v0.1.5 already exists

---

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.

.dockerignore

@@ -0,0 +1,35 @@
+# Git
+.git
+.gitignore
+
+# Rust
+target/
+
+# Documentation
+README.md
+*.md
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Environment
+.env
+.env.local
+
+# Dependencies
+node_modules/
+
+# Build artifacts
+dist/
+build/
+.fastembed_cache

.github/workflows/ci.yml

@@ -25,7 +25,16 @@ jobs:
           key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
 
       - name: Setup Rust
-        run: rustup update stable && rustup default stable
+        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
@@ -35,7 +44,7 @@ jobs:
 
       - name: Clippy
         shell: bash
-        run: cargo clippy --all-targets
+        run: cargo clippy --all
 
       - name: Tests
         shell: bash

.github/workflows/docker.yml

@@ -0,0 +1,46 @@
+name: Build and Push Docker Image
+
+on:
+    tags:
+      - 'v*'
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Log in to Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=branch
+            type=ref,event=pr
+            type=semver,pattern={{version}}
+            type=semver,pattern={{major}}.{{minor}}
+            type=sha
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.github/workflows/release.yml

@@ -32,7 +32,7 @@ jobs:
           key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
 
       - name: Setup Rust
-        run: rustup update stable && rustup default stable
+        run: rustup update stable && rustup default stable && rustup target add wasm32-unknown-unknown
 
       - name: Setup Bun
         uses: oven-sh/setup-bun@v2
@@ -45,7 +45,7 @@ jobs:
 
       - name: Clippy
         shell: bash
-        run: cargo clippy --all-targets
+        run: cargo clippy --all
 
       - name: Tests
         shell: bash
@@ -129,12 +129,17 @@ jobs:
           key: ${{ runner.os }}-${{ matrix.target }}-cargo-${{ hashFiles('**/Cargo.lock') }}
 
       - name: Setup Rust
-        run: rustup update stable && rustup default stable
+        run: rustup update stable && rustup default stable && rustup target add wasm32-unknown-unknown
 
       - name: Add target
         run: rustup target add ${{ matrix.target }}
 
-      - name: Build binary
+      - 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

.gitignore

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

Cargo.toml

@@ -3,17 +3,17 @@ 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/cli"
-]
+    "integration/helm-chart-tool",
+    "integration/llama-runner",
+    "integration/gemma-runner",
+    "integration/cli",
+    "crates/chat-ui"
+, "integration/utils"]
 default-members = ["crates/predict-otron-9000"]
 resolver = "2"
 
 [workspace.package]
-version = "0.1.2"
+version = "0.1.6"
 
 # Compiler optimization profiles for the workspace
 [profile.release]
@@ -42,8 +42,3 @@ overflow-checks = true
 opt-level = 3
 debug = true
 lto = "thin"
-
-[[workspace.metadata.leptos]]
-# project name
-bin-package = "leptos-app"
-lib-package = "leptos-app"

Dockerfile

@@ -0,0 +1,50 @@
+# Multi-stage build for predict-otron-9000 workspace
+FROM rust:1 AS builder
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    pkg-config \
+    libssl-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create app directory
+WORKDIR /app
+
+# Copy workspace files
+COPY Cargo.toml Cargo.lock ./
+COPY crates/ ./crates/
+COPY integration/ ./integration/
+
+# Build all 3 main server binaries in release mode
+RUN cargo build --release -p predict-otron-9000 --bin predict-otron-9000 --no-default-features -p embeddings-engine --bin embeddings-engine -p inference-engine --bin inference-engine
+
+# Runtime stage
+FROM debian:bookworm-slim AS runtime
+
+# Install runtime dependencies
+RUN apt-get update && apt-get install -y \
+    ca-certificates \
+    libssl3 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create app user
+RUN useradd -r -s /bin/false -m -d /app appuser
+
+# Set working directory
+WORKDIR /app
+
+# Copy binaries from builder stage
+COPY --from=builder /app/target/release/predict-otron-9000 ./bin/
+COPY --from=builder /app/target/release/embeddings-engine ./bin/
+COPY --from=builder /app/target/release/inference-engine ./bin/
+# Make binaries executable and change ownership
+RUN chmod +x ./bin/* && chown -R appuser:appuser /app
+
+# Switch to non-root user
+USER appuser
+
+# Expose ports (adjust as needed based on your services)
+EXPOSE 8080 8081 8082
+
+# Default command (can be overridden)
+CMD ["./bin/predict-otron-9000"]

README.md

@@ -2,19 +2,26 @@
     predict-otron-9000
 </h1>
 <p align="center">
-Powerful local AI inference with OpenAI-compatible APIs
+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.
+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.
 
 
+~~~shell
+./scripts/run.sh
+~~~
+
+
 ## Project Overview
 
 The predict-otron-9000 is a flexible AI platform that provides:
@@ -40,26 +47,30 @@ The system supports both CPU and GPU acceleration (CUDA/Metal), with intelligent
 
 ### 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/
 ├── predict-otron-9000/     # Main orchestration server (Rust 2024)
 ├── inference-engine/       # Multi-model inference orchestrator (Rust 2021)
+├── embeddings-engine/     # FastEmbed embeddings service (Rust 2024)
+└── chat-ui/               # WASM web frontend (Rust 2021)
+
+integration/
+├── cli/                   # CLI client crate (Rust 2024)
+│   └── package/
+│       └── cli.ts         # TypeScript/Bun CLI client
 ├── 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)
 ├── helm-chart-tool/       # Kubernetes deployment tooling (Rust 2024)
-└── scripts/
-    └── cli.ts             # TypeScript/Bun CLI client
+└── utils/                 # Shared utilities (Rust 2021)
 ```
 
 ### 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): cargo leptos SSR app
+- **Web Frontend** (port 8788): chat-ui WASM app
 - **CLI Client**: TypeScript/Bun client for testing and automation
 
 ### Deployment Modes
@@ -85,11 +96,6 @@ 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
@@ -134,11 +140,6 @@ 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
 
@@ -152,26 +153,26 @@ 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 integration/cli/package && bun run cli.ts --list-models
 
 # Chat completion
-bun run scripts/cli.ts "What is the capital of France?"
+cd integration/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 integration/cli/package && bun run cli.ts --model gemma-3-1b-it --prompt "Hello, world!"
 
 # Show help
-bun run scripts/cli.ts --help
+cd integration/cli/package && bun run cli.ts --help
 ```
 
 ## API Usage
@@ -287,7 +288,7 @@ cargo test --workspace
 
 **End-to-end test script:**
 ```bash
-./smoke_test.sh
+./scripts/smoke_test.sh
 ```
 
 This script:
@@ -376,7 +377,7 @@ 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:**
@@ -435,8 +436,7 @@ 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`
-3. Check RUSTFLAGS in leptos-app/run.sh
+2. Check RUSTFLAGS in chat-ui/run.sh
 
 ### Network/Timeout Issues
 **Symptom:** First-time model downloads timing out  
@@ -467,24 +467,23 @@ curl -s http://localhost:8080/v1/models | jq
 
 **CLI client test:**
 ```bash
-bun run scripts/cli.ts "What is 2+2?"
+cd integration/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
-./smoke_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.

bun.lock

@@ -4,7 +4,7 @@
     "": {
       "name": "predict-otron-9000",
     },
-    "crates/cli/package": {
+    "integration/cli/package": {
       "name": "cli",
       "dependencies": {
         "install": "^0.13.0",
@@ -13,7 +13,7 @@
     },
   },
   "packages": {
-    "cli": ["cli@workspace:crates/cli/package"],
+    "cli": ["cli@workspace:integration/cli/package"],
 
     "install": ["install@0.13.0", "", {}, "sha512-zDml/jzr2PKU9I8J/xyZBQn8rPCAY//UOYNmR01XwNwyfhEWObo2SWfSl1+0tm1u6PhxLwDnfsT/6jB7OUxqFA=="],
 

crates/chat-ui/Cargo.toml

@@ -1,8 +1,9 @@
 [package]
-name = "leptos-app"
-version.workspace = true
+name = "chat-ui"
+version = "0.1.0"
 edition = "2021"
 
+
 [lib]
 crate-type = ["cdylib", "rlib"]
 
@@ -15,45 +16,33 @@ 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 }
-
-# Chat interface dependencies
+wasm-bindgen-futures = "0.4"
+js-sys = "0.3"
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
-async-openai-wasm = { version = "0.29", default-features = false }
-futures-util = "0.3"
-js-sys = { version = "0.3", optional = true }
-either = { version = "1.9", features = ["serde"] }
-
-web-sys = { version = "0.3", optional = true, features = [
-    "console",
-    "Window",
-    "Document",
-    "Element",
-    "HtmlElement",
-    "HtmlInputElement",
-    "HtmlSelectElement",
-    "HtmlTextAreaElement",
-    "Event",
-    "EventTarget",
-    "KeyboardEvent",
+reqwest = { version = "0.12", features = ["json"] }
+web-sys = { version = "0.3", features = [
+    "console", 
+    "EventSource", 
+    "MessageEvent", 
+    "Window", 
+    "Request", 
+    "RequestInit", 
+    "Response", 
+    "Headers",
+    "ReadableStream",
+    "ReadableStreamDefaultReader",
+    "TextDecoder",
+    "TextDecoderOptions",
+    "HtmlInputElement"
 ] }
-
-[dependencies.uuid]
-version = "1.0"
-features = [
-    "v4",
-    "fast-rng", 
-    "macro-diagnostics",
-    "js",
-]
+gloo-net = { version = "0.6", features = ["http"] }
 
 [features]
 hydrate = [
     "leptos/hydrate",
     "dep:console_error_panic_hook",
     "dep:wasm-bindgen",
-    "dep:js-sys",
-    "dep:web-sys",
 ]
 ssr = [
     "dep:axum",
@@ -73,8 +62,9 @@ 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 +74,7 @@ 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 +122,8 @@ 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"
+
+[[bin]]
+name = "chat-ui"
+path = "src/main.rs"

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 +1,617 @@
+#[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("")); // 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);
+                    selected_model.set(String::from("gemma-3-1b-it"));
+                }
+                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/embeddings-engine/Cargo.toml

@@ -25,15 +25,9 @@ rand = "0.8.5"
 async-openai = "0.28.3"
 once_cell = "1.19.0"
 
-
-
-[package.metadata.compose]
-image = "ghcr.io/geoffsee/embeddings-service:latest"
-port = 8080
-
-
 # generates kubernetes manifests
 [package.metadata.kube]
-image = "ghcr.io/geoffsee/embeddings-service:latest"
+image = "ghcr.io/geoffsee/predict-otron-9000:latest"
+cmd = ["./bin/embeddings-engine"]
 replicas = 1
 port = 8080

crates/embeddings-engine/Dockerfile

@@ -1,42 +0,0 @@
-# ---- Build stage ----
-FROM rust:1-slim-bullseye AS builder
-
-WORKDIR /usr/src/app
-
-# Install build dependencies
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        pkg-config \
-        libssl-dev \
-        build-essential \
-        && rm -rf /var/lib/apt/lists/*
-
-# Cache deps first
-COPY . ./
-RUN rm -rf src
-RUN mkdir src && echo "fn main() {}" > src/main.rs && echo "// lib" > src/lib.rs && cargo build --release
-RUN rm -rf src
-
-# Copy real sources and build
-COPY . .
-RUN cargo build --release
-
-# ---- Runtime stage ----
-FROM debian:bullseye-slim
-
-# Install only what the compiled binary needs
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        libssl1.1 \
-        ca-certificates \
-        && rm -rf /var/lib/apt/lists/*
-
-# Copy binary from builder
-COPY --from=builder /usr/src/app/target/release/embeddings-engine /usr/local/bin/
-
-# Run as non-root user for safety
-RUN useradd -m appuser
-USER appuser
-
-EXPOSE 8080
-CMD ["embeddings-engine"]

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. 
-This crate wraps the fastembed crate to provide embeddings and partially adapts the openai specification.  
+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.
+
+## 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

@@ -1,43 +1,225 @@
 use async_openai::types::{CreateEmbeddingRequest, EmbeddingInput};
-use axum::{Json, Router, response::Json as ResponseJson, routing::post};
+use axum::{Json, Router, http::StatusCode, response::Json as ResponseJson, routing::post};
 use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
 use once_cell::sync::Lazy;
+use serde::Serialize;
+use std::collections::HashMap;
+use std::sync::{Arc, RwLock};
 use tower_http::trace::TraceLayer;
-use tracing;
 
-// Persistent model instance (singleton pattern)
-static EMBEDDING_MODEL: Lazy<TextEmbedding> = Lazy::new(|| {
-    tracing::info!("Initializing persistent embedding model (singleton)");
+// Cache for multiple embedding models
+static MODEL_CACHE: Lazy<RwLock<HashMap<EmbeddingModel, Arc<TextEmbedding>>>> =
+    Lazy::new(|| RwLock::new(HashMap::new()));
+
+#[derive(Serialize)]
+pub struct ModelInfo {
+    pub id: String,
+    pub object: String,
+    pub owned_by: String,
+    pub description: String,
+    pub dimensions: usize,
+}
+
+#[derive(Serialize)]
+pub struct ModelsResponse {
+    pub object: String,
+    pub data: Vec<ModelInfo>,
+}
+
+// Function to convert model name strings to EmbeddingModel enum variants
+fn parse_embedding_model(model_name: &str) -> Result<EmbeddingModel, String> {
+    match model_name {
+        // Sentence Transformers models
+        "sentence-transformers/all-MiniLM-L6-v2" | "all-minilm-l6-v2" => {
+            Ok(EmbeddingModel::AllMiniLML6V2)
+        }
+        "sentence-transformers/all-MiniLM-L6-v2-q" | "all-minilm-l6-v2-q" => {
+            Ok(EmbeddingModel::AllMiniLML6V2Q)
+        }
+        "sentence-transformers/all-MiniLM-L12-v2" | "all-minilm-l12-v2" => {
+            Ok(EmbeddingModel::AllMiniLML12V2)
+        }
+        "sentence-transformers/all-MiniLM-L12-v2-q" | "all-minilm-l12-v2-q" => {
+            Ok(EmbeddingModel::AllMiniLML12V2Q)
+        }
+
+        // BGE models
+        "BAAI/bge-base-en-v1.5" | "bge-base-en-v1.5" => Ok(EmbeddingModel::BGEBaseENV15),
+        "BAAI/bge-base-en-v1.5-q" | "bge-base-en-v1.5-q" => Ok(EmbeddingModel::BGEBaseENV15Q),
+        "BAAI/bge-large-en-v1.5" | "bge-large-en-v1.5" => Ok(EmbeddingModel::BGELargeENV15),
+        "BAAI/bge-large-en-v1.5-q" | "bge-large-en-v1.5-q" => Ok(EmbeddingModel::BGELargeENV15Q),
+        "BAAI/bge-small-en-v1.5" | "bge-small-en-v1.5" => Ok(EmbeddingModel::BGESmallENV15),
+        "BAAI/bge-small-en-v1.5-q" | "bge-small-en-v1.5-q" => Ok(EmbeddingModel::BGESmallENV15Q),
+        "BAAI/bge-small-zh-v1.5" | "bge-small-zh-v1.5" => Ok(EmbeddingModel::BGESmallZHV15),
+        "BAAI/bge-large-zh-v1.5" | "bge-large-zh-v1.5" => Ok(EmbeddingModel::BGELargeZHV15),
+
+        // Nomic models
+        "nomic-ai/nomic-embed-text-v1" | "nomic-embed-text-v1" => {
+            Ok(EmbeddingModel::NomicEmbedTextV1)
+        }
+        "nomic-ai/nomic-embed-text-v1.5" | "nomic-embed-text-v1.5" | "nomic-text-embed" => {
+            Ok(EmbeddingModel::NomicEmbedTextV15)
+        }
+        "nomic-ai/nomic-embed-text-v1.5-q" | "nomic-embed-text-v1.5-q" => {
+            Ok(EmbeddingModel::NomicEmbedTextV15Q)
+        }
+
+        // Paraphrase models
+        "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+        | "paraphrase-multilingual-minilm-l12-v2" => Ok(EmbeddingModel::ParaphraseMLMiniLML12V2),
+        "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-q"
+        | "paraphrase-multilingual-minilm-l12-v2-q" => Ok(EmbeddingModel::ParaphraseMLMiniLML12V2Q),
+        "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
+        | "paraphrase-multilingual-mpnet-base-v2" => Ok(EmbeddingModel::ParaphraseMLMpnetBaseV2),
+
+        // ModernBert
+        "lightonai/modernbert-embed-large" | "modernbert-embed-large" => {
+            Ok(EmbeddingModel::ModernBertEmbedLarge)
+        }
+
+        // Multilingual E5 models
+        "intfloat/multilingual-e5-small" | "multilingual-e5-small" => {
+            Ok(EmbeddingModel::MultilingualE5Small)
+        }
+        "intfloat/multilingual-e5-base" | "multilingual-e5-base" => {
+            Ok(EmbeddingModel::MultilingualE5Base)
+        }
+        "intfloat/multilingual-e5-large" | "multilingual-e5-large" => {
+            Ok(EmbeddingModel::MultilingualE5Large)
+        }
+
+        // Mixedbread models
+        "mixedbread-ai/mxbai-embed-large-v1" | "mxbai-embed-large-v1" => {
+            Ok(EmbeddingModel::MxbaiEmbedLargeV1)
+        }
+        "mixedbread-ai/mxbai-embed-large-v1-q" | "mxbai-embed-large-v1-q" => {
+            Ok(EmbeddingModel::MxbaiEmbedLargeV1Q)
+        }
+
+        // GTE models
+        "Alibaba-NLP/gte-base-en-v1.5" | "gte-base-en-v1.5" => Ok(EmbeddingModel::GTEBaseENV15),
+        "Alibaba-NLP/gte-base-en-v1.5-q" | "gte-base-en-v1.5-q" => {
+            Ok(EmbeddingModel::GTEBaseENV15Q)
+        }
+        "Alibaba-NLP/gte-large-en-v1.5" | "gte-large-en-v1.5" => Ok(EmbeddingModel::GTELargeENV15),
+        "Alibaba-NLP/gte-large-en-v1.5-q" | "gte-large-en-v1.5-q" => {
+            Ok(EmbeddingModel::GTELargeENV15Q)
+        }
+
+        // CLIP model
+        "Qdrant/clip-ViT-B-32-text" | "clip-vit-b-32" => Ok(EmbeddingModel::ClipVitB32),
+
+        // Jina model
+        "jinaai/jina-embeddings-v2-base-code" | "jina-embeddings-v2-base-code" => {
+            Ok(EmbeddingModel::JinaEmbeddingsV2BaseCode)
+        }
+
+        _ => Err(format!("Unsupported embedding model: {}", model_name)),
+    }
+}
+
+// Function to get model dimensions
+fn get_model_dimensions(model: &EmbeddingModel) -> usize {
+    match model {
+        EmbeddingModel::AllMiniLML6V2 | EmbeddingModel::AllMiniLML6V2Q => 384,
+        EmbeddingModel::AllMiniLML12V2 | EmbeddingModel::AllMiniLML12V2Q => 384,
+        EmbeddingModel::BGEBaseENV15 | EmbeddingModel::BGEBaseENV15Q => 768,
+        EmbeddingModel::BGELargeENV15 | EmbeddingModel::BGELargeENV15Q => 1024,
+        EmbeddingModel::BGESmallENV15 | EmbeddingModel::BGESmallENV15Q => 384,
+        EmbeddingModel::BGESmallZHV15 => 512,
+        EmbeddingModel::BGELargeZHV15 => 1024,
+        EmbeddingModel::NomicEmbedTextV1
+        | EmbeddingModel::NomicEmbedTextV15
+        | EmbeddingModel::NomicEmbedTextV15Q => 768,
+        EmbeddingModel::ParaphraseMLMiniLML12V2 | EmbeddingModel::ParaphraseMLMiniLML12V2Q => 384,
+        EmbeddingModel::ParaphraseMLMpnetBaseV2 => 768,
+        EmbeddingModel::ModernBertEmbedLarge => 1024,
+        EmbeddingModel::MultilingualE5Small => 384,
+        EmbeddingModel::MultilingualE5Base => 768,
+        EmbeddingModel::MultilingualE5Large => 1024,
+        EmbeddingModel::MxbaiEmbedLargeV1 | EmbeddingModel::MxbaiEmbedLargeV1Q => 1024,
+        EmbeddingModel::GTEBaseENV15 | EmbeddingModel::GTEBaseENV15Q => 768,
+        EmbeddingModel::GTELargeENV15 | EmbeddingModel::GTELargeENV15Q => 1024,
+        EmbeddingModel::ClipVitB32 => 512,
+        EmbeddingModel::JinaEmbeddingsV2BaseCode => 768,
+    }
+}
+
+// Function to get or create a model from cache
+fn get_or_create_model(embedding_model: EmbeddingModel) -> Result<Arc<TextEmbedding>, String> {
+    // First try to get from cache (read lock)
+    {
+        let cache = MODEL_CACHE
+            .read()
+            .map_err(|e| format!("Failed to acquire read lock: {}", e))?;
+        if let Some(model) = cache.get(&embedding_model) {
+            tracing::debug!("Using cached model: {:?}", embedding_model);
+            return Ok(Arc::clone(model));
+        }
+    }
+
+    // Model not in cache, create it (write lock)
+    let mut cache = MODEL_CACHE
+        .write()
+        .map_err(|e| format!("Failed to acquire write lock: {}", e))?;
+
+    // Double-check after acquiring write lock
+    if let Some(model) = cache.get(&embedding_model) {
+        tracing::debug!("Using cached model (double-check): {:?}", embedding_model);
+        return Ok(Arc::clone(model));
+    }
+
+    tracing::info!("Initializing new embedding model: {:?}", embedding_model);
     let model_start_time = std::time::Instant::now();
 
     let model = TextEmbedding::try_new(
-        InitOptions::new(EmbeddingModel::NomicEmbedTextV15).with_show_download_progress(true),
+        InitOptions::new(embedding_model.clone()).with_show_download_progress(true),
     )
-    .expect("Failed to initialize persistent embedding model");
+    .map_err(|e| format!("Failed to initialize model {:?}: {}", embedding_model, e))?;
 
     let model_init_time = model_start_time.elapsed();
     tracing::info!(
-        "Persistent embedding model initialized in {:.2?}",
+        "Embedding model {:?} initialized in {:.2?}",
+        embedding_model,
         model_init_time
     );
 
-    model
-});
+    let model_arc = Arc::new(model);
+    cache.insert(embedding_model.clone(), Arc::clone(&model_arc));
+    Ok(model_arc)
+}
 
 pub async fn embeddings_create(
     Json(payload): Json<CreateEmbeddingRequest>,
-) -> ResponseJson<serde_json::Value> {
+) -> Result<ResponseJson<serde_json::Value>, (StatusCode, String)> {
     // Start timing the entire process
     let start_time = std::time::Instant::now();
 
-    // Phase 1: Access persistent model instance
+    // Phase 1: Parse and get the embedding model
     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 embedding_model = match parse_embedding_model(&payload.model) {
+        Ok(model) => model,
+        Err(e) => {
+            tracing::error!("Invalid model requested: {}", e);
+            return Err((StatusCode::BAD_REQUEST, format!("Invalid model: {}", e)));
+        }
+    };
+
+    let model = match get_or_create_model(embedding_model.clone()) {
+        Ok(model) => model,
+        Err(e) => {
+            tracing::error!("Failed to get/create model: {}", e);
+            return Err((
+                StatusCode::INTERNAL_SERVER_ERROR,
+                format!("Model initialization failed: {}", e),
+            ));
+        }
+    };
+
     let model_access_time = model_start_time.elapsed();
     tracing::debug!(
-        "Persistent model access completed in {:.2?}",
+        "Model access/creation completed in {:.2?}",
         model_access_time
     );
 
@@ -65,9 +247,13 @@ pub async fn embeddings_create(
     // 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 embeddings = model.embed(texts_from_embedding_input, None).map_err(|e| {
+        tracing::error!("Failed to generate embeddings: {}", e);
+        (
+            StatusCode::INTERNAL_SERVER_ERROR,
+            format!("Embedding generation failed: {}", e),
+        )
+    })?;
 
     let embedding_generation_time = embedding_start_time.elapsed();
     tracing::info!(
@@ -117,8 +303,9 @@ pub async fn embeddings_create(
             // Generate a random non-zero embedding
             use rand::Rng;
             let mut rng = rand::thread_rng();
-            let mut random_embedding = Vec::with_capacity(768);
-            for _ in 0..768 {
+            let expected_dimensions = get_model_dimensions(&embedding_model);
+            let mut random_embedding = Vec::with_capacity(expected_dimensions);
+            for _ in 0..expected_dimensions {
                 // Generate random values between -1.0 and 1.0, excluding 0
                 let mut val = 0.0;
                 while val == 0.0 {
@@ -138,18 +325,19 @@ pub async fn embeddings_create(
             random_embedding
         } else {
             // Check if dimensions parameter is provided and pad the embeddings if necessary
-            let mut padded_embedding = embeddings[0].clone();
+            let padded_embedding = embeddings[0].clone();
 
-            // If the client expects 768 dimensions but our model produces fewer, pad with zeros
-            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
+            // Use the actual model dimensions instead of hardcoded 768
+            let actual_dimensions = padded_embedding.len();
+            let expected_dimensions = get_model_dimensions(&embedding_model);
+
+            if actual_dimensions != expected_dimensions {
+                tracing::warn!(
+                    "Model {:?} produced {} dimensions but expected {}",
+                    embedding_model,
+                    actual_dimensions,
+                    expected_dimensions
                 );
-                padded_embedding.extend(vec![0.0; padding_needed]);
             }
 
             padded_embedding
@@ -203,11 +391,234 @@ pub async fn embeddings_create(
         postprocessing_time
     );
 
-    ResponseJson(response)
+    Ok(ResponseJson(response))
+}
+
+pub async fn models_list() -> ResponseJson<ModelsResponse> {
+    let models = vec![
+        ModelInfo {
+            id: "sentence-transformers/all-MiniLM-L6-v2".to_string(),
+            object: "model".to_string(),
+            owned_by: "sentence-transformers".to_string(),
+            description: "Sentence Transformer model, MiniLM-L6-v2".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "sentence-transformers/all-MiniLM-L6-v2-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "sentence-transformers".to_string(),
+            description: "Quantized Sentence Transformer model, MiniLM-L6-v2".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "sentence-transformers/all-MiniLM-L12-v2".to_string(),
+            object: "model".to_string(),
+            owned_by: "sentence-transformers".to_string(),
+            description: "Sentence Transformer model, MiniLM-L12-v2".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "sentence-transformers/all-MiniLM-L12-v2-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "sentence-transformers".to_string(),
+            description: "Quantized Sentence Transformer model, MiniLM-L12-v2".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "BAAI/bge-base-en-v1.5".to_string(),
+            object: "model".to_string(),
+            owned_by: "BAAI".to_string(),
+            description: "v1.5 release of the base English model".to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "BAAI/bge-base-en-v1.5-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "BAAI".to_string(),
+            description: "Quantized v1.5 release of the base English model".to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "BAAI/bge-large-en-v1.5".to_string(),
+            object: "model".to_string(),
+            owned_by: "BAAI".to_string(),
+            description: "v1.5 release of the large English model".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "BAAI/bge-large-en-v1.5-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "BAAI".to_string(),
+            description: "Quantized v1.5 release of the large English model".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "BAAI/bge-small-en-v1.5".to_string(),
+            object: "model".to_string(),
+            owned_by: "BAAI".to_string(),
+            description: "v1.5 release of the fast and default English model".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "BAAI/bge-small-en-v1.5-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "BAAI".to_string(),
+            description: "Quantized v1.5 release of the fast and default English model".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "BAAI/bge-small-zh-v1.5".to_string(),
+            object: "model".to_string(),
+            owned_by: "BAAI".to_string(),
+            description: "v1.5 release of the small Chinese model".to_string(),
+            dimensions: 512,
+        },
+        ModelInfo {
+            id: "BAAI/bge-large-zh-v1.5".to_string(),
+            object: "model".to_string(),
+            owned_by: "BAAI".to_string(),
+            description: "v1.5 release of the large Chinese model".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "nomic-ai/nomic-embed-text-v1".to_string(),
+            object: "model".to_string(),
+            owned_by: "nomic-ai".to_string(),
+            description: "8192 context length english model".to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "nomic-ai/nomic-embed-text-v1.5".to_string(),
+            object: "model".to_string(),
+            owned_by: "nomic-ai".to_string(),
+            description: "v1.5 release of the 8192 context length english model".to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "nomic-ai/nomic-embed-text-v1.5-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "nomic-ai".to_string(),
+            description: "Quantized v1.5 release of the 8192 context length english model"
+                .to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2".to_string(),
+            object: "model".to_string(),
+            owned_by: "sentence-transformers".to_string(),
+            description: "Multi-lingual model".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "sentence-transformers".to_string(),
+            description: "Quantized Multi-lingual model".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "sentence-transformers/paraphrase-multilingual-mpnet-base-v2".to_string(),
+            object: "model".to_string(),
+            owned_by: "sentence-transformers".to_string(),
+            description: "Sentence-transformers model for tasks like clustering or semantic search"
+                .to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "lightonai/modernbert-embed-large".to_string(),
+            object: "model".to_string(),
+            owned_by: "lightonai".to_string(),
+            description: "Large model of ModernBert Text Embeddings".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "intfloat/multilingual-e5-small".to_string(),
+            object: "model".to_string(),
+            owned_by: "intfloat".to_string(),
+            description: "Small model of multilingual E5 Text Embeddings".to_string(),
+            dimensions: 384,
+        },
+        ModelInfo {
+            id: "intfloat/multilingual-e5-base".to_string(),
+            object: "model".to_string(),
+            owned_by: "intfloat".to_string(),
+            description: "Base model of multilingual E5 Text Embeddings".to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "intfloat/multilingual-e5-large".to_string(),
+            object: "model".to_string(),
+            owned_by: "intfloat".to_string(),
+            description: "Large model of multilingual E5 Text Embeddings".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "mixedbread-ai/mxbai-embed-large-v1".to_string(),
+            object: "model".to_string(),
+            owned_by: "mixedbread-ai".to_string(),
+            description: "Large English embedding model from MixedBreed.ai".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "mixedbread-ai/mxbai-embed-large-v1-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "mixedbread-ai".to_string(),
+            description: "Quantized Large English embedding model from MixedBreed.ai".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "Alibaba-NLP/gte-base-en-v1.5".to_string(),
+            object: "model".to_string(),
+            owned_by: "Alibaba-NLP".to_string(),
+            description: "Base multilingual embedding model from Alibaba".to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "Alibaba-NLP/gte-base-en-v1.5-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "Alibaba-NLP".to_string(),
+            description: "Quantized Base multilingual embedding model from Alibaba".to_string(),
+            dimensions: 768,
+        },
+        ModelInfo {
+            id: "Alibaba-NLP/gte-large-en-v1.5".to_string(),
+            object: "model".to_string(),
+            owned_by: "Alibaba-NLP".to_string(),
+            description: "Large multilingual embedding model from Alibaba".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "Alibaba-NLP/gte-large-en-v1.5-q".to_string(),
+            object: "model".to_string(),
+            owned_by: "Alibaba-NLP".to_string(),
+            description: "Quantized Large multilingual embedding model from Alibaba".to_string(),
+            dimensions: 1024,
+        },
+        ModelInfo {
+            id: "Qdrant/clip-ViT-B-32-text".to_string(),
+            object: "model".to_string(),
+            owned_by: "Qdrant".to_string(),
+            description: "CLIP text encoder based on ViT-B/32".to_string(),
+            dimensions: 512,
+        },
+        ModelInfo {
+            id: "jinaai/jina-embeddings-v2-base-code".to_string(),
+            object: "model".to_string(),
+            owned_by: "jinaai".to_string(),
+            description: "Jina embeddings v2 base code".to_string(),
+            dimensions: 768,
+        },
+    ];
+
+    ResponseJson(ModelsResponse {
+        object: "list".to_string(),
+        data: models,
+    })
 }
 
 pub fn create_embeddings_router() -> Router {
     Router::new()
         .route("/v1/embeddings", post(embeddings_create))
+        // .route("/v1/models", get(models_list))
         .layer(TraceLayer::new_for_http())
 }

crates/embeddings-engine/src/main.rs

@@ -4,8 +4,6 @@ use axum::{
     response::Json as ResponseJson,
     routing::{get, post},
 };
-use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
-use serde::{Deserialize, Serialize};
 use std::env;
 use tower_http::trace::TraceLayer;
 use tracing;
@@ -13,127 +11,28 @@ use tracing;
 const DEFAULT_SERVER_HOST: &str = "127.0.0.1";
 const DEFAULT_SERVER_PORT: &str = "8080";
 
+use embeddings_engine;
+
 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),
-    )
-    .expect("Failed to initialize model");
+) -> Result<ResponseJson<serde_json::Value>, axum::response::Response> {
+    match embeddings_engine::embeddings_create(Json(payload)).await {
+        Ok(response) => Ok(response),
+        Err((status_code, message)) => Err(axum::response::Response::builder()
+            .status(status_code)
+            .body(axum::body::Body::from(message))
+            .unwrap()),
+    }
+}
 
-    let embedding_input = payload.input;
-
-    let texts_from_embedding_input = match embedding_input {
-        EmbeddingInput::String(text) => vec![text],
-        EmbeddingInput::StringArray(texts) => texts,
-        EmbeddingInput::IntegerArray(_) => {
-            panic!("Integer array input not supported for text embeddings");
-        }
-        EmbeddingInput::ArrayOfIntegerArray(_) => {
-            panic!("Array of integer arrays not supported for text embeddings");
-        }
-    };
-
-    let embeddings = model
-        .embed(texts_from_embedding_input, None)
-        .expect("failed to embed document");
-
-    // 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())]
-    );
-
-    // 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
-    );
-
-    // Create the final embedding
-    let final_embedding = {
-        // Check if the embedding is all zeros
-        let all_zeros = embeddings[0].iter().all(|&x| x == 0.0);
-        if all_zeros {
-            tracing::warn!("Embedding is all zeros. Generating random non-zero embedding.");
-
-            // Generate a random non-zero embedding
-            use rand::Rng;
-            let mut rng = rand::thread_rng();
-            let mut random_embedding = Vec::with_capacity(768);
-            for _ in 0..768 {
-                // Generate random values between -1.0 and 1.0, excluding 0
-                let mut val = 0.0;
-                while val == 0.0 {
-                    val = rng.gen_range(-1.0..1.0);
-                }
-                random_embedding.push(val);
-            }
-
-            // Normalize the random embedding
-            let norm: f32 = random_embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
-            for i in 0..random_embedding.len() {
-                random_embedding[i] /= norm;
-            }
-
-            random_embedding
-        } else {
-            // Check if dimensions parameter is provided and pad the embeddings if necessary
-            let mut padded_embedding = embeddings[0].clone();
-
-            // If the client expects 768 dimensions but our model produces fewer, pad with zeros
-            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
-                );
-                padded_embedding.extend(vec![0.0; padding_needed]);
-            }
-
-            padded_embedding
-        }
-    };
-
-    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())]
-    );
-
-    // Return a response that matches the OpenAI API format
-    let response = serde_json::json!({
-        "object": "list",
-        "data": [
-            {
-                "object": "embedding",
-                "index": 0,
-                "embedding": final_embedding
-            }
-        ],
-        "model": payload.model,
-        "usage": {
-            "prompt_tokens": 0,
-            "total_tokens": 0
-        }
-    });
-    ResponseJson(response)
+async fn models_list() -> ResponseJson<embeddings_engine::ModelsResponse> {
+    embeddings_engine::models_list().await
 }
 
 fn create_app() -> Router {
     Router::new()
         .route("/v1/embeddings", post(embeddings_create))
+        .route("/v1/models", get(models_list))
         .layer(TraceLayer::new_for_http())
 }
 use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};

crates/inference-engine/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "inference-engine"
 version.workspace = true
-edition = "2021"
+edition = "2024"
 
 [dependencies]
 candle-core = { git = "https://github.com/huggingface/candle.git" }
@@ -31,13 +31,21 @@ 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" }
-llama-runner = { path = "../llama-runner" }
+gemma-runner = { path = "../../integration/gemma-runner" }
+llama-runner = { path = "../../integration/llama-runner" }
+embeddings-engine = { path = "../embeddings-engine" }
+
+[target.'cfg(target_os = "linux")'.dependencies]
+candle-core = { git = "https://github.com/huggingface/candle.git", default-features = false }
+candle-nn = { git = "https://github.com/huggingface/candle.git", default-features = false }
+candle-transformers = { git = "https://github.com/huggingface/candle.git", default-features = false }
 
 [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"] }
+gemma-runner = { path = "../../integration/gemma-runner", features = ["metal"] }
+llama-runner = { path = "../../integration/llama-runner", features = ["metal"] }
 
 
 [dev-dependencies]
@@ -61,15 +69,13 @@ bindgen_cuda = { version = "0.1.1", optional = true }
 [features]
 bin = []
 
-
-
-[package.metadata.compose]
-image = "ghcr.io/geoffsee/inference-engine:latest"
-port = 8080
-
+[[bin]]
+name = "inference-engine"
+path = "src/main.rs"
 
 # generates kubernetes manifests
 [package.metadata.kube]
-image = "ghcr.io/geoffsee/inference-service:latest"
-replicas = 1
+image = "ghcr.io/geoffsee/predict-otron-9000:latest"
+cmd = ["./bin/inference-engine"]
 port = 8080
+replicas = 1

crates/inference-engine/Dockerfile

@@ -1,86 +0,0 @@
-# ---- Build stage ----
-FROM rust:1-slim-bullseye AS builder
-
-WORKDIR /usr/src/app
-
-# Install build dependencies including CUDA toolkit for GPU support
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        pkg-config \
-        libssl-dev \
-        build-essential \
-        wget \
-        gnupg2 \
-        curl \
-        && rm -rf /var/lib/apt/lists/*
-
-# Install CUDA toolkit (optional, for GPU support)
-# This is a minimal CUDA installation for building
-RUN wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.0-1_all.deb && \
-    dpkg -i cuda-keyring_1.0-1_all.deb && \
-    apt-get update && \
-    apt-get install -y --no-install-recommends \
-        cuda-minimal-build-11-8 \
-        libcublas-dev-11-8 \
-        libcurand-dev-11-8 \
-        && rm -rf /var/lib/apt/lists/* \
-        && rm cuda-keyring_1.0-1_all.deb
-
-# Set CUDA environment variables
-ENV CUDA_HOME=/usr/local/cuda
-ENV PATH=${CUDA_HOME}/bin:${PATH}
-ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
-
-# Copy the entire workspace to get access to all crates
-COPY . ./
-
-# Cache dependencies first - create dummy source files
-RUN rm -rf crates/inference-engine/src
-RUN mkdir -p crates/inference-engine/src && \
-    echo "fn main() {}" > crates/inference-engine/src/main.rs && \
-    echo "fn main() {}" > crates/inference-engine/src/cli_main.rs && \
-    echo "// lib" > crates/inference-engine/src/lib.rs && \
-    cargo build --release --bin cli --package inference-engine
-
-# Remove dummy source and copy real sources
-RUN rm -rf crates/inference-engine/src
-COPY . .
-
-# Build the actual CLI binary
-RUN cargo build --release --bin cli --package inference-engine
-
-# ---- Runtime stage ----
-FROM debian:bullseye-slim
-
-# Install runtime dependencies
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        libssl1.1 \
-        ca-certificates \
-        && rm -rf /var/lib/apt/lists/*
-
-# Install CUDA runtime libraries (optional, for GPU support at runtime)
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        wget \
-        gnupg2 \
-        && wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.0-1_all.deb \
-        && dpkg -i cuda-keyring_1.0-1_all.deb \
-        && apt-get update \
-        && apt-get install -y --no-install-recommends \
-            cuda-cudart-11-8 \
-            libcublas11 \
-            libcurand10 \
-        && rm -rf /var/lib/apt/lists/* \
-        && rm cuda-keyring_1.0-1_all.deb \
-        && apt-get purge -y wget gnupg2
-
-# Copy binary from builder
-COPY --from=builder /usr/src/app/target/release/cli /usr/local/bin/inference-cli
-
-# Run as non-root user for safety
-RUN useradd -m appuser
-USER appuser
-
-EXPOSE 8080
-CMD ["inference-cli"]

crates/inference-engine/src/lib.rs

@@ -8,7 +8,7 @@ pub mod server;
 // Re-export key components for easier access
 pub use inference::ModelInference;
 pub use model::{Model, Which};
-pub use server::{create_router, AppState};
+pub use server::{AppState, create_router};
 
 use std::env;
 use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};

crates/inference-engine/src/main.rs

@@ -0,0 +1,26 @@
+use inference_engine::{AppState, create_router, get_server_config, init_tracing};
+use tokio::net::TcpListener;
+use tracing::info;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    init_tracing();
+
+    let app_state = AppState::default();
+    let app = create_router(app_state);
+
+    let (server_host, server_port, server_address) = get_server_config();
+    let listener = TcpListener::bind(&server_address).await?;
+
+    info!(
+        "Inference Engine server starting on http://{}",
+        server_address
+    );
+    info!("Available endpoints:");
+    info!("  POST /v1/chat/completions - OpenAI-compatible chat completions");
+    info!("  GET  /v1/models         - List available models");
+
+    axum::serve(listener, app).await?;
+
+    Ok(())
+}

crates/inference-engine/src/model.rs

@@ -1,49 +1,9 @@
-// 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};
 
-#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
-pub enum Which {
-    #[value(name = "2b")]
-    Base2B,
-    #[value(name = "7b")]
-    Base7B,
-    #[value(name = "2b-it")]
-    Instruct2B,
-    #[value(name = "7b-it")]
-    Instruct7B,
-    #[value(name = "1.1-2b-it")]
-    InstructV1_1_2B,
-    #[value(name = "1.1-7b-it")]
-    InstructV1_1_7B,
-    #[value(name = "code-2b")]
-    CodeBase2B,
-    #[value(name = "code-7b")]
-    CodeBase7B,
-    #[value(name = "code-2b-it")]
-    CodeInstruct2B,
-    #[value(name = "code-7b-it")]
-    CodeInstruct7B,
-    #[value(name = "2-2b")]
-    BaseV2_2B,
-    #[value(name = "2-2b-it")]
-    InstructV2_2B,
-    #[value(name = "2-9b")]
-    BaseV2_9B,
-    #[value(name = "2-9b-it")]
-    InstructV2_9B,
-    #[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,
-}
-
+#[derive(Clone, Debug)]
 pub enum Model {
     V1(Model1),
     V2(Model2),
@@ -66,48 +26,131 @@ impl Model {
     }
 }
 
+#[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")]
+    Base7B,
+    #[value(name = "2b-it")]
+    Instruct2B,
+    #[value(name = "7b-it")]
+    Instruct7B,
+    #[value(name = "1.1-2b-it")]
+    InstructV1_1_2B,
+    #[value(name = "1.1-7b-it")]
+    InstructV1_1_7B,
+
+    // CodeGemma
+    #[value(name = "code-2b")]
+    CodeBase2B,
+    #[value(name = "code-7b")]
+    CodeBase7B,
+    #[value(name = "code-2b-it")]
+    CodeInstruct2B,
+    #[value(name = "code-7b-it")]
+    CodeInstruct7B,
+
+    // Gemma 2
+    #[value(name = "2-2b")]
+    BaseV2_2B,
+    #[value(name = "2-2b-it")]
+    InstructV2_2B,
+    #[value(name = "2-9b")]
+    BaseV2_9B,
+    #[value(name = "2-9b-it")]
+    InstructV2_9B,
+
+    // Gemma 3
+    #[value(name = "3-1b")]
+    BaseV3_1B,
+    #[value(name = "3-1b-it")]
+    InstructV3_1B,
+
+    // Llama 3.2 (use aliases instead of duplicate variants)
+    #[value(name = "llama-3.2-1b")]
+    Llama32_1B,
+    #[value(name = "llama-3.2-1b-it", alias = "llama-3.2-1b-instruct")]
+    Llama32_1BInstruct,
+    #[value(name = "llama-3.2-3b")]
+    Llama32_3B,
+    #[value(name = "llama-3.2-3b-it", alias = "llama-3.2-3b-instruct")]
+    Llama32_3BInstruct,
+}
+
 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(),
-            Self::InstructV1_1_7B => "google/gemma-1.1-7b-it".to_string(),
-            Self::Base2B => "google/gemma-2b".to_string(),
-            Self::Base7B => "google/gemma-7b".to_string(),
-            Self::Instruct2B => "google/gemma-2b-it".to_string(),
-            Self::Instruct7B => "google/gemma-7b-it".to_string(),
-            Self::CodeBase2B => "google/codegemma-2b".to_string(),
-            Self::CodeBase7B => "google/codegemma-7b".to_string(),
-            Self::CodeInstruct2B => "google/codegemma-2b-it".to_string(),
-            Self::CodeInstruct7B => "google/codegemma-7b-it".to_string(),
-            Self::BaseV2_2B => "google/gemma-2-2b".to_string(),
-            Self::InstructV2_2B => "google/gemma-2-2b-it".to_string(),
-            Self::BaseV2_9B => "google/gemma-2-9b".to_string(),
-            Self::InstructV2_9B => "google/gemma-2-9b-it".to_string(),
-            Self::BaseV3_1B => "google/gemma-3-1b-pt".to_string(),
-            Self::InstructV3_1B => "google/gemma-3-1b-it".to_string(),
-            Self::LlamaInstruct3_2_1B => "meta-llama/Llama-3.2-1B-Instruct".to_string(),
-            Self::LlamaInstruct3_2_3B => "meta-llama/Llama-3.2-3B-Instruct".to_string(),
+            // Gemma 1.x
+            Self::Base2B => m("google/gemma-2b", GemmaV1, false),
+            Self::Base7B => m("google/gemma-7b", GemmaV1, false),
+            Self::Instruct2B => m("google/gemma-2b-it", GemmaV1, true),
+            Self::Instruct7B => m("google/gemma-7b-it", GemmaV1, true),
+            Self::InstructV1_1_2B => m("google/gemma-1.1-2b-it", GemmaV1, true),
+            Self::InstructV1_1_7B => m("google/gemma-1.1-7b-it", GemmaV1, true),
+
+            // CodeGemma
+            Self::CodeBase2B => m("google/codegemma-2b", GemmaV1, false),
+            Self::CodeBase7B => m("google/codegemma-7b", GemmaV1, false),
+            Self::CodeInstruct2B => m("google/codegemma-2b-it", GemmaV1, true),
+            Self::CodeInstruct7B => m("google/codegemma-7b-it", GemmaV1, true),
+
+            // Gemma 2
+            Self::BaseV2_2B => m("google/gemma-2-2b", GemmaV2, false),
+            Self::InstructV2_2B => m("google/gemma-2-2b-it", GemmaV2, true),
+            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::Base2B
-            | Self::Base7B
-            | Self::CodeBase2B
-            | Self::CodeBase7B
-            | Self::BaseV2_2B
-            | Self::BaseV2_9B
-            | Self::BaseV3_1B => false,
-            _ => true,
-        }
+        self.meta().instruct
     }
 
     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/server.rs

@@ -1,26 +1,28 @@
 use axum::{
+    Json, Router,
     extract::State,
     http::StatusCode,
-    response::{sse::Event, sse::Sse, IntoResponse},
+    response::{IntoResponse, sse::Event, sse::Sse},
     routing::{get, post},
-    Json, Router,
 };
 use futures_util::stream::{self, Stream};
 use std::convert::Infallible;
+use std::str::FromStr;
 use std::sync::Arc;
-use tokio::sync::{mpsc, Mutex};
+use tokio::sync::{Mutex, mpsc};
 use tokio_stream::wrappers::UnboundedReceiverStream;
 use tower_http::cors::{Any, CorsLayer};
 use uuid::Uuid;
 
+use crate::Which;
 use crate::openai_types::{
     ChatCompletionChoice, ChatCompletionChunk, ChatCompletionChunkChoice, ChatCompletionRequest,
     ChatCompletionResponse, Delta, Message, MessageContent, Model, ModelListResponse, Usage,
 };
-use crate::Which;
 use either::Either;
-use gemma_runner::{run_gemma_api, GemmaInferenceConfig};
-use llama_runner::{run_llama_inference, LlamaInferenceConfig};
+use embeddings_engine::models_list;
+use gemma_runner::{GemmaInferenceConfig, WhichModel, run_gemma_api};
+use llama_runner::{LlamaInferenceConfig, run_llama_inference};
 use serde_json::Value;
 // -------------------------
 // Shared app state
@@ -34,7 +36,7 @@ pub enum ModelType {
 
 #[derive(Clone)]
 pub struct AppState {
-    pub model_type: ModelType,
+    pub model_type: Option<ModelType>,
     pub model_id: String,
     pub gemma_config: Option<GemmaInferenceConfig>,
     pub llama_config: Option<LlamaInferenceConfig>,
@@ -42,13 +44,19 @@ 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,
+            model: None,
             ..Default::default()
         };
+
         Self {
-            model_type: ModelType::Gemma,
-            model_id: "gemma-3-1b-it".to_string(),
+            model_type: None,
+            model_id: default_model_id,
             gemma_config: Some(gemma_config),
             llama_config: None,
         }
@@ -59,6 +67,33 @@ 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" => Some(Which::Llama32_1B),
+        "llama-3.2-1b-instruct" => Some(Which::Llama32_1BInstruct),
+        "llama-3.2-3b" => Some(Which::Llama32_3B),
+        "llama-3.2-3b-instruct" => Some(Which::Llama32_3BInstruct),
+        _ => None,
+    }
+}
+
 fn normalize_model_id(model_id: &str) -> String {
     model_id.to_lowercase().replace("_", "-")
 }
@@ -116,90 +151,115 @@ 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
-    let configured_model = state.model_id.clone();
-    let requested_model = request.model.clone();
-    if requested_model.to_lowercase() != "default" {
-        let normalized_requested = normalize_model_id(&requested_model);
-        let normalized_configured = normalize_model_id(&configured_model);
-        if normalized_requested != normalized_configured {
+    // Use the model specified in the request
+    let model_id = request.model.clone();
+    let which_model = model_id_to_which(&model_id);
+
+    // Validate that the requested model is supported
+    let which_model = match which_model {
+        Some(model) => model,
+        None => {
             return Err((
                 StatusCode::BAD_REQUEST,
                 Json(serde_json::json!({
                     "error": {
-                        "message": format!(
-                            "Requested model '{}' is not available. This server is running '{}' only.",
-                            requested_model, configured_model
-                        ),
-                        "type": "model_mismatch"
+                        "message": format!("Unsupported model: {}", model_id),
+                        "type": "model_not_supported"
                     }
                 })),
             ));
         }
-    }
-
-    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 {
-        ModelType::Gemma => build_gemma_prompt(&request.messages),
-        ModelType::Llama => {
-            // For Llama, just use the last user message for now
-            request
-                .messages
-                .last()
-                .and_then(|m| m.content.as_ref())
-                .and_then(|c| match c {
-                    MessageContent(Either::Left(text)) => Some(text.clone()),
-                    _ => None,
-                })
-                .unwrap_or_default()
-        }
+    let prompt = if which_model.is_llama_model() {
+        // For Llama, just use the last user message for now
+        request
+            .messages
+            .last()
+            .and_then(|m| m.content.as_ref())
+            .and_then(|c| match c {
+                MessageContent(Either::Left(text)) => Some(text.clone()),
+                _ => None,
+            })
+            .unwrap_or_default()
+    } else {
+        build_gemma_prompt(&request.messages)
     };
 
     // Get streaming receiver based on model type
-    let rx =
-        match state.model_type {
-            ModelType::Gemma => {
-                if let Some(mut config) = state.gemma_config {
-                    config.prompt = prompt.clone();
-                    config.max_tokens = max_tokens;
-                    run_gemma_api(config).map_err(|e| (
-                    StatusCode::INTERNAL_SERVER_ERROR,
+    let rx = if which_model.is_llama_model() {
+        // Create Llama configuration dynamically
+        let llama_model = match which_model {
+            Which::Llama32_1B => llama_runner::WhichModel::Llama32_1B,
+            Which::Llama32_1BInstruct => llama_runner::WhichModel::Llama32_1BInstruct,
+            Which::Llama32_3B => llama_runner::WhichModel::Llama32_3B,
+            Which::Llama32_3BInstruct => llama_runner::WhichModel::Llama32_3BInstruct,
+            _ => {
+                return Err((
+                    StatusCode::BAD_REQUEST,
                     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" }
-                        })),
-                    ));
-                }
-            }
-            ModelType::Llama => {
-                if let Some(mut config) = state.llama_config {
-                    config.prompt = prompt.clone();
-                    config.max_tokens = max_tokens;
-                    run_llama_inference(config).map_err(|e| (
-                    StatusCode::INTERNAL_SERVER_ERROR,
-                    Json(serde_json::json!({
-                        "error": { "message": format!("Error initializing Llama model: {}", e) }
-                    }))
-                ))?
-                } else {
-                    return Err((
-                        StatusCode::INTERNAL_SERVER_ERROR,
-                        Json(serde_json::json!({
-                            "error": { "message": "Llama configuration not available" }
-                        })),
-                    ));
-                }
+                        "error": { "message": format!("Model {} is not a Llama model", model_id) }
+                    })),
+                ));
             }
         };
+        let mut config = LlamaInferenceConfig::new(llama_model);
+        config.prompt = prompt.clone();
+        config.max_tokens = max_tokens;
+        run_llama_inference(config).map_err(|e| {
+            (
+                StatusCode::INTERNAL_SERVER_ERROR,
+                Json(serde_json::json!({
+                    "error": { "message": format!("Error initializing Llama model: {}", e) }
+                })),
+            )
+        })?
+    } else {
+        // Create Gemma configuration dynamically
+        let gemma_model = match which_model {
+            Which::Base2B => gemma_runner::WhichModel::Base2B,
+            Which::Base7B => gemma_runner::WhichModel::Base7B,
+            Which::Instruct2B => gemma_runner::WhichModel::Instruct2B,
+            Which::Instruct7B => gemma_runner::WhichModel::Instruct7B,
+            Which::InstructV1_1_2B => gemma_runner::WhichModel::InstructV1_1_2B,
+            Which::InstructV1_1_7B => gemma_runner::WhichModel::InstructV1_1_7B,
+            Which::CodeBase2B => gemma_runner::WhichModel::CodeBase2B,
+            Which::CodeBase7B => gemma_runner::WhichModel::CodeBase7B,
+            Which::CodeInstruct2B => gemma_runner::WhichModel::CodeInstruct2B,
+            Which::CodeInstruct7B => gemma_runner::WhichModel::CodeInstruct7B,
+            Which::BaseV2_2B => gemma_runner::WhichModel::BaseV2_2B,
+            Which::InstructV2_2B => gemma_runner::WhichModel::InstructV2_2B,
+            Which::BaseV2_9B => gemma_runner::WhichModel::BaseV2_9B,
+            Which::InstructV2_9B => gemma_runner::WhichModel::InstructV2_9B,
+            Which::BaseV3_1B => gemma_runner::WhichModel::BaseV3_1B,
+            Which::InstructV3_1B => gemma_runner::WhichModel::InstructV3_1B,
+            _ => {
+                return Err((
+                    StatusCode::BAD_REQUEST,
+                    Json(serde_json::json!({
+                        "error": { "message": format!("Model {} is not a Gemma model", model_id) }
+                    })),
+                ));
+            }
+        };
+
+        let mut config = GemmaInferenceConfig {
+            model: Some(gemma_model),
+            ..Default::default()
+        };
+        config.prompt = prompt.clone();
+        config.max_tokens = max_tokens;
+        run_gemma_api(config).map_err(|e| {
+            (
+                StatusCode::INTERNAL_SERVER_ERROR,
+                Json(serde_json::json!({
+                    "error": { "message": format!("Error initializing Gemma model: {}", e) }
+                })),
+            )
+        })?
+    };
 
     // Collect all tokens from the stream
     let mut completion = String::new();
@@ -258,27 +318,25 @@ 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
-    let configured_model = state.model_id.clone();
-    let requested_model = request.model.clone();
-    if requested_model.to_lowercase() != "default" {
-        let normalized_requested = normalize_model_id(&requested_model);
-        let normalized_configured = normalize_model_id(&configured_model);
-        if normalized_requested != normalized_configured {
+    // Use the model specified in the request
+    let model_id = request.model.clone();
+    let which_model = model_id_to_which(&model_id);
+
+    // Validate that the requested model is supported
+    let which_model = match which_model {
+        Some(model) => model,
+        None => {
             return Err((
                 StatusCode::BAD_REQUEST,
                 Json(serde_json::json!({
                     "error": {
-                        "message": format!(
-                            "Requested model '{}' is not available. This server is running '{}' only.",
-                            requested_model, configured_model
-                        ),
-                        "type": "model_mismatch"
+                        "message": format!("Unsupported model: {}", model_id),
+                        "type": "model_not_supported"
                     }
                 })),
             ));
         }
-    }
+    };
 
     // Generate a unique ID and metadata
     let response_id = format!("chatcmpl-{}", Uuid::new_v4().to_string().replace('-', ""));
@@ -286,24 +344,22 @@ 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 {
-        ModelType::Gemma => build_gemma_prompt(&request.messages),
-        ModelType::Llama => {
-            // For Llama, just use the last user message for now
-            request
-                .messages
-                .last()
-                .and_then(|m| m.content.as_ref())
-                .and_then(|c| match c {
-                    MessageContent(Either::Left(text)) => Some(text.clone()),
-                    _ => None,
-                })
-                .unwrap_or_default()
-        }
+    let prompt = if which_model.is_llama_model() {
+        // For Llama, just use the last user message for now
+        request
+            .messages
+            .last()
+            .and_then(|m| m.content.as_ref())
+            .and_then(|c| match c {
+                MessageContent(Either::Left(text)) => Some(text.clone()),
+                _ => None,
+            })
+            .unwrap_or_default()
+    } else {
+        build_gemma_prompt(&request.messages)
     };
     tracing::debug!("Formatted prompt: {}", prompt);
 
@@ -330,51 +386,78 @@ async fn handle_streaming_request(
     }
 
     // Get streaming receiver based on model type
-    let model_rx = match state.model_type {
-        ModelType::Gemma => {
-            if let Some(mut config) = state.gemma_config {
-                config.prompt = prompt.clone();
-                config.max_tokens = max_tokens;
-                match run_gemma_api(config) {
-                    Ok(rx) => rx,
-                    Err(e) => {
-                        return Err((
-                            StatusCode::INTERNAL_SERVER_ERROR,
-                            Json(serde_json::json!({
-                                "error": { "message": format!("Error initializing Gemma model: {}", e) }
-                            })),
-                        ));
-                    }
-                }
-            } else {
+    let model_rx = if which_model.is_llama_model() {
+        // Create Llama configuration dynamically
+        let llama_model = match which_model {
+            Which::Llama32_1B => llama_runner::WhichModel::Llama32_1B,
+            Which::Llama32_1BInstruct => llama_runner::WhichModel::Llama32_1BInstruct,
+            Which::Llama32_3B => llama_runner::WhichModel::Llama32_3B,
+            Which::Llama32_3BInstruct => llama_runner::WhichModel::Llama32_3BInstruct,
+            _ => {
                 return Err((
                     StatusCode::INTERNAL_SERVER_ERROR,
                     Json(serde_json::json!({
-                        "error": { "message": "Gemma configuration not available" }
+                        "error": { "message": format!("Model {} is not a Llama model", model_id) }
+                    })),
+                ));
+            }
+        };
+        let mut config = LlamaInferenceConfig::new(llama_model);
+        config.prompt = prompt.clone();
+        config.max_tokens = max_tokens;
+        match run_llama_inference(config) {
+            Ok(rx) => rx,
+            Err(e) => {
+                return Err((
+                    StatusCode::INTERNAL_SERVER_ERROR,
+                    Json(serde_json::json!({
+                        "error": { "message": format!("Error initializing Llama model: {}", e) }
                     })),
                 ));
             }
         }
-        ModelType::Llama => {
-            if let Some(mut config) = state.llama_config {
-                config.prompt = prompt.clone();
-                config.max_tokens = max_tokens;
-                match run_llama_inference(config) {
-                    Ok(rx) => rx,
-                    Err(e) => {
-                        return Err((
-                            StatusCode::INTERNAL_SERVER_ERROR,
-                            Json(serde_json::json!({
-                                "error": { "message": format!("Error initializing Llama model: {}", e) }
-                            })),
-                        ));
-                    }
-                }
-            } else {
+    } else {
+        // Create Gemma configuration dynamically
+        let gemma_model = match which_model {
+            Which::Base2B => gemma_runner::WhichModel::Base2B,
+            Which::Base7B => gemma_runner::WhichModel::Base7B,
+            Which::Instruct2B => gemma_runner::WhichModel::Instruct2B,
+            Which::Instruct7B => gemma_runner::WhichModel::Instruct7B,
+            Which::InstructV1_1_2B => gemma_runner::WhichModel::InstructV1_1_2B,
+            Which::InstructV1_1_7B => gemma_runner::WhichModel::InstructV1_1_7B,
+            Which::CodeBase2B => gemma_runner::WhichModel::CodeBase2B,
+            Which::CodeBase7B => gemma_runner::WhichModel::CodeBase7B,
+            Which::CodeInstruct2B => gemma_runner::WhichModel::CodeInstruct2B,
+            Which::CodeInstruct7B => gemma_runner::WhichModel::CodeInstruct7B,
+            Which::BaseV2_2B => gemma_runner::WhichModel::BaseV2_2B,
+            Which::InstructV2_2B => gemma_runner::WhichModel::InstructV2_2B,
+            Which::BaseV2_9B => gemma_runner::WhichModel::BaseV2_9B,
+            Which::InstructV2_9B => gemma_runner::WhichModel::InstructV2_9B,
+            Which::BaseV3_1B => gemma_runner::WhichModel::BaseV3_1B,
+            Which::InstructV3_1B => gemma_runner::WhichModel::InstructV3_1B,
+            _ => {
                 return Err((
                     StatusCode::INTERNAL_SERVER_ERROR,
                     Json(serde_json::json!({
-                        "error": { "message": "Llama configuration not available" }
+                        "error": { "message": format!("Model {} is not a Gemma model", model_id) }
+                    })),
+                ));
+            }
+        };
+
+        let mut config = GemmaInferenceConfig {
+            model: Some(gemma_model),
+            ..Default::default()
+        };
+        config.prompt = prompt.clone();
+        config.max_tokens = max_tokens;
+        match run_gemma_api(config) {
+            Ok(rx) => rx,
+            Err(e) => {
+                return Err((
+                    StatusCode::INTERNAL_SERVER_ERROR,
+                    Json(serde_json::json!({
+                        "error": { "message": format!("Error initializing Gemma model: {}", e) }
                     })),
                 ));
             }
@@ -500,173 +583,93 @@ 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![
-        // Gemma models
-        Model {
-            id: "gemma-2b".to_string(),
-            object: "model".to_string(),
-            created: 1686935002, // Using same timestamp as OpenAI example
-            owned_by: "google".to_string(),
-        },
-        Model {
-            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(),
-        },
+    let which_variants = vec![
+        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 mut 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: model_id.to_string(),
+                object: "model".to_string(),
+                created: 1686935002,
+                owned_by: owned_by.to_string(),
+            }
+        })
+        .collect();
+
+    // Get embeddings models and convert them to inference Model format
+    let embeddings_response = models_list().await;
+    let embeddings_models: Vec<Model> = embeddings_response
+        .0
+        .data
+        .into_iter()
+        .map(|embedding_model| Model {
+            id: embedding_model.id,
+            object: embedding_model.object,
+            created: 1686935002,
+            owned_by: format!(
+                "{} - {}",
+                embedding_model.owned_by, embedding_model.description
+            ),
+        })
+        .collect();
+
+    // Add embeddings models to the main models list
+    models.extend(embeddings_models);
+
     Json(ModelListResponse {
         object: "list".to_string(),
         data: models,

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

@@ -1,520 +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 async_openai_wasm::config::OpenAIConfig;
-#[cfg(feature = "hydrate")]
-use async_openai_wasm::types::{FinishReason, Role};
-#[cfg(feature = "hydrate")]
-use async_openai_wasm::{
-    types::{
-        ChatCompletionRequestAssistantMessageArgs, ChatCompletionRequestSystemMessageArgs,
-        ChatCompletionRequestUserMessageArgs, CreateChatCompletionRequestArgs,
-        Model as OpenAIModel,
-    },
-    Client,
-};
-#[cfg(feature = "hydrate")]
-use futures_util::StreamExt;
-#[cfg(feature = "hydrate")]
-use js_sys::Date;
-#[cfg(feature = "hydrate")]
-use leptos::task::spawn_local;
-#[cfg(feature = "hydrate")]
-use serde::{Deserialize, Serialize};
-#[cfg(feature = "hydrate")]
-use std::collections::VecDeque;
-#[cfg(feature = "hydrate")]
-use uuid::Uuid;
-#[cfg(feature = "hydrate")]
-use web_sys::{HtmlInputElement, KeyboardEvent, SubmitEvent};
-
-#[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 crate::app::*;
-    use axum::Router;
-    use leptos::prelude::*;
-    use leptos_axum::{generate_route_list, LeptosRoutes};
-
-    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

@@ -1,38 +0,0 @@
-#[cfg(feature = "ssr")]
-#[tokio::main]
-async fn main() {
-    use axum::Router;
-    use leptos::logging::log;
-    use leptos::prelude::*;
-    use leptos_app::app::*;
-    use leptos_axum::{generate_route_list, LeptosRoutes};
-
-    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/predict-otron-9000/Cargo.toml

@@ -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" }
@@ -28,24 +28,24 @@ 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 = true }
 
 mime_guess = "2.0.5"
-
-
-[package.metadata.compose]
-name = "predict-otron-9000"
-image = "ghcr.io/geoffsee/predict-otron-9000:latest"
-port = 8080
-
+log = "0.4.27"
 
 # generates kubernetes manifests
 [package.metadata.kube]
 image = "ghcr.io/geoffsee/predict-otron-9000:latest"
 replicas = 1
 port = 8080
+cmd = ["./bin/predict-otron-9000"]
 # 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>" }
+
+[features]
+default = ["ui"]
+ui = ["dep:chat-ui"]

crates/predict-otron-9000/Dockerfile

@@ -1,89 +0,0 @@
-# ---- Build stage ----
-FROM rust:1-slim-bullseye AS builder
-
-WORKDIR /usr/src/app
-
-# Install build dependencies including CUDA toolkit for GPU support (needed for inference-engine dependency)
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        pkg-config \
-        libssl-dev \
-        build-essential \
-        wget \
-        gnupg2 \
-        curl \
-        && rm -rf /var/lib/apt/lists/*
-
-# Install CUDA toolkit (required for inference-engine dependency)
-# This is a minimal CUDA installation for building
-RUN wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.0-1_all.deb && \
-    dpkg -i cuda-keyring_1.0-1_all.deb && \
-    apt-get update && \
-    apt-get install -y --no-install-recommends \
-        cuda-minimal-build-11-8 \
-        libcublas-dev-11-8 \
-        libcurand-dev-11-8 \
-        && rm -rf /var/lib/apt/lists/* \
-        && rm cuda-keyring_1.0-1_all.deb
-
-# Set CUDA environment variables
-ENV CUDA_HOME=/usr/local/cuda
-ENV PATH=${CUDA_HOME}/bin:${PATH}
-ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
-
-# Copy the entire workspace to get access to all crates (needed for local dependencies)
-COPY . ./
-
-# Cache dependencies first - create dummy source files for all crates
-RUN rm -rf crates/predict-otron-9000/src crates/inference-engine/src crates/embeddings-engine/src
-RUN mkdir -p crates/predict-otron-9000/src crates/inference-engine/src crates/embeddings-engine/src && \
-    echo "fn main() {}" > crates/predict-otron-9000/src/main.rs && \
-    echo "fn main() {}" > crates/inference-engine/src/main.rs && \
-    echo "fn main() {}" > crates/inference-engine/src/cli_main.rs && \
-    echo "// lib" > crates/inference-engine/src/lib.rs && \
-    echo "fn main() {}" > crates/embeddings-engine/src/main.rs && \
-    echo "// lib" > crates/embeddings-engine/src/lib.rs && \
-    cargo build --release --bin predict-otron-9000 --package predict-otron-9000
-
-# Remove dummy sources and copy real sources
-RUN rm -rf crates/predict-otron-9000/src crates/inference-engine/src crates/embeddings-engine/src
-COPY . .
-
-# Build the actual binary
-RUN cargo build --release --bin predict-otron-9000 --package predict-otron-9000
-
-# ---- Runtime stage ----
-FROM debian:bullseye-slim
-
-# Install runtime dependencies
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        libssl1.1 \
-        ca-certificates \
-        && rm -rf /var/lib/apt/lists/*
-
-# Install CUDA runtime libraries (required for inference-engine dependency)
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        wget \
-        gnupg2 \
-        && wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.0-1_all.deb \
-        && dpkg -i cuda-keyring_1.0-1_all.deb \
-        && apt-get update \
-        && apt-get install -y --no-install-recommends \
-            cuda-cudart-11-8 \
-            libcublas11 \
-            libcurand10 \
-        && rm -rf /var/lib/apt/lists/* \
-        && rm cuda-keyring_1.0-1_all.deb \
-        && apt-get purge -y wget gnupg2
-
-# Copy binary from builder
-COPY --from=builder /usr/src/app/target/release/predict-otron-9000 /usr/local/bin/
-
-# Run as non-root user for safety
-RUN useradd -m appuser
-USER appuser
-
-EXPOSE 8080
-CMD ["predict-otron-9000"]

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

@@ -39,29 +39,12 @@ impl Default for ServerMode {
     }
 }
 
-#[derive(Debug, Clone, Deserialize, Serialize)]
+#[derive(Debug, Clone, Deserialize, Serialize, Default)]
 pub struct Services {
     pub inference_url: Option<String>,
     pub embeddings_url: Option<String>,
 }
 
-impl Default for Services {
-    fn default() -> Self {
-        Self {
-            inference_url: None,
-            embeddings_url: None,
-        }
-    }
-}
-
-fn inference_service_url() -> String {
-    "http://inference-service:8080".to_string()
-}
-
-fn embeddings_service_url() -> String {
-    "http://embeddings-service:8080".to_string()
-}
-
 impl Default for ServerConfig {
     fn default() -> Self {
         Self {
@@ -118,8 +101,7 @@ impl ServerConfig {
                     "HighAvailability mode configured but services not well defined! \n## Config Used:\n {}",
                     config_string
                 );
-                let err = std::io::Error::new(
-                    std::io::ErrorKind::Other,
+                let err = std::io::Error::other(
                     "HighAvailability mode configured but services not well defined!",
                 );
                 return Err(err);

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

@@ -126,7 +126,7 @@ use crate::config::ServerConfig;
 /// - 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
+///   HTTP client configured for proxying requests
 #[derive(Clone)]
 pub struct ProxyClient {
     client: Client,

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

@@ -4,22 +4,60 @@ mod middleware;
 mod standalone_mode;
 
 use crate::standalone_mode::create_standalone_router;
-use axum::response::IntoResponse;
 use axum::routing::get;
-use axum::{Router, http::Uri, response::Html, serve};
+use axum::{Router, serve};
 use config::ServerConfig;
 use ha_mode::create_ha_router;
-use inference_engine::AppState;
 use middleware::{MetricsLayer, MetricsLoggerFuture, MetricsStore};
-use rust_embed::Embed;
 use std::env;
-use std::path::Component::ParentDir;
+
+#[cfg(feature = "ui")]
+use axum::http::StatusCode as AxumStatusCode;
+#[cfg(feature = "ui")]
+use axum::http::Uri;
+#[cfg(feature = "ui")]
+use axum::http::header;
+#[cfg(feature = "ui")]
+use axum::response::IntoResponse;
+#[cfg(feature = "ui")]
+use mime_guess::from_path;
+#[cfg(feature = "ui")]
+use rust_embed::Embed;
 use tokio::net::TcpListener;
-use tower_http::classify::ServerErrorsFailureClass::StatusCode;
 use tower_http::cors::{Any, CorsLayer};
 use tower_http::trace::TraceLayer;
 use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};
 
+#[cfg(feature = "ui")]
+#[derive(Embed)]
+#[folder = "../../target/site"]
+#[include = "*.js"]
+#[include = "*.wasm"]
+#[include = "*.css"]
+#[include = "*.ico"]
+struct Asset;
+
+#[cfg(feature = "ui")]
+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
@@ -77,20 +115,28 @@ async fn main() {
     // Create metrics layer
     let metrics_layer = MetricsLayer::new(metrics_store);
 
-    // Create the leptos router for the web frontend
-    let leptos_router = leptos_app::create_leptos_router();
-
     // Merge the service router with base routes and add middleware layers
-    let app = Router::new()
+    let mut app = Router::new()
         .route("/health", get(|| async { "ok" }))
-        .merge(service_router)
-        .merge(leptos_router) // Add leptos web frontend routes
+        .merge(service_router);
+
+    // Add UI routes if the UI feature is enabled
+    #[cfg(feature = "ui")]
+    {
+        let leptos_config = chat_ui::app::AppConfig::default();
+        let leptos_router = chat_ui::app::create_router(leptos_config.config.leptos_options);
+        app = app
+            .route("/pkg/{*path}", get(static_handler))
+            .merge(leptos_router);
+    }
+
+    let app = app
         .layer(metrics_layer) // Add metrics tracking
         .layer(cors)
         .layer(TraceLayer::new_for_http());
 
     // Server configuration
-    let server_host = env::var("SERVER_HOST").unwrap_or_else(|_| String::from(default_host));
+    let server_host = env::var("SERVER_HOST").unwrap_or_else(|_| default_host.to_string());
 
     let server_port = env::var("SERVER_PORT")
         .map(|v| v.parse::<u16>().unwrap_or(default_port))
@@ -105,12 +151,14 @@ async fn main() {
     );
     tracing::info!("Performance metrics tracking enabled - summary logs every 60 seconds");
     tracing::info!("Available endpoints:");
+    #[cfg(feature = "ui")]
     tracing::info!("  GET  / - Leptos chat web application");
     tracing::info!("  GET  /health - Health check");
+    tracing::info!("  POST /v1/models - List Models");
     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) {

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

@@ -2,11 +2,12 @@ use crate::config::ServerConfig;
 use axum::Router;
 use inference_engine::AppState;
 
-pub fn create_standalone_router(server_config: ServerConfig) -> Router {
+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 with correct model configuration
+    // 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

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
@@ -61,34 +61,33 @@ graph TD
             A[predict-otron-9000<br/>Edition: 2024<br/>Port: 8080]
         end
         
-        subgraph "AI Services"
+        subgraph "AI Services (crates/)"
             B[inference-engine<br/>Edition: 2021<br/>Port: 8080<br/>Multi-model orchestrator]
-            J[gemma-runner<br/>Edition: 2021<br/>Gemma via Candle]
-            K[llama-runner<br/>Edition: 2021<br/>Llama via Candle]
             C[embeddings-engine<br/>Edition: 2024<br/>Port: 8080<br/>FastEmbed]
         end
         
-        subgraph "Frontend"
-            D[leptos-app<br/>Edition: 2021<br/>Port: 3000/8788<br/>WASM/SSR]
+        subgraph "Frontend (crates/)"
+            D[chat-ui<br/>Edition: 2021<br/>Port: 8788<br/>WASM UI]
         end
         
-        subgraph "Tooling"
+        
+        subgraph "Integration Tools (integration/)"
             L[helm-chart-tool<br/>Edition: 2024<br/>K8s deployment]
+            E[cli<br/>Edition: 2024<br/>TypeScript/Bun CLI]
+            M[gemma-runner<br/>Edition: 2021<br/>Gemma via Candle]
+            N[llama-runner<br/>Edition: 2021<br/>Llama via Candle]
+            O[utils<br/>Edition: 2021<br/>Shared utilities]
         end
     end
     
-    subgraph "External Tooling"
-        E[scripts/cli.ts<br/>TypeScript/Bun<br/>OpenAI SDK]
-    end
-    
     subgraph "Dependencies"
         A --> B
         A --> C
         A --> D
-        B --> J
-        B --> K
-        J -.-> F[Candle 0.9.1]
-        K -.-> F
+        B --> M
+        B --> N
+        M -.-> F[Candle 0.9.1]
+        N -.-> F
         C -.-> G[FastEmbed 4.x]
         D -.-> H[Leptos 0.8.0]
         E -.-> I[OpenAI SDK 5.16+]
@@ -96,12 +95,13 @@ graph TD
     
     style A fill:#e1f5fe
     style B fill:#f3e5f5
-    style J fill:#f3e5f5
-    style K fill:#f3e5f5
     style C fill:#e8f5e8
     style D fill:#fff3e0
     style E fill:#fce4ec
     style L fill:#fff9c4
+    style M fill:#f3e5f5
+    style N fill:#f3e5f5
+    style O fill:#fff9c4
 ```
 
 ## Deployment Configurations
@@ -193,7 +193,7 @@ 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"

integration/cli/README.md

@@ -3,7 +3,7 @@
 A Rust/Typescript Hybrid
 
 ```console
-./cli [options] [prompt]
+bun run cli.ts [options] [prompt]
 
 Simple CLI tool for testing the local OpenAI-compatible API server.
 
@@ -14,10 +14,11 @@ Options:
   --help              Show this help message
 
 Examples:
-  ./cli "What is the capital of France?"
-  ./cli --model gemma-3-1b-it --prompt "Hello, world!"
-  ./cli --prompt "Who was the 16th president of the United States?"
-  ./cli --list-models
+  cd integration/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
 ```

integration/cli/build.rs

@@ -24,8 +24,7 @@ fn run_build() -> io::Result<()> {
     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()))?;
+    let bun_tgt = BunTarget::from_cargo_env().map_err(|e| io::Error::other(e.to_string()))?;
 
     // Optional: warn if using a Bun target that’s marked unsupported in your chart
     if matches!(bun_tgt, BunTarget::WindowsArm64) {
@@ -54,13 +53,12 @@ fn run_build() -> io::Result<()> {
 
     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}"),
-        ));
+        return Err(io::Error::other(format!(
+            "bun install failed with status {code}"
+        )));
     }
 
-    let target = env::var("TARGET").unwrap();
+    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")
@@ -87,7 +85,7 @@ fn run_build() -> io::Result<()> {
     } 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"));
+        return Err(io::Error::other("bun build failed"));
     }
 
     // Ensure the output is executable (after it exists)

integration/cli/package/bun.lock

@@ -0,0 +1,17 @@
+{
+  "lockfileVersion": 1,
+  "workspaces": {
+    "": {
+      "name": "cli",
+      "dependencies": {
+        "install": "^0.13.0",
+        "openai": "^5.16.0",
+      },
+    },
+  },
+  "packages": {
+    "install": ["install@0.13.0", "", {}, "sha512-zDml/jzr2PKU9I8J/xyZBQn8rPCAY//UOYNmR01XwNwyfhEWObo2SWfSl1+0tm1u6PhxLwDnfsT/6jB7OUxqFA=="],
+
+    "openai": ["openai@5.19.1", "", { "peerDependencies": { "ws": "^8.18.0", "zod": "^3.23.8" }, "optionalPeers": ["ws", "zod"], "bin": { "openai": "bin/cli" } }, "sha512-zSqnUF7oR9ksmpusKkpUgkNrj8Sl57U+OyzO8jzc7LUjTMg4DRfR3uCm+EIMA6iw06sRPNp4t7ojp3sCpEUZRQ=="],
+  }
+}

integration/cli/src/main.rs

@@ -25,7 +25,7 @@ fn main() -> io::Result<()> {
     // Run it
     let status = Command::new(&tmp).arg("--version").status()?;
     if !status.success() {
-        return Err(io::Error::new(io::ErrorKind::Other, "client-cli failed"));
+        return Err(io::Error::other("client-cli failed"));
     }
 
     Ok(())

integration/gemma-runner/Cargo.toml

@@ -10,15 +10,15 @@ edition = "2021"
 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" }
 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"] }

integration/gemma-runner/src/gemma_api.rs

@@ -1,27 +1,24 @@
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
 use anyhow::{Error as E, Result};
 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_examples::token_output_stream::TokenOutputStream;
+use candle_core::{DType, Device, Tensor};
 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::fmt;
+use std::str::FromStr;
 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)]
+#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
 pub enum WhichModel {
     #[value(name = "gemma-2b")]
     Base2B,
@@ -57,6 +54,56 @@ pub enum WhichModel {
     InstructV3_1B,
 }
 
+impl FromStr for WhichModel {
+    type Err = String;
+
+    fn from_str(s: &str) -> Result<Self, Self::Err> {
+        match s {
+            "gemma-2b" => Ok(Self::Base2B),
+            "gemma-7b" => Ok(Self::Base7B),
+            "gemma-2b-it" => Ok(Self::Instruct2B),
+            "gemma-7b-it" => Ok(Self::Instruct7B),
+            "gemma-1.1-2b-it" => Ok(Self::InstructV1_1_2B),
+            "gemma-1.1-7b-it" => Ok(Self::InstructV1_1_7B),
+            "codegemma-2b" => Ok(Self::CodeBase2B),
+            "codegemma-7b" => Ok(Self::CodeBase7B),
+            "codegemma-2b-it" => Ok(Self::CodeInstruct2B),
+            "codegemma-7b-it" => Ok(Self::CodeInstruct7B),
+            "gemma-2-2b" => Ok(Self::BaseV2_2B),
+            "gemma-2-2b-it" => Ok(Self::InstructV2_2B),
+            "gemma-2-9b" => Ok(Self::BaseV2_9B),
+            "gemma-2-9b-it" => Ok(Self::InstructV2_9B),
+            "gemma-3-1b" => Ok(Self::BaseV3_1B),
+            "gemma-3-1b-it" => Ok(Self::InstructV3_1B),
+            _ => Err(format!("Unknown model: {}", s)),
+        }
+    }
+}
+
+impl fmt::Display for WhichModel {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        let name = match self {
+            Self::Base2B => "gemma-2b",
+            Self::Base7B => "gemma-7b",
+            Self::Instruct2B => "gemma-2b-it",
+            Self::Instruct7B => "gemma-7b-it",
+            Self::InstructV1_1_2B => "gemma-1.1-2b-it",
+            Self::InstructV1_1_7B => "gemma-1.1-7b-it",
+            Self::CodeBase2B => "codegemma-2b",
+            Self::CodeBase7B => "codegemma-7b",
+            Self::CodeInstruct2B => "codegemma-2b-it",
+            Self::CodeInstruct7B => "codegemma-7b-it",
+            Self::BaseV2_2B => "gemma-2-2b",
+            Self::InstructV2_2B => "gemma-2-2b-it",
+            Self::BaseV2_9B => "gemma-2-9b",
+            Self::InstructV2_9B => "gemma-2-9b-it",
+            Self::BaseV3_1B => "gemma-3-1b",
+            Self::InstructV3_1B => "gemma-3-1b-it",
+        };
+        write!(f, "{}", name)
+    }
+}
+
 enum Model {
     V1(Model1),
     V2(Model2),
@@ -85,9 +132,9 @@ 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 +145,7 @@ 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>,
@@ -144,8 +191,6 @@ impl TextGeneration {
         // Make sure stdout isn't holding anything (if caller also prints).
         std::io::stdout().flush()?;
 
-        let mut generated_tokens = 0usize;
-
         let eos_token = match self.tokenizer.get_token("<eos>") {
             Some(token) => token,
             None => anyhow::bail!("cannot find the <eos> token"),
@@ -182,7 +227,6 @@ impl TextGeneration {
 
             let next_token = self.logits_processor.sample(&logits)?;
             tokens.push(next_token);
-            generated_tokens += 1;
 
             if next_token == eos_token || next_token == eot_token {
                 break;
@@ -209,7 +253,7 @@ impl TextGeneration {
 pub struct GemmaInferenceConfig {
     pub tracing: bool,
     pub prompt: String,
-    pub model: WhichModel,
+    pub model: Option<WhichModel>,
     pub cpu: bool,
     pub dtype: Option<String>,
     pub model_id: Option<String>,
@@ -228,7 +272,7 @@ impl Default for GemmaInferenceConfig {
         Self {
             tracing: false,
             prompt: "Hello".to_string(),
-            model: WhichModel::InstructV2_2B,
+            model: Some(WhichModel::InstructV2_2B),
             cpu: false,
             dtype: None,
             model_id: None,
@@ -262,10 +306,10 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
 
     println!(
         "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        utils::with_avx(),
-        utils::with_neon(),
-        utils::with_simd128(),
-        utils::with_f16c()
+        candle_core::utils::with_avx(),
+        candle_core::utils::with_neon(),
+        candle_core::utils::with_simd128(),
+        candle_core::utils::with_f16c()
     );
 
     let device = device(cfg.cpu)?;
@@ -285,28 +329,30 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
         }
     };
     println!("Using dtype: {:?}", dtype);
+    println!("Raw model string: {:?}", cfg.model_id);
 
     let start = std::time::Instant::now();
     let api = Api::new()?;
 
     let model_id = cfg.model_id.unwrap_or_else(|| {
         match cfg.model {
-            WhichModel::Base2B => "google/gemma-2b",
-            WhichModel::Base7B => "google/gemma-7b",
-            WhichModel::Instruct2B => "google/gemma-2b-it",
-            WhichModel::Instruct7B => "google/gemma-7b-it",
-            WhichModel::InstructV1_1_2B => "google/gemma-1.1-2b-it",
-            WhichModel::InstructV1_1_7B => "google/gemma-1.1-7b-it",
-            WhichModel::CodeBase2B => "google/codegemma-2b",
-            WhichModel::CodeBase7B => "google/codegemma-7b",
-            WhichModel::CodeInstruct2B => "google/codegemma-2b-it",
-            WhichModel::CodeInstruct7B => "google/codegemma-7b-it",
-            WhichModel::BaseV2_2B => "google/gemma-2-2b",
-            WhichModel::InstructV2_2B => "google/gemma-2-2b-it",
-            WhichModel::BaseV2_9B => "google/gemma-2-9b",
-            WhichModel::InstructV2_9B => "google/gemma-2-9b-it",
-            WhichModel::BaseV3_1B => "google/gemma-3-1b-pt",
-            WhichModel::InstructV3_1B => "google/gemma-3-1b-it",
+            Some(WhichModel::Base2B) => "google/gemma-2b",
+            Some(WhichModel::Base7B) => "google/gemma-7b",
+            Some(WhichModel::Instruct2B) => "google/gemma-2b-it",
+            Some(WhichModel::Instruct7B) => "google/gemma-7b-it",
+            Some(WhichModel::InstructV1_1_2B) => "google/gemma-1.1-2b-it",
+            Some(WhichModel::InstructV1_1_7B) => "google/gemma-1.1-7b-it",
+            Some(WhichModel::CodeBase2B) => "google/codegemma-2b",
+            Some(WhichModel::CodeBase7B) => "google/codegemma-7b",
+            Some(WhichModel::CodeInstruct2B) => "google/codegemma-2b-it",
+            Some(WhichModel::CodeInstruct7B) => "google/codegemma-7b-it",
+            Some(WhichModel::BaseV2_2B) => "google/gemma-2-2b",
+            Some(WhichModel::InstructV2_2B) => "google/gemma-2-2b-it",
+            Some(WhichModel::BaseV2_9B) => "google/gemma-2-9b",
+            Some(WhichModel::InstructV2_9B) => "google/gemma-2-9b-it",
+            Some(WhichModel::BaseV3_1B) => "google/gemma-3-1b-pt",
+            Some(WhichModel::InstructV3_1B) => "google/gemma-3-1b-it",
+            None => "google/gemma-2-2b-it", // default fallback
         }
         .to_string()
     });
@@ -317,8 +363,10 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
     let tokenizer_filename = repo.get("tokenizer.json")?;
     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")?,
+        Some(WhichModel::BaseV3_1B) | Some(WhichModel::InstructV3_1B) => {
+            vec![repo.get("model.safetensors")?]
+        }
+        _ => hub_load_safetensors(&repo, "model.safetensors.index.json")?,
     };
     println!("Retrieved files in {:?}", start.elapsed());
 
@@ -328,29 +376,31 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
     let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
 
     let model: Model = match cfg.model {
-        WhichModel::Base2B
-        | WhichModel::Base7B
-        | WhichModel::Instruct2B
-        | WhichModel::Instruct7B
-        | WhichModel::InstructV1_1_2B
-        | WhichModel::InstructV1_1_7B
-        | WhichModel::CodeBase2B
-        | WhichModel::CodeBase7B
-        | WhichModel::CodeInstruct2B
-        | WhichModel::CodeInstruct7B => {
+        Some(WhichModel::Base2B)
+        | Some(WhichModel::Base7B)
+        | Some(WhichModel::Instruct2B)
+        | Some(WhichModel::Instruct7B)
+        | Some(WhichModel::InstructV1_1_2B)
+        | Some(WhichModel::InstructV1_1_7B)
+        | Some(WhichModel::CodeBase2B)
+        | Some(WhichModel::CodeBase7B)
+        | Some(WhichModel::CodeInstruct2B)
+        | Some(WhichModel::CodeInstruct7B) => {
             let config: Config1 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
             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 => {
+        Some(WhichModel::BaseV2_2B)
+        | Some(WhichModel::InstructV2_2B)
+        | Some(WhichModel::BaseV2_9B)
+        | Some(WhichModel::InstructV2_9B)
+        | None => {
+            // default to V2 model
             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)
         }
-        WhichModel::BaseV3_1B | WhichModel::InstructV3_1B => {
+        Some(WhichModel::BaseV3_1B) | Some(WhichModel::InstructV3_1B) => {
             let config: Config3 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
             let model = Model3::new(cfg.use_flash_attn, &config, vb)?;
             Model::V3(model)
@@ -370,7 +420,7 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
     );
 
     let prompt = match cfg.model {
-        WhichModel::InstructV3_1B => {
+        Some(WhichModel::InstructV3_1B) => {
             format!(
                 "<start_of_turn>user\n{}<end_of_turn>\n<start_of_turn>model\n",
                 cfg.prompt

integration/gemma-runner/src/gemma_cli.rs

@@ -67,7 +67,7 @@ pub fn run_cli() -> anyhow::Result<()> {
     let cfg = GemmaInferenceConfig {
         tracing: args.tracing,
         prompt: args.prompt,
-        model: args.model,
+        model: Some(args.model),
         cpu: args.cpu,
         dtype: args.dtype,
         model_id: args.model_id,

integration/gemma-runner/src/main.rs

@@ -6,10 +6,8 @@ mod gemma_api;
 mod gemma_cli;
 
 use anyhow::Error;
-use clap::{Parser, ValueEnum};
 
 use crate::gemma_cli::run_cli;
-use std::io::Write;
 
 /// just a placeholder, not used for anything
 fn main() -> std::result::Result<(), Error> {

integration/helm-chart-tool/README.md

@@ -64,14 +64,9 @@ version = "0.1.0"
 
 # Required: Kubernetes metadata
 [package.metadata.kube]
-image = "ghcr.io/myorg/my-service:latest"
+image = "ghcr.io/geoffsee/predict-otron-9000:latest"
 replicas = 1
 port = 8080
-
-# Optional: Docker Compose metadata (currently not used but parsed)
-[package.metadata.compose]
-image = "ghcr.io/myorg/my-service:latest"
-port = 8080
 ```
 
 ### Required Fields
@@ -137,7 +132,7 @@ 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)

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

@@ -1,9 +1,8 @@
 use anyhow::{Context, Result};
 use clap::{Arg, Command};
-use serde::{Deserialize, Serialize};
-use std::collections::HashMap;
+use serde::Deserialize;
 use std::fs;
-use std::path::{Path, PathBuf};
+use std::path::Path;
 use walkdir::WalkDir;
 
 #[derive(Debug, Deserialize)]
@@ -20,7 +19,6 @@ struct Package {
 #[derive(Debug, Deserialize)]
 struct Metadata {
     kube: Option<KubeMetadata>,
-    compose: Option<ComposeMetadata>,
 }
 
 #[derive(Debug, Deserialize)]
@@ -30,12 +28,6 @@ struct KubeMetadata {
     port: u16,
 }
 
-#[derive(Debug, Deserialize)]
-struct ComposeMetadata {
-    image: Option<String>,
-    port: Option<u16>,
-}
-
 #[derive(Debug, Clone)]
 struct ServiceInfo {
     name: String,
@@ -105,7 +97,9 @@ fn discover_services(workspace_path: &str) -> Result<Vec<ServiceInfo>> {
         .into_iter()
         .filter_map(|e| e.ok())
     {
-        if entry.file_name() == "Cargo.toml" && entry.path() != workspace_root.join("Cargo.toml") {
+        if entry.file_name() == "Cargo.toml"
+            && entry.path() != workspace_root.join("../../../Cargo.toml")
+        {
             if let Ok(service_info) = parse_cargo_toml(entry.path()) {
                 services.push(service_info);
             }
@@ -375,7 +369,7 @@ spec:
     Ok(())
 }
 
-fn generate_ingress_template(templates_dir: &Path, services: &[ServiceInfo]) -> Result<()> {
+fn generate_ingress_template(templates_dir: &Path, _services: &[ServiceInfo]) -> Result<()> {
     let ingress_template = r#"{{- if .Values.ingress.enabled -}}
 apiVersion: networking.k8s.io/v1
 kind: Ingress

integration/llama-runner/Cargo.toml

@@ -5,8 +5,8 @@ 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-nn = { 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"

integration/llama-runner/src/lib.rs

@@ -1,6 +1,5 @@
 pub mod llama_api;
 
-use clap::ValueEnum;
 pub use llama_api::{run_llama_inference, LlamaInferenceConfig, WhichModel};
 
 // Re-export constants and types that might be needed

integration/llama-runner/src/llama_api.rs

@@ -57,6 +57,27 @@ pub struct LlamaInferenceConfig {
     pub repeat_last_n: usize,
 }
 
+impl LlamaInferenceConfig {
+    pub fn new(model: WhichModel) -> Self {
+        Self {
+            prompt: String::new(),
+            model,
+            cpu: false,
+            temperature: 1.0,
+            top_p: None,
+            top_k: None,
+            seed: 42,
+            max_tokens: 512,
+            no_kv_cache: false,
+            dtype: None,
+            model_id: None,
+            revision: None,
+            use_flash_attn: true,
+            repeat_penalty: 1.1,
+            repeat_last_n: 64,
+        }
+    }
+}
 impl Default for LlamaInferenceConfig {
     fn default() -> Self {
         Self {
@@ -81,8 +102,8 @@ impl Default for LlamaInferenceConfig {
             max_tokens: 512,
 
             // Performance flags
-            no_kv_cache: false,   // keep cache ON for speed
-            use_flash_attn: true, // great speed boost if supported
+            no_kv_cache: false,    // keep cache ON for speed
+            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()),

integration/llama-runner/src/main.rs

@@ -6,9 +6,6 @@ mod llama_api;
 mod llama_cli;
 
 use anyhow::Result;
-use clap::{Parser, ValueEnum};
-
-use std::io::Write;
 
 use crate::llama_cli::run_cli;
 

integration/utils/Cargo.toml

@@ -0,0 +1,96 @@
+[package]
+name = "utils"
+edition = "2021"
+
+[lib]
+path = "src/lib.rs"
+
+[dependencies]
+accelerate-src = {version = "0.3.2", optional = true }
+candle-flash-attn = {version = "0.9.1", optional = true }
+candle-onnx = {version = "0.9.1", optional = true }
+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"]
+
+# Platform-specific candle dependencies
+[target.'cfg(target_os = "linux")'.dependencies]
+candle-nn = {version = "0.9.1", default-features = false }
+candle-transformers = {version = "0.9.1", default-features = false }
+candle-core = {version = "0.9.1", default-features = false }
+
+[target.'cfg(not(target_os = "linux"))'.dependencies]
+candle-nn = {version = "0.9.1" }
+candle-transformers = {version = "0.9.1" }
+candle-core = {version = "0.9.1" }

integration/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)
+}

integration/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)
+}

integration/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",
+];

integration/utils/src/lib.rs

@@ -0,0 +1,156 @@
+extern crate candle_core;
+extern crate candle_transformers;
+extern crate tokenizers;
+
+pub mod audio;
+pub mod bs1770;
+pub mod coco_classes;
+pub mod imagenet;
+pub mod token_output_stream;
+pub mod wav;
+use candle_core::{
+    utils::{cuda_is_available, metal_is_available},
+    Device, Tensor,
+};
+
+pub fn device(cpu: bool) -> Result<Device, anyhow::Error> {
+    if cpu {
+        Ok(Device::Cpu)
+    } else if cuda_is_available() {
+        Ok(Device::new_cuda(0)?)
+    } else if metal_is_available() {
+        Ok(Device::new_metal(0)?)
+    } else {
+        #[cfg(all(target_os = "macos", target_arch = "aarch64"))]
+        {
+            println!(
+                "Running on CPU, to run on GPU(metal), build this example with `--features metal`"
+            );
+        }
+        #[cfg(not(all(target_os = "macos", target_arch = "aarch64")))]
+        {
+            println!("Running on CPU, to run on GPU, build this example with `--features cuda`");
+        }
+        Ok(Device::Cpu)
+    }
+}
+
+pub fn load_image<P: AsRef<std::path::Path>>(
+    p: P,
+    resize_longest: Option<usize>,
+) -> Result<(Tensor, usize, usize), anyhow::Error> {
+    let img = image::ImageReader::open(p)?
+        .decode()
+        .map_err(candle_core::Error::wrap)?;
+    let (initial_h, initial_w) = (img.height() as usize, img.width() as usize);
+    let img = match resize_longest {
+        None => img,
+        Some(resize_longest) => {
+            let (height, width) = (img.height(), img.width());
+            let resize_longest = resize_longest as u32;
+            let (height, width) = if height < width {
+                let h = (resize_longest * height) / width;
+                (h, resize_longest)
+            } else {
+                let w = (resize_longest * width) / height;
+                (resize_longest, w)
+            };
+            img.resize_exact(width, height, image::imageops::FilterType::CatmullRom)
+        }
+    };
+    let (height, width) = (img.height() as usize, img.width() as usize);
+    let img = img.to_rgb8();
+    let data = img.into_raw();
+    let data = Tensor::from_vec(data, (height, width, 3), &Device::Cpu)?.permute((2, 0, 1))?;
+    Ok((data, initial_h, initial_w))
+}
+
+pub fn load_image_and_resize<P: AsRef<std::path::Path>>(
+    p: P,
+    width: usize,
+    height: usize,
+) -> candle_core::Result<Tensor> {
+    let img = image::ImageReader::open(p)?
+        .decode()
+        .map_err(candle_core::Error::wrap)?
+        .resize_to_fill(
+            width as u32,
+            height as u32,
+            image::imageops::FilterType::Triangle,
+        );
+    let img = img.to_rgb8();
+    let data = img.into_raw();
+    Tensor::from_vec(data, (width, height, 3), &Device::Cpu)?.permute((2, 0, 1))
+}
+
+/// 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<(), anyhow::Error> {
+    let p = p.as_ref();
+    let (channel, height, width) = img.dims3()?;
+    if channel != 3 {
+        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 => anyhow::bail!("error saving image {p:?}"),
+        };
+    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>, 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 => anyhow::bail!("no weight map in {json_file:?}"),
+        Some(serde_json::Value::Object(map)) => 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() {
+        if let Some(file) = value.as_str() {
+            safetensors_files.insert(file.to_string());
+        }
+    }
+    let safetensors_files = safetensors_files
+        .iter()
+        .map(|v| repo.get(v).map_err(std::io::Error::other))
+        .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>, 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 => anyhow::bail!("no weight map in {json_file:?}"),
+        Some(serde_json::Value::Object(map)) => 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() {
+        if let Some(file) = value.as_str() {
+            safetensors_files.insert(file);
+        }
+    }
+    let safetensors_files: Vec<_> = safetensors_files
+        .into_iter()
+        .map(|v| path.join(v))
+        .collect();
+    Ok(safetensors_files)
+}

integration/utils/src/main.rs

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

integration/utils/src/token_output_stream.rs

@@ -0,0 +1,85 @@
+use candle_core::Result;
+use tokenizers::Tokenizer;
+
+pub struct TokenOutputStream {
+    tokenizer: tokenizers::Tokenizer,
+    tokens: Vec<u32>,
+    prev_index: usize,
+    current_index: usize,
+}
+
+impl TokenOutputStream {
+    pub fn new(tokenizer: tokenizers::Tokenizer) -> Self {
+        Self {
+            tokenizer,
+            tokens: Vec::new(),
+            prev_index: 0,
+            current_index: 0,
+        }
+    }
+
+    pub fn into_inner(self) -> tokenizers::Tokenizer {
+        self.tokenizer
+    }
+
+    fn decode(&self, tokens: &[u32]) -> Result<String> {
+        match self.tokenizer.decode(tokens, true) {
+            Ok(str) => Ok(str),
+            Err(err) => candle_core::bail!("cannot decode: {err}"),
+        }
+    }
+
+    // https://github.com/huggingface/text-generation-inference/blob/5ba53d44a18983a4de32d122f4cb46f4a17d9ef6/server/text_generation_server/models/model.py#L68
+    pub fn next_token(&mut self, token: u32) -> Result<Option<String>> {
+        let prev_text = if self.tokens.is_empty() {
+            String::new()
+        } else {
+            let tokens = &self.tokens[self.prev_index..self.current_index];
+            self.decode(tokens)?
+        };
+        self.tokens.push(token);
+        let text = self.decode(&self.tokens[self.prev_index..])?;
+        if text.len() > prev_text.len() && text.chars().last().unwrap().is_alphanumeric() {
+            let text = text.split_at(prev_text.len());
+            self.prev_index = self.current_index;
+            self.current_index = self.tokens.len();
+            Ok(Some(text.1.to_string()))
+        } else {
+            Ok(None)
+        }
+    }
+
+    pub fn decode_rest(&self) -> Result<Option<String>> {
+        let prev_text = if self.tokens.is_empty() {
+            String::new()
+        } else {
+            let tokens = &self.tokens[self.prev_index..self.current_index];
+            self.decode(tokens)?
+        };
+        let text = self.decode(&self.tokens[self.prev_index..])?;
+        if text.len() > prev_text.len() {
+            let text = text.split_at(prev_text.len());
+            Ok(Some(text.1.to_string()))
+        } else {
+            Ok(None)
+        }
+    }
+
+    pub fn decode_all(&self) -> Result<String> {
+        self.decode(&self.tokens)
+    }
+
+    pub fn get_token(&self, token_s: &str) -> Option<u32> {
+        self.tokenizer.get_vocab(true).get(token_s).copied()
+    }
+
+    pub fn tokenizer(&self) -> &tokenizers::Tokenizer {
+        &self.tokenizer
+    }
+
+    pub fn clear(&mut self) {
+        self.tokens.clear();
+        self.prev_index = 0;
+        self.current_index = 0;
+    }
+}

integration/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(())
+}

package.json

@@ -1,8 +1,8 @@
 {
   "name": "predict-otron-9000",
-  "workspaces": ["crates/cli/package"],
+  "workspaces": ["integration/cli/package"],
   "scripts": {
     "# WORKSPACE ALIASES": "#",
-    "cli": "bun --filter crates/cli/package"
+    "cli": "bun --filter integration/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_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