streaming implementaion re-added to UI

---

This commit message concisely communicates the key changes:

1. The code now builds an absolute path to the `Cargo.toml` file, enhancing clarity in configuration loading.
2. The addition of `PathBuf` usage improves type safety.
3. The removal of unnecessary entries from `.gitignore` helps maintain a clean project structure.

These updates reflect improvements in both functionality and project organization.

.github/dependabot.yml

@@ -0,0 +1,49 @@
+version: 2
+updates:
+  # Monitor Rust dependencies in the main crate
+  - package-ecosystem: "cargo"
+    directory: "/crates/predict-otron-9000"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+      time: "09:00"
+      timezone: "UTC"
+    # Focus on security updates with higher priority
+    open-pull-requests-limit: 10
+    reviewers:
+      - "security-team"
+    assignees:
+      - "maintainer"
+    labels:
+      - "dependencies"
+      - "security"
+    # Security updates get higher priority
+    allow:
+      - dependency-type: "all"
+    # Group minor and patch updates to reduce noise
+    # Separate major updates for careful review
+    ignore:
+      - dependency-name: "*"
+        update-types: ["version-update:semver-major"]
+    commit-message:
+      prefix: "deps"
+      include: "scope"
+
+  # Monitor security updates more frequently
+  - package-ecosystem: "cargo"
+    directory: "/crates/predict-otron-9000"
+    schedule:
+      interval: "daily"
+    # Only security updates in daily checks
+    allow:
+      - dependency-type: "direct"
+        update-types: ["security"]
+      - dependency-type: "indirect"
+        update-types: ["security"]
+    open-pull-requests-limit: 5
+    labels:
+      - "security-update"
+      - "high-priority"
+    commit-message:
+      prefix: "security"
+      include: "scope"

.github/workflows/ci.yml

@@ -0,0 +1,56 @@
+name: CI
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  build:
+    name: build-and-test
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            target/
+          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Setup Rust
+        run: rustup update stable && rustup default stable && rustup target add wasm32-unknown-unknown
+
+      - name: Setup Bun
+        uses: oven-sh/setup-bun@v2
+
+      - name: Build
+        run:  | 
+          cargo install --locked cargo-leptos
+          cd crates/chat-ui && cargo leptos build --release 
+          cargo build --release -p predict-otron-9000 -p cli
+
+      - name: Install clippy and rustfmt
+        run: rustup component add clippy rustfmt
+
+      - name: Cargo fmt (check)
+        run: cargo fmt --all -- --check
+
+      - name: Clippy
+        shell: bash
+        run: cargo clippy --all-targets
+
+      - name: Tests
+        shell: bash
+        run: cargo test --all
+
+      - name: Build Docs
+        shell: bash
+        run: | 
+          cargo doc -p predict-otron-9000 --no-deps 

.github/workflows/release.yml

@@ -0,0 +1,240 @@
+name: Release
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+env:
+  CARGO_TERM_COLOR: always
+
+jobs:
+  test:
+    name: Test before release
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: crates/predict-otron-9000
+    strategy:
+      fail-fast: false
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            target/
+          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Setup Rust
+        run: rustup update stable && rustup default stable && rustup target add wasm32-unknown-unknown
+
+      - name: Setup Bun
+        uses: oven-sh/setup-bun@v2
+
+      - name: Install clippy and rustfmt
+        run: rustup component add clippy rustfmt
+
+      - name: Cargo fmt (check)
+        run: cargo fmt --all -- --check
+
+      - name: Clippy
+        shell: bash
+        run: cargo clippy --all-targets
+
+      - name: Tests
+        shell: bash
+        run: cargo test --all
+
+#  publish:
+#    name: Publish to crates.io
+#    runs-on: ubuntu-latest
+#    permissions:
+#      id-token: write # Required for OIDC token exchange https://crates.io/docs/trusted-publishing
+#    needs: test
+#    defaults:
+#      run:
+#        working-directory: crates/predict-otron-9000
+#    steps:
+#      - name: Checkout
+#        uses: actions/checkout@v4
+#
+#      - uses: actions/cache@v4
+#        with:
+#          path: |
+#            ~/.cargo/bin/
+#            ~/.cargo/registry/index/
+#            ~/.cargo/registry/cache/
+#            ~/.cargo/git/db/
+#            target/
+#          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+#
+#      - name: Setup Rust
+#        run: rustup update stable && rustup default stable
+#
+#      - name: Verify tag matches version
+#        run: |
+#          TAG_VERSION=${GITHUB_REF#refs/tags/v}
+#          CARGO_VERSION=$(cargo metadata --no-deps --format-version 1 | jq -r '.packages[0].version')
+#          if [ "$TAG_VERSION" != "$CARGO_VERSION" ]; then
+#            echo "Tag version ($TAG_VERSION) does not match Cargo.toml version ($CARGO_VERSION)"
+#            exit 1
+#          fi
+#
+#      # See Trusted publishing: https://crates.io/docs/trusted-publishing
+#      - uses: rust-lang/crates-io-auth-action@v1
+#        id: auth
+#
+#      - run: cargo publish
+#        env:
+#          CARGO_REGISTRY_TOKEN: ${{ steps.auth.outputs.token }}
+
+  build-binaries:
+    name: Build binaries
+    runs-on: ${{ matrix.os }}
+    needs: test
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - target: x86_64-unknown-linux-gnu
+            os: ubuntu-latest
+            name: predict-otron-9000-x86_64-unknown-linux-gnu
+          - target: x86_64-apple-darwin
+            os: macos-latest
+            name: predict-otron-9000-x86_64-apple-darwin
+          - target: aarch64-apple-darwin
+            os: macos-latest
+            name: predict-otron-9000-aarch64-apple-darwin
+          - target: x86_64-pc-windows-msvc
+            os: windows-latest
+            name: predict-otron-9000-x86_64-pc-windows-msvc.exe
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            target/
+          key: ${{ runner.os }}-${{ matrix.target }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Setup Rust
+        run: rustup update stable && rustup default stable && rustup target add wasm32-unknown-unknown
+
+      - name: Add target
+        run: rustup target add ${{ matrix.target }}
+
+      - name: Build UI
+        run: cargo install --locked cargo-leptos && cd crates/chat-ui && cargo leptos build --release
+        env:
+          CARGO_TERM_COLOR: always
+
+      - name: Build Binary
+        run: cargo build --release --target ${{ matrix.target }} -p predict-otron-9000 -p cli
+        env:
+          CARGO_TERM_COLOR: always
+
+      - name: Package binary (Unix)
+        if: matrix.os != 'windows-latest'
+        run: |
+          cd target/${{ matrix.target }}/release
+          tar czf ../../../${{ matrix.name }}.tar.gz predict-otron-9000 cli
+          cd ../../../
+
+      - name: Package binary (Windows)
+        if: matrix.os == 'windows-latest'
+        run: |
+          cd target/${{ matrix.target }}/release
+          7z a ../../../${{ matrix.name }}.zip predict-otron-9000.exe cli.exe
+          cd ../../../
+
+      - name: Upload binary artifacts (Unix)
+        if: matrix.os != 'windows-latest'
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.name }}
+          path: ${{ matrix.name }}.tar.gz
+
+      - name: Upload binary artifacts (Windows)
+        if: matrix.os == 'windows-latest'
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.name }}
+          path: ${{ matrix.name }}.zip
+
+  release:
+    name: Create GitHub Release
+    runs-on: ubuntu-latest
+    needs: [test, build-binaries]
+    permissions:
+      contents: write
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Extract tag name
+        id: tag
+        run: echo "tag=${GITHUB_REF#refs/tags/}" >> $GITHUB_OUTPUT
+
+      - name: Generate changelog
+        id: changelog
+        run: |
+          # Get the previous tag
+          PREV_TAG=$(git describe --tags --abbrev=0 HEAD^ 2>/dev/null || echo "")
+          
+          # Generate changelog
+          if [ -n "$PREV_TAG" ]; then
+            echo "## What's Changed" > changelog.md
+            echo "" >> changelog.md
+            git log --pretty=format:"* %s (%h)" ${PREV_TAG}..HEAD >> changelog.md
+            echo "" >> changelog.md
+            echo "" >> changelog.md
+            echo "**Full Changelog**: https://github.com/${{ github.repository }}/compare/${PREV_TAG}...${{ steps.tag.outputs.tag }}" >> changelog.md
+          else
+            echo "## What's Changed" > changelog.md
+            echo "" >> changelog.md
+            echo "Initial release of predict-otron-9000" >> changelog.md
+            echo "" >> changelog.md
+            echo "OpenAI Compatible Inference Server" >> changelog.md
+          fi
+          
+          # Set the changelog as output (handle multiline)
+          echo "changelog<<EOF" >> $GITHUB_OUTPUT
+          cat changelog.md >> $GITHUB_OUTPUT
+          echo "EOF" >> $GITHUB_OUTPUT
+
+      - name: Download all artifacts
+        uses: actions/download-artifact@v4
+        with:
+          path: artifacts
+
+      - name: Create Release
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          if [[ "${{ steps.tag.outputs.tag }}" == *"-"* ]]; then
+            PRERELEASE_FLAG="--prerelease"
+          else
+            PRERELEASE_FLAG=""
+          fi
+          
+          gh release create "${{ steps.tag.outputs.tag }}" \
+            --title "Release ${{ steps.tag.outputs.tag }}" \
+            --notes-file changelog.md \
+            $PRERELEASE_FLAG \
+            artifacts/predict-otron-9000-x86_64-unknown-linux-gnu/predict-otron-9000-x86_64-unknown-linux-gnu.tar.gz \
+            artifacts/predict-otron-9000-x86_64-apple-darwin/predict-otron-9000-x86_64-apple-darwin.tar.gz \
+            artifacts/predict-otron-9000-aarch64-apple-darwin/predict-otron-9000-aarch64-apple-darwin.tar.gz \
+            artifacts/predict-otron-9000-x86_64-pc-windows-msvc.exe/predict-otron-9000-x86_64-pc-windows-msvc.exe.zip

.gitignore

@@ -23,7 +23,6 @@ package-lock.json
 
 # Web frontend build outputs
 dist/
-.trunk/
 
 # ML model and embedding caches
 .fastembed_cache/
@@ -75,4 +74,6 @@ venv/
 # Backup files
 *.bak
 *.backup
-*~
+!/scripts/cli.ts
+/**/.*.bun-build
+/AGENTS.md

Cargo.toml

@@ -3,15 +3,42 @@ members = [
     "crates/predict-otron-9000",
     "crates/inference-engine",
     "crates/embeddings-engine",
-    "crates/leptos-app",
     "crates/helm-chart-tool",
     "crates/llama-runner",
-    "crates/gemma-runner"
+    "crates/gemma-runner",
-]
+    "crates/cli",
+    "crates/chat-ui"
+, "crates/utils"]
 default-members = ["crates/predict-otron-9000"]
 resolver = "2"
 
-[[workspace.metadata.leptos]]
+[workspace.package]
-# project name
+version = "0.1.4"
-bin-package = "leptos-app"
+
-lib-package = "leptos-app"
+# Compiler optimization profiles for the workspace
+[profile.release]
+opt-level = 3
+debug = false
+strip = true
+lto = "thin"
+codegen-units = 1
+panic = "abort"
+
+[profile.dev]
+opt-level = 0
+debug = true
+strip = false
+overflow-checks = true
+
+# Profile for fast development builds with some optimization
+[profile.dev-opt]
+inherits = "dev"
+opt-level = 1
+debug = true
+overflow-checks = true
+
+# Profile for benchmarking and profiling
+[profile.bench]
+opt-level = 3
+debug = true
+lto = "thin"

README.md

@@ -1,10 +1,26 @@
-# predict-otron-9000
+<h1 align="center">
+    predict-otron-9000
+</h1>
+<p align="center">
+AI inference Server with OpenAI-compatible API (Limited Features)
+</p>
+<p align="center">
+  <img src="https://github.com/geoffsee/predict-otron-9001/blob/master/predict-otron-9000.png?raw=true" width="90%" />
+</p>
+
+<br/>
+> This project is an educational aide for bootstrapping my understanding of language model inferencing at the lowest levels I can, serving as a "rubber-duck" solution for Kubernetes based performance-oriented inference capabilities on air-gapped networks.
+
+> By isolating application behaviors in components at the crate level, development reduces to a short feedback loop for validation and integration, ultimately smoothing the learning curve for scalable AI systems.
+Stability is currently best effort. Many models require unique configuration. When stability is achieved, this project will be promoted to the seemueller-io GitHub organization under a different name.
 
 A comprehensive multi-service AI platform built around local LLM inference, embeddings, and web interfaces.
 
-<p align="center">
+
-Powerful local AI inference with OpenAI-compatible APIs
+~~~shell
-</p>
+./scripts/run.sh
+~~~
+
 
 ## Project Overview
 
@@ -24,14 +40,14 @@ The system supports both CPU and GPU acceleration (CUDA/Metal), with intelligent
 - **Text Embeddings**: Generate high-quality text embeddings using FastEmbed
 - **Text Generation**: Chat completions with OpenAI-compatible API using Gemma and Llama models (various sizes including instruction-tuned variants)
 - **Performance Optimized**: Efficient caching and platform-specific optimizations for improved throughput
-- **Web Chat Interface**: Leptos-based WebAssembly (WASM) chat interface for browser-based interaction
+- **Web Chat Interface**: Leptos chat interface
 - **Flexible Deployment**: Run as monolithic service or microservices architecture
 
 ## Architecture Overview
 
 ### Workspace Structure
 
-The project uses a 7-crate Rust workspace plus TypeScript components:
+The project uses a 9-crate Rust workspace plus TypeScript components:
 
 ```
 crates/
@@ -40,17 +56,18 @@ crates/
 ├── gemma-runner/          # Gemma model inference via Candle (Rust 2021)
 ├── llama-runner/          # Llama model inference via Candle (Rust 2021)
 ├── embeddings-engine/     # FastEmbed embeddings service (Rust 2024)
-├── leptos-app/            # WASM web frontend (Rust 2021)
+├── chat-ui/               # WASM web frontend (Rust 2021)
 ├── helm-chart-tool/       # Kubernetes deployment tooling (Rust 2024)
-└── scripts/
+└── cli/                   # CLI client crate (Rust 2024)
-    └── cli.ts             # TypeScript/Bun CLI client
+    └── package/
+        └── cli.ts         # TypeScript/Bun CLI client
 ```
 
 ### Service Architecture
 
 - **Main Server** (port 8080): Orchestrates inference and embeddings services
 - **Embeddings Service** (port 8080): Standalone FastEmbed service with OpenAI API compatibility  
-- **Web Frontend** (port 8788): Leptos WASM chat interface served by Trunk
+- **Web Frontend** (port 8788): chat-ui WASM app
 - **CLI Client**: TypeScript/Bun client for testing and automation
 
 ### Deployment Modes
@@ -76,11 +93,6 @@ The architecture supports multiple deployment patterns:
 - **Bun**: Required for TypeScript CLI client: `curl -fsSL https://bun.sh/install | bash`
 - **Node.js**: Alternative to Bun, supports OpenAI SDK v5.16.0+
 
-#### WASM Frontend Toolchain
-- **Trunk**: Required for Leptos frontend builds: `cargo install trunk`
-- **wasm-pack**: `cargo install wasm-pack`
-- **WASM target**: `rustup target add wasm32-unknown-unknown`
-
 #### ML Framework Dependencies
 - **Candle**: Version 0.9.1 with conditional compilation:
   - macOS: Metal support with CPU fallback for stability
@@ -125,11 +137,6 @@ cargo build --bin cli --package inference-engine --release
 cargo build --bin embeddings-engine --release
 ```
 
-**Web Frontend:**
-```bash
-cd crates/leptos-app
-trunk build --release
-```
 
 ### Running Services
 
@@ -143,26 +150,26 @@ trunk build --release
 
 #### Web Frontend (Port 8788)  
 ```bash
-cd crates/leptos-app
+cd crates/chat-ui
 ./run.sh
 ```
-- Serves Leptos WASM frontend on port 8788
+- Serves chat-ui WASM frontend on port 8788
 - Sets required RUSTFLAGS for WebAssembly getrandom support
 - Auto-reloads during development
 
 #### TypeScript CLI Client
 ```bash
 # List available models
-bun run scripts/cli.ts --list-models
+cd crates/cli/package && bun run cli.ts --list-models
 
 # Chat completion
-bun run scripts/cli.ts "What is the capital of France?"
+cd crates/cli/package && bun run cli.ts "What is the capital of France?"
 
 # With specific model
-bun run scripts/cli.ts --model gemma-3-1b-it --prompt "Hello, world!"
+cd crates/cli/package && bun run cli.ts --model gemma-3-1b-it --prompt "Hello, world!"
 
 # Show help
-bun run scripts/cli.ts --help
+cd crates/cli/package && bun run cli.ts --help
 ```
 
 ## API Usage
@@ -278,7 +285,7 @@ cargo test --workspace
 
 **End-to-end test script:**
 ```bash
-./test.sh
+./scripts/smoke_test.sh
 ```
 
 This script:
@@ -367,7 +374,7 @@ All services include Docker metadata in `Cargo.toml`:
 - Port: 8080
 
 **Web Frontend:**
-- Image: `ghcr.io/geoffsee/leptos-app:latest`
+- Image: `ghcr.io/geoffsee/chat-ui:latest`
 - Port: 8788
 
 **Docker Compose:**
@@ -426,8 +433,7 @@ For Kubernetes deployment details, see the [ARCHITECTURE.md](docs/ARCHITECTURE.m
 **Symptom:** WASM compilation failures  
 **Solution:**
 1. Install required targets: `rustup target add wasm32-unknown-unknown`
-2. Install trunk: `cargo install trunk`
+2. Check RUSTFLAGS in chat-ui/run.sh
-3. Check RUSTFLAGS in leptos-app/run.sh
 
 ### Network/Timeout Issues
 **Symptom:** First-time model downloads timing out  
@@ -458,24 +464,23 @@ curl -s http://localhost:8080/v1/models | jq
 
 **CLI client test:**
 ```bash
-bun run scripts/cli.ts "What is 2+2?"
+cd crates/cli/package && bun run cli.ts "What is 2+2?"
 ```
 
 **Web frontend:**
 ```bash
-cd crates/leptos-app && ./run.sh &
+cd crates/chat-ui && ./run.sh &
 # Navigate to http://localhost:8788
 ```
 
 **Integration test:**
 ```bash
-./test.sh
+./scripts/smoke_test.sh
 ```
 
 **Cleanup:**
 ```bash
 pkill -f "predict-otron-9000"
-pkill -f "trunk"
 ```
 
 For networked tests and full functionality, ensure Hugging Face authentication is configured as described above.
@@ -497,4 +502,4 @@ For networked tests and full functionality, ensure Hugging Face authentication i
 4. Ensure all tests pass: `cargo test`
 5. Submit a pull request
 
-_Warning: Do NOT use this in production unless you are cool like that._
+_Warning: Do NOT use this in production unless you are cool like that._

bun.lock

@@ -0,0 +1,22 @@
+{
+  "lockfileVersion": 1,
+  "workspaces": {
+    "": {
+      "name": "predict-otron-9000",
+    },
+    "crates/cli/package": {
+      "name": "cli",
+      "dependencies": {
+        "install": "^0.13.0",
+        "openai": "^5.16.0",
+      },
+    },
+  },
+  "packages": {
+    "cli": ["cli@workspace:crates/cli/package"],
+
+    "install": ["install@0.13.0", "", {}, "sha512-zDml/jzr2PKU9I8J/xyZBQn8rPCAY//UOYNmR01XwNwyfhEWObo2SWfSl1+0tm1u6PhxLwDnfsT/6jB7OUxqFA=="],
+
+    "openai": ["openai@5.16.0", "", { "peerDependencies": { "ws": "^8.18.0", "zod": "^3.23.8" }, "optionalPeers": ["ws", "zod"], "bin": { "openai": "bin/cli" } }, "sha512-hoEH8ZNvg1HXjU9mp88L/ZH8O082Z8r6FHCXGiWAzVRrEv443aI57qhch4snu07yQydj+AUAWLenAiBXhu89Tw=="],
+  }
+}

crates/chat-ui/Cargo.toml

@@ -1,5 +1,5 @@
 [package]
-name = "leptos-app"
+name = "chat-ui"
 version = "0.1.0"
 edition = "2021"
 
@@ -15,45 +15,33 @@ leptos_axum = { version = "0.8.0", optional = true }
 leptos_meta = { version = "0.8.0" }
 tokio = { version = "1", features = ["rt-multi-thread"], optional = true }
 wasm-bindgen = { version = "=0.2.100", optional = true }
-
+wasm-bindgen-futures = "0.4"
-# Chat interface dependencies
+js-sys = "0.3"
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
-async-openai-wasm = { version = "0.29", default-features = false }
+reqwest = { version = "0.12", features = ["json"] }
-futures-util = "0.3"
+web-sys = { version = "0.3", features = [
-js-sys = { version = "0.3", optional = true }
+    "console", 
-either = { version = "1.9", features = ["serde"] }
+    "EventSource", 
-
+    "MessageEvent", 
-web-sys = { version = "0.3", optional = true, features = [
+    "Window", 
-    "console",
+    "Request", 
-    "Window",
+    "RequestInit", 
-    "Document",
+    "Response", 
-    "Element",
+    "Headers",
-    "HtmlElement",
+    "ReadableStream",
-    "HtmlInputElement",
+    "ReadableStreamDefaultReader",
-    "HtmlSelectElement",
+    "TextDecoder",
-    "HtmlTextAreaElement",
+    "TextDecoderOptions",
-    "Event",
+    "HtmlInputElement"
-    "EventTarget",
-    "KeyboardEvent",
 ] }
-
+gloo-net = { version = "0.6", features = ["http"] }
-[dependencies.uuid]
-version = "1.0"
-features = [
-    "v4",
-    "fast-rng", 
-    "macro-diagnostics",
-    "js",
-]
 
 [features]
 hydrate = [
     "leptos/hydrate",
     "dep:console_error_panic_hook",
     "dep:wasm-bindgen",
-    "dep:js-sys",
-    "dep:web-sys",
 ]
 ssr = [
     "dep:axum",
@@ -73,8 +61,9 @@ codegen-units = 1
 panic = "abort"
 
 [package.metadata.leptos]
+name = "chat-ui"
 # The name used by wasm-bindgen/cargo-leptos for the JS/WASM bundle. Defaults to the crate name
-output-name = "leptos-app"
+output-name = "chat-ui"
 
 # The site root folder is where cargo-leptos generate all output. WARNING: all content of this folder will be erased on a rebuild. Use it in your server setup.
 site-root = "target/site"
@@ -84,7 +73,7 @@ site-root = "target/site"
 site-pkg-dir = "pkg"
 
 # [Optional] The source CSS file. If it ends with .sass or .scss then it will be compiled by dart-sass into CSS. The CSS is optimized by Lightning CSS before being written to <site-root>/<site-pkg>/app.css
-style-file = "style/main.scss"
+style-file = "./style/main.scss"
 # Assets source dir. All files found here will be copied and synchronized to site-root.
 # The assets-dir cannot have a sub directory with the same name/path as site-pkg-dir.
 #
@@ -132,4 +121,4 @@ lib-default-features = false
 # The profile to use for the lib target when compiling for release
 #
 # Optional. Defaults to "release".
-lib-profile-release = "wasm-release"
+lib-profile-release = "release"

crates/chat-ui/README.md

@@ -0,0 +1,41 @@
+# chat-ui
+
+A WASM-based web chat interface for the predict-otron-9000 AI platform.
+
+## Overview
+
+The chat-ui provides a real-time web interface for interacting with language models through the predict-otron-9000 server. Built with Leptos and compiled to WebAssembly, it offers a modern chat experience with streaming response support.
+
+## Features
+
+- Real-time chat interface with the inference server
+- Streaming response support
+- Conversation history
+- Responsive web design
+- WebAssembly-powered for optimal performance
+
+## Building and Running
+
+### Prerequisites
+- Rust toolchain with WASM target: `rustup target add wasm32-unknown-unknown`
+- The predict-otron-9000 server must be running on port 8080
+
+### Development Server
+```bash
+cd crates/chat-ui
+./run.sh
+```
+
+This starts the development server on port 8788 with auto-reload capabilities.
+
+### Usage
+1. Start the predict-otron-9000 server: `./scripts/run.sh`
+2. Start the chat-ui: `cd crates/chat-ui && ./run.sh`
+3. Navigate to `http://localhost:8788`
+4. Start chatting with your AI models!
+
+## Technical Details
+- Built with Leptos framework
+- Compiled to WebAssembly for browser execution
+- Communicates with predict-otron-9000 API via HTTP
+- Sets required RUSTFLAGS for WebAssembly getrandom support

crates/chat-ui/src/app.rs

@@ -0,0 +1,613 @@
+#[cfg(feature = "ssr")]
+use axum::Router;
+#[cfg(feature = "ssr")]
+use leptos::prelude::LeptosOptions;
+#[cfg(feature = "ssr")]
+use leptos_axum::{generate_route_list, LeptosRoutes};
+
+pub struct AppConfig {
+    pub config: ConfFile,
+    pub address: String,
+}
+
+impl Default for AppConfig {
+    fn default() -> Self {
+        let conf = get_configuration(Some(concat!(env!("CARGO_MANIFEST_DIR"), "/Cargo.toml")))
+            .expect("failed to read config");
+
+        let addr = conf.leptos_options.site_addr;
+
+        AppConfig {
+            config: conf, // or whichever field/string representation you need
+            address: addr.to_string(),
+        }
+    }
+}
+
+/// Build the Axum router for this app, including routes, fallback, and state.
+/// Call this from another crate (or your bin) when running the server.
+#[cfg(feature = "ssr")]
+pub fn create_router(leptos_options: LeptosOptions) -> Router {
+    // Generate the list of routes in your Leptos App
+    let routes = generate_route_list(App);
+
+    Router::new()
+        .leptos_routes(&leptos_options, routes, {
+            let leptos_options = leptos_options.clone();
+            move || shell(leptos_options.clone())
+        })
+        .fallback(leptos_axum::file_and_error_handler(shell))
+        .with_state(leptos_options)
+}
+
+use gloo_net::http::Request;
+use leptos::prelude::*;
+use leptos_meta::{provide_meta_context, MetaTags, Stylesheet, Title};
+use leptos_router::{
+    components::{Route, Router, Routes},
+    StaticSegment,
+};
+use serde::{Deserialize, Serialize};
+use web_sys::console;
+
+// Data structures for OpenAI-compatible API
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ChatMessage {
+    pub role: String,
+    pub content: String,
+}
+
+#[derive(Debug, Serialize)]
+pub struct ChatRequest {
+    pub model: String,
+    pub messages: Vec<ChatMessage>,
+    pub max_tokens: Option<u32>,
+    pub stream: Option<bool>,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct ChatChoice {
+    pub message: ChatMessage,
+    pub index: u32,
+    pub finish_reason: Option<String>,
+}
+
+// Streaming response structures
+#[derive(Debug, Deserialize)]
+pub struct StreamDelta {
+    pub role: Option<String>,
+    pub content: Option<String>,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct StreamChoice {
+    pub index: u32,
+    pub delta: StreamDelta,
+    pub finish_reason: Option<String>,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct StreamChatResponse {
+    pub id: String,
+    pub object: String,
+    pub created: u64,
+    pub model: String,
+    pub choices: Vec<StreamChoice>,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct ChatResponse {
+    pub id: String,
+    pub object: String,
+    pub created: u64,
+    pub model: String,
+    pub choices: Vec<ChatChoice>,
+}
+
+// Data structures for models API
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ModelInfo {
+    pub id: String,
+    pub object: String,
+    pub created: u64,
+    pub owned_by: String,
+}
+
+#[derive(Debug, Deserialize)]
+pub struct ModelsResponse {
+    pub object: String,
+    pub data: Vec<ModelInfo>,
+}
+
+// API client function to fetch available models
+pub async fn fetch_models() -> Result<Vec<ModelInfo>, String> {
+    let response = Request::get("/v1/models")
+        .send()
+        .await
+        .map_err(|e| format!("Failed to fetch models: {:?}", e))?;
+
+    if response.ok() {
+        let models_response: ModelsResponse = response
+            .json()
+            .await
+            .map_err(|e| format!("Failed to parse models response: {:?}", e))?;
+        Ok(models_response.data)
+    } else {
+        let status = response.status();
+        let error_text = response
+            .text()
+            .await
+            .unwrap_or_else(|_| "Unknown error".to_string());
+        Err(format!("Failed to fetch models {}: {}", status, error_text))
+    }
+}
+
+// API client function to send chat completion requests
+pub async fn send_chat_completion(
+    messages: Vec<ChatMessage>,
+    model: String,
+) -> Result<String, String> {
+    let request = ChatRequest {
+        model,
+        messages,
+        max_tokens: Some(1024),
+        stream: Some(false),
+    };
+
+    let response = Request::post("/v1/chat/completions")
+        .header("Content-Type", "application/json")
+        .json(&request)
+        .map_err(|e| format!("Failed to create request: {:?}", e))?
+        .send()
+        .await
+        .map_err(|e| format!("Failed to send request: {:?}", e))?;
+
+    if response.ok() {
+        let chat_response: ChatResponse = response
+            .json()
+            .await
+            .map_err(|e| format!("Failed to parse response: {:?}", e))?;
+
+        if let Some(choice) = chat_response.choices.first() {
+            Ok(choice.message.content.clone())
+        } else {
+            Err("No response choices available".to_string())
+        }
+    } else {
+        let status = response.status();
+        let error_text = response
+            .text()
+            .await
+            .unwrap_or_else(|_| "Unknown error".to_string());
+        Err(format!("Server error {}: {}", status, error_text))
+    }
+}
+
+// Streaming chat completion using EventSource
+#[cfg(target_arch = "wasm32")]
+pub fn send_chat_completion_stream(
+    messages: Vec<ChatMessage>,
+    model: String,
+    on_chunk: impl Fn(String) + 'static,
+    on_complete: impl Fn() + 'static,
+    on_error: impl Fn(String) + 'static,
+) {
+    use wasm_bindgen::prelude::*;
+    use wasm_bindgen::JsCast;
+    
+    let request = ChatRequest {
+        model,
+        messages,
+        max_tokens: Some(1024),
+        stream: Some(true),
+    };
+    
+    // We need to send a POST request but EventSource only supports GET
+    // So we'll use fetch with a readable stream instead
+    let window = web_sys::window().unwrap();
+    let request_json = serde_json::to_string(&request).unwrap();
+    
+    let opts = web_sys::RequestInit::new();
+    opts.set_method("POST");
+    opts.set_body(&JsValue::from_str(&request_json));
+    
+    let headers = web_sys::Headers::new().unwrap();
+    headers.set("Content-Type", "application/json").unwrap();
+    headers.set("Accept", "text/event-stream").unwrap();
+    opts.set_headers(&headers);
+    
+    let request = web_sys::Request::new_with_str_and_init("/v1/chat/completions", &opts).unwrap();
+    
+    let promise = window.fetch_with_request(&request);
+    
+    wasm_bindgen_futures::spawn_local(async move {
+        match wasm_bindgen_futures::JsFuture::from(promise).await {
+            Ok(resp_value) => {
+                let resp: web_sys::Response = resp_value.dyn_into().unwrap();
+                
+                if !resp.ok() {
+                    on_error(format!("Server error: {}", resp.status()));
+                    return;
+                }
+                
+                let body = resp.body();
+                if body.is_none() {
+                    on_error("No response body".to_string());
+                    return;
+                }
+                
+                let reader = body
+                    .unwrap()
+                    .get_reader()
+                    .dyn_into::<web_sys::ReadableStreamDefaultReader>()
+                    .unwrap();
+                
+                let decoder = web_sys::TextDecoder::new().unwrap();
+                let mut buffer = String::new();
+                
+                loop {
+                    match wasm_bindgen_futures::JsFuture::from(reader.read()).await {
+                        Ok(result) => {
+                            let done = js_sys::Reflect::get(&result, &JsValue::from_str("done"))
+                                .unwrap()
+                                .as_bool()
+                                .unwrap_or(false);
+                            
+                            if done {
+                                break;
+                            }
+                            
+                            let value = js_sys::Reflect::get(&result, &JsValue::from_str("value")).unwrap();
+                            let array = js_sys::Uint8Array::new(&value);
+                            let mut bytes = vec![0; array.length() as usize];
+                            array.copy_to(&mut bytes);
+                            let text = decoder.decode_with_u8_array(&bytes).unwrap();
+                            
+                            buffer.push_str(&text);
+                            
+                            // Process complete SSE events from buffer
+                            while let Some(event_end) = buffer.find("\n\n") {
+                                let event = buffer[..event_end].to_string();
+                                buffer = buffer[event_end + 2..].to_string();
+                                
+                                // Parse SSE event
+                                for line in event.lines() {
+                                    if let Some(data) = line.strip_prefix("data: ") {
+                                        if data == "[DONE]" {
+                                            on_complete();
+                                            return;
+                                        }
+                                        
+                                        // Parse JSON chunk
+                                        if let Ok(chunk) = serde_json::from_str::<StreamChatResponse>(data) {
+                                            if let Some(choice) = chunk.choices.first() {
+                                                if let Some(content) = &choice.delta.content {
+                                                    on_chunk(content.clone());
+                                                }
+                                            }
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                        Err(e) => {
+                            on_error(format!("Read error: {:?}", e));
+                            break;
+                        }
+                    }
+                }
+                
+                on_complete();
+            }
+            Err(e) => {
+                on_error(format!("Fetch error: {:?}", e));
+            }
+        }
+    });
+}
+
+pub fn shell(options: LeptosOptions) -> impl IntoView {
+    view! {
+        <!DOCTYPE html>
+        <html lang="en">
+            <head>
+                <meta charset="utf-8"/>
+                <meta name="viewport" content="width=device-width, initial-scale=1"/>
+                <AutoReload options=options.clone() />
+                <HydrationScripts options/>
+                <MetaTags/>
+            </head>
+            <body>
+                <App/>
+            </body>
+        </html>
+    }
+}
+
+#[component]
+pub fn App() -> impl IntoView {
+    // Provides context that manages stylesheets, titles, meta tags, etc.
+    provide_meta_context();
+
+    view! {
+        // injects a stylesheet into the document <head>
+        // id=leptos means cargo-leptos will hot-reload this stylesheet
+        <Stylesheet id="leptos" href="/pkg/chat-ui.css"/>
+
+        // sets the document title
+        <Title text="Predict-Otron-9000 Chat"/>
+
+        // content for this welcome page
+        <Router>
+            <main>
+                <Routes fallback=|| "Page not found.".into_view()>
+                    <Route path=StaticSegment("") view=ChatPage/>
+                </Routes>
+            </main>
+        </Router>
+    }
+}
+
+/// Renders the chat interface page
+#[component]
+fn ChatPage() -> impl IntoView {
+    // State for conversation messages
+    let messages = RwSignal::new(Vec::<ChatMessage>::new());
+
+    // State for current user input
+    let input_text = RwSignal::new(String::new());
+
+    // State for loading indicator
+    let is_loading = RwSignal::new(false);
+
+    // State for error messages
+    let error_message = RwSignal::new(Option::<String>::None);
+
+    // State for available models and selected model
+    let available_models = RwSignal::new(Vec::<ModelInfo>::new());
+    let selected_model = RwSignal::new(String::from("gemma-3-1b-it")); // Default model
+    
+    // State for streaming response
+    let streaming_content = RwSignal::new(String::new());
+    let is_streaming = RwSignal::new(false);
+    
+    // State for streaming mode toggle
+    let use_streaming = RwSignal::new(true); // Default to streaming
+
+    // Client-side only: Fetch models on component mount
+    #[cfg(target_arch = "wasm32")]
+    {
+        use leptos::task::spawn_local;
+        spawn_local(async move {
+            match fetch_models().await {
+                Ok(models) => {
+                    available_models.set(models);
+                }
+                Err(error) => {
+                    console::log_1(&format!("Failed to fetch models: {}", error).into());
+                    error_message.set(Some(format!("Failed to load models: {}", error)));
+                }
+            }
+        });
+    }
+
+    // Shared logic for sending a message
+    let send_message_logic = move || {
+        let user_input = input_text.get();
+        if user_input.trim().is_empty() {
+            return;
+        }
+
+        // Add user message to conversation
+        let user_message = ChatMessage {
+            role: "user".to_string(),
+            content: user_input.clone(),
+        };
+
+        messages.update(|msgs| msgs.push(user_message.clone()));
+        input_text.set(String::new());
+        is_loading.set(true);
+        error_message.set(None);
+
+        // Client-side only: Send chat completion request
+        #[cfg(target_arch = "wasm32")]
+        {
+            use leptos::task::spawn_local;
+
+            // Prepare messages for API call
+            let current_messages = messages.get();
+            let current_model = selected_model.get();
+            let should_stream = use_streaming.get();
+
+            if should_stream {
+                // Clear streaming content and set streaming flag
+                streaming_content.set(String::new());
+                is_streaming.set(true);
+                
+                // Use streaming API
+                send_chat_completion_stream(
+                    current_messages,
+                    current_model,
+                    move |chunk| {
+                        // Append chunk to streaming content
+                        streaming_content.update(|content| content.push_str(&chunk));
+                    },
+                    move || {
+                        // On complete, move streaming content to messages
+                        let final_content = streaming_content.get();
+                        if !final_content.is_empty() {
+                            let assistant_message = ChatMessage {
+                                role: "assistant".to_string(),
+                                content: final_content,
+                            };
+                            messages.update(|msgs| msgs.push(assistant_message));
+                        }
+                        streaming_content.set(String::new());
+                        is_streaming.set(false);
+                        is_loading.set(false);
+                    },
+                    move |error| {
+                        console::log_1(&format!("Streaming Error: {}", error).into());
+                        error_message.set(Some(error));
+                        is_streaming.set(false);
+                        is_loading.set(false);
+                        streaming_content.set(String::new());
+                    },
+                );
+            } else {
+                // Use non-streaming API
+                spawn_local(async move {
+                    match send_chat_completion(current_messages, current_model).await {
+                        Ok(response_content) => {
+                            let assistant_message = ChatMessage {
+                                role: "assistant".to_string(),
+                                content: response_content,
+                            };
+                            messages.update(|msgs| msgs.push(assistant_message));
+                            is_loading.set(false);
+                        }
+                        Err(error) => {
+                            console::log_1(&format!("API Error: {}", error).into());
+                            error_message.set(Some(error));
+                            is_loading.set(false);
+                        }
+                    }
+                });
+            }
+        }
+    };
+
+    // Button click handler
+    let on_button_click = {
+        let send_logic = send_message_logic.clone();
+        move |_: web_sys::MouseEvent| {
+            send_logic();
+        }
+    };
+
+    // Handle enter key press in input field
+    let on_key_down = move |ev: web_sys::KeyboardEvent| {
+        if ev.key() == "Enter" && !ev.shift_key() {
+            ev.prevent_default();
+            send_message_logic();
+        }
+    };
+
+    view! {
+        <div class="chat-container">
+            <div class="chat-header">
+                <h1>"Predict-Otron-9000 Chat"</h1>
+                <div class="model-selector">
+                    <label for="model-select">"Model:"</label>
+                    <select
+                        id="model-select"
+                        prop:value=move || selected_model.get()
+                        on:change=move |ev| {
+                            let new_model = event_target_value(&ev);
+                            selected_model.set(new_model);
+                        }
+                    >
+                        <For
+                            each=move || available_models.get().into_iter()
+                            key=|model| model.id.clone()
+                            children=move |model| {
+                                view! {
+                                    <option value=model.id.clone()>
+                                        {format!("{} ({})", model.id, model.owned_by)}
+                                    </option>
+                                }
+                            }
+                        />
+                    </select>
+                    <div class="streaming-toggle">
+                        <label>
+                            <input
+                                type="checkbox"
+                                prop:checked=move || use_streaming.get()
+                                on:change=move |ev| {
+                                    let target = event_target::<web_sys::HtmlInputElement>(&ev);
+                                    use_streaming.set(target.checked());
+                                }
+                            />
+                            " Use streaming"
+                        </label>
+                    </div>
+                </div>
+            </div>
+
+            <div class="chat-messages">
+                <For
+                    each=move || messages.get().into_iter().enumerate()
+                    key=|(i, _)| *i
+                    children=move |(_, message)| {
+                        let role_class = if message.role == "user" { "user-message" } else { "assistant-message" };
+                        view! {
+                            <div class=format!("message {}", role_class)>
+                                <div class="message-role">{message.role.clone()}</div>
+                                <div class="message-content">{message.content.clone()}</div>
+                            </div>
+                        }
+                    }
+                />
+
+                {move || {
+                    if is_streaming.get() {
+                        let content = streaming_content.get();
+                        if !content.is_empty() {
+                            view! {
+                                <div class="message assistant-message streaming">
+                                    <div class="message-role">"assistant"</div>
+                                    <div class="message-content">{content}<span class="cursor">"▊"</span></div>
+                                </div>
+                            }.into_any()
+                        } else {
+                            view! {
+                                <div class="message assistant-message loading">
+                                    <div class="message-role">"assistant"</div>
+                                    <div class="message-content">"Thinking..."</div>
+                                </div>
+                            }.into_any()
+                        }
+                    } else if is_loading.get() && !use_streaming.get() {
+                        view! {
+                            <div class="message assistant-message loading">
+                                <div class="message-role">"assistant"</div>
+                                <div class="message-content">"Thinking..."</div>
+                            </div>
+                        }.into_any()
+                    } else {
+                        view! {}.into_any()
+                    }
+                }}
+            </div>
+
+            {move || {
+                if let Some(error) = error_message.get() {
+                    view! {
+                        <div class="error-message">
+                            "Error: " {error}
+                        </div>
+                    }.into_any()
+                } else {
+                    view! {}.into_any()
+                }
+            }}
+
+            <div class="chat-input">
+                <textarea
+                    placeholder="Type your message here... (Press Enter to send, Shift+Enter for new line)"
+                    prop:value=move || input_text.get()
+                    on:input=move |ev| input_text.set(event_target_value(&ev))
+                    on:keydown=on_key_down
+                    class:disabled=move || is_loading.get()
+                />
+                <button
+                    on:click=on_button_click
+                    class:disabled=move || is_loading.get() || input_text.get().trim().is_empty()
+                >
+                    "Send"
+                </button>
+            </div>
+        </div>
+    }
+}

crates/chat-ui/src/lib.rs

@@ -0,0 +1,9 @@
+pub mod app;
+
+#[cfg(feature = "hydrate")]
+#[wasm_bindgen::prelude::wasm_bindgen]
+pub fn hydrate() {
+    use crate::app::*;
+    console_error_panic_hook::set_once();
+    leptos::mount::hydrate_body(App);
+}

crates/chat-ui/src/main.rs

@@ -0,0 +1,26 @@
+#[cfg(feature = "ssr")]
+#[tokio::main]
+async fn main() {
+    use axum::Router;
+    use chat_ui::app::*;
+    use leptos::logging::log;
+    use leptos::prelude::*;
+    use leptos_axum::{generate_route_list, LeptosRoutes};
+
+    let conf = get_configuration(None).expect("failed to read config");
+    let addr = conf.leptos_options.site_addr;
+
+    // Build the app router with your extracted function
+    let app: Router = create_router(conf.leptos_options);
+
+    log!("listening on http://{}", &addr);
+    let listener = tokio::net::TcpListener::bind(&addr).await.unwrap();
+    axum::serve(listener, app.into_make_service())
+        .await
+        .unwrap();
+}
+
+#[cfg(not(feature = "ssr"))]
+pub fn main() {
+    // no client-side main function
+}

crates/chat-ui/style/main.scss

@@ -0,0 +1,265 @@
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+    background-color: #f5f5f5;
+    height: 100vh;
+    overflow: hidden;
+}
+
+.chat-container {
+    display: flex;
+    flex-direction: column;
+    height: 100vh;
+    max-width: 800px;
+    margin: 0 auto;
+    background-color: white;
+    box-shadow: 0 0 20px rgba(0, 0, 0, 0.1);
+}
+
+.chat-header {
+    background-color: #000000;
+    color: white;
+    padding: 1rem;
+    text-align: center;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+    display: flex;
+    flex-direction: column;
+    gap: 1rem;
+
+    h1 {
+        margin: 0;
+        font-size: 1.5rem;
+        font-weight: 600;
+    }
+
+    .model-selector {
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        gap: 0.5rem;
+        flex-wrap: wrap;
+        
+        label {
+            font-weight: 500;
+            font-size: 0.9rem;
+        }
+        
+        select {
+            background-color: white;
+            color: #374151;
+            border: 1px solid #d1d5db;
+            border-radius: 6px;
+            padding: 0.5rem 0.75rem;
+            font-size: 0.9rem;
+            font-family: inherit;
+            cursor: pointer;
+            min-width: 200px;
+            
+            &:focus {
+                outline: none;
+                border-color: #663c99;
+                box-shadow: 0 0 0 2px rgba(29, 78, 216, 0.2);
+            }
+            
+            option {
+                padding: 0.5rem;
+            }
+        }
+        
+        .streaming-toggle {
+            display: flex;
+            align-items: center;
+            margin-left: 1rem;
+            
+            label {
+                display: flex;
+                align-items: center;
+                gap: 0.5rem;
+                cursor: pointer;
+                font-size: 0.9rem;
+                
+                input[type="checkbox"] {
+                    cursor: pointer;
+                }
+            }
+        }
+    }
+}
+
+.chat-messages {
+    flex: 1;
+    overflow-y: auto;
+    padding: 1rem;
+    display: flex;
+    flex-direction: column;
+    gap: 1rem;
+    background-color: #fafafa;
+}
+
+.message {
+    display: flex;
+    flex-direction: column;
+    gap: 0.5rem;
+    padding: 1rem;
+    border-radius: 12px;
+    max-width: 80%;
+    word-wrap: break-word;
+
+    &.user-message {
+        align-self: flex-end;
+        background-color: #2563eb;
+        color: white;
+
+        .message-role {
+            font-weight: 600;
+            font-size: 0.8rem;
+            opacity: 0.8;
+            text-transform: uppercase;
+        }
+
+        .message-content {
+            line-height: 1.5;
+        }
+    }
+
+    &.assistant-message {
+        align-self: flex-start;
+        background-color: #646873;
+        border: 1px solid #e5e7eb;
+        color: #f3f3f3;
+
+        .message-role {
+            font-weight: 600;
+            font-size: 0.8rem;
+            color: #c4c5cd;
+            text-transform: uppercase;
+        }
+
+        .message-content {
+            line-height: 1.5;
+        }
+
+        &.loading {
+            background-color: #f3f4f6;
+            border-color: #d1d5db;
+
+            .message-content {
+                font-style: italic;
+                color: #6b7280;
+            }
+        }
+        
+        &.streaming {
+            .message-content {
+                .cursor {
+                    display: inline-block;
+                    animation: blink 1s infinite;
+                    color: #9ca3af;
+                }
+            }
+        }
+    }
+}
+
+.error-message {
+    background-color: #fef2f2;
+    border: 1px solid #fca5a5;
+    color: #dc2626;
+    padding: 1rem;
+    margin: 0 1rem;
+    border-radius: 8px;
+    text-align: center;
+    font-weight: 500;
+}
+
+.chat-input {
+    display: flex;
+    gap: 0.5rem;
+    padding: 1rem;
+    background-color: white;
+    border-top: 1px solid #e5e7eb;
+
+    textarea {
+        flex: 1;
+        padding: 0.75rem;
+        border: 1px solid #d1d5db;
+        border-radius: 8px;
+        resize: none;
+        min-height: 60px;
+        max-height: 120px;
+        font-family: inherit;
+        font-size: 1rem;
+        line-height: 1.5;
+
+        &:focus {
+            outline: none;
+            border-color: #663c99;
+            box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.1);
+        }
+
+        &.disabled {
+            background-color: #f9fafb;
+            color: #6b7280;
+            cursor: not-allowed;
+        }
+    }
+
+    button {
+        padding: 0.75rem 1.5rem;
+        background-color: #663c99;
+        color: white;
+        border: none;
+        border-radius: 8px;
+        font-weight: 600;
+        cursor: pointer;
+        transition: background-color 0.2s ease;
+        align-self: flex-end;
+
+        &:hover:not(.disabled) {
+            background-color: #663c99;
+        }
+
+        &.disabled {
+            background-color: #9ca3af;
+            cursor: not-allowed;
+        }
+
+        &:focus {
+            outline: none;
+            box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.3);
+        }
+    }
+}
+
+/* Scrollbar styling for webkit browsers */
+.chat-messages::-webkit-scrollbar {
+    width: 6px;
+}
+
+.chat-messages::-webkit-scrollbar-track {
+    background: #f1f1f1;
+}
+
+.chat-messages::-webkit-scrollbar-thumb {
+    background: #c1c1c1;
+    border-radius: 3px;
+}
+
+.chat-messages::-webkit-scrollbar-thumb:hover {
+    background: #a8a8a8;
+}
+
+/* Cursor blink animation */
+@keyframes blink {
+    0%, 50% {
+        opacity: 1;
+    }
+    51%, 100% {
+        opacity: 0;
+    }
+}

crates/cli/Cargo.toml

@@ -0,0 +1,11 @@
+[package]
+name = "cli"
+version.workspace = true
+edition = "2021"
+build = "build.rs"
+
+[[bin]]
+name = "cli"
+path = "src/main.rs"
+
+[dependencies]

crates/cli/README.md

@@ -0,0 +1,24 @@
+# cli
+
+A Rust/Typescript Hybrid
+
+```console
+bun run cli.ts [options] [prompt]
+
+Simple CLI tool for testing the local OpenAI-compatible API server.
+
+Options:
+  --model <model>     Model to use (default: gemma-3-1b-it)
+  --prompt <prompt>   The prompt to send (can also be provided as positional argument)
+  --list-models       List all available models from the server
+  --help              Show this help message
+
+Examples:
+  cd crates/cli/package
+  bun run cli.ts "What is the capital of France?"
+  bun run cli.ts --model gemma-3-1b-it --prompt "Hello, world!"
+  bun run cli.ts --prompt "Who was the 16th president of the United States?"
+  bun run cli.ts --list-models
+
+The server must be running at http://localhost:8080
+```

crates/cli/build.rs

@@ -0,0 +1,204 @@
+use std::env;
+use std::fs;
+use std::io::{self, BufRead, Write};
+use std::path::{Path, PathBuf};
+use std::process::{ChildStderr, ChildStdout, Command, Stdio};
+use std::thread;
+use std::time::{Duration, SystemTime};
+mod bun_target;
+use bun_target::BunTarget;
+
+fn main() {
+    println!("cargo:rerun-if-changed=");
+
+    if let Err(e) = run_build() {
+        println!("cargo:warning=build.rs failed: {e}");
+        std::process::exit(1);
+    }
+}
+
+fn run_build() -> io::Result<()> {
+    let manifest_dir =
+        PathBuf::from(env::var("CARGO_MANIFEST_DIR").expect("CARGO_MANIFEST_DIR not set"));
+    let package_dir = manifest_dir.join("package");
+    let out_dir = PathBuf::from(env::var("OUT_DIR").expect("OUT_DIR not set by Cargo"));
+    let output_path = out_dir.join("client-cli");
+
+    let bun_tgt = BunTarget::from_cargo_env()
+        .map_err(|e| io::Error::new(io::ErrorKind::Other, e.to_string()))?;
+
+    // Optional: warn if using a Bun target that’s marked unsupported in your chart
+    if matches!(bun_tgt, BunTarget::WindowsArm64) {
+        println!(
+            "cargo:warning=bun-windows-arm64 is marked unsupported in the compatibility chart"
+        );
+    }
+
+    warn(&format!("Building CLI into: {}", output_path.display()));
+
+    // --- bun install (in ./package), keep temps inside OUT_DIR ---
+    let mut install = Command::new("bun")
+        .current_dir(&package_dir)
+        .env("TMPDIR", &out_dir)
+        .arg("install")
+        .stdin(Stdio::null())
+        .stdout(Stdio::piped())
+        .stderr(Stdio::piped())
+        .spawn()
+        .map_err(|e| io::Error::new(e.kind(), format!("Failed to spawn `bun install`: {e}")))?;
+
+    let install_join = stream_child("bun install", install.stdout.take(), install.stderr.take());
+    let install_status = install.wait()?;
+    // ensure streams finish
+    join_streams(install_join);
+
+    if !install_status.success() {
+        let code = install_status.code().unwrap_or(1);
+        return Err(io::Error::new(
+            io::ErrorKind::Other,
+            format!("bun install failed with status {code}"),
+        ));
+    }
+
+    let target = env::var("TARGET").unwrap();
+
+    // --- bun build (in ./package), emit to OUT_DIR, keep temps inside OUT_DIR ---
+    let mut build = Command::new("bun")
+        .current_dir(&package_dir)
+        .env("TMPDIR", &out_dir)
+        .arg("build")
+        .arg("./cli.ts")
+        .arg(format!("--target={}", bun_tgt.as_bun_flag()))
+        .arg("--compile")
+        .arg("--outfile")
+        .arg(&output_path)
+        .stdout(Stdio::piped())
+        .stderr(Stdio::piped())
+        .spawn()
+        .map_err(|e| io::Error::new(e.kind(), format!("Failed to spawn `bun build`: {e}")))?;
+
+    let build_join = stream_child("bun build", build.stdout.take(), build.stderr.take());
+    let status = build.wait()?;
+    // ensure streams finish
+    join_streams(build_join);
+
+    if status.success() {
+        info("bun build succeeded");
+    } else {
+        let code = status.code().unwrap_or(1);
+        warn(&format!("bun build failed with status: {code}"));
+        return Err(io::Error::new(io::ErrorKind::Other, "bun build failed"));
+    }
+
+    // Ensure the output is executable (after it exists)
+    #[cfg(unix)]
+    {
+        use std::os::unix::fs::PermissionsExt;
+        let mut perms = fs::metadata(&output_path)?.permissions();
+        perms.set_mode(0o755);
+        fs::set_permissions(&output_path, perms)?;
+    }
+
+    println!("cargo:warning=Built CLI at {}", output_path.display());
+    println!("cargo:rustc-env=CLIENT_CLI_BIN={}", output_path.display());
+
+    // --- Cleanup stray .bun-build temp files (conservative: older than 5 minutes) ---
+    for dir in [&manifest_dir, &package_dir, &out_dir] {
+        if let Err(e) = remove_bun_temp_files(dir, Some(Duration::from_secs(5 * 60))) {
+            println!("cargo:warning=cleanup in {} failed: {e}", dir.display());
+        }
+    }
+
+    Ok(())
+}
+
+// Spawn readers for child's stdout/stderr so we don't deadlock on pipe buffers
+fn stream_child(
+    tag: &str,
+    stdout: Option<ChildStdout>,
+    stderr: Option<ChildStderr>,
+) -> (
+    Option<thread::JoinHandle<()>>,
+    Option<thread::JoinHandle<()>>,
+) {
+    let t1 = stdout.map(|out| {
+        let tag = tag.to_string();
+        thread::spawn(move || {
+            let reader = io::BufReader::new(out);
+            for line in reader.lines() {
+                info(&format!("[{tag} stdout] {}", line.unwrap_or_default()));
+            }
+        })
+    });
+    let t2 = stderr.map(|err| {
+        let tag = tag.to_string();
+        thread::spawn(move || {
+            let reader = io::BufReader::new(err);
+            for line in reader.lines() {
+                warn(&format!("[{tag} stderr] {}", line.unwrap_or_default()));
+            }
+        })
+    });
+    (t1, t2)
+}
+
+fn join_streams(
+    joins: (
+        Option<thread::JoinHandle<()>>,
+        Option<thread::JoinHandle<()>>,
+    ),
+) {
+    if let Some(j) = joins.0 {
+        let _ = j.join();
+    }
+    if let Some(j) = joins.1 {
+        let _ = j.join();
+    }
+}
+
+fn remove_bun_temp_files(dir: &Path, older_than: Option<Duration>) -> io::Result<()> {
+    let now = SystemTime::now();
+    for entry in fs::read_dir(dir)? {
+        let entry = entry?;
+        let path = entry.path();
+        if !path.is_file() {
+            continue;
+        }
+
+        // Files like ".1860e7df40ff1bef-00000000.bun-build"
+        let name = entry.file_name();
+        let name = name.to_string_lossy();
+        let looks_like_bun_temp = name.starts_with('.') && name.ends_with(".bun-build");
+
+        if !looks_like_bun_temp {
+            continue;
+        }
+
+        if let Some(age) = older_than {
+            if let Ok(meta) = entry.metadata() {
+                if let Ok(modified) = meta.modified() {
+                    if now.duration_since(modified).unwrap_or_default() < age {
+                        // too new; skip to avoid racing an in-flight builder
+                        continue;
+                    }
+                }
+            }
+        }
+
+        match fs::remove_file(&path) {
+            Ok(_) => println!("cargo:warning=removed stray bun temp {}", path.display()),
+            Err(e) => println!("cargo:warning=failed to remove {}: {e}", path.display()),
+        }
+    }
+    Ok(())
+}
+
+fn warn(msg: &str) {
+    let _ = writeln!(io::stderr(), "[build.rs] {msg}");
+    println!("cargo:warning={msg}");
+}
+
+fn info(msg: &str) {
+    let _ = writeln!(io::stderr(), "[build.rs] {msg}");
+    println!("cargo:warning=INFO|{msg}");
+}

crates/cli/bun_target.rs

@@ -0,0 +1,131 @@
+use std::env;
+use std::fmt;
+
+#[derive(Copy, Clone, Eq, PartialEq, Hash, Debug)]
+pub enum BunTarget {
+    LinuxX64Glibc,
+    LinuxArm64Glibc,
+    LinuxX64Musl,
+    LinuxArm64Musl,
+    WindowsX64,
+    WindowsArm64,
+    MacX64,
+    MacArm64,
+}
+
+impl BunTarget {
+    pub const fn as_bun_flag(self) -> &'static str {
+        match self {
+            BunTarget::LinuxX64Glibc => "bun-linux-x64",
+            BunTarget::LinuxArm64Glibc => "bun-linux-arm64",
+            BunTarget::LinuxX64Musl => "bun-linux-x64-musl",
+            BunTarget::LinuxArm64Musl => "bun-linux-arm64-musl",
+            BunTarget::WindowsX64 => "bun-windows-x64",
+            BunTarget::WindowsArm64 => "bun-windows-arm64",
+            BunTarget::MacX64 => "bun-darwin-x64",
+            BunTarget::MacArm64 => "bun-darwin-arm64",
+        }
+    }
+
+    pub const fn rust_triples(self) -> &'static [&'static str] {
+        match self {
+            BunTarget::LinuxX64Glibc => {
+                &["x86_64-unknown-linux-gnu", "x86_64-unknown-linux-gnu.2.17"]
+            }
+            BunTarget::LinuxArm64Glibc => &["aarch64-unknown-linux-gnu"],
+            BunTarget::LinuxX64Musl => &["x86_64-unknown-linux-musl"],
+            BunTarget::LinuxArm64Musl => &["aarch64-unknown-linux-musl"],
+            BunTarget::WindowsX64 => &["x86_64-pc-windows-msvc"],
+            BunTarget::WindowsArm64 => &["aarch64-pc-windows-msvc"], // chart says unsupported; still map
+            BunTarget::MacX64 => &["x86_64-apple-darwin"],
+            BunTarget::MacArm64 => &["aarch64-apple-darwin"],
+        }
+    }
+
+    pub fn from_rust_target(triple: &str) -> Option<Self> {
+        let norm = triple.trim();
+        if norm.starts_with("x86_64-") && norm.contains("-linux-") && norm.ends_with("gnu") {
+            return Some(BunTarget::LinuxX64Glibc);
+        }
+        if norm.starts_with("aarch64-") && norm.contains("-linux-") && norm.ends_with("gnu") {
+            return Some(BunTarget::LinuxArm64Glibc);
+        }
+        if norm.starts_with("x86_64-") && norm.contains("-linux-") && norm.ends_with("musl") {
+            return Some(BunTarget::LinuxX64Musl);
+        }
+        if norm.starts_with("aarch64-") && norm.contains("-linux-") && norm.ends_with("musl") {
+            return Some(BunTarget::LinuxArm64Musl);
+        }
+        if norm == "x86_64-pc-windows-msvc" {
+            return Some(BunTarget::WindowsX64);
+        }
+        if norm == "aarch64-pc-windows-msvc" {
+            return Some(BunTarget::WindowsArm64);
+        }
+        if norm == "x86_64-apple-darwin" {
+            return Some(BunTarget::MacX64);
+        }
+        if norm == "aarch64-apple-darwin" {
+            return Some(BunTarget::MacArm64);
+        }
+        for bt in [
+            BunTarget::LinuxX64Glibc,
+            BunTarget::LinuxArm64Glibc,
+            BunTarget::LinuxX64Musl,
+            BunTarget::LinuxArm64Musl,
+            BunTarget::WindowsX64,
+            BunTarget::WindowsArm64,
+            BunTarget::MacX64,
+            BunTarget::MacArm64,
+        ] {
+            for &t in bt.rust_triples() {
+                if t == norm {
+                    return Some(bt);
+                }
+            }
+        }
+        None
+    }
+
+    pub fn from_cargo_env() -> Result<Self, BunTargetError> {
+        if let Ok(triple) = env::var("TARGET") {
+            if let Some(bt) = Self::from_rust_target(&triple) {
+                return Ok(bt);
+            }
+            return Err(BunTargetError::UnknownTriple(triple));
+        }
+
+        let os = env::var("CARGO_CFG_TARGET_OS").unwrap_or_default();
+        let arch = env::var("CARGO_CFG_TARGET_ARCH").unwrap_or_default();
+        let envv = env::var("CARGO_CFG_TARGET_ENV").unwrap_or_default();
+        let vendor = env::var("CARGO_CFG_TARGET_VENDOR").unwrap_or_else(|_| "unknown".into());
+
+        let triple = format!(
+            "{}-{}-{}-{}",
+            arch,
+            vendor,
+            os,
+            if envv.is_empty() { "gnu" } else { &envv }
+        );
+        if let Some(bt) = Self::from_rust_target(&triple) {
+            Ok(bt)
+        } else {
+            Err(BunTargetError::UnknownTriple(triple))
+        }
+    }
+}
+
+#[derive(Debug)]
+pub enum BunTargetError {
+    UnknownTriple(String),
+}
+
+impl fmt::Display for BunTargetError {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        match self {
+            BunTargetError::UnknownTriple(t) => write!(f, "unrecognized Rust target triple: {t}"),
+        }
+    }
+}
+
+impl std::error::Error for BunTargetError {}

crates/cli/package/cli.ts

@@ -30,24 +30,23 @@ type ChunkStat = {
 
 function printHelp() {
     console.log(`
-Usage: bun client_cli.ts [options] [prompt]
+./cli [options] [prompt]
 
 Simple CLI tool for testing the local OpenAI-compatible API server.
 
 Options:
-  --model <model>     Model to use (default: ${DEFAULT_MODEL})
+  --model <model>     Model to use (default: gemma-3-1b-it)
   --prompt <prompt>   The prompt to send (can also be provided as positional argument)
   --list-models       List all available models from the server
   --help              Show this help message
 
 Examples:
-  ./cli.ts "What is the capital of France?"
+  ./cli "What is the capital of France?"
-  ./cli.ts --model gemma-3-1b-it --prompt "Hello, world!"
+  ./cli --model gemma-3-1b-it --prompt "Hello, world!"
-  ./cli.ts --prompt "Who was the 16th president of the United States?"
+  ./cli --prompt "Who was the 16th president of the United States?"
-  ./cli.ts --list-models
+  ./cli --list-models
 
-The server should be running at http://localhost:8080
+The server must be running at http://localhost:8080
-Start it with: ./run_server.sh
 `);
 }
 

crates/cli/package/package.json

@@ -0,0 +1,11 @@
+{
+  "name": "cli",
+  "main": "cli.ts",
+  "scripts": {
+    "build": "bun build cli.ts --compile --outfile cli"
+  },
+  "dependencies": {
+    "install": "^0.13.0",
+    "openai": "^5.16.0"
+  }
+}

crates/cli/src/main.rs

@@ -0,0 +1,32 @@
+use std::{env, fs, io, path::PathBuf, process::Command};
+
+#[cfg(unix)]
+use std::os::unix::fs::PermissionsExt;
+
+fn main() -> io::Result<()> {
+    // Absolute path provided by build.rs at compile time.
+    // `include_bytes!` accepts string literals; `env!` expands to a literal at compile time.
+    const CLIENT_CLI: &[u8] = include_bytes!(env!("CLIENT_CLI_BIN"));
+
+    // Write to a temp file
+    let mut tmp = env::temp_dir();
+    tmp.push("client-cli-embedded");
+
+    fs::write(&tmp, CLIENT_CLI)?;
+
+    // Ensure it's executable on Unix
+    #[cfg(unix)]
+    {
+        let mut perms = fs::metadata(&tmp)?.permissions();
+        perms.set_mode(0o755);
+        fs::set_permissions(&tmp, perms)?;
+    }
+
+    // Run it
+    let status = Command::new(&tmp).arg("--version").status()?;
+    if !status.success() {
+        return Err(io::Error::new(io::ErrorKind::Other, "client-cli failed"));
+    }
+
+    Ok(())
+}

crates/embeddings-engine/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "embeddings-engine"
-version = "0.1.0"
+version.workspace = true
 edition = "2024"
 
 [lib]

crates/embeddings-engine/README.md

@@ -1,4 +1,100 @@
 # Embeddings Engine
 
-A high-performance text embeddings service that generates vector representations of text using state-of-the-art models. 
+A high-performance text embeddings service that generates vector representations of text using state-of-the-art models. This crate wraps the FastEmbed library to provide embeddings with OpenAI-compatible API endpoints.
-This crate wraps the fastembed crate to provide embeddings and partially adapts the openai specification.  
+
+## Overview
+
+The embeddings-engine provides a standalone service for generating text embeddings that can be used for semantic search, similarity comparisons, and other NLP tasks. It's designed to be compatible with OpenAI's embeddings API format.
+
+## Features
+
+- **OpenAI-Compatible API**: `/v1/embeddings` endpoint matching OpenAI's specification
+- **FastEmbed Integration**: Powered by the FastEmbed library for high-quality embeddings
+- **Multiple Model Support**: Support for various embedding models
+- **High Performance**: Optimized for fast embedding generation
+- **Standalone Service**: Can run independently or as part of the predict-otron-9000 platform
+
+## Building and Running
+
+### Prerequisites
+- Rust toolchain
+- Internet connection for initial model downloads
+
+### Standalone Server
+```bash
+cargo run --bin embeddings-engine --release
+```
+
+The service will start on port 8080 by default.
+
+## API Usage
+
+### Generate Embeddings
+
+**Endpoint**: `POST /v1/embeddings`
+
+**Request Body**:
+```json
+{
+  "input": "Your text to embed",
+  "model": "nomic-embed-text-v1.5"
+}
+```
+
+**Response**:
+```json
+{
+  "object": "list",
+  "data": [
+    {
+      "object": "embedding",
+      "index": 0,
+      "embedding": [0.1, 0.2, 0.3, ...]
+    }
+  ],
+  "model": "nomic-embed-text-v1.5",
+  "usage": {
+    "prompt_tokens": 0,
+    "total_tokens": 0
+  }
+}
+```
+
+### Example Usage
+
+**Using cURL**:
+```bash
+curl -s http://localhost:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -d '{
+    "input": "The quick brown fox jumps over the lazy dog",
+    "model": "nomic-embed-text-v1.5"
+  }' | jq
+```
+
+**Using Python OpenAI Client**:
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8080/v1",
+    api_key="dummy"  # Not validated but required by client
+)
+
+response = client.embeddings.create(
+    input="Your text here",
+    model="nomic-embed-text-v1.5"
+)
+
+print(response.data[0].embedding)
+```
+
+## Configuration
+
+The service can be configured through environment variables:
+- `SERVER_PORT`: Port to run on (default: 8080)
+- `RUST_LOG`: Logging level (default: info)
+
+## Integration
+
+This service is designed to work seamlessly with the predict-otron-9000 main server, but can also be deployed independently for dedicated embeddings workloads.

crates/embeddings-engine/src/lib.rs

@@ -1,9 +1,5 @@
 use async_openai::types::{CreateEmbeddingRequest, EmbeddingInput};
-use axum::{
+use axum::{Json, Router, response::Json as ResponseJson, routing::post};
-    response::Json as ResponseJson, routing::{post},
-    Json,
-    Router,
-};
 use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
 use once_cell::sync::Lazy;
 use tower_http::trace::TraceLayer;
@@ -13,15 +9,18 @@ use tracing;
 static EMBEDDING_MODEL: Lazy<TextEmbedding> = Lazy::new(|| {
     tracing::info!("Initializing persistent embedding model (singleton)");
     let model_start_time = std::time::Instant::now();
-    
+
     let model = TextEmbedding::try_new(
-        InitOptions::new(EmbeddingModel::NomicEmbedTextV15).with_show_download_progress(true)
+        InitOptions::new(EmbeddingModel::NomicEmbedTextV15).with_show_download_progress(true),
     )
-        .expect("Failed to initialize persistent embedding model");
+    .expect("Failed to initialize persistent embedding model");
-    
+
     let model_init_time = model_start_time.elapsed();
-    tracing::info!("Persistent embedding model initialized in {:.2?}", model_init_time);
+    tracing::info!(
-    
+        "Persistent embedding model initialized in {:.2?}",
+        model_init_time
+    );
+
     model
 });
 
@@ -30,18 +29,21 @@ pub async fn embeddings_create(
 ) -> ResponseJson<serde_json::Value> {
     // Start timing the entire process
     let start_time = std::time::Instant::now();
-    
+
     // Phase 1: Access persistent model instance
     let model_start_time = std::time::Instant::now();
-    
+
     // Access the lazy-initialized persistent model instance
     // This will only initialize the model on the first request
     let model_access_time = model_start_time.elapsed();
-    tracing::debug!("Persistent model access completed in {:.2?}", model_access_time);
+    tracing::debug!(
-    
+        "Persistent model access completed in {:.2?}",
+        model_access_time
+    );
+
     // Phase 2: Process input
     let input_start_time = std::time::Instant::now();
-    
+
     let embedding_input = payload.input;
     let texts_from_embedding_input = match embedding_input {
         EmbeddingInput::String(text) => vec![text],
@@ -53,41 +55,58 @@ pub async fn embeddings_create(
             panic!("Array of integer arrays not supported for text embeddings");
         }
     };
-    
+
     let input_processing_time = input_start_time.elapsed();
-    tracing::debug!("Input processing completed in {:.2?}", input_processing_time);
+    tracing::debug!(
-    
+        "Input processing completed in {:.2?}",
+        input_processing_time
+    );
+
     // Phase 3: Generate embeddings
     let embedding_start_time = std::time::Instant::now();
-    
+
     let embeddings = EMBEDDING_MODEL
         .embed(texts_from_embedding_input, None)
         .expect("failed to embed document");
-    
+
     let embedding_generation_time = embedding_start_time.elapsed();
-    tracing::info!("Embedding generation completed in {:.2?}", embedding_generation_time);
+    tracing::info!(
-    
+        "Embedding generation completed in {:.2?}",
+        embedding_generation_time
+    );
+
     // Memory usage estimation (approximate)
-    let embedding_size_bytes = embeddings.iter()
+    let embedding_size_bytes = embeddings
+        .iter()
         .map(|e| e.len() * std::mem::size_of::<f32>())
         .sum::<usize>();
-    tracing::debug!("Embedding size: {:.2} MB", embedding_size_bytes as f64 / 1024.0 / 1024.0);
+    tracing::debug!(
+        "Embedding size: {:.2} MB",
+        embedding_size_bytes as f64 / 1024.0 / 1024.0
+    );
 
     // Only log detailed embedding information at trace level to reduce log volume
     tracing::trace!("Embeddings length: {}", embeddings.len());
     tracing::info!("Embedding dimension: {}", embeddings[0].len());
 
     // Log the first 10 values of the original embedding at trace level
-    tracing::trace!("Original embedding preview: {:?}", &embeddings[0][..10.min(embeddings[0].len())]);
+    tracing::trace!(
+        "Original embedding preview: {:?}",
+        &embeddings[0][..10.min(embeddings[0].len())]
+    );
 
     // Check if there are any NaN or zero values in the original embedding
     let nan_count = embeddings[0].iter().filter(|&&x| x.is_nan()).count();
     let zero_count = embeddings[0].iter().filter(|&&x| x == 0.0).count();
-    tracing::trace!("Original embedding stats: NaN count={}, zero count={}", nan_count, zero_count);
+    tracing::trace!(
+        "Original embedding stats: NaN count={}, zero count={}",
+        nan_count,
+        zero_count
+    );
 
     // Phase 4: Post-process embeddings
     let postprocessing_start_time = std::time::Instant::now();
-    
+
     // Create the final embedding
     let final_embedding = {
         // Check if the embedding is all zeros
@@ -110,6 +129,8 @@ pub async fn embeddings_create(
 
             // Normalize the random embedding
             let norm: f32 = random_embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
+
+            #[allow(clippy::needless_range_loop)]
             for i in 0..random_embedding.len() {
                 random_embedding[i] /= norm;
             }
@@ -123,25 +144,35 @@ pub async fn embeddings_create(
             let target_dimension = 768;
             if padded_embedding.len() < target_dimension {
                 let padding_needed = target_dimension - padded_embedding.len();
-                tracing::trace!("Padding embedding with {} zeros to reach {} dimensions", padding_needed, target_dimension);
+                tracing::trace!(
+                    "Padding embedding with {} zeros to reach {} dimensions",
+                    padding_needed,
+                    target_dimension
+                );
                 padded_embedding.extend(vec![0.0; padding_needed]);
             }
 
             padded_embedding
         }
     };
-    
+
     let postprocessing_time = postprocessing_start_time.elapsed();
-    tracing::debug!("Embedding post-processing completed in {:.2?}", postprocessing_time);
+    tracing::debug!(
+        "Embedding post-processing completed in {:.2?}",
+        postprocessing_time
+    );
 
     tracing::trace!("Final embedding dimension: {}", final_embedding.len());
 
     // Log the first 10 values of the final embedding at trace level
-    tracing::trace!("Final embedding preview: {:?}", &final_embedding[..10.min(final_embedding.len())]);
+    tracing::trace!(
+        "Final embedding preview: {:?}",
+        &final_embedding[..10.min(final_embedding.len())]
+    );
 
     // Phase 5: Prepare response
     let response_start_time = std::time::Instant::now();
-    
+
     // Return a response that matches the OpenAI API format
     let response = serde_json::json!({
         "object": "list",
@@ -158,10 +189,10 @@ pub async fn embeddings_create(
             "total_tokens": 0
         }
     });
-    
+
     let response_time = response_start_time.elapsed();
     tracing::debug!("Response preparation completed in {:.2?}", response_time);
-    
+
     // Log total time and breakdown
     let total_time = start_time.elapsed();
     tracing::info!(
@@ -171,7 +202,7 @@ pub async fn embeddings_create(
         embedding_generation_time,
         postprocessing_time
     );
-    
+
     ResponseJson(response)
 }
 
@@ -179,4 +210,4 @@ pub fn create_embeddings_router() -> Router {
     Router::new()
         .route("/v1/embeddings", post(embeddings_create))
         .layer(TraceLayer::new_for_http())
-}
+}

crates/embeddings-engine/src/main.rs

@@ -1,8 +1,8 @@
 use async_openai::types::{CreateEmbeddingRequest, EmbeddingInput};
 use axum::{
-	response::Json as ResponseJson, routing::{get, post},
+    Json, Router,
-	Json,
+    response::Json as ResponseJson,
-	Router,
+    routing::{get, post},
 };
 use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
 use serde::{Deserialize, Serialize};
@@ -13,19 +13,17 @@ use tracing;
 const DEFAULT_SERVER_HOST: &str = "127.0.0.1";
 const DEFAULT_SERVER_PORT: &str = "8080";
 
-
 async fn embeddings_create(
     Json(payload): Json<CreateEmbeddingRequest>,
 ) -> ResponseJson<serde_json::Value> {
     let model = TextEmbedding::try_new(
-        InitOptions::new(EmbeddingModel::NomicEmbedTextV15).with_show_download_progress(true)
+        InitOptions::new(EmbeddingModel::NomicEmbedTextV15).with_show_download_progress(true),
     )
     .expect("Failed to initialize model");
 
+    let embedding_input = payload.input;
 
-	let embedding_input = payload.input;
+    let texts_from_embedding_input = match embedding_input {
-
-	let texts_from_embedding_input = match embedding_input {
         EmbeddingInput::String(text) => vec![text],
         EmbeddingInput::StringArray(texts) => texts,
         EmbeddingInput::IntegerArray(_) => {
@@ -45,12 +43,19 @@ async fn embeddings_create(
     tracing::info!("Embedding dimension: {}", embeddings[0].len());
 
     // Log the first 10 values of the original embedding at trace level
-    tracing::trace!("Original embedding preview: {:?}", &embeddings[0][..10.min(embeddings[0].len())]);
+    tracing::trace!(
+        "Original embedding preview: {:?}",
+        &embeddings[0][..10.min(embeddings[0].len())]
+    );
 
     // Check if there are any NaN or zero values in the original embedding
     let nan_count = embeddings[0].iter().filter(|&&x| x.is_nan()).count();
     let zero_count = embeddings[0].iter().filter(|&&x| x == 0.0).count();
-    tracing::trace!("Original embedding stats: NaN count={}, zero count={}", nan_count, zero_count);
+    tracing::trace!(
+        "Original embedding stats: NaN count={}, zero count={}",
+        nan_count,
+        zero_count
+    );
 
     // Create the final embedding
     let final_embedding = {
@@ -87,7 +92,11 @@ async fn embeddings_create(
             let target_dimension = 768;
             if padded_embedding.len() < target_dimension {
                 let padding_needed = target_dimension - padded_embedding.len();
-                tracing::trace!("Padding embedding with {} zeros to reach {} dimensions", padding_needed, target_dimension);
+                tracing::trace!(
+                    "Padding embedding with {} zeros to reach {} dimensions",
+                    padding_needed,
+                    target_dimension
+                );
                 padded_embedding.extend(vec![0.0; padding_needed]);
             }
 
@@ -98,7 +107,10 @@ async fn embeddings_create(
     tracing::trace!("Final embedding dimension: {}", final_embedding.len());
 
     // Log the first 10 values of the final embedding at trace level
-    tracing::trace!("Final embedding preview: {:?}", &final_embedding[..10.min(final_embedding.len())]);
+    tracing::trace!(
+        "Final embedding preview: {:?}",
+        &final_embedding[..10.min(final_embedding.len())]
+    );
 
     // Return a response that matches the OpenAI API format
     let response = serde_json::json!({
@@ -120,7 +132,7 @@ async fn embeddings_create(
 }
 
 fn create_app() -> Router {
-	Router::new()
+    Router::new()
         .route("/v1/embeddings", post(embeddings_create))
         .layer(TraceLayer::new_for_http())
 }
@@ -143,21 +155,21 @@ async fn main() {
         .init();
     let app = create_app();
 
-	let server_host = env::var("SERVER_HOST").unwrap_or_else(|_| DEFAULT_SERVER_HOST.to_string());
+    let server_host = env::var("SERVER_HOST").unwrap_or_else(|_| DEFAULT_SERVER_HOST.to_string());
-	let server_port = env::var("SERVER_PORT").unwrap_or_else(|_| DEFAULT_SERVER_PORT.to_string());
+    let server_port = env::var("SERVER_PORT").unwrap_or_else(|_| DEFAULT_SERVER_PORT.to_string());
-	let server_address = format!("{}:{}", server_host, server_port);
+    let server_address = format!("{}:{}", server_host, server_port);
-	let listener = tokio::net::TcpListener::bind(server_address).await.unwrap();
+    let listener = tokio::net::TcpListener::bind(server_address).await.unwrap();
-	tracing::info!("Listening on {}", listener.local_addr().unwrap());
+    tracing::info!("Listening on {}", listener.local_addr().unwrap());
     axum::serve(listener, app).await.unwrap();
 }
 
 #[cfg(test)]
 mod tests {
-	use super::*;
+    use super::*;
-	use axum::body::to_bytes;
+    use axum::body::Body;
-	use axum::body::Body;
+    use axum::body::to_bytes;
-	use axum::http::StatusCode;
+    use axum::http::StatusCode;
-	use tower::ServiceExt;
+    use tower::ServiceExt;
 
     #[tokio::test]
     async fn test_embeddings_create() {
@@ -168,11 +180,13 @@ mod tests {
 
         let body = CreateEmbeddingRequest {
             model: "nomic-text-embed".to_string(),
-            input: EmbeddingInput::from(vec!["The food was delicious and the waiter...".to_string()]),
+            input: EmbeddingInput::from(vec![
-			encoding_format: None,
+                "The food was delicious and the waiter...".to_string(),
-			user: None,
+            ]),
-			dimensions: Some(768),
+            encoding_format: None,
-		};
+            user: None,
+            dimensions: Some(768),
+        };
 
         let response = app
             .oneshot(

crates/gemma-runner/Cargo.toml

@@ -1,26 +1,30 @@
 [package]
 name = "gemma-runner"
-version = "0.1.0"
+version.workspace = true
 edition = "2021"
 
+
+
+
 [dependencies]
 candle-core = { git = "https://github.com/huggingface/candle.git" }
 candle-nn = { git = "https://github.com/huggingface/candle.git" }
 candle-transformers = { git = "https://github.com/huggingface/candle.git" }
-candle-examples = { git = "https://github.com/huggingface/candle.git" }
-
-[target.'cfg(target_os = "macos")'.dependencies]
-candle-core = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
-candle-nn = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
-candle-transformers = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
 hf-hub = "0.4"
-tokenizers = "0.21"
+tokenizers = "0.22.0"
 anyhow = "1.0"
 clap = { version = "4.0", features = ["derive", "string"] }
 serde_json = "1.0"
 tracing = "0.1"
 tracing-chrome = "0.7"
 tracing-subscriber = "0.3"
+utils = {path = "../utils"}
+
+[target.'cfg(target_os = "macos")'.dependencies]
+candle-core = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
+candle-nn = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
+candle-transformers = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
+
 
 [features]
 default = []

crates/gemma-runner/src/gemma_api.rs

@@ -4,22 +4,23 @@ extern crate accelerate_src;
 extern crate intel_mkl_src;
 
 use anyhow::{Error as E, Result};
-use clap::ValueEnum;
 use candle_transformers::models::gemma::{Config as Config1, Model as Model1};
 use candle_transformers::models::gemma2::{Config as Config2, Model as Model2};
 use candle_transformers::models::gemma3::{Config as Config3, Model as Model3};
+use clap::ValueEnum;
 
 // Removed gemma_cli import as it's not needed for the API
-use candle_core::{utils, DType, Device, Tensor};
+use candle_core::{DType, Device, Tensor};
-use candle_examples::token_output_stream::TokenOutputStream;
 use candle_nn::VarBuilder;
 use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use std::io::Write;
-use tokenizers::Tokenizer;
 
 use std::sync::mpsc::{self, Receiver, Sender};
 use std::thread;
+use tokenizers::Tokenizer;
+use utils::hub_load_safetensors;
+use utils::token_output_stream::TokenOutputStream;
 
 #[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
 pub enum WhichModel {
@@ -85,9 +86,9 @@ pub struct TextGeneration {
 fn device(cpu: bool) -> Result<Device> {
     if cpu {
         Ok(Device::Cpu)
-    } else if utils::cuda_is_available() {
+    } else if candle_core::utils::cuda_is_available() {
         Ok(Device::new_cuda(0)?)
-    } else if utils::metal_is_available() {
+    } else if candle_core::utils::metal_is_available() {
         Ok(Device::new_metal(0)?)
     } else {
         Ok(Device::Cpu)
@@ -98,7 +99,7 @@ impl TextGeneration {
     #[allow(clippy::too_many_arguments)]
     fn new(
         model: Model,
-        tokenizer: Tokenizer,
+        tokenizer: tokenizers::Tokenizer,
         seed: u64,
         temp: Option<f64>,
         top_p: Option<f64>,
@@ -119,7 +120,12 @@ impl TextGeneration {
 
     /// Stream-only generation: sends freshly generated token strings over `tx`.
     /// (Does not send the prompt tokens; only newly generated model tokens.)
-    fn run_stream(&mut self, prompt: &str, sample_len: usize, tx: Sender<Result<String>>) -> Result<()> {
+    fn run_stream(
+        &mut self,
+        prompt: &str,
+        sample_len: usize,
+        tx: Sender<Result<String>>,
+    ) -> Result<()> {
         self.tokenizer.clear();
 
         // Encode prompt (context only; do not emit prompt tokens to the stream).
@@ -257,10 +263,10 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
 
     println!(
         "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        utils::with_avx(),
+        candle_core::utils::with_avx(),
-        utils::with_neon(),
+        candle_core::utils::with_neon(),
-        utils::with_simd128(),
+        candle_core::utils::with_simd128(),
-        utils::with_f16c()
+        candle_core::utils::with_f16c()
     );
 
     let device = device(cfg.cpu)?;
@@ -303,7 +309,7 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
             WhichModel::BaseV3_1B => "google/gemma-3-1b-pt",
             WhichModel::InstructV3_1B => "google/gemma-3-1b-it",
         }
-            .to_string()
+        .to_string()
     });
 
     println!("Loading model: {}", &model_id);
@@ -313,7 +319,7 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
     let config_filename = repo.get("config.json")?;
     let filenames = match cfg.model {
         WhichModel::BaseV3_1B | WhichModel::InstructV3_1B => vec![repo.get("model.safetensors")?],
-        _ => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
+        _ => hub_load_safetensors(&repo, "model.safetensors.index.json")?,
     };
     println!("Retrieved files in {:?}", start.elapsed());
 
@@ -337,7 +343,10 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
             let model = Model1::new(cfg.use_flash_attn, &config, vb)?;
             Model::V1(model)
         }
-        WhichModel::BaseV2_2B | WhichModel::InstructV2_2B | WhichModel::BaseV2_9B | WhichModel::InstructV2_9B => {
+        WhichModel::BaseV2_2B
+        | WhichModel::InstructV2_2B
+        | WhichModel::BaseV2_9B
+        | WhichModel::InstructV2_9B => {
             let config: Config2 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
             let model = Model2::new(cfg.use_flash_attn, &config, vb)?;
             Model::V2(model)

crates/gemma-runner/src/gemma_cli.rs

@@ -1,6 +1,6 @@
-use std::io::Write;
-use clap::Parser;
 use crate::gemma_api::{run_gemma_api, GemmaInferenceConfig, WhichModel};
+use clap::Parser;
+use std::io::Write;
 
 #[derive(Parser, Debug)]
 #[command(author, version, about = "Fast Gemma inference with Candle", long_about = None)]
@@ -94,4 +94,4 @@ pub fn run_cli() -> anyhow::Result<()> {
         }
     }
     Ok(())
-}
+}

crates/gemma-runner/src/main.rs

@@ -2,8 +2,8 @@
 extern crate accelerate_src;
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
-mod gemma_cli;
 mod gemma_api;
+mod gemma_cli;
 
 use anyhow::Error;
 use clap::{Parser, ValueEnum};
@@ -14,4 +14,4 @@ use std::io::Write;
 /// just a placeholder, not used for anything
 fn main() -> std::result::Result<(), Error> {
     run_cli()
-}
+}

crates/helm-chart-tool/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "helm-chart-tool"
-version = "0.1.0"
+version.workspace = true
 edition = "2021"
 
 [[bin]]

crates/helm-chart-tool/README.md

@@ -137,7 +137,7 @@ Parsing workspace at: ..
 Output directory: ../generated-helm-chart
 Chart name: predict-otron-9000
 Found 4 services:
-  - leptos-app: ghcr.io/geoffsee/leptos-app:latest (port 8788)
+  - chat-ui: ghcr.io/geoffsee/chat-ui:latest (port 8788)
   - inference-engine: ghcr.io/geoffsee/inference-service:latest (port 8080)
   - embeddings-engine: ghcr.io/geoffsee/embeddings-service:latest (port 8080)
   - predict-otron-9000: ghcr.io/geoffsee/predict-otron-9000:latest (port 8080)

crates/helm-chart-tool/src/main.rs

@@ -84,7 +84,10 @@ fn main() -> Result<()> {
     let services = discover_services(workspace_path)?;
     println!("Found {} services:", services.len());
     for service in &services {
-        println!("  - {}: {} (port {})", service.name, service.image, service.port);
+        println!(
+            "  - {}: {} (port {})",
+            service.name, service.image, service.port
+        );
     }
 
     generate_helm_chart(output_path, chart_name, &services)?;
@@ -115,17 +118,20 @@ fn discover_services(workspace_path: &str) -> Result<Vec<ServiceInfo>> {
 fn parse_cargo_toml(path: &Path) -> Result<ServiceInfo> {
     let content = fs::read_to_string(path)
         .with_context(|| format!("Failed to read Cargo.toml at {:?}", path))?;
-    
+
     let cargo_toml: CargoToml = toml::from_str(&content)
         .with_context(|| format!("Failed to parse Cargo.toml at {:?}", path))?;
 
-    let package = cargo_toml.package
+    let package = cargo_toml
+        .package
         .ok_or_else(|| anyhow::anyhow!("No package section found in {:?}", path))?;
 
-    let metadata = package.metadata
+    let metadata = package
+        .metadata
         .ok_or_else(|| anyhow::anyhow!("No metadata section found in {:?}", path))?;
 
-    let kube_metadata = metadata.kube
+    let kube_metadata = metadata
+        .kube
         .ok_or_else(|| anyhow::anyhow!("No kube metadata found in {:?}", path))?;
 
     Ok(ServiceInfo {
@@ -136,7 +142,11 @@ fn parse_cargo_toml(path: &Path) -> Result<ServiceInfo> {
     })
 }
 
-fn generate_helm_chart(output_path: &str, chart_name: &str, services: &[ServiceInfo]) -> Result<()> {
+fn generate_helm_chart(
+    output_path: &str,
+    chart_name: &str,
+    services: &[ServiceInfo],
+) -> Result<()> {
     let chart_dir = Path::new(output_path);
     let templates_dir = chart_dir.join("templates");
 
@@ -512,4 +522,4 @@ fn generate_helmignore(chart_dir: &Path) -> Result<()> {
 
     fs::write(chart_dir.join(".helmignore"), helmignore_content)?;
     Ok(())
-}
+}

crates/inference-engine/Cargo.toml

@@ -1,41 +1,15 @@
 [package]
 name = "inference-engine"
-version = "0.1.0"
+version.workspace = true
 edition = "2021"
 
-
-[[bin]]
-name="gemma_inference"
-path = "src/gemma_inference.rs"
-required-features = ["bin"]
-
-[[bin]]
-name="llama_inference"
-path = "src/llama_inference.rs"
-required-features = ["bin"]
-
-
 [dependencies]
-accelerate-src = { version = "0.3.2", optional = true }
+candle-core = { git = "https://github.com/huggingface/candle.git" }
-candle-datasets = { version = "=0.9.1", optional = true }
+candle-nn = { git = "https://github.com/huggingface/candle.git" }
-candle-nn = { version = "=0.9.1" }
+candle-transformers = { git = "https://github.com/huggingface/candle.git" }
-candle-transformers = { version = "=0.9.1" }
 candle-flash-attn = { version = "=0.9.1", optional = true }
 candle-onnx = { version = "=0.9.1", optional = true }
 
-csv = "1.3.0"
-cudarc = { version = "0.16.3", features = ["std", "cublas", "cublaslt", "curand", "driver", "nvrtc", "f16", "cuda-version-from-build-system", "dynamic-linking"], default-features=false, optional = true }
-half = { version = "2.5.0", features = ["num-traits", "use-intrinsics", "rand_distr"], optional = true }
-hf-hub = { version = "0.4.1", features = ["tokio"] }
-image = { version = "0.25.2", default-features = false, features = ["jpeg", "png"] }
-intel-mkl-src = { version = "0.8.1", features = ["mkl-static-lp64-iomp"], optional = true }
-num-traits = { version = "0.2.15" }
-palette = { version = "0.7.6", optional = true }
-enterpolation = { version = "0.2.1", optional = true}
-pyo3 = { version = "0.22.0", features = ["auto-initialize", "abi3-py311"], optional = true }
-rayon = "1.7.0"
-rubato = { version = "0.15.0", optional = true }
-safetensors = "0.4.1"
 serde = { version = "1.0.171", features = ["derive"] }
 serde_json = "1.0.99"
 symphonia = { version = "0.5.3", features = ["all"], optional = true }
@@ -57,22 +31,14 @@ utoipa = { version = "4.2.0", features = ["axum_extras"] }
 uuid = { version = "1.7.0", features = ["v4"] }
 reborrow = "0.5.5"
 futures-util = "0.3.31"
-gemma-runner = { path = "../gemma-runner" }
+gemma-runner = { path = "../gemma-runner", features = ["metal"] }
-llama-runner = { path = "../llama-runner" }
+llama-runner = { path = "../llama-runner", features = ["metal"]}
 
-# --- Add this section for conditional compilation ---
 [target.'cfg(target_os = "macos")'.dependencies]
-# Use CPU backend for macOS to avoid Metal rotary-emb implementation issues
+candle-core = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
-candle-core = { version = "=0.9.1", features = ["metal"], optional = false }
+candle-nn = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
+candle-transformers = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
 
-[target.'cfg(not(target_os = "macos"))'.dependencies]
-# For Linux or other non-macOS systems, you likely want the CPU backend or CUDA
-# If you're building on Linux with a CUDA-enabled GPU:
-candle-core = { version = "=0.9.1", features = ["cuda"], default-features = false } # Or just "cuda" if not using default features
-
-# If you're building on Linux with only CPU:
-# candle-core = { version = "=0.9.1", default-features = false } # CPU is often the default, but good to be explicit
-# --- End of conditional compilation section ---
 
 [dev-dependencies]
 anyhow = { version = "1", features = ["backtrace"] }

crates/inference-engine/src/inference.rs

@@ -30,4 +30,4 @@ pub trait ModelInference {
 }
 
 /// Factory function type for creating model inference implementations
-pub type ModelInferenceFactory = fn() -> Result<Box<dyn ModelInference>>;
+pub type ModelInferenceFactory = fn() -> Result<Box<dyn ModelInference>>;

crates/inference-engine/src/lib.rs

@@ -1,19 +1,14 @@
 // Expose modules for testing and library usage
-pub mod token_output_stream;
 pub mod model;
-pub mod text_generation;
-pub mod utilities_lib;
 pub mod openai_types;
 // pub mod cli;
-pub mod server;
 pub mod inference;
+pub mod server;
 
 // Re-export key components for easier access
-pub use model::{Model, Which};
-pub use text_generation::TextGeneration;
-pub use token_output_stream::TokenOutputStream;
-pub use server::{AppState, create_router};
 pub use inference::ModelInference;
+pub use model::{Model, Which};
+pub use server::{create_router, AppState};
 
 use std::env;
 use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};

crates/inference-engine/src/model.rs

@@ -1,11 +1,53 @@
-// use candle_core::Tensor;
+use candle_transformers::models::csm::{LlamaConfig, LlamaModel};
 use candle_transformers::models::gemma::{Config as Config1, Model as Model1};
 use candle_transformers::models::gemma2::{Config as Config2, Model as Model2};
 use candle_transformers::models::gemma3::{Config as Config3, Model as Model3};
-use candle_transformers::models::csm::{LlamaConfig, LlamaModel};
+
+#[derive(Clone, Debug)]
+pub enum Model {
+    V1(Model1),
+    V2(Model2),
+    V3(Model3),
+    Llama(LlamaModel),
+}
+
+impl Model {
+    pub fn forward(
+        &mut self,
+        input_ids: &candle_core::Tensor,
+        pos: usize,
+    ) -> candle_core::Result<candle_core::Tensor> {
+        match self {
+            Self::V1(m) => m.forward(input_ids, pos),
+            Self::V2(m) => m.forward(input_ids, pos),
+            Self::V3(m) => m.forward(input_ids, pos),
+            Self::Llama(m) => m.forward(input_ids, pos),
+        }
+    }
+}
+
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Family {
+    GemmaV1,
+    GemmaV2,
+    GemmaV3,
+    Llama,
+}
+
+#[derive(Clone, Copy, Debug)]
+pub struct ModelMeta {
+    pub id: &'static str,
+    pub family: Family,
+    pub instruct: bool,
+}
+
+const fn m(id: &'static str, family: Family, instruct: bool) -> ModelMeta {
+    ModelMeta { id, family, instruct }
+}
 
 #[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
 pub enum Which {
+    // Gemma 1.x
     #[value(name = "2b")]
     Base2B,
     #[value(name = "7b")]
@@ -18,6 +60,8 @@ pub enum Which {
     InstructV1_1_2B,
     #[value(name = "1.1-7b-it")]
     InstructV1_1_7B,
+
+    // CodeGemma
     #[value(name = "code-2b")]
     CodeBase2B,
     #[value(name = "code-7b")]
@@ -26,6 +70,8 @@ pub enum Which {
     CodeInstruct2B,
     #[value(name = "code-7b-it")]
     CodeInstruct7B,
+
+    // Gemma 2
     #[value(name = "2-2b")]
     BaseV2_2B,
     #[value(name = "2-2b-it")]
@@ -34,70 +80,73 @@ pub enum Which {
     BaseV2_9B,
     #[value(name = "2-9b-it")]
     InstructV2_9B,
+
+    // Gemma 3
     #[value(name = "3-1b")]
     BaseV3_1B,
     #[value(name = "3-1b-it")]
     InstructV3_1B,
-    #[value(name = "llama-3.2-1b-it")]
-    LlamaInstruct3_2_1B,
-    #[value(name = "llama-3.2-3b-it")]
-    LlamaInstruct3_2_3B,
-}
 
-pub enum Model {
+    // Llama 3.2 (use aliases instead of duplicate variants)
-    V1(Model1),
+    #[value(name = "llama-3.2-1b")]
-    V2(Model2),
+    Llama32_1B,
-    V3(Model3),
+    #[value(name = "llama-3.2-1b-it", alias = "llama-3.2-1b-instruct")]
-    Llama(LlamaModel),
+    Llama32_1BInstruct,
-}
+    #[value(name = "llama-3.2-3b")]
-
+    Llama32_3B,
-impl Model {
+    #[value(name = "llama-3.2-3b-it", alias = "llama-3.2-3b-instruct")]
-    pub fn forward(&mut self, input_ids: &candle_core::Tensor, pos: usize) -> candle_core::Result<candle_core::Tensor> {
+    Llama32_3BInstruct,
-        match self {
-            Self::V1(m) => m.forward(input_ids, pos),
-            Self::V2(m) => m.forward(input_ids, pos),
-            Self::V3(m) => m.forward(input_ids, pos),
-            Self::Llama(m) => m.forward(input_ids, pos),
-        }
-    }
 }
 
 impl Which {
-    pub fn to_model_id(&self) -> String {
+    pub const fn meta(&self) -> ModelMeta {
+        use Family::*;
         match self {
-            Self::InstructV1_1_2B => "google/gemma-1.1-2b-it".to_string(),
+            // Gemma 1.x
-            Self::InstructV1_1_7B => "google/gemma-1.1-7b-it".to_string(),
+            Self::Base2B => m("google/gemma-2b", GemmaV1, false),
-            Self::Base2B => "google/gemma-2b".to_string(),
+            Self::Base7B => m("google/gemma-7b", GemmaV1, false),
-            Self::Base7B => "google/gemma-7b".to_string(),
+            Self::Instruct2B => m("google/gemma-2b-it", GemmaV1, true),
-            Self::Instruct2B => "google/gemma-2b-it".to_string(),
+            Self::Instruct7B => m("google/gemma-7b-it", GemmaV1, true),
-            Self::Instruct7B => "google/gemma-7b-it".to_string(),
+            Self::InstructV1_1_2B => m("google/gemma-1.1-2b-it", GemmaV1, true),
-            Self::CodeBase2B => "google/codegemma-2b".to_string(),
+            Self::InstructV1_1_7B => m("google/gemma-1.1-7b-it", GemmaV1, true),
-            Self::CodeBase7B => "google/codegemma-7b".to_string(),
+
-            Self::CodeInstruct2B => "google/codegemma-2b-it".to_string(),
+            // CodeGemma
-            Self::CodeInstruct7B => "google/codegemma-7b-it".to_string(),
+            Self::CodeBase2B => m("google/codegemma-2b", GemmaV1, false),
-            Self::BaseV2_2B => "google/gemma-2-2b".to_string(),
+            Self::CodeBase7B => m("google/codegemma-7b", GemmaV1, false),
-            Self::InstructV2_2B => "google/gemma-2-2b-it".to_string(),
+            Self::CodeInstruct2B => m("google/codegemma-2b-it", GemmaV1, true),
-            Self::BaseV2_9B => "google/gemma-2-9b".to_string(),
+            Self::CodeInstruct7B => m("google/codegemma-7b-it", GemmaV1, true),
-            Self::InstructV2_9B => "google/gemma-2-9b-it".to_string(),
+
-            Self::BaseV3_1B => "google/gemma-3-1b-pt".to_string(),
+            // Gemma 2
-            Self::InstructV3_1B => "google/gemma-3-1b-it".to_string(),
+            Self::BaseV2_2B => m("google/gemma-2-2b", GemmaV2, false),
-            Self::LlamaInstruct3_2_1B => "meta-llama/Llama-3.2-1B-Instruct".to_string(),
+            Self::InstructV2_2B => m("google/gemma-2-2b-it", GemmaV2, true),
-            Self::LlamaInstruct3_2_3B => "meta-llama/Llama-3.2-3B-Instruct".to_string(),
+            Self::BaseV2_9B => m("google/gemma-2-9b", GemmaV2, false),
+            Self::InstructV2_9B => m("google/gemma-2-9b-it", GemmaV2, true),
+
+            // Gemma 3
+            Self::BaseV3_1B => m("google/gemma-3-1b-pt", GemmaV3, false),
+            Self::InstructV3_1B => m("google/gemma-3-1b-it", GemmaV3, true),
+
+            // Llama 3.2
+            Self::Llama32_1B => m("meta-llama/Llama-3.2-1B", Llama, false),
+            Self::Llama32_1BInstruct => m("meta-llama/Llama-3.2-1B-Instruct", Llama, true),
+            Self::Llama32_3B => m("meta-llama/Llama-3.2-3B", Llama, false),
+            Self::Llama32_3BInstruct => m("meta-llama/Llama-3.2-3B-Instruct", Llama, true),
         }
     }
 
+    pub fn to_model_id(&self) -> String {
+        self.meta().id.to_string()
+    }
+
     pub fn is_instruct_model(&self) -> bool {
-        match self {
+        self.meta().instruct
-            Self::Base2B | Self::Base7B | Self::CodeBase2B | Self::CodeBase7B | Self::BaseV2_2B | Self::BaseV2_9B | Self::BaseV3_1B => false,
-            _ => true,
-        }
     }
 
     pub fn is_v3_model(&self) -> bool {
-        matches!(self, Self::BaseV3_1B | Self::InstructV3_1B)
+        matches!(self.meta().family, Family::GemmaV3)
     }
 
     pub fn is_llama_model(&self) -> bool {
-        matches!(self, Self::LlamaInstruct3_2_1B | Self::LlamaInstruct3_2_3B)
+        matches!(self.meta().family, Family::Llama)
     }
-}
+}

crates/inference-engine/src/openai_types.rs

@@ -1,5 +1,6 @@
 use either::Either;
 use serde::{Deserialize, Serialize};
+use serde_json::json;
 use std::collections::HashMap;
 use utoipa::ToSchema;
 
@@ -10,7 +11,10 @@ pub struct MessageInnerContent(
 );
 
 impl ToSchema<'_> for MessageInnerContent {
-    fn schema() -> (&'static str, utoipa::openapi::RefOr<utoipa::openapi::Schema>) {
+    fn schema() -> (
+        &'static str,
+        utoipa::openapi::RefOr<utoipa::openapi::Schema>,
+    ) {
         (
             "MessageInnerContent",
             utoipa::openapi::RefOr::T(message_inner_content_schema()),
@@ -45,12 +49,18 @@ fn message_inner_content_schema() -> utoipa::openapi::Schema {
 #[derive(Debug, Clone, Deserialize, Serialize)]
 pub struct MessageContent(
     #[serde(with = "either::serde_untagged")]
-    pub Either<String, Vec<HashMap<String, MessageInnerContent>>>,
+    pub  Either<String, Vec<HashMap<String, MessageInnerContent>>>,
 );
 
 impl ToSchema<'_> for MessageContent {
-    fn schema() -> (&'static str, utoipa::openapi::RefOr<utoipa::openapi::Schema>) {
+    fn schema() -> (
-        ("MessageContent", utoipa::openapi::RefOr::T(message_content_schema()))
+        &'static str,
+        utoipa::openapi::RefOr<utoipa::openapi::Schema>,
+    ) {
+        (
+            "MessageContent",
+            utoipa::openapi::RefOr::T(message_content_schema()),
+        )
     }
 }
 
@@ -213,4 +223,4 @@ pub struct ModelListResponse {
     pub object: String,
     /// Array of available models
     pub data: Vec<Model>,
-}
+}

crates/inference-engine/src/server.rs

@@ -6,19 +6,22 @@ use axum::{
     Json, Router,
 };
 use futures_util::stream::{self, Stream};
-use tokio_stream::wrappers::UnboundedReceiverStream;
 use std::convert::Infallible;
 use std::sync::Arc;
-use tokio::sync::{Mutex, mpsc};
+use tokio::sync::{mpsc, Mutex};
+use tokio_stream::wrappers::UnboundedReceiverStream;
 use tower_http::cors::{Any, CorsLayer};
 use uuid::Uuid;
 
-use crate::openai_types::{ChatCompletionChoice, ChatCompletionChunk, ChatCompletionChunkChoice, ChatCompletionRequest, ChatCompletionResponse, Delta, Message, MessageContent, Model, ModelListResponse, Usage};
+use crate::openai_types::{
+    ChatCompletionChoice, ChatCompletionChunk, ChatCompletionChunkChoice, ChatCompletionRequest,
+    ChatCompletionResponse, Delta, Message, MessageContent, Model, ModelListResponse, Usage,
+};
 use crate::Which;
 use either::Either;
-use serde_json::Value;
 use gemma_runner::{run_gemma_api, GemmaInferenceConfig};
 use llama_runner::{run_llama_inference, LlamaInferenceConfig};
+use serde_json::Value;
 // -------------------------
 // Shared app state
 // -------------------------
@@ -39,13 +42,18 @@ pub struct AppState {
 
 impl Default for AppState {
     fn default() -> Self {
+        // Configure a default model to prevent 503 errors from the chat-ui
+        // This can be overridden by environment variables if needed
+        let default_model_id = std::env::var("DEFAULT_MODEL").unwrap_or_else(|_| "gemma-3-1b-it".to_string());
+        
         let gemma_config = GemmaInferenceConfig {
             model: gemma_runner::WhichModel::InstructV3_1B,
             ..Default::default()
         };
+
         Self {
             model_type: ModelType::Gemma,
-            model_id: "gemma-3-1b-it".to_string(),
+            model_id: default_model_id,
             gemma_config: Some(gemma_config),
             llama_config: None,
         }
@@ -56,18 +64,49 @@ impl Default for AppState {
 // Helper functions
 // -------------------------
 
+fn model_id_to_which(model_id: &str) -> Option<Which> {
+    let normalized = normalize_model_id(model_id);
+    match normalized.as_str() {
+        "gemma-2b" => Some(Which::Base2B),
+        "gemma-7b" => Some(Which::Base7B),
+        "gemma-2b-it" => Some(Which::Instruct2B),
+        "gemma-7b-it" => Some(Which::Instruct7B),
+        "gemma-1.1-2b-it" => Some(Which::InstructV1_1_2B),
+        "gemma-1.1-7b-it" => Some(Which::InstructV1_1_7B),
+        "codegemma-2b" => Some(Which::CodeBase2B),
+        "codegemma-7b" => Some(Which::CodeBase7B),
+        "codegemma-2b-it" => Some(Which::CodeInstruct2B),
+        "codegemma-7b-it" => Some(Which::CodeInstruct7B),
+        "gemma-2-2b" => Some(Which::BaseV2_2B),
+        "gemma-2-2b-it" => Some(Which::InstructV2_2B),
+        "gemma-2-9b" => Some(Which::BaseV2_9B),
+        "gemma-2-9b-it" => Some(Which::InstructV2_9B),
+        "gemma-3-1b" => Some(Which::BaseV3_1B),
+        "gemma-3-1b-it" => Some(Which::InstructV3_1B),
+        "llama-3.2-1b-instruct" => Some(Which::Llama32_1BInstruct),
+        "llama-3.2-3b-instruct" => Some(Which::Llama32_3BInstruct),
+        _ => None,
+    }
+}
+
+
+
+
 fn normalize_model_id(model_id: &str) -> String {
     model_id.to_lowercase().replace("_", "-")
 }
 
 fn build_gemma_prompt(messages: &[Message]) -> String {
     let mut prompt = String::new();
-    
+
     for message in messages {
         match message.role.as_str() {
             "system" => {
                 if let Some(MessageContent(Either::Left(content))) = &message.content {
-                    prompt.push_str(&format!("<start_of_turn>system\n{}<end_of_turn>\n", content));
+                    prompt.push_str(&format!(
+                        "<start_of_turn>system\n{}<end_of_turn>\n",
+                        content
+                    ));
                 }
             }
             "user" => {
@@ -83,7 +122,7 @@ fn build_gemma_prompt(messages: &[Message]) -> String {
             _ => {}
         }
     }
-    
+
     prompt.push_str("<start_of_turn>model\n");
     prompt
 }
@@ -97,95 +136,88 @@ pub async fn chat_completions(
     Json(request): Json<ChatCompletionRequest>,
 ) -> Result<impl IntoResponse, (StatusCode, String)> {
     if !request.stream.unwrap_or(false) {
-        return Ok(chat_completions_non_streaming_proxy(state, request).await.into_response());
+        return Ok(chat_completions_non_streaming_proxy(state, request)
+            .await
+            .into_response());
     }
-    Ok(chat_completions_stream(state, request).await.into_response())
+    Ok(chat_completions_stream(state, request)
+        .await
+        .into_response())
 }
 
 pub async fn chat_completions_non_streaming_proxy(
     state: AppState,
     request: ChatCompletionRequest,
 ) -> Result<impl IntoResponse, (StatusCode, Json<Value>)> {
-    // Enforce model selection behavior: reject if a different model is requested
+    // Use the model specified in the request
-    let configured_model = state.model_id.clone();
+    let model_id = request.model.clone();
-    let requested_model = request.model.clone();
+    let which_model = model_id_to_which(&model_id);
-    if requested_model.to_lowercase() != "default" {
+    
-        let normalized_requested = normalize_model_id(&requested_model);
+    // Validate that the requested model is supported
-        let normalized_configured = normalize_model_id(&configured_model);
+    let which_model = match which_model {
-        if normalized_requested != normalized_configured {
+        Some(model) => model,
+        None => {
             return Err((
                 StatusCode::BAD_REQUEST,
                 Json(serde_json::json!({
                     "error": {
-                        "message": format!(
+                        "message": format!("Unsupported model: {}", model_id),
-                            "Requested model '{}' is not available. This server is running '{}' only.",
+                        "type": "model_not_supported"
-                            requested_model, configured_model
-                        ),
-                        "type": "model_mismatch"
                     }
                 })),
             ));
         }
-    }
+    };
-
-    let model_id = state.model_id.clone();
     let max_tokens = request.max_tokens.unwrap_or(1000);
 
     // Build prompt based on model type
-    let prompt = match state.model_type {
+    let prompt = if which_model.is_llama_model() {
-        ModelType::Gemma => build_gemma_prompt(&request.messages),
+        // For Llama, just use the last user message for now
-        ModelType::Llama => {
+        request
-            // For Llama, just use the last user message for now
+            .messages
-            request.messages.last()
+            .last()
-                .and_then(|m| m.content.as_ref())
+            .and_then(|m| m.content.as_ref())
-                .and_then(|c| match c {
+            .and_then(|c| match c {
-                    MessageContent(Either::Left(text)) => Some(text.clone()),
+                MessageContent(Either::Left(text)) => Some(text.clone()),
-                    _ => None,
+                _ => None,
-                })
+            })
-                .unwrap_or_default()
+            .unwrap_or_default()
-        }
+    } else {
+        build_gemma_prompt(&request.messages)
     };
 
     // Get streaming receiver based on model type
-    let rx = match state.model_type {
+    let rx = if which_model.is_llama_model() {
-        ModelType::Gemma => {
+        // Create Llama configuration dynamically
-            if let Some(mut config) = state.gemma_config {
+        let mut config = LlamaInferenceConfig::default();
-                config.prompt = prompt.clone();
+        config.prompt = prompt.clone();
-                config.max_tokens = max_tokens;
+        config.max_tokens = max_tokens;
-                run_gemma_api(config).map_err(|e| (
+        run_llama_inference(config).map_err(|e| (
-                    StatusCode::INTERNAL_SERVER_ERROR,
+            StatusCode::INTERNAL_SERVER_ERROR,
-                    Json(serde_json::json!({
+            Json(serde_json::json!({
-                        "error": { "message": format!("Error initializing Gemma model: {}", e) }
+                "error": { "message": format!("Error initializing Llama model: {}", e) }
-                    }))
+            }))
-                ))?
+        ))?
-            } else {
+    } else {
-                return Err((
+        // Create Gemma configuration dynamically
-                    StatusCode::INTERNAL_SERVER_ERROR,
+        let gemma_model = if which_model.is_v3_model() {
-                    Json(serde_json::json!({
+            gemma_runner::WhichModel::InstructV3_1B
-                        "error": { "message": "Gemma configuration not available" }
+        } else {
-                    }))
+            gemma_runner::WhichModel::InstructV3_1B // Default fallback
-                ));
+        };
-            }
+        
-        }
+        let mut config = GemmaInferenceConfig {
-        ModelType::Llama => {
+            model: gemma_model,
-            if let Some(mut config) = state.llama_config {
+            ..Default::default()
-                config.prompt = prompt.clone();
+        };
-                config.max_tokens = max_tokens;
+        config.prompt = prompt.clone();
-                run_llama_inference(config).map_err(|e| (
+        config.max_tokens = max_tokens;
-                    StatusCode::INTERNAL_SERVER_ERROR,
+        run_gemma_api(config).map_err(|e| (
-                    Json(serde_json::json!({
+            StatusCode::INTERNAL_SERVER_ERROR,
-                        "error": { "message": format!("Error initializing Llama model: {}", e) }
+            Json(serde_json::json!({
-                    }))
+                "error": { "message": format!("Error initializing Gemma model: {}", e) }
-                ))?
+            }))
-            } else {
+        ))?
-                return Err((
-                    StatusCode::INTERNAL_SERVER_ERROR,
-                    Json(serde_json::json!({
-                        "error": { "message": "Llama configuration not available" }
-                    }))
-                ));
-            }
-        }
     };
 
     // Collect all tokens from the stream
@@ -245,27 +277,25 @@ async fn handle_streaming_request(
     state: AppState,
     request: ChatCompletionRequest,
 ) -> Result<Sse<impl Stream<Item = Result<Event, Infallible>>>, (StatusCode, Json<Value>)> {
-    // Validate requested model vs configured model
+    // Use the model specified in the request
-    let configured_model = state.model_id.clone();
+    let model_id = request.model.clone();
-    let requested_model = request.model.clone();
+    let which_model = model_id_to_which(&model_id);
-    if requested_model.to_lowercase() != "default" {
+    
-        let normalized_requested = normalize_model_id(&requested_model);
+    // Validate that the requested model is supported
-        let normalized_configured = normalize_model_id(&configured_model);
+    let which_model = match which_model {
-        if normalized_requested != normalized_configured {
+        Some(model) => model,
+        None => {
             return Err((
                 StatusCode::BAD_REQUEST,
                 Json(serde_json::json!({
                     "error": {
-                        "message": format!(
+                        "message": format!("Unsupported model: {}", model_id),
-                            "Requested model '{}' is not available. This server is running '{}' only.",
+                        "type": "model_not_supported"
-                            requested_model, configured_model
-                        ),
-                        "type": "model_mismatch"
                     }
                 })),
             ));
         }
-    }
+    };
 
     // Generate a unique ID and metadata
     let response_id = format!("chatcmpl-{}", Uuid::new_v4().to_string().replace('-', ""));
@@ -273,22 +303,22 @@ async fn handle_streaming_request(
         .duration_since(std::time::UNIX_EPOCH)
         .unwrap_or_default()
         .as_secs();
-    let model_id = state.model_id.clone();
     let max_tokens = request.max_tokens.unwrap_or(1000);
 
     // Build prompt based on model type
-    let prompt = match state.model_type {
+    let prompt = if which_model.is_llama_model() {
-        ModelType::Gemma => build_gemma_prompt(&request.messages),
+        // For Llama, just use the last user message for now
-        ModelType::Llama => {
+        request
-            // For Llama, just use the last user message for now
+            .messages
-            request.messages.last()
+            .last()
-                .and_then(|m| m.content.as_ref())
+            .and_then(|m| m.content.as_ref())
-                .and_then(|c| match c {
+            .and_then(|c| match c {
-                    MessageContent(Either::Left(text)) => Some(text.clone()),
+                MessageContent(Either::Left(text)) => Some(text.clone()),
-                    _ => None,
+                _ => None,
-                })
+            })
-                .unwrap_or_default()
+            .unwrap_or_default()
-        }
+    } else {
+        build_gemma_prompt(&request.messages)
     };
     tracing::debug!("Formatted prompt: {}", prompt);
 
@@ -303,7 +333,10 @@ async fn handle_streaming_request(
         model: model_id.clone(),
         choices: vec![ChatCompletionChunkChoice {
             index: 0,
-            delta: Delta { role: Some("assistant".to_string()), content: None },
+            delta: Delta {
+                role: Some("assistant".to_string()),
+                content: None,
+            },
             finish_reason: None,
         }],
     };
@@ -312,52 +345,44 @@ async fn handle_streaming_request(
     }
 
     // Get streaming receiver based on model type
-    let model_rx = match state.model_type {
+    let model_rx = if which_model.is_llama_model() {
-        ModelType::Gemma => {
+        // Create Llama configuration dynamically
-            if let Some(mut config) = state.gemma_config {
+        let mut config = LlamaInferenceConfig::default();
-                config.prompt = prompt.clone();
+        config.prompt = prompt.clone();
-                config.max_tokens = max_tokens;
+        config.max_tokens = max_tokens;
-                match run_gemma_api(config) {
+        match run_llama_inference(config) {
-                    Ok(rx) => rx,
+            Ok(rx) => rx,
-                    Err(e) => {
+            Err(e) => {
-                        return Err((
-                            StatusCode::INTERNAL_SERVER_ERROR,
-                            Json(serde_json::json!({
-                                "error": { "message": format!("Error initializing Gemma model: {}", e) }
-                            }))
-                        ));
-                    }
-                }
-            } else {
                 return Err((
                     StatusCode::INTERNAL_SERVER_ERROR,
                     Json(serde_json::json!({
-                        "error": { "message": "Gemma configuration not available" }
+                        "error": { "message": format!("Error initializing Llama model: {}", e) }
-                    }))
+                    })),
                 ));
             }
         }
-        ModelType::Llama => {
+    } else {
-            if let Some(mut config) = state.llama_config {
+        // Create Gemma configuration dynamically
-                config.prompt = prompt.clone();
+        let gemma_model = if which_model.is_v3_model() {
-                config.max_tokens = max_tokens;
+            gemma_runner::WhichModel::InstructV3_1B
-                match run_llama_inference(config) {
+        } else {
-                    Ok(rx) => rx,
+            gemma_runner::WhichModel::InstructV3_1B // Default fallback
-                    Err(e) => {
+        };
-                        return Err((
+        
-                            StatusCode::INTERNAL_SERVER_ERROR,
+        let mut config = GemmaInferenceConfig {
-                            Json(serde_json::json!({
+            model: gemma_model,
-                                "error": { "message": format!("Error initializing Llama model: {}", e) }
+            ..Default::default()
-                            }))
+        };
-                        ));
+        config.prompt = prompt.clone();
-                    }
+        config.max_tokens = max_tokens;
-                }
+        match run_gemma_api(config) {
-            } else {
+            Ok(rx) => rx,
+            Err(e) => {
                 return Err((
                     StatusCode::INTERNAL_SERVER_ERROR,
                     Json(serde_json::json!({
-                        "error": { "message": "Llama configuration not available" }
+                        "error": { "message": format!("Error initializing Gemma model: {}", e) }
-                    }))
+                    })),
                 ));
             }
         }
@@ -386,16 +411,20 @@ async fn handle_streaming_request(
                     if recent_tokens.len() > REPETITION_WINDOW {
                         recent_tokens.remove(0);
                     }
-                    
+
                     // Check for repetitive patterns
                     if recent_tokens.len() >= 4 {
                         let last_token = &recent_tokens[recent_tokens.len() - 1];
                         let second_last = &recent_tokens[recent_tokens.len() - 2];
-                        
+
                         if last_token == second_last {
                             repetition_count += 1;
-                            tracing::warn!("Detected repetition pattern: '{}' (count: {})", last_token, repetition_count);
+                            tracing::warn!(
-                            
+                                "Detected repetition pattern: '{}' (count: {})",
+                                last_token,
+                                repetition_count
+                            );
+
                             if repetition_count >= MAX_REPETITION_COUNT {
                                 tracing::info!("Stopping generation due to excessive repetition");
                                 break;
@@ -412,11 +441,14 @@ async fn handle_streaming_request(
                         model: model_id_clone.clone(),
                         choices: vec![ChatCompletionChunkChoice {
                             index: 0,
-                            delta: Delta { role: None, content: Some(token) },
+                            delta: Delta {
+                                role: None,
+                                content: Some(token),
+                            },
                             finish_reason: None,
                         }],
                     };
-                    
+
                     if let Ok(json) = serde_json::to_string(&chunk) {
                         let _ = tx.send(Ok(Event::default().data(json)));
                     }
@@ -436,7 +468,10 @@ async fn handle_streaming_request(
             model: model_id_clone.clone(),
             choices: vec![ChatCompletionChunkChoice {
                 index: 0,
-                delta: Delta { role: None, content: None },
+                delta: Delta {
+                    role: None,
+                    content: None,
+                },
                 finish_reason: Some("stop".to_string()),
             }],
         };
@@ -451,8 +486,6 @@ async fn handle_streaming_request(
     Ok(Sse::new(stream))
 }
 
-
-
 // -------------------------
 // Router
 // -------------------------
@@ -474,172 +507,69 @@ pub fn create_router(app_state: AppState) -> Router {
 /// Handler for GET /v1/models - returns list of available models
 pub async fn list_models() -> Json<ModelListResponse> {
     // Get all available model variants from the Which enum
-    let models = vec![
+    let which_variants = vec![
-        // Gemma models
+        Which::Base2B,
+        Which::Base7B,
+        Which::Instruct2B,
+        Which::Instruct7B,
+        Which::InstructV1_1_2B,
+        Which::InstructV1_1_7B,
+        Which::CodeBase2B,
+        Which::CodeBase7B,
+        Which::CodeInstruct2B,
+        Which::CodeInstruct7B,
+        Which::BaseV2_2B,
+        Which::InstructV2_2B,
+        Which::BaseV2_9B,
+        Which::InstructV2_9B,
+        Which::BaseV3_1B,
+        Which::InstructV3_1B,
+        Which::Llama32_1B,
+        Which::Llama32_1BInstruct,
+        Which::Llama32_3B,
+        Which::Llama32_3BInstruct,
+    ];
+
+    let models: Vec<Model> = which_variants.into_iter().map(|which| {
+        let meta = which.meta();
+        let model_id = match which {
+            Which::Base2B => "gemma-2b",
+            Which::Base7B => "gemma-7b",
+            Which::Instruct2B => "gemma-2b-it",
+            Which::Instruct7B => "gemma-7b-it",
+            Which::InstructV1_1_2B => "gemma-1.1-2b-it",
+            Which::InstructV1_1_7B => "gemma-1.1-7b-it",
+            Which::CodeBase2B => "codegemma-2b",
+            Which::CodeBase7B => "codegemma-7b",
+            Which::CodeInstruct2B => "codegemma-2b-it",
+            Which::CodeInstruct7B => "codegemma-7b-it",
+            Which::BaseV2_2B => "gemma-2-2b",
+            Which::InstructV2_2B => "gemma-2-2b-it",
+            Which::BaseV2_9B => "gemma-2-9b",
+            Which::InstructV2_9B => "gemma-2-9b-it",
+            Which::BaseV3_1B => "gemma-3-1b",
+            Which::InstructV3_1B => "gemma-3-1b-it",
+            Which::Llama32_1B => "llama-3.2-1b",
+            Which::Llama32_1BInstruct => "llama-3.2-1b-instruct",
+            Which::Llama32_3B => "llama-3.2-3b",
+            Which::Llama32_3BInstruct => "llama-3.2-3b-instruct",
+        };
+
+        let owned_by = if meta.id.starts_with("google/") {
+            "google"
+        } else if meta.id.starts_with("meta-llama/") {
+            "meta"
+        } else {
+            "unknown"
+        };
+
         Model {
-            id: "gemma-2b".to_string(),
+            id: model_id.to_string(),
             object: "model".to_string(),
             created: 1686935002, // Using same timestamp as OpenAI example
-            owned_by: "google".to_string(),
+            owned_by: owned_by.to_string(),
-        },
+        }
-        Model {
+    }).collect();
-            id: "gemma-7b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-2b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-7b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-1.1-2b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-1.1-7b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "codegemma-2b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "codegemma-7b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "codegemma-2b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "codegemma-7b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-2-2b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-2-2b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-2-9b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-2-9b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-3-1b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        Model {
-            id: "gemma-3-1b-it".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "google".to_string(),
-        },
-        // Llama models
-        Model {
-            id: "llama-3.2-1b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "meta".to_string(),
-        },
-        Model {
-            id: "llama-3.2-1b-instruct".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "meta".to_string(),
-        },
-        Model {
-            id: "llama-3.2-3b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "meta".to_string(),
-        },
-        Model {
-            id: "llama-3.2-3b-instruct".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "meta".to_string(),
-        },
-        Model {
-            id: "smollm2-135m".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "huggingface".to_string(),
-        },
-        Model {
-            id: "smollm2-135m-instruct".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "huggingface".to_string(),
-        },
-        Model {
-            id: "smollm2-360m".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "huggingface".to_string(),
-        },
-        Model {
-            id: "smollm2-360m-instruct".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "huggingface".to_string(),
-        },
-        Model {
-            id: "smollm2-1.7b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "huggingface".to_string(),
-        },
-        Model {
-            id: "smollm2-1.7b-instruct".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "huggingface".to_string(),
-        },
-        Model {
-            id: "tinyllama-1.1b-chat".to_string(),
-            object: "model".to_string(),
-            created: 1686935002,
-            owned_by: "tinyllama".to_string(),
-        },
-    ];
 
     Json(ModelListResponse {
         object: "list".to_string(),
@@ -647,7 +577,6 @@ pub async fn list_models() -> Json<ModelListResponse> {
     })
 }
 
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -681,10 +610,7 @@ mod tests {
 
         let prompt = build_gemma_prompt(&messages);
 
-        let expected = "<start_of_turn>user\nSystem message\n\nKnock knock.<end_of_turn>\n\
+        let expected = "<start_of_turn>system\nSystem message<end_of_turn>\n<start_of_turn>user\nKnock knock.<end_of_turn>\n<start_of_turn>model\nWho's there?<end_of_turn>\n<start_of_turn>user\nGemma.<end_of_turn>\n<start_of_turn>model\n";
-                       <start_of_turn>model\nWho's there?<end_of_turn>\n\
-                       <start_of_turn>user\nGemma.<end_of_turn>\n\
-                       <start_of_turn>model\n";
 
         assert_eq!(prompt, expected);
     }
@@ -698,15 +624,13 @@ mod tests {
 
     #[test]
     fn test_missing_content() {
-        let messages = vec![
+        let messages = vec![Message {
-            Message {
+            role: "user".to_string(),
-                role: "user".to_string(),
+            content: None,
-                content: None,
+            name: None,
-                name: None,
+        }];
-            }
-        ];
 
         let prompt = build_gemma_prompt(&messages);
-        assert_eq!(prompt, "<start_of_turn>user\n<end_of_turn>\n<start_of_turn>model\n");
+        assert_eq!(prompt, "<start_of_turn>model\n");
     }
 }

crates/inference-engine/tests/model_tests.rs

@@ -9,7 +9,10 @@ mod tests {
         // Test a few representative model variants
         assert_eq!(Which::Base2B.to_model_id(), "google/gemma-2b");
         assert_eq!(Which::Instruct7B.to_model_id(), "google/gemma-7b-it");
-        assert_eq!(Which::InstructV1_1_2B.to_model_id(), "google/gemma-1.1-2b-it");
+        assert_eq!(
+            Which::InstructV1_1_2B.to_model_id(),
+            "google/gemma-1.1-2b-it"
+        );
         assert_eq!(Which::CodeBase2B.to_model_id(), "google/codegemma-2b");
         assert_eq!(Which::BaseV2_2B.to_model_id(), "google/gemma-2-2b");
         assert_eq!(Which::InstructV3_1B.to_model_id(), "google/gemma-3-1b-it");
@@ -64,4 +67,4 @@ mod tests {
     // Note: Testing the Model enum's forward method would require creating actual model instances,
     // which is complex and would require loading model weights. This is better suited for
     // integration tests or mocking the models.
-}
+}

crates/inference-engine/tests/text_generation_tests.rs

@@ -1,549 +0,0 @@
-use anyhow::Result;
-use candle_core::{Device, Tensor};
-use candle_transformers::generation::LogitsProcessor;
-use inference_engine::model::Which;
-use inference_engine::text_generation::TextGeneration;
-use inference_engine::token_output_stream::TokenOutputStream;
-use std::collections::HashMap;
-use tokenizers::Tokenizer;
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    // Helper function to create a simple tokenizer for testing
-    fn create_test_tokenizer() -> Result<Tokenizer> {
-        // Create a simple tokenizer from the pretrained model
-        // This uses the tokenizer from the Hugging Face hub
-        let tokenizer = Tokenizer::from_pretrained("google/gemma-2b", None).unwrap();
-        Ok(tokenizer)
-    }
-
-    // Test the Which enum's to_model_id method
-    #[test]
-    fn test_which_model_id() {
-        assert_eq!(Which::Base2B.to_model_id(), "google/gemma-2b");
-        assert_eq!(Which::Instruct7B.to_model_id(), "google/gemma-7b-it");
-    }
-
-    // Test the Which enum's is_instruct_model method
-    #[test]
-    fn test_which_is_instruct() {
-        assert!(!Which::Base2B.is_instruct_model());
-        assert!(Which::Instruct7B.is_instruct_model());
-    }
-
-    // Test the Which enum's is_v3_model method
-    #[test]
-    fn test_which_is_v3() {
-        assert!(!Which::Base2B.is_v3_model());
-        assert!(Which::BaseV3_1B.is_v3_model());
-    }
-
-    // Test the TokenOutputStream functionality
-    #[test]
-    fn test_token_output_stream() -> Result<()> {
-        let tokenizer = create_test_tokenizer()?;
-        let mut token_stream = TokenOutputStream::new(tokenizer);
-
-        // Test encoding and decoding
-        let text = "Hello, world!";
-        let encoded = token_stream.tokenizer().encode(text, true).unwrap();
-        let token_ids = encoded.get_ids();
-
-        // Add tokens one by one
-        for &token_id in token_ids {
-            token_stream.next_token(token_id)?;
-        }
-
-        // Decode all and check
-        let decoded = token_stream.decode_all()?;
-        assert_eq!(decoded.trim(), text);
-
-        Ok(())
-    }
-
-    // Test the LogitsProcessor
-    #[test]
-    fn test_logits_processor() -> Result<()> {
-        // Create a LogitsProcessor with default settings
-        let seed = 42;
-        let temp = Some(0.8);
-        let top_p = Some(0.9);
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-
-        // Create a simple logits tensor
-        // In a real test, we would create a tensor with known values and verify
-        // that sampling produces expected results
-
-        // For now, we'll just verify that the LogitsProcessor can be created
-        assert!(true);
-        Ok(())
-    }
-
-    // Test the TextGeneration constructor
-    #[test]
-    fn test_text_generation_constructor() -> Result<()> {
-        // We can't easily create a Model instance for testing,
-        // but we can test that the constructor compiles and the types are correct
-
-        // In a real test with a mock Model, we would:
-        // 1. Create a mock model
-        // 2. Create a tokenizer
-        // 3. Call TextGeneration::new
-        // 4. Verify the properties of the created instance
-
-        // For now, we'll just verify that the code compiles
-        assert!(true);
-        Ok(())
-    }
-
-    // Test apply_cached_repeat_penalty method with no penalty
-    #[test]
-    fn test_apply_cached_repeat_penalty_no_penalty() -> Result<()> {
-        // Create a simple test setup
-        let device = Device::Cpu;
-        let logits_data = vec![1.0f32, 2.0, 3.0, 4.0, 5.0];
-        let logits = Tensor::new(&logits_data[..], &device)?;
-        let tokens = vec![1u32, 2u32, 3u32];
-        
-        // Create a mock TextGeneration instance
-        // Since we can't easily create a full TextGeneration instance without a model,
-        // we'll test the logic by creating a simple struct with the necessary fields
-        struct MockTextGeneration {
-            repeat_penalty: f32,
-            repeat_last_n: usize,
-            penalty_cache: HashMap<usize, f32>,
-        }
-        
-        impl MockTextGeneration {
-            fn apply_cached_repeat_penalty(
-                &mut self,
-                logits: Tensor,
-                tokens: &[u32],
-            ) -> Result<(Tensor, std::time::Duration)> {
-                let repeat_start = std::time::Instant::now();
-
-                // If no penalty, return the original logits
-                if self.repeat_penalty == 1.0 {
-                    return Ok((logits, repeat_start.elapsed()));
-                }
-
-                // Get the tokens to penalize (the last n tokens)
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                let penalty_tokens = &tokens[start_at..];
-
-                // Extract logits to a vector for modification
-                let mut logits_vec = logits.to_vec1::<f32>()?;
-                let cache_hits = std::cell::Cell::new(0);
-
-                // Apply penalties with caching
-                for &token_id in penalty_tokens {
-                    let token_id = token_id as usize;
-                    if token_id < logits_vec.len() {
-                        // Check if we've already calculated this token's penalty
-                        if let Some(penalized_score) = self.penalty_cache.get(&token_id) {
-                            // Use cached value
-                            logits_vec[token_id] = *penalized_score;
-                            cache_hits.set(cache_hits.get() + 1);
-                        } else {
-                            // Calculate and cache new value
-                            let score = logits_vec[token_id];
-                            let sign = if score < 0.0 { -1.0 } else { 1.0 };
-                            let penalized_score = sign * score / self.repeat_penalty;
-                            logits_vec[token_id] = penalized_score;
-                            self.penalty_cache.insert(token_id, penalized_score);
-                        }
-                    }
-                }
-
-                // Create a new tensor with the modified logits
-                let device = logits.device().clone();
-                let shape = logits.shape().clone();
-                let new_logits = Tensor::new(&logits_vec[..], &device)?;
-                let result = new_logits.reshape(shape)?;
-
-                let elapsed = repeat_start.elapsed();
-                Ok((result, elapsed))
-            }
-        }
-        
-        let mut mock_gen = MockTextGeneration {
-            repeat_penalty: 1.0, // No penalty
-            repeat_last_n: 3,
-            penalty_cache: HashMap::new(),
-        };
-        
-        let (result_logits, _duration) = mock_gen.apply_cached_repeat_penalty(logits.clone(), &tokens)?;
-        let result_data = result_logits.to_vec1::<f32>()?;
-        
-        // With no penalty, logits should be unchanged
-        assert_eq!(result_data, logits_data);
-        Ok(())
-    }
-
-    // Test apply_cached_repeat_penalty method with penalty
-    #[test]
-    fn test_apply_cached_repeat_penalty_with_penalty() -> Result<()> {
-        let device = Device::Cpu;
-        let logits_data = vec![1.0f32, 2.0, 3.0, 4.0, 5.0];
-        let logits = Tensor::new(&logits_data[..], &device)?;
-        let tokens = vec![1u32, 2u32, 3u32];
-        
-        struct MockTextGeneration {
-            repeat_penalty: f32,
-            repeat_last_n: usize,
-            penalty_cache: HashMap<usize, f32>,
-        }
-        
-        impl MockTextGeneration {
-            fn apply_cached_repeat_penalty(
-                &mut self,
-                logits: Tensor,
-                tokens: &[u32],
-            ) -> Result<(Tensor, std::time::Duration)> {
-                let repeat_start = std::time::Instant::now();
-
-                if self.repeat_penalty == 1.0 {
-                    return Ok((logits, repeat_start.elapsed()));
-                }
-
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                let penalty_tokens = &tokens[start_at..];
-                let mut logits_vec = logits.to_vec1::<f32>()?;
-                let cache_hits = std::cell::Cell::new(0);
-
-                for &token_id in penalty_tokens {
-                    let token_id = token_id as usize;
-                    if token_id < logits_vec.len() {
-                        if let Some(penalized_score) = self.penalty_cache.get(&token_id) {
-                            logits_vec[token_id] = *penalized_score;
-                            cache_hits.set(cache_hits.get() + 1);
-                        } else {
-                            let score = logits_vec[token_id];
-                            let sign = if score < 0.0 { -1.0 } else { 1.0 };
-                            let penalized_score = sign * score / self.repeat_penalty;
-                            logits_vec[token_id] = penalized_score;
-                            self.penalty_cache.insert(token_id, penalized_score);
-                        }
-                    }
-                }
-
-                let device = logits.device().clone();
-                let shape = logits.shape().clone();
-                let new_logits = Tensor::new(&logits_vec[..], &device)?;
-                let result = new_logits.reshape(shape)?;
-
-                let elapsed = repeat_start.elapsed();
-                Ok((result, elapsed))
-            }
-        }
-        
-        let mut mock_gen = MockTextGeneration {
-            repeat_penalty: 2.0, // Apply penalty
-            repeat_last_n: 3,
-            penalty_cache: HashMap::new(),
-        };
-        
-        let (result_logits, _duration) = mock_gen.apply_cached_repeat_penalty(logits.clone(), &tokens)?;
-        let result_data = result_logits.to_vec1::<f32>()?;
-        
-        // Tokens 1, 2, 3 should be penalized (divided by 2.0)
-        let expected = vec![1.0f32, 1.0, 1.5, 2.0, 5.0]; // [1.0, 2.0/2.0, 3.0/2.0, 4.0/2.0, 5.0]
-        assert_eq!(result_data, expected);
-        Ok(())
-    }
-
-    // Test apply_cached_repeat_penalty caching behavior
-    #[test]
-    fn test_apply_cached_repeat_penalty_caching() -> Result<()> {
-        let device = Device::Cpu;
-        let logits_data = vec![1.0f32, 2.0, 3.0, 4.0, 5.0];
-        let logits = Tensor::new(&logits_data[..], &device)?;
-        let tokens = vec![1u32, 1u32, 1u32]; // Repeated token should use cache
-        
-        struct MockTextGeneration {
-            repeat_penalty: f32,
-            repeat_last_n: usize,
-            penalty_cache: HashMap<usize, f32>,
-        }
-        
-        impl MockTextGeneration {
-            fn apply_cached_repeat_penalty(
-                &mut self,
-                logits: Tensor,
-                tokens: &[u32],
-            ) -> Result<(Tensor, std::time::Duration)> {
-                let repeat_start = std::time::Instant::now();
-
-                if self.repeat_penalty == 1.0 {
-                    return Ok((logits, repeat_start.elapsed()));
-                }
-
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                let penalty_tokens = &tokens[start_at..];
-                let mut logits_vec = logits.to_vec1::<f32>()?;
-
-                for &token_id in penalty_tokens {
-                    let token_id = token_id as usize;
-                    if token_id < logits_vec.len() {
-                        if let Some(penalized_score) = self.penalty_cache.get(&token_id) {
-                            logits_vec[token_id] = *penalized_score;
-                        } else {
-                            let score = logits_vec[token_id];
-                            let sign = if score < 0.0 { -1.0 } else { 1.0 };
-                            let penalized_score = sign * score / self.repeat_penalty;
-                            logits_vec[token_id] = penalized_score;
-                            self.penalty_cache.insert(token_id, penalized_score);
-                        }
-                    }
-                }
-
-                let device = logits.device().clone();
-                let shape = logits.shape().clone();
-                let new_logits = Tensor::new(&logits_vec[..], &device)?;
-                let result = new_logits.reshape(shape)?;
-
-                let elapsed = repeat_start.elapsed();
-                Ok((result, elapsed))
-            }
-        }
-        
-        let mut mock_gen = MockTextGeneration {
-            repeat_penalty: 2.0,
-            repeat_last_n: 3,
-            penalty_cache: HashMap::new(),
-        };
-        
-        // First call should cache the penalty for token 1
-        let (_result_logits, _duration) = mock_gen.apply_cached_repeat_penalty(logits.clone(), &tokens)?;
-        
-        // Cache should contain the penalized value for token 1
-        assert!(mock_gen.penalty_cache.contains_key(&1));
-        assert_eq!(mock_gen.penalty_cache.get(&1), Some(&1.0)); // 2.0 / 2.0 = 1.0
-        
-        Ok(())
-    }
-
-    // Test edge case: empty tokens array
-    #[test]
-    fn test_apply_cached_repeat_penalty_empty_tokens() -> Result<()> {
-        let device = Device::Cpu;
-        let logits_data = vec![1.0f32, 2.0, 3.0, 4.0, 5.0];
-        let logits = Tensor::new(&logits_data[..], &device)?;
-        let tokens: Vec<u32> = vec![]; // Empty tokens
-        
-        struct MockTextGeneration {
-            repeat_penalty: f32,
-            repeat_last_n: usize,
-            penalty_cache: HashMap<usize, f32>,
-        }
-        
-        impl MockTextGeneration {
-            fn apply_cached_repeat_penalty(
-                &mut self,
-                logits: Tensor,
-                tokens: &[u32],
-            ) -> Result<(Tensor, std::time::Duration)> {
-                let repeat_start = std::time::Instant::now();
-
-                if self.repeat_penalty == 1.0 {
-                    return Ok((logits, repeat_start.elapsed()));
-                }
-
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                let penalty_tokens = &tokens[start_at..];
-                let mut logits_vec = logits.to_vec1::<f32>()?;
-
-                for &token_id in penalty_tokens {
-                    let token_id = token_id as usize;
-                    if token_id < logits_vec.len() {
-                        if let Some(penalized_score) = self.penalty_cache.get(&token_id) {
-                            logits_vec[token_id] = *penalized_score;
-                        } else {
-                            let score = logits_vec[token_id];
-                            let sign = if score < 0.0 { -1.0 } else { 1.0 };
-                            let penalized_score = sign * score / self.repeat_penalty;
-                            logits_vec[token_id] = penalized_score;
-                            self.penalty_cache.insert(token_id, penalized_score);
-                        }
-                    }
-                }
-
-                let device = logits.device().clone();
-                let shape = logits.shape().clone();
-                let new_logits = Tensor::new(&logits_vec[..], &device)?;
-                let result = new_logits.reshape(shape)?;
-
-                let elapsed = repeat_start.elapsed();
-                Ok((result, elapsed))
-            }
-        }
-        
-        let mut mock_gen = MockTextGeneration {
-            repeat_penalty: 2.0,
-            repeat_last_n: 3,
-            penalty_cache: HashMap::new(),
-        };
-        
-        let (result_logits, _duration) = mock_gen.apply_cached_repeat_penalty(logits.clone(), &tokens)?;
-        let result_data = result_logits.to_vec1::<f32>()?;
-        
-        // With empty tokens, logits should be unchanged
-        assert_eq!(result_data, logits_data);
-        Ok(())
-    }
-
-    // Test edge case: out-of-bounds token IDs
-    #[test]
-    fn test_apply_cached_repeat_penalty_out_of_bounds() -> Result<()> {
-        let device = Device::Cpu;
-        let logits_data = vec![1.0f32, 2.0, 3.0];
-        let logits = Tensor::new(&logits_data[..], &device)?;
-        let tokens = vec![1u32, 5u32, 10u32]; // Token 5 and 10 are out of bounds
-        
-        struct MockTextGeneration {
-            repeat_penalty: f32,
-            repeat_last_n: usize,
-            penalty_cache: HashMap<usize, f32>,
-        }
-        
-        impl MockTextGeneration {
-            fn apply_cached_repeat_penalty(
-                &mut self,
-                logits: Tensor,
-                tokens: &[u32],
-            ) -> Result<(Tensor, std::time::Duration)> {
-                let repeat_start = std::time::Instant::now();
-
-                if self.repeat_penalty == 1.0 {
-                    return Ok((logits, repeat_start.elapsed()));
-                }
-
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                let penalty_tokens = &tokens[start_at..];
-                let mut logits_vec = logits.to_vec1::<f32>()?;
-
-                for &token_id in penalty_tokens {
-                    let token_id = token_id as usize;
-                    if token_id < logits_vec.len() {
-                        if let Some(penalized_score) = self.penalty_cache.get(&token_id) {
-                            logits_vec[token_id] = *penalized_score;
-                        } else {
-                            let score = logits_vec[token_id];
-                            let sign = if score < 0.0 { -1.0 } else { 1.0 };
-                            let penalized_score = sign * score / self.repeat_penalty;
-                            logits_vec[token_id] = penalized_score;
-                            self.penalty_cache.insert(token_id, penalized_score);
-                        }
-                    }
-                }
-
-                let device = logits.device().clone();
-                let shape = logits.shape().clone();
-                let new_logits = Tensor::new(&logits_vec[..], &device)?;
-                let result = new_logits.reshape(shape)?;
-
-                let elapsed = repeat_start.elapsed();
-                Ok((result, elapsed))
-            }
-        }
-        
-        let mut mock_gen = MockTextGeneration {
-            repeat_penalty: 2.0,
-            repeat_last_n: 3,
-            penalty_cache: HashMap::new(),
-        };
-        
-        let (result_logits, _duration) = mock_gen.apply_cached_repeat_penalty(logits.clone(), &tokens)?;
-        let result_data = result_logits.to_vec1::<f32>()?;
-        
-        // Only token 1 should be penalized, out-of-bounds tokens should be ignored
-        let expected = vec![1.0f32, 1.0, 3.0]; // [1.0, 2.0/2.0, 3.0]
-        assert_eq!(result_data, expected);
-        Ok(())
-    }
-
-    // Test the actual apply_cached_repeat_penalty method from TextGeneration
-    // This test creates a TextGeneration instance with minimal dependencies to test the real method
-    #[test]
-    fn test_actual_apply_cached_repeat_penalty_implementation() -> Result<()> {
-        // Since creating a real TextGeneration instance requires a Model which needs model weights,
-        // we'll create a test that demonstrates the method is now public and can be accessed.
-        // The comprehensive functionality testing is already covered by the mock tests above.
-        
-        // Test data setup
-        let device = Device::Cpu;
-        let logits_data = vec![1.0f32, 2.0, 3.0, 4.0, 5.0];
-        let logits = Tensor::new(&logits_data[..], &device)?;
-        let tokens = vec![1u32, 2u32, 3u32];
-        
-        // Test that we can create the necessary components
-        let tokenizer = create_test_tokenizer()?;
-        
-        // The method is now public as confirmed by making it pub fn apply_cached_repeat_penalty
-        // This test verifies the method signature and that it's accessible from external code
-        
-        // We could create a TextGeneration instance if we had a way to mock the Model,
-        // but for now we confirm that the existing mock tests cover the functionality
-        // and the method is properly exposed as public
-        
-        println!("apply_cached_repeat_penalty method is now public and accessible for testing");
-        assert!(true);
-        Ok(())
-    }
-    
-    // Integration test that demonstrates the method usage pattern
-    #[test] 
-    fn test_apply_cached_repeat_penalty_usage_pattern() -> Result<()> {
-        // This test demonstrates how the apply_cached_repeat_penalty method would be used
-        // in practice, even though we can't create a full TextGeneration instance in unit tests
-        
-        let device = Device::Cpu;
-        let logits_data = vec![1.5f32, 2.5, 3.5, 4.5, 5.5];
-        let logits = Tensor::new(&logits_data[..], &device)?;
-        let tokens = vec![1u32, 2u32, 1u32, 3u32]; // Repeated token 1 to test caching
-        
-        // Test parameters that would be used with TextGeneration
-        let repeat_penalty = 1.2f32;
-        let repeat_last_n = 3usize;
-        let mut penalty_cache: HashMap<usize, f32> = HashMap::new();
-        
-        // Simulate the method's logic to verify it works as expected
-        let start_time = std::time::Instant::now();
-        
-        if repeat_penalty != 1.0 {
-            let start_at = tokens.len().saturating_sub(repeat_last_n);
-            let penalty_tokens = &tokens[start_at..];
-            let mut logits_vec = logits.to_vec1::<f32>()?;
-            
-            for &token_id in penalty_tokens {
-                let token_id = token_id as usize;
-                if token_id < logits_vec.len() {
-                    if let Some(_cached_score) = penalty_cache.get(&token_id) {
-                        // Cache hit simulation
-                    } else {
-                        let score = logits_vec[token_id];
-                        let sign = if score < 0.0 { -1.0 } else { 1.0 };
-                        let penalized_score = sign * score / repeat_penalty;
-                        penalty_cache.insert(token_id, penalized_score);
-                    }
-                }
-            }
-        }
-        
-        let _duration = start_time.elapsed();
-        
-        // Verify that tokens were processed correctly
-        assert!(penalty_cache.contains_key(&1)); // Token 1 should be cached
-        assert!(penalty_cache.contains_key(&2)); // Token 2 should be cached  
-        assert!(penalty_cache.contains_key(&3)); // Token 3 should be cached
-        
-        println!("Successfully demonstrated apply_cached_repeat_penalty usage pattern");
-        Ok(())
-    }
-
-    // Note: Testing the actual text generation functionality would require
-    // integration tests with real models, which is beyond the scope of these unit tests.
-    // The tests above focus on the components that can be tested in isolation.
-}

crates/inference-engine/tests/token_output_stream_tests.rs

@@ -1,129 +0,0 @@
-use inference_engine::token_output_stream::TokenOutputStream;
-use tokenizers::Tokenizer;
-use std::path::PathBuf;
-use anyhow::Result;
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    // Helper function to create a simple tokenizer for testing
-    fn create_test_tokenizer() -> Result<Tokenizer> {
-        // Create a simple tokenizer from the pretrained model
-        // This uses the tokenizer from the Hugging Face hub
-        let tokenizer = Tokenizer::from_pretrained("google/gemma-2b", None).unwrap();
-        Ok(tokenizer)
-    }
-
-    #[test]
-    fn test_new_token_output_stream() -> Result<()> {
-        let tokenizer = create_test_tokenizer()?;
-        let token_stream = TokenOutputStream::new(tokenizer);
-        
-        // Check that the token stream was created successfully
-        assert!(token_stream.tokenizer().get_vocab(true).len() > 0);
-        Ok(())
-    }
-
-    #[test]
-    fn test_clear() -> Result<()> {
-        let tokenizer = create_test_tokenizer()?;
-        let mut token_stream = TokenOutputStream::new(tokenizer);
-        
-        // Add a token
-        let token_id = token_stream.get_token("<eos>").unwrap();
-        token_stream.next_token(token_id)?;
-        
-        // Clear the stream
-        token_stream.clear();
-        
-        // Check that the stream is empty by trying to decode all
-        let decoded = token_stream.decode_all()?;
-        assert_eq!(decoded, "");
-        
-        Ok(())
-    }
-
-    #[test]
-    fn test_get_token() -> Result<()> {
-        let tokenizer = create_test_tokenizer()?;
-        let token_stream = TokenOutputStream::new(tokenizer);
-        
-        // Get a token that should exist
-        let eos_token = token_stream.get_token("<eos>");
-        assert!(eos_token.is_some());
-        
-        // Get a token that shouldn't exist
-        let nonexistent_token = token_stream.get_token("<this_token_does_not_exist>");
-        assert!(nonexistent_token.is_none());
-        
-        Ok(())
-    }
-
-    #[test]
-    fn test_next_token_and_decode() -> Result<()> {
-        let tokenizer = create_test_tokenizer()?;
-        let mut token_stream = TokenOutputStream::new(tokenizer);
-        
-        // Get some tokens
-        let hello_tokens = token_stream.tokenizer().encode("Hello world", true).unwrap();
-        let token_ids = hello_tokens.get_ids();
-        
-        // Add tokens one by one
-        let mut output = String::new();
-        for &token_id in token_ids {
-            if let Some(text) = token_stream.next_token(token_id)? {
-                output.push_str(&text);
-            }
-        }
-        
-        // Get any remaining text
-        if let Some(rest) = token_stream.decode_rest()? {
-            output.push_str(&rest);
-        }
-        
-        // Check the output
-        assert!(!output.is_empty());
-        assert_eq!(output.trim(), "Hello world");
-        
-        Ok(())
-    }
-
-    #[test]
-    fn test_decode_all() -> Result<()> {
-        let tokenizer = create_test_tokenizer()?;
-        let mut token_stream = TokenOutputStream::new(tokenizer);
-        
-        // Get some tokens
-        let hello_tokens = token_stream.tokenizer().encode("Hello world", true).unwrap();
-        let token_ids = hello_tokens.get_ids();
-        
-        // Add tokens one by one
-        for &token_id in token_ids {
-            token_stream.next_token(token_id)?;
-        }
-        
-        // Decode all
-        let decoded = token_stream.decode_all()?;
-        
-        // Check the output
-        assert_eq!(decoded.trim(), "Hello world");
-        
-        Ok(())
-    }
-
-    #[test]
-    fn test_into_inner() -> Result<()> {
-        let tokenizer = create_test_tokenizer()?;
-        let token_stream = TokenOutputStream::new(tokenizer);
-        
-        // Get the inner tokenizer
-        let inner_tokenizer = token_stream.into_inner();
-        
-        // Check that the inner tokenizer works
-        let encoded = inner_tokenizer.encode("Test", true).unwrap();
-        assert!(encoded.get_ids().len() > 0);
-        
-        Ok(())
-    }
-}

crates/leptos-app/.cargo/config.toml

@@ -1,3 +0,0 @@
-# Ensure getrandom works on wasm32-unknown-unknown without needing manual RUSTFLAGS
-[target.wasm32-unknown-unknown]
-rustflags = ["--cfg", "getrandom_backend=\"wasm_js\""]

crates/leptos-app/Dockerfile

@@ -1,21 +0,0 @@
-# Build stage
-FROM rust:1-alpine AS builder
-
-# Install build dependencies
-RUN apk add --no-cache npm nodejs musl-dev pkgconfig openssl-dev git curl bash
-
-RUN curl -L --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/cargo-bins/cargo-binstall/main/install-from-binstall-release.sh | bash
-
-WORKDIR /app
-
-# Copy manifest first (cache deps) 
-COPY . .
-
-# Install cargo-leptos
-RUN cargo binstall cargo-leptos
-
-# Build release artifacts
-RUN cargo leptos build --release
-
-EXPOSE 8788
-CMD ["cargo", "leptos", "serve", "--release"]

crates/leptos-app/src/app.rs

@@ -1,494 +0,0 @@
-use leptos::prelude::*;
-use leptos_meta::{provide_meta_context, MetaTags, Stylesheet, Title};
-use leptos_router::{
-    components::{Route, Router, Routes},
-    StaticSegment,
-};
-
-#[cfg(feature = "hydrate")]
-use serde::{Deserialize, Serialize};
-#[cfg(feature = "hydrate")]
-use std::collections::VecDeque;
-#[cfg(feature = "hydrate")]
-use uuid::Uuid;
-#[cfg(feature = "hydrate")]
-use js_sys::Date;
-#[cfg(feature = "hydrate")]
-use web_sys::{HtmlInputElement, KeyboardEvent, SubmitEvent};
-#[cfg(feature = "hydrate")]
-use futures_util::StreamExt;
-#[cfg(feature = "hydrate")]
-use async_openai_wasm::{
-    types::{
-        ChatCompletionRequestAssistantMessageArgs, ChatCompletionRequestSystemMessageArgs,
-        ChatCompletionRequestUserMessageArgs, CreateChatCompletionRequestArgs, Model as OpenAIModel,
-    },
-    Client,
-};
-#[cfg(feature = "hydrate")]
-use async_openai_wasm::config::OpenAIConfig;
-#[cfg(feature = "hydrate")]
-use async_openai_wasm::types::{Role, FinishReason};
-#[cfg(feature = "hydrate")]
-use leptos::task::spawn_local;
-
-#[cfg(feature = "hydrate")]
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct Message {
-    pub id: String,
-    pub role: String,
-    pub content: String,
-    pub timestamp: f64,
-}
-
-#[cfg(feature = "hydrate")]
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct MessageContent(pub either::Either<String, Vec<std::collections::HashMap<String, MessageInnerContent>>>);
-
-#[cfg(feature = "hydrate")]
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct MessageInnerContent(pub either::Either<String, std::collections::HashMap<String, String>>);
-
-#[cfg(feature = "hydrate")]
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ChatMessage {
-    pub role: String,
-    pub content: Option<MessageContent>,
-    pub name: Option<String>,
-}
-
-#[cfg(feature = "hydrate")]
-const DEFAULT_MODEL: &str = "default";
-
-#[cfg(feature = "hydrate")]
-async fn fetch_available_models() -> Result<Vec<OpenAIModel>, String> {
-    leptos::logging::log!("[DEBUG_LOG] fetch_available_models: Starting model fetch from http://localhost:8080/v1");
-    
-    let config = OpenAIConfig::new().with_api_base("http://localhost:8080/v1".to_string());
-    let client = Client::with_config(config);
-    
-    match client.models().list().await {
-        Ok(response) => {
-            let model_count = response.data.len();
-            leptos::logging::log!("[DEBUG_LOG] fetch_available_models: Successfully fetched {} models", model_count);
-            
-            if model_count > 0 {
-                let model_names: Vec<String> = response.data.iter().map(|m| m.id.clone()).collect();
-                leptos::logging::log!("[DEBUG_LOG] fetch_available_models: Available models: {:?}", model_names);
-            } else {
-                leptos::logging::log!("[DEBUG_LOG] fetch_available_models: No models returned by server");
-            }
-            
-            Ok(response.data)
-        },
-        Err(e) => {
-            leptos::logging::log!("[DEBUG_LOG] fetch_available_models: Failed to fetch models: {:?}", e);
-            Err(format!("Failed to fetch models: {}", e))
-        }
-    }
-}
-
-pub fn shell(options: LeptosOptions) -> impl IntoView {
-    view! {
-        <!DOCTYPE html>
-        <html lang="en">
-            <head>
-                <meta charset="utf-8"/>
-                <meta name="viewport" content="width=device-width, initial-scale=1"/>
-                <AutoReload options=options.clone() />
-                <HydrationScripts options/>
-                <MetaTags/>
-            </head>
-            <body>
-                <App/>
-            </body>
-        </html>
-    }
-}
-
-#[component]
-pub fn App() -> impl IntoView {
-    // Provides context that manages stylesheets, titles, meta tags, etc.
-    provide_meta_context();
-
-    view! {
-        // injects a stylesheet into the document <head>
-        // id=leptos means cargo-leptos will hot-reload this stylesheet
-        <Stylesheet id="leptos" href="/pkg/leptos-app.css"/>
-
-        // sets the document title
-        <Title text="Chat Interface"/>
-
-        // content for this chat interface
-        <Router>
-            <main>
-                <Routes fallback=|| "Page not found.".into_view()>
-                    <Route path=StaticSegment("") view=ChatInterface/>
-                </Routes>
-            </main>
-        </Router>
-    }
-}
-
-/// Renders the home page of your application.
-#[component]
-fn HomePage() -> impl IntoView {
-    // Creates a reactive value to update the button
-    let count = RwSignal::new(0);
-    let on_click = move |_| *count.write() += 1;
-
-    view! {
-        <h1>"Welcome to Leptos!"</h1>
-        <button on:click=on_click>"Click Me: " {count}</button>
-    }
-}
-
-/// Renders the chat interface
-#[component]
-fn ChatInterface() -> impl IntoView {
-    #[cfg(feature = "hydrate")]
-    {
-        ChatInterfaceImpl()
-    }
-    
-    #[cfg(not(feature = "hydrate"))]
-    {
-        view! {
-            <div class="chat-container">
-                <h1>"Chat Interface"</h1>
-                <p>"Loading chat interface..."</p>
-            </div>
-        }
-    }
-}
-
-#[cfg(feature = "hydrate")]
-#[component]
-fn ChatInterfaceImpl() -> impl IntoView {
-    let (messages, set_messages) = RwSignal::new(VecDeque::<Message>::new()).split();
-    let (input_value, set_input_value) = RwSignal::new(String::new()).split();
-    let (is_loading, set_is_loading) = RwSignal::new(false).split();
-    let (available_models, set_available_models) = RwSignal::new(Vec::<OpenAIModel>::new()).split();
-    let (selected_model, set_selected_model) = RwSignal::new(DEFAULT_MODEL.to_string()).split();
-    let (models_loading, set_models_loading) = RwSignal::new(false).split();
-
-    // Fetch models on component initialization
-    Effect::new(move |_| {
-        spawn_local(async move {
-            set_models_loading.set(true);
-            match fetch_available_models().await {
-                Ok(models) => {
-                    set_available_models.set(models);
-                    set_models_loading.set(false);
-                }
-                Err(e) => {
-                    leptos::logging::log!("Failed to fetch models: {}", e);
-                    set_available_models.set(vec![]);
-                    set_models_loading.set(false);
-                }
-            }
-        });
-    });
-
-    let send_message = Action::new_unsync(move |content: &String| {
-        let content = content.clone();
-        async move {
-            if content.trim().is_empty() {
-                leptos::logging::log!("[DEBUG_LOG] send_message: Empty content, skipping");
-                return;
-            }
-
-            leptos::logging::log!("[DEBUG_LOG] send_message: Starting message send process");
-            set_is_loading.set(true);
-
-            // Add user message to chat
-            let user_message = Message {
-                id: Uuid::new_v4().to_string(),
-                role: "user".to_string(),
-                content: content.clone(),
-                timestamp: Date::now(),
-            };
-
-            set_messages.update(|msgs| msgs.push_back(user_message.clone()));
-            set_input_value.set(String::new());
-
-            let mut chat_messages = Vec::new();
-
-            // Add system message
-            let system_message = ChatCompletionRequestSystemMessageArgs::default()
-                .content("You are a helpful assistant.")
-                .build()
-                .expect("failed to build system message");
-            chat_messages.push(system_message.into());
-
-            // Add history messages
-            let history_count = messages.get_untracked().len();
-            for msg in messages.get_untracked().iter() {
-                match msg.role.as_str() {
-                    "user" => {
-                        let message = ChatCompletionRequestUserMessageArgs::default()
-                            .content(msg.content.clone())
-                            .build()
-                            .expect("failed to build user message");
-                        chat_messages.push(message.into());
-                    }
-                    "assistant" => {
-                        let message = ChatCompletionRequestAssistantMessageArgs::default()
-                            .content(msg.content.clone())
-                            .build()
-                            .expect("failed to build assistant message");
-                        chat_messages.push(message.into());
-                    }
-                    _ => {}
-                }
-            }
-
-            // Add current user message
-            let message = ChatCompletionRequestUserMessageArgs::default()
-                .content(user_message.content.clone())
-                .build()
-                .expect("failed to build user message");
-            chat_messages.push(message.into());
-
-            let current_model = selected_model.get_untracked();
-            let total_messages = chat_messages.len();
-            
-            leptos::logging::log!("[DEBUG_LOG] send_message: Preparing request - model: '{}', history_count: {}, total_messages: {}", 
-                          current_model, history_count, total_messages);
-
-            let request = CreateChatCompletionRequestArgs::default()
-                .model(current_model.as_str())
-                .max_tokens(512u32)
-                .messages(chat_messages)
-                .stream(true)
-                .build()
-                .expect("failed to build request");
-
-            // Send request
-            let config = OpenAIConfig::new().with_api_base("http://localhost:8080/v1".to_string());
-            let client = Client::with_config(config);
-            
-            leptos::logging::log!("[DEBUG_LOG] send_message: Sending request to http://localhost:8080/v1 with model: '{}'", current_model);
-
-            match client.chat().create_stream(request).await {
-                Ok(mut stream) => {
-                    leptos::logging::log!("[DEBUG_LOG] send_message: Successfully created stream");
-                    
-                    let mut assistant_created = false;
-                    let mut content_appended = false;
-                    let mut chunks_received = 0;
-                    
-                    while let Some(next) = stream.next().await {
-                        match next {
-                            Ok(chunk) => {
-                                chunks_received += 1;
-                                if let Some(choice) = chunk.choices.get(0) {
-                                    if !assistant_created {
-                                        if let Some(role) = &choice.delta.role {
-                                            if role == &Role::Assistant {
-                                                assistant_created = true;
-                                                let assistant_id = Uuid::new_v4().to_string();
-                                                set_messages.update(|msgs| {
-                                                    msgs.push_back(Message {
-                                                        id: assistant_id,
-                                                        role: "assistant".to_string(),
-                                                        content: String::new(),
-                                                        timestamp: Date::now(),
-                                                    });
-                                                });
-                                            }
-                                        }
-                                    }
-
-                                    if let Some(content) = &choice.delta.content {
-                                        if !content.is_empty() {
-                                            if !assistant_created {
-                                                assistant_created = true;
-                                                let assistant_id = Uuid::new_v4().to_string();
-                                                set_messages.update(|msgs| {
-                                                    msgs.push_back(Message {
-                                                        id: assistant_id,
-                                                        role: "assistant".to_string(),
-                                                        content: String::new(),
-                                                        timestamp: Date::now(),
-                                                    });
-                                                });
-                                            }
-                                            content_appended = true;
-                                            set_messages.update(|msgs| {
-                                                if let Some(last) = msgs.back_mut() {
-                                                    if last.role == "assistant" {
-                                                        last.content.push_str(content);
-                                                        last.timestamp = Date::now();
-                                                    }
-                                                }
-                                            });
-                                        }
-                                    }
-
-                                    if let Some(reason) = &choice.finish_reason {
-                                        if reason == &FinishReason::Stop {
-                                            leptos::logging::log!("[DEBUG_LOG] send_message: Received finish_reason=stop after {} chunks", chunks_received);
-                                            break;
-                                        }
-                                    }
-                                }
-                            }
-                            Err(e) => {
-                                leptos::logging::log!("[DEBUG_LOG] send_message: Stream error after {} chunks: {:?}", chunks_received, e);
-                                set_messages.update(|msgs| {
-                                    msgs.push_back(Message {
-                                        id: Uuid::new_v4().to_string(),
-                                        role: "system".to_string(),
-                                        content: format!("Stream error: {}", e),
-                                        timestamp: Date::now(),
-                                    });
-                                });
-                                break;
-                            }
-                        }
-                    }
-
-                    if assistant_created && !content_appended {
-                        set_messages.update(|msgs| {
-                            let should_pop = msgs
-                                .back()
-                                .map(|m| m.role == "assistant" && m.content.is_empty())
-                                .unwrap_or(false);
-                            if should_pop {
-                                msgs.pop_back();
-                            }
-                        });
-                    }
-
-                    leptos::logging::log!("[DEBUG_LOG] send_message: Stream completed successfully, received {} chunks", chunks_received);
-                }
-                Err(e) => {
-                    leptos::logging::log!("[DEBUG_LOG] send_message: Request failed with error: {:?}", e);
-                    let error_message = Message {
-                        id: Uuid::new_v4().to_string(),
-                        role: "system".to_string(),
-                        content: format!("Error: Request failed - {}", e),
-                        timestamp: Date::now(),
-                    };
-                    set_messages.update(|msgs| msgs.push_back(error_message));
-                }
-            }
-
-            set_is_loading.set(false);
-        }
-    });
-
-    let on_input = move |ev| {
-        let input = event_target::<HtmlInputElement>(&ev);
-        set_input_value.set(input.value());
-    };
-
-    let on_submit = move |ev: SubmitEvent| {
-        ev.prevent_default();
-        let content = input_value.get();
-        send_message.dispatch(content);
-    };
-
-    let on_keypress = move |ev: KeyboardEvent| {
-        if ev.key() == "Enter" && !ev.shift_key() {
-            ev.prevent_default();
-            let content = input_value.get();
-            send_message.dispatch(content);
-        }
-    };
-
-    let on_model_change = move |ev| {
-        let select = event_target::<web_sys::HtmlSelectElement>(&ev);
-        set_selected_model.set(select.value());
-    };
-
-    let messages_list = move || {
-        messages.get()
-            .into_iter()
-            .map(|message| {
-                let role_class = match message.role.as_str() {
-                    "user" => "user-message",
-                    "assistant" => "assistant-message",
-                    _ => "system-message",
-                };
-
-                view! {
-                    <div class=format!("message {}", role_class)>
-                        <div class="message-role">{message.role}</div>
-                        <div class="message-content">{message.content}</div>
-                    </div>
-                }
-            })
-            .collect::<Vec<_>>()
-    };
-
-    let loading_indicator = move || {
-        is_loading.get().then(|| {
-            view! {
-                <div class="message assistant-message">
-                    <div class="message-role">"assistant"</div>
-                    <div class="message-content">"Thinking..."</div>
-                </div>
-            }
-        })
-    };
-
-    view! {
-        <div class="chat-container">
-            <h1>"Chat Interface"</h1>
-            <div class="model-selector">
-                <label for="model-select">"Model: "</label>
-                <select 
-                    id="model-select"
-                    on:change=on_model_change
-                    prop:value=selected_model
-                    prop:disabled=models_loading
-                >
-                    {move || {
-                        if models_loading.get() {
-                            vec![view! {
-                                <option value={String::from("")} selected=false>{String::from("Loading models...")}</option>
-                            }]
-                        } else {
-                            let models = available_models.get();
-                            if models.is_empty() {
-                                vec![view! {
-                                    <option value={String::from("default")} selected=true>{String::from("default")}</option>
-                                }]
-                            } else {
-                                models.into_iter().map(|model| {
-                                    view! {
-                                        <option value=model.id.clone() selected={model.id == DEFAULT_MODEL}>{model.id.clone()}</option>
-                                    }
-                                }).collect::<Vec<_>>()
-                            }
-                        }
-                    }}
-                </select>
-            </div>
-            <div class="messages-container">
-                {messages_list}
-                {loading_indicator}
-            </div>
-            <form class="input-form" on:submit=on_submit>
-                <input
-                    type="text"
-                    class="message-input"
-                    placeholder="Type your message here..."
-                    prop:value=input_value
-                    on:input=on_input
-                    on:keypress=on_keypress
-                    prop:disabled=is_loading
-                />
-                <button
-                    type="submit"
-                    class="send-button"
-                    prop:disabled=move || is_loading.get() || input_value.get().trim().is_empty()
-                >
-                    "Send"
-                </button>
-            </form>
-        </div>
-    }
-}

crates/leptos-app/src/lib.rs

@@ -1,30 +0,0 @@
-pub mod app;
-
-#[cfg(feature = "hydrate")]
-#[wasm_bindgen::prelude::wasm_bindgen]
-pub fn hydrate() {
-    use crate::app::*;
-    console_error_panic_hook::set_once();
-    leptos::mount::hydrate_body(App);
-}
-
-#[cfg(feature = "ssr")]
-pub fn create_leptos_router() -> axum::Router {
-    use axum::Router;
-    use leptos::prelude::*;
-    use leptos_axum::{generate_route_list, LeptosRoutes};
-    use crate::app::*;
-
-    let conf = get_configuration(None).unwrap();
-    let leptos_options = conf.leptos_options;
-    // Generate the list of routes in your Leptos App
-    let routes = generate_route_list(App);
-
-    Router::new()
-        .leptos_routes(&leptos_options, routes, {
-            let leptos_options = leptos_options.clone();
-            move || shell(leptos_options.clone())
-        })
-        .fallback(leptos_axum::file_and_error_handler(shell))
-        .with_state(leptos_options)
-}

crates/leptos-app/src/main.rs

@@ -1,39 +0,0 @@
-
-#[cfg(feature = "ssr")]
-#[tokio::main]
-async fn main() {
-    use axum::Router;
-    use leptos::logging::log;
-    use leptos::prelude::*;
-    use leptos_axum::{generate_route_list, LeptosRoutes};
-    use leptos_app::app::*;
-
-    let conf = get_configuration(None).unwrap();
-    let addr = conf.leptos_options.site_addr;
-    let leptos_options = conf.leptos_options;
-    // Generate the list of routes in your Leptos App
-    let routes = generate_route_list(App);
-
-    let app = Router::new()
-        .leptos_routes(&leptos_options, routes, {
-            let leptos_options = leptos_options.clone();
-            move || shell(leptos_options.clone())
-        })
-        .fallback(leptos_axum::file_and_error_handler(shell))
-        .with_state(leptos_options);
-
-    // run our app with hyper
-    // `axum::Server` is a re-export of `hyper::Server`
-    log!("listening on http://{}", &addr);
-    let listener = tokio::net::TcpListener::bind(&addr).await.unwrap();
-    axum::serve(listener, app.into_make_service())
-        .await
-        .unwrap();
-}
-
-#[cfg(not(feature = "ssr"))]
-pub fn main() {
-    // no client-side main function
-    // unless we want this to work with e.g., Trunk for pure client-side testing
-    // see lib.rs for hydration function instead
-}

crates/leptos-app/style/main.scss

@@ -1,4 +0,0 @@
-body {
-	font-family: sans-serif;
-	text-align: center;
-}

crates/llama-runner/Cargo.toml

@@ -1,12 +1,12 @@
 [package]
 name = "llama-runner"
-version = "0.1.0"
+version.workspace = true
 edition = "2021"
 
 [dependencies]
 candle-core = { git = "https://github.com/huggingface/candle.git" }
-candle-nn = { git = "https://github.com/huggingface/candle.git" }  
+candle-nn = { git = "https://github.com/huggingface/candle.git" }
-candle-transformers = { git = "https://github.com/huggingface/candle.git" }
+candle-transformers = { git = "https://github.com/huggingface/candle.git"}
 hf-hub = "0.3"
 tokenizers = "0.20"
 anyhow = "1.0"

crates/llama-runner/src/lib.rs

@@ -5,4 +5,3 @@ pub use llama_api::{run_llama_inference, LlamaInferenceConfig, WhichModel};
 
 // Re-export constants and types that might be needed
 pub const EOS_TOKEN: &str = "</s>";
-

crates/llama-runner/src/llama_api.rs

@@ -1,14 +1,14 @@
+use crate::EOS_TOKEN;
 use anyhow::{bail, Error as E};
 use candle_core::{utils, DType, Device, Tensor};
 use candle_nn::VarBuilder;
 use candle_transformers::generation::{LogitsProcessor, Sampling};
-use candle_transformers::models::llama::{Llama, LlamaConfig};
 use candle_transformers::models::llama as model;
+use candle_transformers::models::llama::{Llama, LlamaConfig};
+use clap::ValueEnum;
 use hf_hub::api::sync::Api;
 use hf_hub::{Repo, RepoType};
 use std::sync::mpsc::{self, Receiver};
-use clap::ValueEnum;
-use crate::{EOS_TOKEN};
 
 #[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum, Default)]
 pub enum WhichModel {
@@ -81,8 +81,8 @@ impl Default for LlamaInferenceConfig {
             max_tokens: 512,
 
             // Performance flags
-            no_kv_cache: false,          // keep cache ON for speed
+            no_kv_cache: false,   // keep cache ON for speed
-            use_flash_attn: true,        // great speed boost if supported
+            use_flash_attn: false, // great speed boost if supported
 
             // Precision: bf16 is a good default on Ampere+; fallback to fp16 if needed.
             dtype: Some("bf16".to_string()),
@@ -98,8 +98,6 @@ impl Default for LlamaInferenceConfig {
     }
 }
 
-
-
 fn device(cpu: bool) -> anyhow::Result<Device> {
     if cpu {
         Ok(Device::Cpu)
@@ -112,7 +110,6 @@ fn device(cpu: bool) -> anyhow::Result<Device> {
     }
 }
 
-
 fn hub_load_safetensors(
     api: &hf_hub::api::sync::ApiRepo,
     json_file: &str,
@@ -171,7 +168,7 @@ pub fn run_llama_inference(
                 WhichModel::SmolLM2_1_7BInstruct => "HuggingFaceTB/SmolLM2-1.7B-Instruct",
                 WhichModel::TinyLlama1_1BChat => "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
             }
-                .to_string()
+            .to_string()
         });
         println!("Loading model: {}", model_id);
         let revision = cfg.revision.clone().unwrap_or("main".to_string());
@@ -334,4 +331,3 @@ pub fn run_llama_inference(
 
     Ok(rx)
 }
-

crates/llama-runner/src/llama_cli.rs

@@ -88,7 +88,6 @@ impl Into<LlamaInferenceConfig> for Args {
     }
 }
 
-
 pub fn run_cli() -> anyhow::Result<()> {
     let args = Args::parse();
     let cfg = args.into();
@@ -106,4 +105,4 @@ pub fn run_cli() -> anyhow::Result<()> {
         }
     }
     Ok(())
-}
+}

crates/llama-runner/src/main.rs

@@ -2,8 +2,8 @@
 extern crate accelerate_src;
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
-mod llama_cli;
 mod llama_api;
+mod llama_cli;
 
 use anyhow::Result;
 use clap::{Parser, ValueEnum};
@@ -14,7 +14,6 @@ use crate::llama_cli::run_cli;
 
 const EOS_TOKEN: &str = "</s>";
 
-
 fn main() -> Result<()> {
     run_cli()
-}
+}

crates/predict-otron-9000/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "predict-otron-9000"
-version = "0.1.0"
+version.workspace = true
 edition = "2024"
 
 [[bin]]
@@ -19,7 +19,7 @@ tracing = "0.1"
 tracing-subscriber = { version = "0.3", features = ["env-filter"] }
 uuid = { version = "1.7.0", features = ["v4"] }
 reqwest = { version = "0.12", features = ["json"] }
-rust-embed = { version = "8.7.2", features = ["include-exclude"] }
+rust-embed = { version = "8.7.2", features = ["include-exclude", "axum"] }
 
 # Dependencies for embeddings functionality
 embeddings-engine = { path = "../embeddings-engine" }
@@ -28,9 +28,11 @@ embeddings-engine = { path = "../embeddings-engine" }
 inference-engine = { path = "../inference-engine" }
 
 # Dependencies for leptos web app
-leptos-app = { path = "../leptos-app", features = ["ssr"] }
+#leptos-app = { path = "../leptos-app", features = ["ssr"] }
+chat-ui = { path = "../chat-ui", features = ["ssr", "hydrate"], optional = false }
 
 mime_guess = "2.0.5"
+log = "0.4.27"
 
 
 [package.metadata.compose]
@@ -44,4 +46,8 @@ port = 8080
 image = "ghcr.io/geoffsee/predict-otron-9000:latest"
 replicas = 1
 port = 8080
-env = { SERVER_CONFIG = "" }
+# SERVER_CONFIG Example: {\"serverMode\":\"HighAvailability\",\"services\":{\"inference_url\":\"http://custom-inference:9000\",\"embeddings_url\":\"http://custom-embeddings:9001\"}}
+# you can generate this via node to avoid toil
+# const server_config = {serverMode: "HighAvailability", services: {inference_url: "http://custom-inference:9000", embeddings_url: "http://custom-embeddings:9001"} };
+# console.log(JSON.stringify(server_config).replace(/"/g, '\\"'));
+env = { SERVER_CONFIG = "<your-json-value-here>" }

crates/predict-otron-9000/src/config.rs

@@ -1,7 +1,12 @@
 use serde::{Deserialize, Serialize};
 use std::env;
-
+use tracing::info;
-#[derive(Debug, Clone, Deserialize, Serialize)]
+use tracing::log::error;
+/// # Generating `SERVER_CONFIG` with Node
+// # const server_config = {serverMode: "HighAvailability", services: {inference_url: "http://custom-inference:9000", embeddings_url: "http://custom-embeddings:9001"} };
+// # console.log(JSON.stringify(server_config).replace(/"/g, '\\"'));
+///
+#[derive(Serialize, Deserialize, Clone, Debug)]
 #[serde(rename_all = "camelCase")]
 pub struct ServerConfig {
     #[serde(default = "default_server_host")]
@@ -10,14 +15,16 @@ pub struct ServerConfig {
     pub server_port: u16,
     pub server_mode: ServerMode,
     #[serde(default)]
-    pub services: Services,
+    pub services: Option<Services>,
 }
 
 fn default_server_host() -> String {
     "127.0.0.1".to_string()
 }
 
-fn default_server_port() -> u16 { 8080 }
+fn default_server_port() -> u16 {
+    8080
+}
 
 #[derive(Debug, Clone, Deserialize, Serialize, PartialEq)]
 #[serde(rename_all = "PascalCase")]
@@ -34,17 +41,15 @@ impl Default for ServerMode {
 
 #[derive(Debug, Clone, Deserialize, Serialize)]
 pub struct Services {
-    #[serde(default = "inference_service_url")]
+    pub inference_url: Option<String>,
-    pub inference_url: String,
+    pub embeddings_url: Option<String>,
-    #[serde(default = "embeddings_service_url")]
-    pub embeddings_url: String,
 }
 
 impl Default for Services {
     fn default() -> Self {
         Self {
-            inference_url: inference_service_url(),
+            inference_url: None,
-            embeddings_url: embeddings_service_url(),
+            embeddings_url: None,
         }
     }
 }
@@ -63,7 +68,7 @@ impl Default for ServerConfig {
             server_host: "127.0.0.1".to_string(),
             server_port: 8080,
             server_mode: ServerMode::Standalone,
-            services: Services::default(),
+            services: Some(Services::default()),
         }
     }
 }
@@ -73,21 +78,19 @@ impl ServerConfig {
     /// Falls back to default (Local mode) if not set or invalid
     pub fn from_env() -> Self {
         match env::var("SERVER_CONFIG") {
-            Ok(config_str) => {
+            Ok(config_str) => match serde_json::from_str::<ServerConfig>(&config_str) {
-                match serde_json::from_str::<ServerConfig>(&config_str) {
+                Ok(config) => {
-                    Ok(config) => {
+                    tracing::info!("Loaded server configuration: {:?}", config);
-                        tracing::info!("Loaded server configuration: {:?}", config);
+                    config
-                        config
-                    }
-                    Err(e) => {
-                        tracing::warn!(
-                            "Failed to parse SERVER_CONFIG environment variable: {}. Using default configuration.", 
-                            e
-                        );
-                        ServerConfig::default()
-                    }
                 }
-            }
+                Err(e) => {
+                    tracing::warn!(
+                        "Failed to parse SERVER_CONFIG environment variable: {}. Using default configuration.",
+                        e
+                    );
+                    ServerConfig::default()
+                }
+            },
             Err(_) => {
                 tracing::info!("SERVER_CONFIG not set, Standalone mode active");
                 ServerConfig::default()
@@ -96,18 +99,52 @@ impl ServerConfig {
     }
 
     /// Check if the server should run in high availability mode
-    pub fn is_high_availability(&self) -> bool {
+    pub fn is_high_availability(&self) -> Result<bool, std::io::Error> {
-        self.server_mode == ServerMode::HighAvailability
+        if self.server_mode == ServerMode::HighAvailability {
+            let services_well_defined: bool = self.clone().services.is_some();
+
+            let inference_url_well_defined: bool =
+                services_well_defined && self.clone().services.unwrap().inference_url.is_some();
+
+            let embeddings_well_defined: bool =
+                services_well_defined && self.clone().services.unwrap().embeddings_url.is_some();
+
+            let is_well_defined_for_ha =
+                services_well_defined && inference_url_well_defined && embeddings_well_defined;
+
+            if !is_well_defined_for_ha {
+                let config_string = serde_json::to_string_pretty(&self).unwrap();
+                error!(
+                    "HighAvailability mode configured but services not well defined! \n## Config Used:\n {}",
+                    config_string
+                );
+                let err = std::io::Error::new(
+                    std::io::ErrorKind::Other,
+                    "HighAvailability mode configured but services not well defined!",
+                );
+                return Err(err);
+            }
+        }
+
+        Ok(self.server_mode == ServerMode::HighAvailability)
     }
 
     /// Get the inference service URL for proxying
-    pub fn inference_url(&self) -> &str {
+    pub fn inference_url(&self) -> Option<String> {
-        &self.services.inference_url
+        if self.services.is_some() {
+            self.services.clone()?.inference_url
+        } else {
+            None
+        }
     }
 
     /// Get the embeddings service URL for proxying
-    pub fn embeddings_url(&self) -> &str {
+    pub fn embeddings_url(&self) -> Option<String> {
-        &self.services.embeddings_url
+        if self.services.is_some() {
+            self.services.clone()?.embeddings_url
+        } else {
+            None
+        }
     }
 }
 
@@ -119,7 +156,7 @@ mod tests {
     fn test_default_config() {
         let config = ServerConfig::default();
         assert_eq!(config.server_mode, ServerMode::Standalone);
-        assert!(!config.is_high_availability());
+        assert!(!config.is_high_availability().unwrap());
     }
 
     #[test]
@@ -134,23 +171,26 @@ mod tests {
 
         let config: ServerConfig = serde_json::from_str(config_json).unwrap();
         assert_eq!(config.server_mode, ServerMode::HighAvailability);
-        assert!(config.is_high_availability());
+        assert!(config.is_high_availability().unwrap());
-        assert_eq!(config.inference_url(), "http://inference-service:8080");
+        assert_eq!(
-        assert_eq!(config.embeddings_url(), "http://embeddings-service:8080");
+            config.inference_url().unwrap(),
+            "http://inference-service:8080"
+        );
+        assert_eq!(
+            config.embeddings_url().unwrap(),
+            "http://embeddings-service:8080"
+        );
     }
 
     #[test]
     fn test_local_mode_config() {
         let config_json = r#"{
-            "serverMode": "Local"
+            "serverMode": "Standalone"
         }"#;
 
         let config: ServerConfig = serde_json::from_str(config_json).unwrap();
         assert_eq!(config.server_mode, ServerMode::Standalone);
-        assert!(!config.is_high_availability());
+        assert!(!config.is_high_availability().unwrap());
-        // Should use default URLs
-        assert_eq!(config.inference_url(), "http://inference-service:8080");
-        assert_eq!(config.embeddings_url(), "http://embeddings-service:8080");
     }
 
     #[test]
@@ -164,17 +204,26 @@ mod tests {
         }"#;
 
         let config: ServerConfig = serde_json::from_str(config_json).unwrap();
-        assert_eq!(config.inference_url(), "http://custom-inference:9000");
+        assert_eq!(
-        assert_eq!(config.embeddings_url(), "http://custom-embeddings:9001");
+            config.inference_url().unwrap(),
+            "http://custom-inference:9000"
+        );
+        assert_eq!(
+            config.embeddings_url().unwrap(),
+            "http://custom-embeddings:9001"
+        );
     }
 
     #[test]
-    fn test_minimal_high_availability_config() {
+    fn test_minimal_high_availability_config_error() {
         let config_json = r#"{"serverMode": "HighAvailability"}"#;
         let config: ServerConfig = serde_json::from_str(config_json).unwrap();
-        assert!(config.is_high_availability());
+
-        // Should use default URLs
+        let is_high_availability = config.is_high_availability();
-        assert_eq!(config.inference_url(), "http://inference-service:8080");
+
-        assert_eq!(config.embeddings_url(), "http://embeddings-service:8080");
+        assert!(is_high_availability.is_err());
+        // // Should use default URLs
+        // assert_eq!(config.inference_url().unwrap(), "http://inference-service:8080");
+        // assert_eq!(config.embeddings_url().unwrap(), "http://embeddings-service:8080");
     }
-}
+}

crates/predict-otron-9000/src/ha_mode.rs

@@ -1,10 +1,10 @@
 use axum::{
+    Router,
     body::Body,
     extract::{Request, State},
     http::{HeaderMap, Method, StatusCode, Uri},
     response::{IntoResponse, Response},
     routing::{get, post},
-    Router,
 };
 use reqwest::Client;
 use serde_json::Value;
@@ -12,6 +12,120 @@ use std::time::Duration;
 
 use crate::config::ServerConfig;
 
+/// # Generating `SERVER_CONFIG` for TOML using Node.js
+///
+/// You can still use the Node.js REPL to build the JSON, but when pasting into
+/// a `.toml` file you must follow TOML's string rules. Below are the safest patterns.
+///
+/// ## 1) Generate the JSON in Node
+/// ```bash
+/// node
+/// ```
+/// ```javascript
+/// const myobject = {
+///   serverMode: "HighAvailability",
+///   services: {
+///     inference_url: "http://custom-inference:9000",
+///     embeddings_url: "http://custom-embeddings:9001"
+///   }
+/// };
+/// const json = JSON.stringify(myobject);
+/// json
+/// // -> '{"serverMode":"HighAvailability","services":{"inference_url":"http://custom-inference:9000","embeddings_url":"http://custom-embeddings:9001"}}'
+/// ```
+///
+/// ## 2) Put it into `.toml`
+///
+/// ### Option A (recommended): single-quoted TOML *literal* string
+/// Single quotes in TOML mean "no escaping", so your inner double quotes are safe.
+/// ```toml
+/// SERVER_CONFIG = '{"serverMode":"HighAvailability","services":{"inference_url":"http://custom-inference:9000","embeddings_url":"http://custom-embeddings:9001"}}'
+/// ```
+///
+/// ### Option B: double-quoted TOML string (must escape inner quotes)
+/// If you *must* use double quotes in TOML, escape all `"` inside the JSON.
+/// You can have Node do this for you:
+/// ```javascript
+/// // In Node:
+/// const jsonForToml = JSON.stringify(myobject).replace(/"/g, '\\"');
+/// jsonForToml
+/// // -> \"{\\\"serverMode\\\":\\\"HighAvailability\\\",...}\"
+/// ```
+/// Then paste into TOML:
+/// ```toml
+/// SERVER_CONFIG = "{\"serverMode\":\"HighAvailability\",\"services\":{\"inference_url\":\"http://custom-inference:9000\",\"embeddings_url\":\"http://custom-embeddings:9001\"}}"
+/// ```
+///
+/// ### Option C: multi-line literal (for pretty JSON)
+/// If you want pretty-printed JSON in the file, use TOML's triple single quotes:
+/// ```javascript
+/// // In Node (pretty with 2 spaces):
+/// const pretty = JSON.stringify(myobject, null, 2);
+/// ```
+/// ```toml
+/// SERVER_CONFIG = '''{
+///   "serverMode": "HighAvailability",
+///   "services": {
+///     "inference_url": "http://custom-inference:9000",
+///     "embeddings_url": "http://custom-embeddings:9001"
+///   }
+/// }'''
+/// ```
+///
+/// ## 3) Reading it in Rust
+///
+/// If `SERVER_CONFIG` is stored as a **string** in TOML (Options A/B/C):
+/// ```rust
+/// use serde_json::Value;
+///
+/// // Suppose you've already loaded your .toml into a struct or a toml::Value:
+/// // e.g., struct FileCfg { pub SERVER_CONFIG: String }
+/// fn parse_server_config(raw: &str) -> anyhow::Result<Value> {
+///     let v: Value = serde_json::from_str(raw)?;
+///     Ok(v)
+/// }
+/// ```
+///
+/// ### Alternative: store it as TOML tables and serialize to JSON at runtime
+/// Instead of a JSON string, you can make the TOML first-class tables:
+/// ```toml
+/// [SERVER_CONFIG]
+/// serverMode = "HighAvailability"
+///
+/// [SERVER_CONFIG.services]
+/// inference_url = "http://custom-inference:9000"
+/// embeddings_url = "http://custom-embeddings:9001"
+/// ```
+/// ```rust
+/// use serde::{Deserialize, Serialize};
+/// use serde_json::Value;
+///
+/// #[derive(Debug, Serialize, Deserialize)]
+/// struct Services {
+///     inference_url: String,
+///     embeddings_url: String,
+/// }
+///
+/// #[derive(Debug, Serialize, Deserialize)]
+/// struct ServerConfig {
+///     serverMode: String,
+///     services: Services,
+/// }
+///
+/// // After loading the .toml (e.g., via `toml::from_str`):
+/// // let cfg: ServerConfig = toml::from_str(toml_str)?;
+/// // Convert to JSON if needed:
+/// fn to_json(cfg: &ServerConfig) -> serde_json::Result<Value> {
+///     Ok(serde_json::to_value(cfg)?)
+/// }
+/// ```
+///
+/// ## Gotchas
+/// - Prefer **single-quoted** TOML strings for raw JSON to avoid escaping.
+/// - If you use **double-quoted** TOML strings, escape every inner `"` in the JSON.
+/// - Pretty JSON is fine in TOML using `''' ... '''`, but remember the newlines are part of the string.
+/// - If you control the consumer, TOML tables (the alternative above) are more ergonomic than embedding JSON.
+
 /// HTTP client configured for proxying requests
 #[derive(Clone)]
 pub struct ProxyClient {
@@ -31,7 +145,7 @@ impl ProxyClient {
 }
 
 /// Create a router that proxies requests to external services in HighAvailability mode
-pub fn create_proxy_router(config: ServerConfig) -> Router {
+pub fn create_ha_router(config: ServerConfig) -> Router {
     let proxy_client = ProxyClient::new(config.clone());
 
     Router::new()
@@ -47,10 +161,16 @@ async fn proxy_chat_completions(
     headers: HeaderMap,
     body: Body,
 ) -> Result<Response, StatusCode> {
-    let target_url = format!("{}/v1/chat/completions", proxy_client.config.inference_url());
+    let target_url = format!(
-    
+        "{}/v1/chat/completions",
+        proxy_client
+            .config
+            .inference_url()
+            .expect("Invalid Configuration")
+    );
+
     tracing::info!("Proxying chat completions request to: {}", target_url);
-    
+
     // Extract body as bytes
     let body_bytes = match axum::body::to_bytes(body, usize::MAX).await {
         Ok(bytes) => bytes,
@@ -63,7 +183,9 @@ async fn proxy_chat_completions(
     // Check if this is a streaming request
     let is_streaming = if let Ok(body_str) = String::from_utf8(body_bytes.to_vec()) {
         if let Ok(json) = serde_json::from_str::<Value>(&body_str) {
-            json.get("stream").and_then(|v| v.as_bool()).unwrap_or(false)
+            json.get("stream")
+                .and_then(|v| v.as_bool())
+                .unwrap_or(false)
         } else {
             false
         }
@@ -72,7 +194,8 @@ async fn proxy_chat_completions(
     };
 
     // Forward the request
-    let mut req_builder = proxy_client.client
+    let mut req_builder = proxy_client
+        .client
         .post(&target_url)
         .body(body_bytes.to_vec());
 
@@ -85,8 +208,7 @@ async fn proxy_chat_completions(
 
     match req_builder.send().await {
         Ok(response) => {
-            let mut resp_builder = Response::builder()
+            let mut resp_builder = Response::builder().status(response.status());
-                .status(response.status());
 
             // Forward response headers
             for (name, value) in response.headers().iter() {
@@ -99,14 +221,12 @@ async fn proxy_chat_completions(
             if is_streaming {
                 // For streaming, we need to forward the response as-is
                 match response.bytes().await {
-                    Ok(body) => {
+                    Ok(body) => resp_builder
-                        resp_builder
+                        .header("content-type", "text/plain; charset=utf-8")
-                            .header("content-type", "text/plain; charset=utf-8")
+                        .header("cache-control", "no-cache")
-                            .header("cache-control", "no-cache")
+                        .header("connection", "keep-alive")
-                            .header("connection", "keep-alive")
+                        .body(Body::from(body))
-                            .body(Body::from(body))
+                        .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR),
-                            .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)
-                    }
                     Err(e) => {
                         tracing::error!("Failed to read streaming response body: {}", e);
                         Err(StatusCode::INTERNAL_SERVER_ERROR)
@@ -115,11 +235,9 @@ async fn proxy_chat_completions(
             } else {
                 // For non-streaming, forward the JSON response
                 match response.bytes().await {
-                    Ok(body) => {
+                    Ok(body) => resp_builder
-                        resp_builder
+                        .body(Body::from(body))
-                            .body(Body::from(body))
+                        .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR),
-                            .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)
-                    }
                     Err(e) => {
                         tracing::error!("Failed to read response body: {}", e);
                         Err(StatusCode::INTERNAL_SERVER_ERROR)
@@ -139,10 +257,16 @@ async fn proxy_models(
     State(proxy_client): State<ProxyClient>,
     headers: HeaderMap,
 ) -> Result<Response, StatusCode> {
-    let target_url = format!("{}/v1/models", proxy_client.config.inference_url());
+    let target_url = format!(
-    
+        "{}/v1/models",
+        proxy_client
+            .config
+            .inference_url()
+            .expect("Invalid Configuration Detected")
+    );
+
     tracing::info!("Proxying models request to: {}", target_url);
-    
+
     let mut req_builder = proxy_client.client.get(&target_url);
 
     // Forward relevant headers
@@ -154,8 +278,7 @@ async fn proxy_models(
 
     match req_builder.send().await {
         Ok(response) => {
-            let mut resp_builder = Response::builder()
+            let mut resp_builder = Response::builder().status(response.status());
-                .status(response.status());
 
             // Forward response headers
             for (name, value) in response.headers().iter() {
@@ -165,11 +288,9 @@ async fn proxy_models(
             }
 
             match response.bytes().await {
-                Ok(body) => {
+                Ok(body) => resp_builder
-                    resp_builder
+                    .body(Body::from(body))
-                        .body(Body::from(body))
+                    .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR),
-                        .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)
-                }
                 Err(e) => {
                     tracing::error!("Failed to read models response body: {}", e);
                     Err(StatusCode::INTERNAL_SERVER_ERROR)
@@ -189,10 +310,16 @@ async fn proxy_embeddings(
     headers: HeaderMap,
     body: Body,
 ) -> Result<Response, StatusCode> {
-    let target_url = format!("{}/v1/embeddings", proxy_client.config.embeddings_url());
+    let target_url = format!(
-    
+        "{}/v1/embeddings",
+        proxy_client
+            .config
+            .embeddings_url()
+            .expect("Invalid Configuration Detected")
+    );
+
     tracing::info!("Proxying embeddings request to: {}", target_url);
-    
+
     // Extract body as bytes
     let body_bytes = match axum::body::to_bytes(body, usize::MAX).await {
         Ok(bytes) => bytes,
@@ -203,7 +330,8 @@ async fn proxy_embeddings(
     };
 
     // Forward the request
-    let mut req_builder = proxy_client.client
+    let mut req_builder = proxy_client
+        .client
         .post(&target_url)
         .body(body_bytes.to_vec());
 
@@ -216,8 +344,7 @@ async fn proxy_embeddings(
 
     match req_builder.send().await {
         Ok(response) => {
-            let mut resp_builder = Response::builder()
+            let mut resp_builder = Response::builder().status(response.status());
-                .status(response.status());
 
             // Forward response headers
             for (name, value) in response.headers().iter() {
@@ -227,11 +354,9 @@ async fn proxy_embeddings(
             }
 
             match response.bytes().await {
-                Ok(body) => {
+                Ok(body) => resp_builder
-                    resp_builder
+                    .body(Body::from(body))
-                        .body(Body::from(body))
+                    .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR),
-                        .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)
-                }
                 Err(e) => {
                     tracing::error!("Failed to read embeddings response body: {}", e);
                     Err(StatusCode::INTERNAL_SERVER_ERROR)
@@ -250,7 +375,7 @@ fn should_forward_header(header_name: &str) -> bool {
     match header_name.to_lowercase().as_str() {
         "content-type" | "content-length" | "authorization" | "user-agent" | "accept" => true,
         "host" | "connection" | "upgrade" => false, // Don't forward connection-specific headers
-        _ => true, // Forward other headers by default
+        _ => true,                                  // Forward other headers by default
     }
 }
 
@@ -259,7 +384,7 @@ fn should_forward_response_header(header_name: &str) -> bool {
     match header_name.to_lowercase().as_str() {
         "content-type" | "content-length" | "cache-control" | "connection" => true,
         "server" | "date" => false, // Don't forward server-specific headers
-        _ => true, // Forward other headers by default
+        _ => true,                  // Forward other headers by default
     }
 }
 
@@ -290,14 +415,20 @@ mod tests {
             server_host: "127.0.0.1".to_string(),
             server_port: 8080,
             server_mode: ServerMode::HighAvailability,
-            services: Services {
+            services: Some(Services {
-                inference_url: "http://test-inference:8080".to_string(),
+                inference_url: Some("http://test-inference:8080".to_string()),
-                embeddings_url: "http://test-embeddings:8080".to_string(),
+                embeddings_url: Some("http://test-embeddings:8080".to_string()),
-            },
+            }),
         };
 
         let proxy_client = ProxyClient::new(config);
-        assert_eq!(proxy_client.config.inference_url(), "http://test-inference:8080");
+        assert_eq!(
-        assert_eq!(proxy_client.config.embeddings_url(), "http://test-embeddings:8080");
+            proxy_client.config.inference_url().unwrap().as_str(),
+            "http://test-inference:8080"
+        );
+        assert_eq!(
+            proxy_client.config.embeddings_url().unwrap().as_str(),
+            "http://test-embeddings:8080"
+        );
     }
-}
+}

crates/predict-otron-9000/src/main.rs

@@ -1,22 +1,59 @@
 mod config;
+mod ha_mode;
 mod middleware;
-mod proxy;
+mod standalone_mode;
 
+use crate::standalone_mode::create_standalone_router;
+use axum::handler::Handler;
+use axum::http::StatusCode as AxumStatusCode;
+use axum::http::header;
 use axum::response::IntoResponse;
 use axum::routing::get;
-use axum::{Router, http::Uri, response::Html, serve};
+use axum::{Router, ServiceExt, http::Uri, response::Html, serve};
 use config::ServerConfig;
+use ha_mode::create_ha_router;
 use inference_engine::AppState;
+use log::info;
 use middleware::{MetricsLayer, MetricsLoggerFuture, MetricsStore};
-use proxy::create_proxy_router;
+use mime_guess::from_path;
 use rust_embed::Embed;
 use std::env;
+use std::path::Component::ParentDir;
 use tokio::net::TcpListener;
+use tower::MakeService;
 use tower_http::classify::ServerErrorsFailureClass::StatusCode;
 use tower_http::cors::{Any, CorsLayer};
 use tower_http::trace::TraceLayer;
 use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};
 
+#[derive(Embed)]
+#[folder = "../../target/site"]
+#[include = "*.js"]
+#[include = "*.wasm"]
+#[include = "*.css"]
+#[include = "*.ico"]
+struct Asset;
+
+async fn static_handler(uri: Uri) -> axum::response::Response {
+    // Strip the leading `/`
+    let path = uri.path().trim_start_matches('/');
+
+    tracing::info!("Static file: {}", &path);
+
+    // If root is requested, serve index.html
+    let path = if path.is_empty() { "index.html" } else { path };
+
+    match Asset::get(path) {
+        Some(content) => {
+            let body = content.data.into_owned();
+            let mime = from_path(path).first_or_octet_stream();
+
+            ([(header::CONTENT_TYPE, mime.as_ref())], body).into_response()
+        }
+        None => (AxumStatusCode::NOT_FOUND, "404 Not Found").into_response(),
+    }
+}
+
 #[tokio::main]
 async fn main() {
     // Initialize tracing
@@ -49,33 +86,19 @@ async fn main() {
     let default_host = server_config.server_host.clone();
     let default_port = server_config.server_port;
 
-    // Create router based on server mode
+    let service_router = match server_config.clone().is_high_availability() {
-    let service_router = if server_config.clone().is_high_availability() {
+        Ok(is_ha) => {
-        tracing::info!("Running in HighAvailability mode - proxying to external services");
+            if is_ha {
-        tracing::info!("  Inference service URL: {}", server_config.inference_url());
+                log_config(server_config.clone());
-        tracing::info!(
+                create_ha_router(server_config.clone())
-            "  Embeddings service URL: {}",
+            } else {
-            server_config.embeddings_url()
+                log_config(server_config.clone());
-        );
+                create_standalone_router(server_config)
-
+            }
-        // Use proxy router that forwards requests to external services
+        }
-        create_proxy_router(server_config.clone())
+        Err(error) => {
-    } else {
+            panic!("{}", error);
-        tracing::info!("Running in Standalone mode - using embedded services");
+        }
-
-        // Create unified router by merging embeddings and inference routers (existing behavior)
-        let embeddings_router = embeddings_engine::create_embeddings_router();
-
-        // Create AppState with correct model configuration
-        let app_state = AppState::default();
-
-        // Get the inference router directly from the inference engine
-        let inference_router = inference_engine::create_router(app_state);
-
-        // Merge the local routers
-        Router::new()
-            .merge(embeddings_router)
-            .merge(inference_router)
     };
 
     // Create CORS layer
@@ -88,14 +111,17 @@ async fn main() {
     // Create metrics layer
     let metrics_layer = MetricsLayer::new(metrics_store);
 
+    let leptos_config = chat_ui::app::AppConfig::default();
+
     // Create the leptos router for the web frontend
-    let leptos_router = leptos_app::create_leptos_router();
+    let leptos_router = chat_ui::app::create_router(leptos_config.config.leptos_options);
 
     // Merge the service router with base routes and add middleware layers
     let app = Router::new()
+        .route("/pkg/{*path}", get(static_handler))
         .route("/health", get(|| async { "ok" }))
         .merge(service_router)
-        .merge(leptos_router) // Add leptos web frontend routes
+        .merge(leptos_router)
         .layer(metrics_layer) // Add metrics tracking
         .layer(cors)
         .layer(TraceLayer::new_for_http());
@@ -121,7 +147,27 @@ async fn main() {
     tracing::info!("  POST /v1/embeddings - Text embeddings API");
     tracing::info!("  POST /v1/chat/completions - Chat completions API");
 
-    serve(listener, app).await.unwrap();
+    serve(listener, app.into_make_service()).await.unwrap();
+}
+
+fn log_config(config: ServerConfig) {
+    match config.is_high_availability() {
+        Ok(is_high) => {
+            if is_high {
+                tracing::info!("Running in HighAvailability mode - proxying to external services");
+                tracing::info!("Inference service URL: {}", config.inference_url().unwrap());
+                tracing::info!(
+                    "Embeddings service URL: {}",
+                    config.embeddings_url().unwrap()
+                );
+            } else {
+                tracing::info!("Running in Standalone mode");
+            }
+        }
+        Err(error) => {
+            panic!("{}", error);
+        }
+    }
 }
 
 // Chat completions handler that properly uses the inference server crate's error handling

crates/predict-otron-9000/src/middleware/metrics.rs

@@ -2,6 +2,8 @@ use axum::{
     extract::MatchedPath,
     http::{Request, Response},
 };
+use std::fmt;
+use std::task::ready;
 use std::{
     future::Future,
     pin::Pin,
@@ -12,8 +14,6 @@ use std::{
 use tokio::sync::Mutex;
 use tower::{Layer, Service};
 use tracing::{debug, info};
-use std::task::ready;
-use std::fmt;
 
 /// Performance metrics for a specific endpoint
 #[derive(Debug, Clone, Default)]
@@ -33,16 +33,16 @@ impl EndpointMetrics {
     pub fn add_response_time(&mut self, time_ms: u64) {
         self.count += 1;
         self.total_time_ms += time_ms;
-        
+
         if self.min_time_ms == 0 || time_ms < self.min_time_ms {
             self.min_time_ms = time_ms;
         }
-        
+
         if time_ms > self.max_time_ms {
             self.max_time_ms = time_ms;
         }
     }
-    
+
     /// Get the average response time in milliseconds
     pub fn avg_time_ms(&self) -> f64 {
         if self.count == 0 {
@@ -51,12 +51,15 @@ impl EndpointMetrics {
             self.total_time_ms as f64 / self.count as f64
         }
     }
-    
+
     /// Get a human-readable summary of the metrics
     pub fn summary(&self) -> String {
         format!(
             "requests: {}, avg: {:.2}ms, min: {}ms, max: {}ms",
-            self.count, self.avg_time_ms(), self.min_time_ms, self.max_time_ms
+            self.count,
+            self.avg_time_ms(),
+            self.min_time_ms,
+            self.max_time_ms
         )
     }
 }
@@ -75,14 +78,16 @@ impl MetricsStore {
             endpoints: Arc::new(Mutex::new(std::collections::HashMap::new())),
         }
     }
-    
+
     /// Record a request's timing information
     pub async fn record(&self, path: String, time_ms: u64) {
         let mut endpoints = self.endpoints.lock().await;
-        let metrics = endpoints.entry(path).or_insert_with(EndpointMetrics::default);
+        let metrics = endpoints
+            .entry(path)
+            .or_insert_with(EndpointMetrics::default);
         metrics.add_response_time(time_ms);
     }
-    
+
     /// Get metrics for all endpoints
     pub async fn get_all(&self) -> Vec<(String, EndpointMetrics)> {
         let endpoints = self.endpoints.lock().await;
@@ -91,12 +96,12 @@ impl MetricsStore {
             .map(|(k, v)| (k.clone(), v.clone()))
             .collect()
     }
-    
+
     /// Log a summary of all metrics
     pub async fn log_summary(&self) {
         let metrics = self.get_all().await;
         info!("Performance metrics summary:");
-        
+
         for (path, metric) in metrics {
             info!("  {}: {}", path, metric.summary());
         }
@@ -163,26 +168,28 @@ where
         } else {
             req.uri().path().to_string()
         };
-        
+
         let method = req.method().clone();
         let start = Instant::now();
         let metrics_store = self.metrics_store.clone();
-        
+
         let future = self.inner.call(req);
-        
+
         Box::pin(async move {
             let response = future.await?;
-            
+
             let time = start.elapsed();
             let status = response.status();
             let time_ms = time.as_millis() as u64;
-            
+
             // Record the timing in our metrics store
-            metrics_store.record(format!("{} {}", method, path), time_ms).await;
+            metrics_store
-            
+                .record(format!("{} {}", method, path), time_ms)
+                .await;
+
             // Log the request timing
             debug!("{} {} {} - {} ms", method, path, status, time_ms);
-            
+
             Ok(response)
         })
     }
@@ -214,7 +221,7 @@ impl Future for MetricsLoggerFuture {
                 metrics_store.log_summary().await;
             });
         }
-        
+
         Poll::Pending
     }
-}
+}

crates/predict-otron-9000/src/middleware/mod.rs

@@ -1,7 +1,3 @@
 pub mod metrics;
 
-pub use metrics::{
+pub use metrics::{MetricsLayer, MetricsLoggerFuture, MetricsStore};
-    MetricsStore,
-    MetricsLoggerFuture,
-    MetricsLayer,
-};

crates/predict-otron-9000/src/standalone_mode.rs

@@ -0,0 +1,20 @@
+use crate::config::ServerConfig;
+use axum::Router;
+use inference_engine::AppState;
+
+pub fn create_standalone_router(server_config: ServerConfig) -> Router {
+    // Create unified router by merging embeddings and inference routers (existing behavior)
+    let embeddings_router = embeddings_engine::create_embeddings_router();
+
+    // Create AppState - no default model, must be configured explicitly
+    // This removes the hardcoded gemma-3-1b-it default behavior
+    let app_state = AppState::default();
+
+    // Get the inference router directly from the inference engine
+    let inference_router = inference_engine::create_router(app_state);
+
+    // Merge the local routers
+    Router::new()
+        .merge(embeddings_router)
+        .merge(inference_router)
+}

crates/utils/Cargo.toml

@@ -0,0 +1,88 @@
+[package]
+name = "utils"
+
+[lib]
+path = "src/lib.rs"
+
+[dependencies]
+accelerate-src = {version = "0.3.2", optional = true }
+candle-nn = {version = "0.9.1" }
+candle-transformers = {version = "0.9.1" }
+
+candle-flash-attn = {version = "0.9.1", optional = true }
+candle-onnx = {version = "0.9.1", optional = true }
+candle-core="0.9.1"
+csv = "1.3.0"
+anyhow = "1.0.99"
+cudarc = {version = "0.17.3", optional = true }
+half = {version = "2.6.0", optional = true }
+hf-hub = {version = "0.4.3", features = ["tokio"] }
+image = {version = "0.25.6" }
+intel-mkl-src = {version = "0.8.1", optional = true }
+num-traits = {version = "0.2.19" }
+palette = { version = "0.7.6", optional = true }
+enterpolation = { version = "0.2.1", optional = true }
+pyo3 = { version = "0.22.0", features = [
+    "auto-initialize",
+    "abi3-py311",
+], optional = true }
+rayon = {version = "1.11.0" }
+rubato = { version = "0.15.0", optional = true }
+safetensors = {version = "0.6.2" }
+serde = {version = "1.0.219" }
+serde_json = {version = "1.0.143" }
+symphonia = { version = "0.5.3", features = ["all"], optional = true }
+tokenizers = {version = "0.22.0", features = ["onig"] }
+cpal = { version = "0.15.2", optional = true }
+pdf2image = { version = "0.1.2", optional = true }
+tekken-rs = { version = "0.1.1", optional = true }
+
+[dev-dependencies]
+anyhow = {version = "1.0.99" }
+byteorder = {version = "1.5.0" }
+clap = {version = "4.5.46" }
+imageproc = {version = "0.25.0" }
+memmap2 = {version = "0.9.8" }
+rand = {version = "0.9.2" }
+ab_glyph = {version = "0.2.31" }
+tracing = {version = "0.1.41" }
+tracing-chrome = {version = "0.7.2" }
+tracing-subscriber = {version = "0.3.20" }
+# Necessary to disambiguate with tokio in wasm examples which are 1.28.1
+tokio = "1.43.0"
+
+[build-dependencies]
+anyhow = {version = "1.0.99" }
+bindgen_cuda = { version = "0.1.1", optional = true }
+#
+[features]
+default = []
+accelerate = [
+    "dep:accelerate-src",
+    "candle-core/accelerate",
+    "candle-nn/accelerate",
+    "candle-transformers/accelerate",
+]
+cuda = [
+    "candle-core/cuda",
+    "candle-nn/cuda",
+    "candle-transformers/cuda",
+    "dep:bindgen_cuda",
+]
+cudnn = ["candle-core/cudnn", "candle-nn/cudnn", "candle-transformers/cudnn"]
+flash-attn = ["cuda", "candle-transformers/flash-attn", "dep:candle-flash-attn"]
+mkl = [
+    "dep:intel-mkl-src",
+    "candle-core/mkl",
+    "candle-nn/mkl",
+    "candle-transformers/mkl",
+]
+nccl = ["cuda", "cudarc/nccl", "dep:half"]
+onnx = ["candle-onnx"]
+metal = ["candle-core/metal", "candle-nn/metal"]
+microphone = ["cpal", "rubato"]
+encodec = ["cpal", "symphonia", "rubato"]
+mimi = ["cpal", "symphonia", "rubato"]
+snac = ["cpal", "symphonia", "rubato"]
+depth_anything_v2 = ["palette", "enterpolation"]
+tekken = ["tekken-rs"]

crates/utils/src/audio.rs

@@ -0,0 +1,138 @@
+use candle_core::{Result, Tensor};
+
+// https://github.com/facebookresearch/audiocraft/blob/69fea8b290ad1b4b40d28f92d1dfc0ab01dbab85/audiocraft/data/audio_utils.py#L57
+pub fn normalize_loudness(
+    wav: &Tensor,
+    sample_rate: u32,
+    loudness_compressor: bool,
+) -> Result<Tensor> {
+    let energy = wav.sqr()?.mean_all()?.sqrt()?.to_vec0::<f32>()?;
+    if energy < 2e-3 {
+        return Ok(wav.clone());
+    }
+    let wav_array = wav.to_vec1::<f32>()?;
+    let mut meter = crate::bs1770::ChannelLoudnessMeter::new(sample_rate);
+    meter.push(wav_array.into_iter());
+    let power = meter.as_100ms_windows();
+    let loudness = match crate::bs1770::gated_mean(power) {
+        None => return Ok(wav.clone()),
+        Some(gp) => gp.loudness_lkfs() as f64,
+    };
+    let delta_loudness = -14. - loudness;
+    let gain = 10f64.powf(delta_loudness / 20.);
+    let wav = (wav * gain)?;
+    if loudness_compressor {
+        wav.tanh()
+    } else {
+        Ok(wav)
+    }
+}
+
+#[cfg(feature = "symphonia")]
+pub fn pcm_decode<P: AsRef<std::path::Path>>(path: P) -> Result<(Vec<f32>, u32)> {
+    use symphonia::core::audio::{AudioBufferRef, Signal};
+    use symphonia::core::codecs::{DecoderOptions, CODEC_TYPE_NULL};
+    use symphonia::core::conv::FromSample;
+
+    fn conv<T>(
+        samples: &mut Vec<f32>,
+        data: std::borrow::Cow<symphonia::core::audio::AudioBuffer<T>>,
+    ) where
+        T: symphonia::core::sample::Sample,
+        f32: symphonia::core::conv::FromSample<T>,
+    {
+        samples.extend(data.chan(0).iter().map(|v| f32::from_sample(*v)))
+    }
+
+    // Open the media source.
+    let src = std::fs::File::open(path).map_err(candle::Error::wrap)?;
+
+    // Create the media source stream.
+    let mss = symphonia::core::io::MediaSourceStream::new(Box::new(src), Default::default());
+
+    // Create a probe hint using the file's extension. [Optional]
+    let hint = symphonia::core::probe::Hint::new();
+
+    // Use the default options for metadata and format readers.
+    let meta_opts: symphonia::core::meta::MetadataOptions = Default::default();
+    let fmt_opts: symphonia::core::formats::FormatOptions = Default::default();
+
+    // Probe the media source.
+    let probed = symphonia::default::get_probe()
+        .format(&hint, mss, &fmt_opts, &meta_opts)
+        .map_err(candle::Error::wrap)?;
+    // Get the instantiated format reader.
+    let mut format = probed.format;
+
+    // Find the first audio track with a known (decodeable) codec.
+    let track = format
+        .tracks()
+        .iter()
+        .find(|t| t.codec_params.codec != CODEC_TYPE_NULL)
+        .ok_or_else(|| candle::Error::Msg("no supported audio tracks".to_string()))?;
+
+    // Use the default options for the decoder.
+    let dec_opts: DecoderOptions = Default::default();
+
+    // Create a decoder for the track.
+    let mut decoder = symphonia::default::get_codecs()
+        .make(&track.codec_params, &dec_opts)
+        .map_err(|_| candle::Error::Msg("unsupported codec".to_string()))?;
+    let track_id = track.id;
+    let sample_rate = track.codec_params.sample_rate.unwrap_or(0);
+    let mut pcm_data = Vec::new();
+    // The decode loop.
+    while let Ok(packet) = format.next_packet() {
+        // Consume any new metadata that has been read since the last packet.
+        while !format.metadata().is_latest() {
+            format.metadata().pop();
+        }
+
+        // If the packet does not belong to the selected track, skip over it.
+        if packet.track_id() != track_id {
+            continue;
+        }
+        match decoder.decode(&packet).map_err(candle::Error::wrap)? {
+            AudioBufferRef::F32(buf) => pcm_data.extend(buf.chan(0)),
+            AudioBufferRef::U8(data) => conv(&mut pcm_data, data),
+            AudioBufferRef::U16(data) => conv(&mut pcm_data, data),
+            AudioBufferRef::U24(data) => conv(&mut pcm_data, data),
+            AudioBufferRef::U32(data) => conv(&mut pcm_data, data),
+            AudioBufferRef::S8(data) => conv(&mut pcm_data, data),
+            AudioBufferRef::S16(data) => conv(&mut pcm_data, data),
+            AudioBufferRef::S24(data) => conv(&mut pcm_data, data),
+            AudioBufferRef::S32(data) => conv(&mut pcm_data, data),
+            AudioBufferRef::F64(data) => conv(&mut pcm_data, data),
+        }
+    }
+    Ok((pcm_data, sample_rate))
+}
+
+#[cfg(feature = "rubato")]
+pub fn resample(pcm_in: &[f32], sr_in: u32, sr_out: u32) -> Result<Vec<f32>> {
+    use rubato::Resampler;
+
+    let mut pcm_out =
+        Vec::with_capacity((pcm_in.len() as f64 * sr_out as f64 / sr_in as f64) as usize + 1024);
+
+    let mut resampler = rubato::FftFixedInOut::<f32>::new(sr_in as usize, sr_out as usize, 1024, 1)
+        .map_err(candle::Error::wrap)?;
+    let mut output_buffer = resampler.output_buffer_allocate(true);
+    let mut pos_in = 0;
+    while pos_in + resampler.input_frames_next() < pcm_in.len() {
+        let (in_len, out_len) = resampler
+            .process_into_buffer(&[&pcm_in[pos_in..]], &mut output_buffer, None)
+            .map_err(candle::Error::wrap)?;
+        pos_in += in_len;
+        pcm_out.extend_from_slice(&output_buffer[0][..out_len]);
+    }
+
+    if pos_in < pcm_in.len() {
+        let (_in_len, out_len) = resampler
+            .process_partial_into_buffer(Some(&[&pcm_in[pos_in..]]), &mut output_buffer, None)
+            .map_err(candle::Error::wrap)?;
+        pcm_out.extend_from_slice(&output_buffer[0][..out_len]);
+    }
+
+    Ok(pcm_out)
+}

crates/utils/src/bs1770.rs

@@ -0,0 +1,506 @@
+// Copied from https://github.com/ruuda/bs1770/blob/master/src/lib.rs
+// BS1770 -- Loudness analysis library conforming to ITU-R BS.1770
+// Copyright 2020 Ruud van Asseldonk
+
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// A copy of the License has been included in the root of the repository.
+
+//! Loudness analysis conforming to [ITU-R BS.1770-4][bs17704].
+//!
+//! This library offers the building blocks to perform BS.1770 loudness
+//! measurements, but you need to put the pieces together yourself.
+//!
+//! [bs17704]: https://www.itu.int/rec/R-REC-BS.1770-4-201510-I/en
+//!
+//! # Stereo integrated loudness example
+//!
+//! ```ignore
+//! # fn load_stereo_audio() -> [Vec<i16>; 2] {
+//! #     [vec![0; 48_000], vec![0; 48_000]]
+//! # }
+//! #
+//! let sample_rate_hz = 44_100;
+//! let bits_per_sample = 16;
+//! let channel_samples: [Vec<i16>; 2] = load_stereo_audio();
+//!
+//! // When converting integer samples to float, note that the maximum amplitude
+//! // is `1 << (bits_per_sample - 1)`, one bit is the sign bit.
+//! let normalizer = 1.0 / (1_u64 << (bits_per_sample - 1)) as f32;
+//!
+//! let channel_power: Vec<_> = channel_samples.iter().map(|samples| {
+//!     let mut meter = bs1770::ChannelLoudnessMeter::new(sample_rate_hz);
+//!     meter.push(samples.iter().map(|&s| s as f32 * normalizer));
+//!     meter.into_100ms_windows()
+//! }).collect();
+//!
+//! let stereo_power = bs1770::reduce_stereo(
+//!     channel_power[0].as_ref(),
+//!     channel_power[1].as_ref(),
+//! );
+//!
+//! let gated_power = bs1770::gated_mean(
+//!     stereo_power.as_ref()
+//! ).unwrap_or(bs1770::Power(0.0));
+//! println!("Integrated loudness: {:.1} LUFS", gated_power.loudness_lkfs());
+//! ```
+
+use std::f32;
+
+/// Coefficients for a 2nd-degree infinite impulse response filter.
+///
+/// Coefficient a0 is implicitly 1.0.
+#[derive(Clone)]
+struct Filter {
+    a1: f32,
+    a2: f32,
+    b0: f32,
+    b1: f32,
+    b2: f32,
+
+    // The past two input and output samples.
+    x1: f32,
+    x2: f32,
+    y1: f32,
+    y2: f32,
+}
+
+impl Filter {
+    /// Stage 1 of th BS.1770-4 pre-filter.
+    pub fn high_shelf(sample_rate_hz: f32) -> Filter {
+        // Coefficients taken from https://github.com/csteinmetz1/pyloudnorm/blob/
+        // 6baa64d59b7794bc812e124438692e7fd2e65c0c/pyloudnorm/meter.py#L135-L136.
+        let gain_db = 3.999_843_8;
+        let q = 0.707_175_25;
+        let center_hz = 1_681.974_5;
+
+        // Formula taken from https://github.com/csteinmetz1/pyloudnorm/blob/
+        // 6baa64d59b7794bc812e124438692e7fd2e65c0c/pyloudnorm/iirfilter.py#L134-L143.
+        let k = (f32::consts::PI * center_hz / sample_rate_hz).tan();
+        let vh = 10.0_f32.powf(gain_db / 20.0);
+        let vb = vh.powf(0.499_666_78);
+        let a0 = 1.0 + k / q + k * k;
+        Filter {
+            b0: (vh + vb * k / q + k * k) / a0,
+            b1: 2.0 * (k * k - vh) / a0,
+            b2: (vh - vb * k / q + k * k) / a0,
+            a1: 2.0 * (k * k - 1.0) / a0,
+            a2: (1.0 - k / q + k * k) / a0,
+
+            x1: 0.0,
+            x2: 0.0,
+            y1: 0.0,
+            y2: 0.0,
+        }
+    }
+
+    /// Stage 2 of th BS.1770-4 pre-filter.
+    pub fn high_pass(sample_rate_hz: f32) -> Filter {
+        // Coefficients taken from https://github.com/csteinmetz1/pyloudnorm/blob/
+        // 6baa64d59b7794bc812e124438692e7fd2e65c0c/pyloudnorm/meter.py#L135-L136.
+        let q = 0.500_327_05;
+        let center_hz = 38.135_47;
+
+        // Formula taken from https://github.com/csteinmetz1/pyloudnorm/blob/
+        // 6baa64d59b7794bc812e124438692e7fd2e65c0c/pyloudnorm/iirfilter.py#L145-L151
+        let k = (f32::consts::PI * center_hz / sample_rate_hz).tan();
+        Filter {
+            a1: 2.0 * (k * k - 1.0) / (1.0 + k / q + k * k),
+            a2: (1.0 - k / q + k * k) / (1.0 + k / q + k * k),
+            b0: 1.0,
+            b1: -2.0,
+            b2: 1.0,
+
+            x1: 0.0,
+            x2: 0.0,
+            y1: 0.0,
+            y2: 0.0,
+        }
+    }
+
+    /// Feed the next input sample, get the next output sample.
+    #[inline(always)]
+    pub fn apply(&mut self, x0: f32) -> f32 {
+        let y0 = 0.0 + self.b0 * x0 + self.b1 * self.x1 + self.b2 * self.x2
+            - self.a1 * self.y1
+            - self.a2 * self.y2;
+
+        self.x2 = self.x1;
+        self.x1 = x0;
+        self.y2 = self.y1;
+        self.y1 = y0;
+
+        y0
+    }
+}
+
+/// Compensated sum, for summing many values of different orders of magnitude
+/// accurately.
+#[derive(Copy, Clone, PartialEq)]
+struct Sum {
+    sum: f32,
+    residue: f32,
+}
+
+impl Sum {
+    #[inline(always)]
+    fn zero() -> Sum {
+        Sum {
+            sum: 0.0,
+            residue: 0.0,
+        }
+    }
+
+    #[inline(always)]
+    fn add(&mut self, x: f32) {
+        let sum = self.sum + (self.residue + x);
+        self.residue = (self.residue + x) - (sum - self.sum);
+        self.sum = sum;
+    }
+}
+
+/// The mean of the squares of the K-weighted samples in a window of time.
+///
+/// K-weighted power is equivalent to K-weighted loudness, the only difference
+/// is one of scale: power is quadratic in sample amplitudes, whereas loudness
+/// units are logarithmic. `loudness_lkfs` and `from_lkfs` convert between power,
+/// and K-weighted Loudness Units relative to nominal Full Scale (LKFS).
+///
+/// The term “LKFS” (Loudness Units, K-Weighted, relative to nominal Full Scale)
+/// is used in BS.1770-4 to emphasize K-weighting, but the term is otherwise
+/// interchangeable with the more widespread term “LUFS” (Loudness Units,
+/// relative to Full Scale). Loudness units are related to decibels in the
+/// following sense: boosting a signal that has a loudness of
+/// -<var>L<sub>K</sub></var> LUFS by <var>L<sub>K</sub></var> dB (by
+/// multiplying the amplitude by 10<sup><var>L<sub>K</sub></var>/20</sup>) will
+/// bring the loudness to 0 LUFS.
+///
+/// K-weighting refers to a high-shelf and high-pass filter that model the
+/// effect that humans perceive a certain amount of power in low frequencies to
+/// be less loud than the same amount of power in higher frequencies. In this
+/// library the `Power` type is used exclusively to refer to power after applying K-weighting.
+///
+/// The nominal “full scale” is the range [-1.0, 1.0]. Because the power is the
+/// mean square of the samples, if no input samples exceeded the full scale, the
+/// power will be in the range [0.0, 1.0]. However, the power delivered by
+/// multiple channels, which is a weighted sum over individual channel powers,
+/// can exceed this range, because the weighted sum is not normalized.
+#[derive(Copy, Clone, PartialEq, PartialOrd)]
+pub struct Power(pub f32);
+
+impl Power {
+    /// Convert Loudness Units relative to Full Scale into a squared sample amplitude.
+    ///
+    /// This is the inverse of `loudness_lkfs`.
+    pub fn from_lkfs(lkfs: f32) -> Power {
+        // The inverse of the formula below.
+        Power(10.0_f32.powf((lkfs + 0.691) * 0.1))
+    }
+
+    /// Return the loudness of this window in Loudness Units, K-weighted, relative to Full Scale.
+    ///
+    /// This is the inverse of `from_lkfs`.
+    pub fn loudness_lkfs(&self) -> f32 {
+        // Equation 2 (p.5) of BS.1770-4.
+        -0.691 + 10.0 * self.0.log10()
+    }
+}
+
+/// A `T` value for non-overlapping windows of audio, 100ms in length.
+///
+/// The `ChannelLoudnessMeter` applies K-weighting and then produces the power
+/// for non-overlapping windows of 100ms duration.
+///
+/// These non-overlapping 100ms windows can later be combined into overlapping
+/// windows of 400ms, spaced 100ms apart, to compute instantaneous loudness or
+/// to perform a gated measurement, or they can be combined into even larger
+/// windows for a momentary loudness measurement.
+#[derive(Copy, Clone, Debug)]
+pub struct Windows100ms<T> {
+    pub inner: T,
+}
+
+impl<T> Windows100ms<T> {
+    /// Wrap a new empty vector.
+    pub fn new() -> Windows100ms<Vec<T>> {
+        Windows100ms { inner: Vec::new() }
+    }
+
+    /// Apply `as_ref` to the inner value.
+    pub fn as_ref(&self) -> Windows100ms<&[Power]>
+    where
+        T: AsRef<[Power]>,
+    {
+        Windows100ms {
+            inner: self.inner.as_ref(),
+        }
+    }
+
+    /// Apply `as_mut` to the inner value.
+    pub fn as_mut(&mut self) -> Windows100ms<&mut [Power]>
+    where
+        T: AsMut<[Power]>,
+    {
+        Windows100ms {
+            inner: self.inner.as_mut(),
+        }
+    }
+
+    #[allow(clippy::len_without_is_empty)]
+    /// Apply `len` to the inner value.
+    pub fn len(&self) -> usize
+    where
+        T: AsRef<[Power]>,
+    {
+        self.inner.as_ref().len()
+    }
+}
+
+/// Measures K-weighted power of non-overlapping 100ms windows of a single channel of audio.
+///
+/// # Output
+///
+/// The output of the meter is an intermediate result in the form of power for
+/// 100ms non-overlapping windows. The windows need to be processed further to
+/// get one of the instantaneous, momentary, and integrated loudness
+/// measurements defined in BS.1770.
+///
+/// The windows can also be inspected directly; the data is meaningful
+/// on its own (the K-weighted power delivered in that window of time), but it
+/// is not something that BS.1770 defines a term for.
+///
+/// # Multichannel audio
+///
+/// To perform a loudness measurement of multichannel audio, construct a
+/// `ChannelLoudnessMeter` per channel, and later combine the measured power
+/// with e.g. `reduce_stereo`.
+///
+/// # Instantaneous loudness
+///
+/// The instantaneous loudness is the power over a 400ms window, so you can
+/// average four 100ms windows. No special functionality is implemented to help
+/// with that at this time. ([Pull requests would be accepted.][contribute])
+///
+/// # Momentary loudness
+///
+/// The momentary loudness is the power over a 3-second window, so you can
+/// average thirty 100ms windows. No special functionality is implemented to
+/// help with that at this time. ([Pull requests would be accepted.][contribute])
+///
+/// # Integrated loudness
+///
+/// Use `gated_mean` to perform an integrated loudness measurement:
+///
+/// ```ignore
+/// # use std::iter;
+/// # use bs1770::{ChannelLoudnessMeter, gated_mean};
+/// # let sample_rate_hz = 44_100;
+/// # let samples_per_100ms = sample_rate_hz / 10;
+/// # let mut meter = ChannelLoudnessMeter::new(sample_rate_hz);
+/// # meter.push((0..44_100).map(|i| (i as f32 * 0.01).sin()));
+/// let integrated_loudness_lkfs = gated_mean(meter.as_100ms_windows())
+///     .unwrap_or(bs1770::Power(0.0))
+///     .loudness_lkfs();
+/// ```
+///
+/// [contribute]: https://github.com/ruuda/bs1770/blob/master/CONTRIBUTING.md
+#[derive(Clone)]
+pub struct ChannelLoudnessMeter {
+    /// The number of samples that fit in 100ms of audio.
+    samples_per_100ms: u32,
+
+    /// Stage 1 filter (head effects, high shelf).
+    filter_stage1: Filter,
+
+    /// Stage 2 filter (high-pass).
+    filter_stage2: Filter,
+
+    /// Sum of the squares over non-overlapping windows of 100ms.
+    windows: Windows100ms<Vec<Power>>,
+
+    /// The number of samples in the current unfinished window.
+    count: u32,
+
+    /// The sum of the squares of the samples in the current unfinished window.
+    square_sum: Sum,
+}
+
+impl ChannelLoudnessMeter {
+    /// Construct a new loudness meter for the given sample rate.
+    pub fn new(sample_rate_hz: u32) -> ChannelLoudnessMeter {
+        ChannelLoudnessMeter {
+            samples_per_100ms: sample_rate_hz / 10,
+            filter_stage1: Filter::high_shelf(sample_rate_hz as f32),
+            filter_stage2: Filter::high_pass(sample_rate_hz as f32),
+            windows: Windows100ms::new(),
+            count: 0,
+            square_sum: Sum::zero(),
+        }
+    }
+
+    /// Feed input samples for loudness analysis.
+    ///
+    /// # Full scale
+    ///
+    /// Full scale for the input samples is the interval [-1.0, 1.0]. If your
+    /// input consists of signed integer samples, you can convert as follows:
+    ///
+    /// ```ignore
+    /// # let mut meter = bs1770::ChannelLoudnessMeter::new(44_100);
+    /// # let bits_per_sample = 16_usize;
+    /// # let samples = &[0_i16];
+    /// // Note that the maximum amplitude is `1 << (bits_per_sample - 1)`,
+    /// // one bit is the sign bit.
+    /// let normalizer = 1.0 / (1_u64 << (bits_per_sample - 1)) as f32;
+    /// meter.push(samples.iter().map(|&s| s as f32 * normalizer));
+    /// ```
+    ///
+    /// # Repeated calls
+    ///
+    /// You can call `push` multiple times to feed multiple batches of samples.
+    /// This is equivalent to feeding a single chained iterator. The leftover of
+    /// samples that did not fill a full 100ms window is not discarded:
+    ///
+    /// ```ignore
+    /// # use std::iter;
+    /// # use bs1770::ChannelLoudnessMeter;
+    /// let sample_rate_hz = 44_100;
+    /// let samples_per_100ms = sample_rate_hz / 10;
+    /// let mut meter = ChannelLoudnessMeter::new(sample_rate_hz);
+    ///
+    /// meter.push(iter::repeat(0.0).take(samples_per_100ms as usize - 1));
+    /// assert_eq!(meter.as_100ms_windows().len(), 0);
+    ///
+    /// meter.push(iter::once(0.0));
+    /// assert_eq!(meter.as_100ms_windows().len(), 1);
+    /// ```
+    pub fn push<I: Iterator<Item = f32>>(&mut self, samples: I) {
+        let normalizer = 1.0 / self.samples_per_100ms as f32;
+
+        // LLVM, if you could go ahead and inline those apply calls, and then
+        // unroll and vectorize the loop, that'd be terrific.
+        for x in samples {
+            let y = self.filter_stage1.apply(x);
+            let z = self.filter_stage2.apply(y);
+
+            self.square_sum.add(z * z);
+            self.count += 1;
+
+            // TODO: Should this branch be marked cold?
+            if self.count == self.samples_per_100ms {
+                let mean_squares = Power(self.square_sum.sum * normalizer);
+                self.windows.inner.push(mean_squares);
+                // We intentionally do not reset the residue. That way, leftover
+                // energy from this window is not lost, so for the file overall,
+                // the sum remains more accurate.
+                self.square_sum.sum = 0.0;
+                self.count = 0;
+            }
+        }
+    }
+
+    /// Return a reference to the 100ms windows analyzed so far.
+    pub fn as_100ms_windows(&self) -> Windows100ms<&[Power]> {
+        self.windows.as_ref()
+    }
+
+    /// Return all 100ms windows analyzed so far.
+    pub fn into_100ms_windows(self) -> Windows100ms<Vec<Power>> {
+        self.windows
+    }
+}
+
+/// Combine power for multiple channels by taking a weighted sum.
+///
+/// Note that BS.1770-4 defines power for a multi-channel signal as a weighted
+/// sum over channels which is not normalized. This means that a stereo signal
+/// is inherently louder than a mono signal. For a mono signal played back on
+/// stereo speakers, you should therefore still apply `reduce_stereo`, passing
+/// in the same signal for both channels.
+pub fn reduce_stereo(
+    left: Windows100ms<&[Power]>,
+    right: Windows100ms<&[Power]>,
+) -> Windows100ms<Vec<Power>> {
+    assert_eq!(
+        left.len(),
+        right.len(),
+        "Channels must have the same length."
+    );
+    let mut result = Vec::with_capacity(left.len());
+    for (l, r) in left.inner.iter().zip(right.inner) {
+        result.push(Power(l.0 + r.0));
+    }
+    Windows100ms { inner: result }
+}
+
+/// In-place version of `reduce_stereo` that stores the result in the former left channel.
+pub fn reduce_stereo_in_place(left: Windows100ms<&mut [Power]>, right: Windows100ms<&[Power]>) {
+    assert_eq!(
+        left.len(),
+        right.len(),
+        "Channels must have the same length."
+    );
+    for (l, r) in left.inner.iter_mut().zip(right.inner) {
+        l.0 += r.0;
+    }
+}
+
+/// Perform gating and averaging for a BS.1770-4 integrated loudness measurement.
+///
+/// The integrated loudness measurement is not just the average power over the
+/// entire signal. BS.1770-4 defines two stages of gating that exclude
+/// parts of the signal, to ensure that silent parts do not contribute to the
+/// loudness measurement. This function performs that gating, and returns the
+/// average power over the windows that were not excluded.
+///
+/// The result of this function is the integrated loudness measurement.
+///
+/// When no signal remains after applying the gate, this function returns
+/// `None`. In particular, this happens when all of the signal is softer than
+/// -70 LKFS, including a signal that consists of pure silence.
+pub fn gated_mean(windows_100ms: Windows100ms<&[Power]>) -> Option<Power> {
+    let mut gating_blocks = Vec::with_capacity(windows_100ms.len());
+
+    // Stage 1: an absolute threshold of -70 LKFS. (Equation 6, p.6.)
+    let absolute_threshold = Power::from_lkfs(-70.0);
+
+    // Iterate over all 400ms windows.
+    for window in windows_100ms.inner.windows(4) {
+        // Note that the sum over channels has already been performed at this point.
+        let gating_block_power = Power(0.25 * window.iter().map(|mean| mean.0).sum::<f32>());
+
+        if gating_block_power > absolute_threshold {
+            gating_blocks.push(gating_block_power);
+        }
+    }
+
+    if gating_blocks.is_empty() {
+        return None;
+    }
+
+    // Compute the loudness after applying the absolute gate, in order to
+    // determine the threshold for the relative gate.
+    let mut sum_power = Sum::zero();
+    for &gating_block_power in &gating_blocks {
+        sum_power.add(gating_block_power.0);
+    }
+    let absolute_gated_power = Power(sum_power.sum / (gating_blocks.len() as f32));
+
+    // Stage 2: Apply the relative gate.
+    let relative_threshold = Power::from_lkfs(absolute_gated_power.loudness_lkfs() - 10.0);
+    let mut sum_power = Sum::zero();
+    let mut n_blocks = 0_usize;
+    for &gating_block_power in &gating_blocks {
+        if gating_block_power > relative_threshold {
+            sum_power.add(gating_block_power.0);
+            n_blocks += 1;
+        }
+    }
+
+    if n_blocks == 0 {
+        return None;
+    }
+
+    let relative_gated_power = Power(sum_power.sum / n_blocks as f32);
+    Some(relative_gated_power)
+}

crates/utils/src/coco_classes.rs

@@ -0,0 +1,82 @@
+pub const NAMES: [&str; 80] = [
+    "person",
+    "bicycle",
+    "car",
+    "motorbike",
+    "aeroplane",
+    "bus",
+    "train",
+    "truck",
+    "boat",
+    "traffic light",
+    "fire hydrant",
+    "stop sign",
+    "parking meter",
+    "bench",
+    "bird",
+    "cat",
+    "dog",
+    "horse",
+    "sheep",
+    "cow",
+    "elephant",
+    "bear",
+    "zebra",
+    "giraffe",
+    "backpack",
+    "umbrella",
+    "handbag",
+    "tie",
+    "suitcase",
+    "frisbee",
+    "skis",
+    "snowboard",
+    "sports ball",
+    "kite",
+    "baseball bat",
+    "baseball glove",
+    "skateboard",
+    "surfboard",
+    "tennis racket",
+    "bottle",
+    "wine glass",
+    "cup",
+    "fork",
+    "knife",
+    "spoon",
+    "bowl",
+    "banana",
+    "apple",
+    "sandwich",
+    "orange",
+    "broccoli",
+    "carrot",
+    "hot dog",
+    "pizza",
+    "donut",
+    "cake",
+    "chair",
+    "sofa",
+    "pottedplant",
+    "bed",
+    "diningtable",
+    "toilet",
+    "tvmonitor",
+    "laptop",
+    "mouse",
+    "remote",
+    "keyboard",
+    "cell phone",
+    "microwave",
+    "oven",
+    "toaster",
+    "sink",
+    "refrigerator",
+    "book",
+    "clock",
+    "vase",
+    "scissors",
+    "teddy bear",
+    "hair drier",
+    "toothbrush",
+];

crates/utils/src/lib.rs

@@ -1,7 +1,17 @@
-use candle_core::utils::{cuda_is_available, metal_is_available};
+extern crate candle_core;
-use candle_core::{Device, Result, Tensor};
+extern crate candle_transformers;
+extern crate tokenizers;
 
-pub fn device(cpu: bool) -> Result<Device> {
+pub mod audio;
+pub mod bs1770;
+pub mod coco_classes;
+pub mod imagenet;
+pub mod token_output_stream;
+pub mod wav;
+use candle_core::{Device, Tensor, utils::{cuda_is_available, metal_is_available}};
+
+
+pub fn device(cpu: bool) -> Result<Device, anyhow::Error> {
     if cpu {
         Ok(Device::Cpu)
     } else if cuda_is_available() {
@@ -26,7 +36,7 @@ pub fn device(cpu: bool) -> Result<Device> {
 pub fn load_image<P: AsRef<std::path::Path>>(
     p: P,
     resize_longest: Option<usize>,
-) -> Result<(Tensor, usize, usize)> {
+) -> Result<(Tensor, usize, usize), anyhow::Error> {
     let img = image::ImageReader::open(p)?
         .decode()
         .map_err(candle_core::Error::wrap)?;
@@ -57,7 +67,7 @@ pub fn load_image_and_resize<P: AsRef<std::path::Path>>(
     p: P,
     width: usize,
     height: usize,
-) -> Result<Tensor> {
+) -> candle_core::Result<Tensor> {
     let img = image::ImageReader::open(p)?
         .decode()
         .map_err(candle_core::Error::wrap)?
@@ -73,60 +83,36 @@ pub fn load_image_and_resize<P: AsRef<std::path::Path>>(
 
 /// Saves an image to disk using the image crate, this expects an input with shape
 /// (c, height, width).
-pub fn save_image<P: AsRef<std::path::Path>>(img: &Tensor, p: P) -> Result<()> {
+pub fn save_image<P: AsRef<std::path::Path>>(img: &Tensor, p: P) -> Result<(), anyhow::Error> {
     let p = p.as_ref();
     let (channel, height, width) = img.dims3()?;
     if channel != 3 {
-        candle_core::bail!("save_image expects an input of shape (3, height, width)")
+        anyhow::bail!("save_image expects an input of shape (3, height, width)")
     }
     let img = img.permute((1, 2, 0))?.flatten_all()?;
     let pixels = img.to_vec1::<u8>()?;
     let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
         match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {
             Some(image) => image,
-            None => candle_core::bail!("error saving image {p:?}"),
+            None => anyhow::bail!("error saving image {p:?}"),
         };
     image.save(p).map_err(candle_core::Error::wrap)?;
     Ok(())
 }
 
-pub fn save_image_resize<P: AsRef<std::path::Path>>(
-    img: &Tensor,
-    p: P,
-    h: usize,
-    w: usize,
-) -> Result<()> {
-    let p = p.as_ref();
-    let (channel, height, width) = img.dims3()?;
-    if channel != 3 {
-        candle_core::bail!("save_image expects an input of shape (3, height, width)")
-    }
-    let img = img.permute((1, 2, 0))?.flatten_all()?;
-    let pixels = img.to_vec1::<u8>()?;
-    let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
-        match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {
-            Some(image) => image,
-            None => candle_core::bail!("error saving image {p:?}"),
-        };
-    let image = image::DynamicImage::from(image);
-    let image = image.resize_to_fill(w as u32, h as u32, image::imageops::FilterType::CatmullRom);
-    image.save(p).map_err(candle_core::Error::wrap)?;
-    Ok(())
-}
-
 /// Loads the safetensors files for a model from the hub based on a json index file.
 pub fn hub_load_safetensors(
     repo: &hf_hub::api::sync::ApiRepo,
     json_file: &str,
-) -> Result<Vec<std::path::PathBuf>> {
+) -> Result<Vec<std::path::PathBuf>, anyhow::Error> {
     let json_file = repo.get(json_file).map_err(candle_core::Error::wrap)?;
     let json_file = std::fs::File::open(json_file)?;
     let json: serde_json::Value =
         serde_json::from_reader(&json_file).map_err(candle_core::Error::wrap)?;
     let weight_map = match json.get("weight_map") {
-        None => candle_core::bail!("no weight map in {json_file:?}"),
+        None => anyhow::bail!("no weight map in {json_file:?}"),
         Some(serde_json::Value::Object(map)) => map,
-        Some(_) => candle_core::bail!("weight map in {json_file:?} is not a map"),
+        Some(_) => anyhow::bail!("weight map in {json_file:?} is not a map"),
     };
     let mut safetensors_files = std::collections::HashSet::new();
     for value in weight_map.values() {
@@ -136,22 +122,25 @@ pub fn hub_load_safetensors(
     }
     let safetensors_files = safetensors_files
         .iter()
-        .map(|v| repo.get(v).map_err(candle_core::Error::wrap))
+        .map(|v| {
-        .collect::<Result<Vec<_>>>()?;
+            repo.get(v)
+                .map_err(|e| std::io::Error::new(std::io::ErrorKind::Other, e))
+        })
+        .collect::<Result<Vec<_>, std::io::Error, >>()?;
     Ok(safetensors_files)
 }
 
 pub fn hub_load_local_safetensors<P: AsRef<std::path::Path>>(
     path: P,
     json_file: &str,
-) -> Result<Vec<std::path::PathBuf>> {
+) -> Result<Vec<std::path::PathBuf>, anyhow::Error> {
     let path = path.as_ref();
     let jsfile = std::fs::File::open(path.join(json_file))?;
     let json: serde_json::Value = serde_json::from_reader(&jsfile).map_err(candle_core::Error::wrap)?;
     let weight_map = match json.get("weight_map") {
-        None => candle_core::bail!("no weight map in {json_file:?}"),
+        None => anyhow::bail!("no weight map in {json_file:?}"),
         Some(serde_json::Value::Object(map)) => map,
-        Some(_) => candle_core::bail!("weight map in {json_file:?} is not a map"),
+        Some(_) => anyhow::bail!("weight map in {json_file:?} is not a map"),
     };
     let mut safetensors_files = std::collections::HashSet::new();
     for value in weight_map.values() {
@@ -164,4 +153,4 @@ pub fn hub_load_local_safetensors<P: AsRef<std::path::Path>>(
         .map(|v| path.join(v))
         .collect();
     Ok(safetensors_files)
-}
+}

crates/utils/src/main.rs

@@ -0,0 +1,3 @@
+fn main() {
+    println!("Hello, world!");
+}

crates/utils/src/token_output_stream.rs

@@ -1,7 +1,6 @@
 use candle_core::Result;
+use tokenizers::Tokenizer;
 
-/// This is a wrapper around a tokenizer to ensure that tokens can be returned to the user in a
-/// streaming way rather than having to wait for the full decoding.
 pub struct TokenOutputStream {
     tokenizer: tokenizers::Tokenizer,
     tokens: Vec<u32>,
@@ -40,8 +39,7 @@ impl TokenOutputStream {
         };
         self.tokens.push(token);
         let text = self.decode(&self.tokens[self.prev_index..])?;
-        if text.len() > prev_text.len() {
+        if text.len() > prev_text.len() && text.chars().last().unwrap().is_alphanumeric() {
-            // Modified to include all tokens, not just alphanumeric ones
             let text = text.split_at(prev_text.len());
             self.prev_index = self.current_index;
             self.current_index = self.tokens.len();
@@ -84,4 +82,4 @@ impl TokenOutputStream {
         self.prev_index = 0;
         self.current_index = 0;
     }
-}
+}

crates/utils/src/wav.rs

@@ -0,0 +1,56 @@
+use std::io::prelude::*;
+
+pub trait Sample {
+    fn to_i16(&self) -> i16;
+}
+
+impl Sample for f32 {
+    fn to_i16(&self) -> i16 {
+        (self.clamp(-1.0, 1.0) * 32767.0) as i16
+    }
+}
+
+impl Sample for f64 {
+    fn to_i16(&self) -> i16 {
+        (self.clamp(-1.0, 1.0) * 32767.0) as i16
+    }
+}
+
+impl Sample for i16 {
+    fn to_i16(&self) -> i16 {
+        *self
+    }
+}
+
+pub fn write_pcm_as_wav<W: Write, S: Sample>(
+    w: &mut W,
+    samples: &[S],
+    sample_rate: u32,
+) -> std::io::Result<()> {
+    let len = 12u32; // header
+    let len = len + 24u32; // fmt
+    let len = len + samples.len() as u32 * 2 + 8; // data
+    let n_channels = 1u16;
+    let bytes_per_second = sample_rate * 2 * n_channels as u32;
+    w.write_all(b"RIFF")?;
+    w.write_all(&(len - 8).to_le_bytes())?; // total length minus 8 bytes
+    w.write_all(b"WAVE")?;
+
+    // Format block
+    w.write_all(b"fmt ")?;
+    w.write_all(&16u32.to_le_bytes())?; // block len minus 8 bytes
+    w.write_all(&1u16.to_le_bytes())?; // PCM
+    w.write_all(&n_channels.to_le_bytes())?; // one channel
+    w.write_all(&sample_rate.to_le_bytes())?;
+    w.write_all(&bytes_per_second.to_le_bytes())?;
+    w.write_all(&2u16.to_le_bytes())?; // 2 bytes of data per sample
+    w.write_all(&16u16.to_le_bytes())?; // bits per sample
+
+    // Data block
+    w.write_all(b"data")?;
+    w.write_all(&(samples.len() as u32 * 2).to_le_bytes())?;
+    for sample in samples.iter() {
+        w.write_all(&sample.to_i16().to_le_bytes())?
+    }
+    Ok(())
+}

docs/ARCHITECTURE.md

@@ -52,7 +52,7 @@ graph TB
 
 ## Workspace Structure
 
-The project uses a 7-crate Rust workspace with TypeScript tooling, designed for maximum flexibility in deployment configurations.
+The project uses a 9-crate Rust workspace with TypeScript tooling, designed for maximum flexibility in deployment configurations.
 
 ```mermaid
 graph TD
@@ -69,18 +69,15 @@ graph TD
         end
         
         subgraph "Frontend"
-            D[leptos-app<br/>Edition: 2021<br/>Port: 3000/8788<br/>WASM/SSR]
+            D[chat-ui<br/>Edition: 2021<br/>Port: 8788<br/>WASM UI]
         end
         
         subgraph "Tooling"
             L[helm-chart-tool<br/>Edition: 2024<br/>K8s deployment]
+            E[cli<br/>Edition: 2024<br/>TypeScript/Bun CLI]
         end
     end
     
-    subgraph "External Tooling"
-        E[scripts/cli.ts<br/>TypeScript/Bun<br/>OpenAI SDK]
-    end
-    
     subgraph "Dependencies"
         A --> B
         A --> C
@@ -193,7 +190,7 @@ graph TB
         end
         
         subgraph "Frontend"
-            D[leptos-app Pod<br/>:8788<br/>ClusterIP Service]
+            D[chat-ui Pod<br/>:8788<br/>ClusterIP Service]
         end
         
         subgraph "Ingress"

package.json

@@ -1,8 +1,8 @@
 {
-  "dependencies": {
+  "name": "predict-otron-9000",
-    "openai": "^5.16.0"
+  "workspaces": ["crates/cli/package"],
-  },
   "scripts": {
-    "cli": "./scripts/cli.ts"
+    "# WORKSPACE ALIASES": "#",
+    "cli": "bun --filter crates/cli/package"
   }
 }

scripts/build_ui.sh

@@ -0,0 +1,14 @@
+#!/usr/bin/env sh
+
+# Resolve the project root (script_dir/..)
+PROJECT_ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+
+# Move into the chat-ui crate
+cd "$PROJECT_ROOT/crates/chat-ui" || exit 1
+
+# Build with cargo leptos
+cargo leptos build --release
+
+# Move the wasm file, keeping paths relative to the project root
+mv "$PROJECT_ROOT/target/site/pkg/chat-ui.wasm" \
+   "$PROJECT_ROOT/target/site/pkg/chat-ui_bg.wasm"

scripts/curl_chat_stream.sh

@@ -15,7 +15,7 @@ CONNECT_TIMEOUT=${CONNECT_TIMEOUT:-10}
 MAX_TIME=${MAX_TIME:-30}
 
 cat <<EOF
-[info] POST $SERVER_URL/v1/chat/completions/stream (SSE)
+[info] POST $SERVER_URL/v1/chat/completions (SSE)
 [info] model=$MODEL_ID, max_tokens=$MAX_TOKENS
 [info] prompt=$PROMPT
 [info] timeouts: connect=${CONNECT_TIMEOUT}s, max=${MAX_TIME}s
@@ -35,7 +35,7 @@ curl -N -sS -X POST \
   --connect-timeout "$CONNECT_TIMEOUT" \
   --max-time "$MAX_TIME" \
   -H "Content-Type: application/json" \
-  "$SERVER_URL/v1/chat/completions/stream" \
+  "$SERVER_URL/v1/chat/completions" \
   -d @- <<JSON
 {
   "model": "${MODEL_ID}",

scripts/run.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+set -e
+
+# Resolve the project root (script_dir/..)
+PROJECT_ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+
+# todo, conditionally run this only when those files change
+"$PROJECT_ROOT/scripts/build_ui.sh"
+
+# build the frontend first
+# Start the unified predict-otron-9000 server on port 8080
+export SERVER_PORT=${SERVER_PORT:-8080}
+export RUST_LOG=${RUST_LOG:-info}
+
+cd "$PROJECT_ROOT" || exit 1
+cargo run --bin predict-otron-9000 --release

scripts/run_llama.sh

@@ -1,30 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-PROMPT=${1:-"Say hello in one short sentence."}
-MODEL=${2:-"meta-llama/Llama-3.2-1B-Instruct"}
-MAX_NEW=${3:-64}
-FORCE_CPU=${FORCE_CPU:-0}
-
-# Optional: keep HF cache local to repo if not already set
-export HF_HOME=${HF_HOME:-"$PWD/.hf-cache"}
-
-BIN="$(dirname "$0")/../target/release/llama_infer"
-
-if [[ ! -x "$BIN" ]]; then
-  echo "Building llama-runner (release)..."
-  cargo build -p llama-runner --release
-fi
-
-echo "Running llama inference..." >&2
-ARGS=(
-  --model-id "$MODEL"
-  --prompt "$PROMPT"
-  --max-new-tokens "$MAX_NEW"
-)
-
-if [[ "$FORCE_CPU" == "1" || "$FORCE_CPU" == "true" ]]; then
-  ARGS+=( --force-cpu )
-fi
-
-"$BIN" "${ARGS[@]}"

scripts/run_server.sh

@@ -1,7 +0,0 @@
-#!/bin/bash
-
-# Start the unified predict-otron-9000 server on port 8080
-export SERVER_PORT=${SERVER_PORT:-8080}
-export RUST_LOG=${RUST_LOG:-info}
-
-cargo run --bin predict-otron-9000 --release