bump version in Cargo.toml

.github/dependabot.yml

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

.github/workflows/ci.yml

@@ -0,0 +1,47 @@
+name: CI
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  build:
+    name: build-and-test
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            target/
+          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Setup Rust
+        run: rustup update stable && rustup default stable
+
+      - name: Install clippy and rustfmt
+        run: rustup component add clippy rustfmt
+
+      - name: Cargo fmt (check)
+        run: cargo fmt --all -- --check
+
+      - name: Clippy
+        shell: bash
+        run: cargo clippy --all-targets
+
+      - name: Tests
+        shell: bash
+        run: cargo test --all
+
+      - name: Build Docs
+        shell: bash
+        run: | 
+          cargo doc -p predict-otron-9000 --no-deps 

.github/workflows/release.yml

@@ -0,0 +1,232 @@
+name: Release
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+env:
+  CARGO_TERM_COLOR: always
+
+jobs:
+  test:
+    name: Test before release
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: crates/predict-otron-9000
+    strategy:
+      fail-fast: false
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            target/
+          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Setup Rust
+        run: rustup update stable && rustup default stable
+
+      - name: Install clippy and rustfmt
+        run: rustup component add clippy rustfmt
+
+      - name: Cargo fmt (check)
+        run: cargo fmt --all -- --check
+
+      - name: Clippy
+        shell: bash
+        run: cargo clippy --all-targets
+
+      - name: Tests
+        shell: bash
+        run: cargo test --all
+
+#  publish:
+#    name: Publish to crates.io
+#    runs-on: ubuntu-latest
+#    permissions:
+#      id-token: write # Required for OIDC token exchange https://crates.io/docs/trusted-publishing
+#    needs: test
+#    defaults:
+#      run:
+#        working-directory: crates/predict-otron-9000
+#    steps:
+#      - name: Checkout
+#        uses: actions/checkout@v4
+#
+#      - uses: actions/cache@v4
+#        with:
+#          path: |
+#            ~/.cargo/bin/
+#            ~/.cargo/registry/index/
+#            ~/.cargo/registry/cache/
+#            ~/.cargo/git/db/
+#            target/
+#          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+#
+#      - name: Setup Rust
+#        run: rustup update stable && rustup default stable
+#
+#      - name: Verify tag matches version
+#        run: |
+#          TAG_VERSION=${GITHUB_REF#refs/tags/v}
+#          CARGO_VERSION=$(cargo metadata --no-deps --format-version 1 | jq -r '.packages[0].version')
+#          if [ "$TAG_VERSION" != "$CARGO_VERSION" ]; then
+#            echo "Tag version ($TAG_VERSION) does not match Cargo.toml version ($CARGO_VERSION)"
+#            exit 1
+#          fi
+#
+#      # See Trusted publishing: https://crates.io/docs/trusted-publishing
+#      - uses: rust-lang/crates-io-auth-action@v1
+#        id: auth
+#
+#      - run: cargo publish
+#        env:
+#          CARGO_REGISTRY_TOKEN: ${{ steps.auth.outputs.token }}
+
+  build-binaries:
+    name: Build binaries
+    runs-on: ${{ matrix.os }}
+    needs: test
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - target: x86_64-unknown-linux-gnu
+            os: ubuntu-latest
+            name: predict-otron-9000-x86_64-unknown-linux-gnu
+          - target: x86_64-apple-darwin
+            os: macos-latest
+            name: predict-otron-9000-x86_64-apple-darwin
+          - target: aarch64-apple-darwin
+            os: macos-latest
+            name: predict-otron-9000-aarch64-apple-darwin
+          - target: x86_64-pc-windows-msvc
+            os: windows-latest
+            name: predict-otron-9000-x86_64-pc-windows-msvc.exe
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            target/
+          key: ${{ runner.os }}-${{ matrix.target }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Setup Rust
+        run: rustup update stable && rustup default stable
+
+      - name: Add target
+        run: rustup target add ${{ matrix.target }}
+
+      - name: Build binary
+        run: cargo build --release --target ${{ matrix.target }} -p predict-otron-9000
+        env:
+          CARGO_TERM_COLOR: always
+
+      - name: Package binary (Unix)
+        if: matrix.os != 'windows-latest'
+        run: |
+          cd target/${{ matrix.target }}/release
+          tar czf ../../../${{ matrix.name }}.tar.gz predict-otron-9000
+          cd ../../../
+
+      - name: Package binary (Windows)
+        if: matrix.os == 'windows-latest'
+        run: |
+          cd target/${{ matrix.target }}/release
+          7z a ../../../${{ matrix.name }}.zip predict-otron-9000.exe
+          cd ../../../
+
+      - name: Upload binary artifacts (Unix)
+        if: matrix.os != 'windows-latest'
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.name }}
+          path: ${{ matrix.name }}.tar.gz
+
+      - name: Upload binary artifacts (Windows)
+        if: matrix.os == 'windows-latest'
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.name }}
+          path: ${{ matrix.name }}.zip
+
+  release:
+    name: Create GitHub Release
+    runs-on: ubuntu-latest
+    needs: [test, build-binaries]
+    permissions:
+      contents: write
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Extract tag name
+        id: tag
+        run: echo "tag=${GITHUB_REF#refs/tags/}" >> $GITHUB_OUTPUT
+
+      - name: Generate changelog
+        id: changelog
+        run: |
+          # Get the previous tag
+          PREV_TAG=$(git describe --tags --abbrev=0 HEAD^ 2>/dev/null || echo "")
+          
+          # Generate changelog
+          if [ -n "$PREV_TAG" ]; then
+            echo "## What's Changed" > changelog.md
+            echo "" >> changelog.md
+            git log --pretty=format:"* %s (%h)" ${PREV_TAG}..HEAD >> changelog.md
+            echo "" >> changelog.md
+            echo "" >> changelog.md
+            echo "**Full Changelog**: https://github.com/${{ github.repository }}/compare/${PREV_TAG}...${{ steps.tag.outputs.tag }}" >> changelog.md
+          else
+            echo "## What's Changed" > changelog.md
+            echo "" >> changelog.md
+            echo "Initial release of predict-otron-9000" >> changelog.md
+            echo "" >> changelog.md
+            echo "OpenAI Compatible Inference Server" >> changelog.md
+          fi
+          
+          # Set the changelog as output (handle multiline)
+          echo "changelog<<EOF" >> $GITHUB_OUTPUT
+          cat changelog.md >> $GITHUB_OUTPUT
+          echo "EOF" >> $GITHUB_OUTPUT
+
+      - name: Download all artifacts
+        uses: actions/download-artifact@v4
+        with:
+          path: artifacts
+
+      - name: Create Release
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          if [[ "${{ steps.tag.outputs.tag }}" == *"-"* ]]; then
+            PRERELEASE_FLAG="--prerelease"
+          else
+            PRERELEASE_FLAG=""
+          fi
+          
+          gh release create "${{ steps.tag.outputs.tag }}" \
+            --title "Release ${{ steps.tag.outputs.tag }}" \
+            --notes-file changelog.md \
+            $PRERELEASE_FLAG \
+            artifacts/predict-otron-9000-x86_64-unknown-linux-gnu/predict-otron-9000-x86_64-unknown-linux-gnu.tar.gz \
+            artifacts/predict-otron-9000-x86_64-apple-darwin/predict-otron-9000-x86_64-apple-darwin.tar.gz \
+            artifacts/predict-otron-9000-aarch64-apple-darwin/predict-otron-9000-aarch64-apple-darwin.tar.gz \
+            artifacts/predict-otron-9000-x86_64-pc-windows-msvc.exe/predict-otron-9000-x86_64-pc-windows-msvc.exe.zip

.gitignore

@@ -76,3 +76,5 @@ venv/
 *.bak
 *.backup
 *~
+/scripts/cli
+!/scripts/cli.ts

Cargo.toml

@@ -11,6 +11,34 @@ members = [
 default-members = ["crates/predict-otron-9000"]
 resolver = "2"
 
+# Compiler optimization profiles for the workspace
+[profile.release]
+opt-level = 3
+debug = false
+strip = true
+lto = "thin"
+codegen-units = 1
+panic = "abort"
+
+[profile.dev]
+opt-level = 0
+debug = true
+strip = false
+overflow-checks = true
+
+# Profile for fast development builds with some optimization
+[profile.dev-opt]
+inherits = "dev"
+opt-level = 1
+debug = true
+overflow-checks = true
+
+# Profile for benchmarking and profiling
+[profile.bench]
+opt-level = 3
+debug = true
+lto = "thin"
+
 [[workspace.metadata.leptos]]
 # project name
 bin-package = "leptos-app"

README.md

@@ -1,11 +1,20 @@
-# predict-otron-9000
-
-A comprehensive multi-service AI platform built around local LLM inference, embeddings, and web interfaces.
-
+<h1 align="center">
+    predict-otron-9000
+</h1>
 <p align="center">
 Powerful local AI inference with OpenAI-compatible APIs
 </p>
 
+<br/>
+
+> This project is an educational aide for bootstrapping my understanding of language model inferencing at the lowest levels I can, serving as a "rubber-duck" solution for Kubernetes based performance-oriented inference capabilities on air-gapped networks.
+
+> By isolating application behaviors in components at the crate level, development reduces to a short feedback loop for validation and integration, ultimately smoothing the learning curve for scalable AI systems.
+Stability is currently best effort. Many models require unique configuration. When stability is achieved, this project will be promoted to the seemueller-io GitHub organization under a different name.
+
+A comprehensive multi-service AI platform built around local LLM inference, embeddings, and web interfaces.
+
+
 ## Project Overview
 
 The predict-otron-9000 is a flexible AI platform that provides:
@@ -24,7 +33,7 @@ The system supports both CPU and GPU acceleration (CUDA/Metal), with intelligent
 - **Text Embeddings**: Generate high-quality text embeddings using FastEmbed
 - **Text Generation**: Chat completions with OpenAI-compatible API using Gemma and Llama models (various sizes including instruction-tuned variants)
 - **Performance Optimized**: Efficient caching and platform-specific optimizations for improved throughput
-- **Web Chat Interface**: Leptos-based WebAssembly (WASM) chat interface for browser-based interaction
+- **Web Chat Interface**: Leptos chat interface
 - **Flexible Deployment**: Run as monolithic service or microservices architecture
 
 ## Architecture Overview
@@ -50,7 +59,7 @@ crates/
 
 - **Main Server** (port 8080): Orchestrates inference and embeddings services
 - **Embeddings Service** (port 8080): Standalone FastEmbed service with OpenAI API compatibility  
-- **Web Frontend** (port 8788): Leptos WASM chat interface served by Trunk
+- **Web Frontend** (port 8788): cargo leptos SSR app
 - **CLI Client**: TypeScript/Bun client for testing and automation
 
 ### Deployment Modes
@@ -278,7 +287,7 @@ cargo test --workspace
 
 **End-to-end test script:**
 ```bash
-./test.sh
+./smoke_test.sh
 ```
 
 This script:
@@ -469,7 +478,7 @@ cd crates/leptos-app && ./run.sh &
 
 **Integration test:**
 ```bash
-./test.sh
+./smoke_test.sh
 ```
 
 **Cleanup:**
@@ -497,4 +506,4 @@ For networked tests and full functionality, ensure Hugging Face authentication i
 4. Ensure all tests pass: `cargo test`
 5. Submit a pull request
 
-_Warning: Do NOT use this in production unless you are cool like that._
+_Warning: Do NOT use this in production unless you are cool like that._

crates/embeddings-engine/src/lib.rs

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

crates/embeddings-engine/src/main.rs

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

crates/gemma-runner/Cargo.toml

@@ -3,16 +3,14 @@ name = "gemma-runner"
 version = "0.1.0"
 edition = "2021"
 
+
+
+
 [dependencies]
 candle-core = { git = "https://github.com/huggingface/candle.git" }
 candle-nn = { git = "https://github.com/huggingface/candle.git" }
 candle-transformers = { git = "https://github.com/huggingface/candle.git" }
 candle-examples = { git = "https://github.com/huggingface/candle.git" }
-
-[target.'cfg(target_os = "macos")'.dependencies]
-candle-core = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
-candle-nn = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
-candle-transformers = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
 hf-hub = "0.4"
 tokenizers = "0.21"
 anyhow = "1.0"
@@ -22,6 +20,12 @@ tracing = "0.1"
 tracing-chrome = "0.7"
 tracing-subscriber = "0.3"
 
+[target.'cfg(target_os = "macos")'.dependencies]
+candle-core = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
+candle-nn = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
+candle-transformers = { git = "https://github.com/huggingface/candle.git", features = ["metal"] }
+
+
 [features]
 default = []
 cuda = ["candle-core/cuda", "candle-nn/cuda", "candle-transformers/cuda"]

crates/gemma-runner/src/gemma_api.rs

@@ -4,10 +4,10 @@ extern crate accelerate_src;
 extern crate intel_mkl_src;
 
 use anyhow::{Error as E, Result};
-use clap::ValueEnum;
 use candle_transformers::models::gemma::{Config as Config1, Model as Model1};
 use candle_transformers::models::gemma2::{Config as Config2, Model as Model2};
 use candle_transformers::models::gemma3::{Config as Config3, Model as Model3};
+use clap::ValueEnum;
 
 // Removed gemma_cli import as it's not needed for the API
 use candle_core::{utils, DType, Device, Tensor};
@@ -119,7 +119,12 @@ impl TextGeneration {
 
     /// Stream-only generation: sends freshly generated token strings over `tx`.
     /// (Does not send the prompt tokens; only newly generated model tokens.)
-    fn run_stream(&mut self, prompt: &str, sample_len: usize, tx: Sender<Result<String>>) -> Result<()> {
+    fn run_stream(
+        &mut self,
+        prompt: &str,
+        sample_len: usize,
+        tx: Sender<Result<String>>,
+    ) -> Result<()> {
         self.tokenizer.clear();
 
         // Encode prompt (context only; do not emit prompt tokens to the stream).
@@ -303,7 +308,7 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
             WhichModel::BaseV3_1B => "google/gemma-3-1b-pt",
             WhichModel::InstructV3_1B => "google/gemma-3-1b-it",
         }
-            .to_string()
+        .to_string()
     });
 
     println!("Loading model: {}", &model_id);
@@ -337,7 +342,10 @@ pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String
             let model = Model1::new(cfg.use_flash_attn, &config, vb)?;
             Model::V1(model)
         }
-        WhichModel::BaseV2_2B | WhichModel::InstructV2_2B | WhichModel::BaseV2_9B | WhichModel::InstructV2_9B => {
+        WhichModel::BaseV2_2B
+        | WhichModel::InstructV2_2B
+        | WhichModel::BaseV2_9B
+        | WhichModel::InstructV2_9B => {
             let config: Config2 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
             let model = Model2::new(cfg.use_flash_attn, &config, vb)?;
             Model::V2(model)

crates/gemma-runner/src/gemma_cli.rs

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

crates/gemma-runner/src/main.rs

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

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

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

crates/inference-engine/Cargo.toml

@@ -3,39 +3,13 @@ name = "inference-engine"
 version = "0.1.0"
 edition = "2021"
 
-
-[[bin]]
-name="gemma_inference"
-path = "src/gemma_inference.rs"
-required-features = ["bin"]
-
-[[bin]]
-name="llama_inference"
-path = "src/llama_inference.rs"
-required-features = ["bin"]
-
-
 [dependencies]
-accelerate-src = { version = "0.3.2", optional = true }
-candle-datasets = { version = "=0.9.1", optional = true }
-candle-nn = { version = "=0.9.1" }
-candle-transformers = { version = "=0.9.1" }
+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-flash-attn = { version = "=0.9.1", optional = true }
 candle-onnx = { version = "=0.9.1", optional = true }
 
-csv = "1.3.0"
-cudarc = { version = "0.16.3", features = ["std", "cublas", "cublaslt", "curand", "driver", "nvrtc", "f16", "cuda-version-from-build-system", "dynamic-linking"], default-features=false, optional = true }
-half = { version = "2.5.0", features = ["num-traits", "use-intrinsics", "rand_distr"], optional = true }
-hf-hub = { version = "0.4.1", features = ["tokio"] }
-image = { version = "0.25.2", default-features = false, features = ["jpeg", "png"] }
-intel-mkl-src = { version = "0.8.1", features = ["mkl-static-lp64-iomp"], optional = true }
-num-traits = { version = "0.2.15" }
-palette = { version = "0.7.6", optional = true }
-enterpolation = { version = "0.2.1", optional = true}
-pyo3 = { version = "0.22.0", features = ["auto-initialize", "abi3-py311"], optional = true }
-rayon = "1.7.0"
-rubato = { version = "0.15.0", optional = true }
-safetensors = "0.4.1"
 serde = { version = "1.0.171", features = ["derive"] }
 serde_json = "1.0.99"
 symphonia = { version = "0.5.3", features = ["all"], optional = true }
@@ -60,19 +34,11 @@ futures-util = "0.3.31"
 gemma-runner = { path = "../gemma-runner" }
 llama-runner = { path = "../llama-runner" }
 
-# --- Add this section for conditional compilation ---
 [target.'cfg(target_os = "macos")'.dependencies]
-# Use CPU backend for macOS to avoid Metal rotary-emb implementation issues
-candle-core = { version = "=0.9.1", features = ["metal"], optional = false }
+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"] }
 
-[target.'cfg(not(target_os = "macos"))'.dependencies]
-# For Linux or other non-macOS systems, you likely want the CPU backend or CUDA
-# If you're building on Linux with a CUDA-enabled GPU:
-candle-core = { version = "=0.9.1", features = ["cuda"], default-features = false } # Or just "cuda" if not using default features
-
-# If you're building on Linux with only CPU:
-# candle-core = { version = "=0.9.1", default-features = false } # CPU is often the default, but good to be explicit
-# --- End of conditional compilation section ---
 
 [dev-dependencies]
 anyhow = { version = "1", features = ["backtrace"] }

crates/inference-engine/src/inference.rs

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

crates/inference-engine/src/lib.rs

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

crates/inference-engine/src/model.rs

@@ -1,8 +1,8 @@
 // use candle_core::Tensor;
+use candle_transformers::models::csm::{LlamaConfig, LlamaModel};
 use candle_transformers::models::gemma::{Config as Config1, Model as Model1};
 use candle_transformers::models::gemma2::{Config as Config2, Model as Model2};
 use candle_transformers::models::gemma3::{Config as Config3, Model as Model3};
-use candle_transformers::models::csm::{LlamaConfig, LlamaModel};
 
 #[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
 pub enum Which {
@@ -52,7 +52,11 @@ pub enum Model {
 }
 
 impl Model {
-    pub fn forward(&mut self, input_ids: &candle_core::Tensor, pos: usize) -> candle_core::Result<candle_core::Tensor> {
+    pub fn forward(
+        &mut self,
+        input_ids: &candle_core::Tensor,
+        pos: usize,
+    ) -> candle_core::Result<candle_core::Tensor> {
         match self {
             Self::V1(m) => m.forward(input_ids, pos),
             Self::V2(m) => m.forward(input_ids, pos),
@@ -88,7 +92,13 @@ impl Which {
 
     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,
+            Self::Base2B
+            | Self::Base7B
+            | Self::CodeBase2B
+            | Self::CodeBase7B
+            | Self::BaseV2_2B
+            | Self::BaseV2_9B
+            | Self::BaseV3_1B => false,
             _ => true,
         }
     }
@@ -100,4 +110,4 @@ impl Which {
     pub fn is_llama_model(&self) -> bool {
         matches!(self, Self::LlamaInstruct3_2_1B | Self::LlamaInstruct3_2_3B)
     }
-}
+}

crates/inference-engine/src/openai_types.rs

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

crates/inference-engine/src/server.rs

@@ -6,19 +6,22 @@ use axum::{
     Json, Router,
 };
 use futures_util::stream::{self, Stream};
-use tokio_stream::wrappers::UnboundedReceiverStream;
 use std::convert::Infallible;
 use std::sync::Arc;
-use tokio::sync::{Mutex, mpsc};
+use tokio::sync::{mpsc, Mutex};
+use tokio_stream::wrappers::UnboundedReceiverStream;
 use tower_http::cors::{Any, CorsLayer};
 use uuid::Uuid;
 
-use crate::openai_types::{ChatCompletionChoice, ChatCompletionChunk, ChatCompletionChunkChoice, ChatCompletionRequest, ChatCompletionResponse, Delta, Message, MessageContent, Model, ModelListResponse, Usage};
+use crate::openai_types::{
+    ChatCompletionChoice, ChatCompletionChunk, ChatCompletionChunkChoice, ChatCompletionRequest,
+    ChatCompletionResponse, Delta, Message, MessageContent, Model, ModelListResponse, Usage,
+};
 use crate::Which;
 use either::Either;
-use serde_json::Value;
 use gemma_runner::{run_gemma_api, GemmaInferenceConfig};
 use llama_runner::{run_llama_inference, LlamaInferenceConfig};
+use serde_json::Value;
 // -------------------------
 // Shared app state
 // -------------------------
@@ -62,12 +65,15 @@ fn normalize_model_id(model_id: &str) -> String {
 
 fn build_gemma_prompt(messages: &[Message]) -> String {
     let mut prompt = String::new();
-    
+
     for message in messages {
         match message.role.as_str() {
             "system" => {
                 if let Some(MessageContent(Either::Left(content))) = &message.content {
-                    prompt.push_str(&format!("<start_of_turn>system\n{}<end_of_turn>\n", content));
+                    prompt.push_str(&format!(
+                        "<start_of_turn>system\n{}<end_of_turn>\n",
+                        content
+                    ));
                 }
             }
             "user" => {
@@ -83,7 +89,7 @@ fn build_gemma_prompt(messages: &[Message]) -> String {
             _ => {}
         }
     }
-    
+
     prompt.push_str("<start_of_turn>model\n");
     prompt
 }
@@ -97,9 +103,13 @@ pub async fn chat_completions(
     Json(request): Json<ChatCompletionRequest>,
 ) -> Result<impl IntoResponse, (StatusCode, String)> {
     if !request.stream.unwrap_or(false) {
-        return Ok(chat_completions_non_streaming_proxy(state, request).await.into_response());
+        return Ok(chat_completions_non_streaming_proxy(state, request)
+            .await
+            .into_response());
     }
-    Ok(chat_completions_stream(state, request).await.into_response())
+    Ok(chat_completions_stream(state, request)
+        .await
+        .into_response())
 }
 
 pub async fn chat_completions_non_streaming_proxy(
@@ -136,7 +146,9 @@ pub async fn chat_completions_non_streaming_proxy(
         ModelType::Gemma => build_gemma_prompt(&request.messages),
         ModelType::Llama => {
             // For Llama, just use the last user message for now
-            request.messages.last()
+            request
+                .messages
+                .last()
                 .and_then(|m| m.content.as_ref())
                 .and_then(|c| match c {
                     MessageContent(Either::Left(text)) => Some(text.clone()),
@@ -147,46 +159,47 @@ pub async fn chat_completions_non_streaming_proxy(
     };
 
     // 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| (
+    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,
                     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" }
-                    }))
-                ));
+                } 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| (
+            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" }
-                    }))
-                ));
+                } else {
+                    return Err((
+                        StatusCode::INTERNAL_SERVER_ERROR,
+                        Json(serde_json::json!({
+                            "error": { "message": "Llama configuration not available" }
+                        })),
+                    ));
+                }
             }
-        }
-    };
+        };
 
     // Collect all tokens from the stream
     let mut completion = String::new();
@@ -281,7 +294,9 @@ async fn handle_streaming_request(
         ModelType::Gemma => build_gemma_prompt(&request.messages),
         ModelType::Llama => {
             // For Llama, just use the last user message for now
-            request.messages.last()
+            request
+                .messages
+                .last()
                 .and_then(|m| m.content.as_ref())
                 .and_then(|c| match c {
                     MessageContent(Either::Left(text)) => Some(text.clone()),
@@ -303,7 +318,10 @@ async fn handle_streaming_request(
         model: model_id.clone(),
         choices: vec![ChatCompletionChunkChoice {
             index: 0,
-            delta: Delta { role: Some("assistant".to_string()), content: None },
+            delta: Delta {
+                role: Some("assistant".to_string()),
+                content: None,
+            },
             finish_reason: None,
         }],
     };
@@ -324,7 +342,7 @@ async fn handle_streaming_request(
                             StatusCode::INTERNAL_SERVER_ERROR,
                             Json(serde_json::json!({
                                 "error": { "message": format!("Error initializing Gemma model: {}", e) }
-                            }))
+                            })),
                         ));
                     }
                 }
@@ -333,7 +351,7 @@ async fn handle_streaming_request(
                     StatusCode::INTERNAL_SERVER_ERROR,
                     Json(serde_json::json!({
                         "error": { "message": "Gemma configuration not available" }
-                    }))
+                    })),
                 ));
             }
         }
@@ -348,7 +366,7 @@ async fn handle_streaming_request(
                             StatusCode::INTERNAL_SERVER_ERROR,
                             Json(serde_json::json!({
                                 "error": { "message": format!("Error initializing Llama model: {}", e) }
-                            }))
+                            })),
                         ));
                     }
                 }
@@ -357,7 +375,7 @@ async fn handle_streaming_request(
                     StatusCode::INTERNAL_SERVER_ERROR,
                     Json(serde_json::json!({
                         "error": { "message": "Llama configuration not available" }
-                    }))
+                    })),
                 ));
             }
         }
@@ -386,16 +404,20 @@ async fn handle_streaming_request(
                     if recent_tokens.len() > REPETITION_WINDOW {
                         recent_tokens.remove(0);
                     }
-                    
+
                     // Check for repetitive patterns
                     if recent_tokens.len() >= 4 {
                         let last_token = &recent_tokens[recent_tokens.len() - 1];
                         let second_last = &recent_tokens[recent_tokens.len() - 2];
-                        
+
                         if last_token == second_last {
                             repetition_count += 1;
-                            tracing::warn!("Detected repetition pattern: '{}' (count: {})", last_token, repetition_count);
-                            
+                            tracing::warn!(
+                                "Detected repetition pattern: '{}' (count: {})",
+                                last_token,
+                                repetition_count
+                            );
+
                             if repetition_count >= MAX_REPETITION_COUNT {
                                 tracing::info!("Stopping generation due to excessive repetition");
                                 break;
@@ -412,11 +434,14 @@ async fn handle_streaming_request(
                         model: model_id_clone.clone(),
                         choices: vec![ChatCompletionChunkChoice {
                             index: 0,
-                            delta: Delta { role: None, content: Some(token) },
+                            delta: Delta {
+                                role: None,
+                                content: Some(token),
+                            },
                             finish_reason: None,
                         }],
                     };
-                    
+
                     if let Ok(json) = serde_json::to_string(&chunk) {
                         let _ = tx.send(Ok(Event::default().data(json)));
                     }
@@ -436,7 +461,10 @@ async fn handle_streaming_request(
             model: model_id_clone.clone(),
             choices: vec![ChatCompletionChunkChoice {
                 index: 0,
-                delta: Delta { role: None, content: None },
+                delta: Delta {
+                    role: None,
+                    content: None,
+                },
                 finish_reason: Some("stop".to_string()),
             }],
         };
@@ -451,8 +479,6 @@ async fn handle_streaming_request(
     Ok(Sse::new(stream))
 }
 
-
-
 // -------------------------
 // Router
 // -------------------------
@@ -647,7 +673,6 @@ pub async fn list_models() -> Json<ModelListResponse> {
     })
 }
 
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -681,10 +706,7 @@ mod tests {
 
         let prompt = build_gemma_prompt(&messages);
 
-        let expected = "<start_of_turn>user\nSystem message\n\nKnock knock.<end_of_turn>\n\
-                       <start_of_turn>model\nWho's there?<end_of_turn>\n\
-                       <start_of_turn>user\nGemma.<end_of_turn>\n\
-                       <start_of_turn>model\n";
+        let expected = "<start_of_turn>system\nSystem message<end_of_turn>\n<start_of_turn>user\nKnock knock.<end_of_turn>\n<start_of_turn>model\nWho's there?<end_of_turn>\n<start_of_turn>user\nGemma.<end_of_turn>\n<start_of_turn>model\n";
 
         assert_eq!(prompt, expected);
     }
@@ -698,15 +720,13 @@ mod tests {
 
     #[test]
     fn test_missing_content() {
-        let messages = vec![
-            Message {
-                role: "user".to_string(),
-                content: None,
-                name: None,
-            }
-        ];
+        let messages = vec![Message {
+            role: "user".to_string(),
+            content: None,
+            name: None,
+        }];
 
         let prompt = build_gemma_prompt(&messages);
-        assert_eq!(prompt, "<start_of_turn>user\n<end_of_turn>\n<start_of_turn>model\n");
+        assert_eq!(prompt, "<start_of_turn>model\n");
     }
 }

crates/inference-engine/src/token_output_stream.rs

@@ -1,87 +0,0 @@
-use candle_core::Result;
-
-/// This is a wrapper around a tokenizer to ensure that tokens can be returned to the user in a
-/// streaming way rather than having to wait for the full decoding.
-pub struct TokenOutputStream {
-    tokenizer: tokenizers::Tokenizer,
-    tokens: Vec<u32>,
-    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() {
-            // Modified to include all tokens, not just alphanumeric ones
-            let text = text.split_at(prev_text.len());
-            self.prev_index = self.current_index;
-            self.current_index = self.tokens.len();
-            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;
-    }
-}

crates/inference-engine/src/utilities_lib.rs

@@ -1,167 +0,0 @@
-use candle_core::utils::{cuda_is_available, metal_is_available};
-use candle_core::{Device, Result, Tensor};
-
-pub fn device(cpu: bool) -> Result<Device> {
-    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)> {
-    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,
-) -> 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<()> {
-    let p = p.as_ref();
-    let (channel, height, width) = img.dims3()?;
-    if channel != 3 {
-        candle_core::bail!("save_image expects an input of shape (3, height, width)")
-    }
-    let img = img.permute((1, 2, 0))?.flatten_all()?;
-    let pixels = img.to_vec1::<u8>()?;
-    let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
-        match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {
-            Some(image) => image,
-            None => candle_core::bail!("error saving image {p:?}"),
-        };
-    image.save(p).map_err(candle_core::Error::wrap)?;
-    Ok(())
-}
-
-pub fn save_image_resize<P: AsRef<std::path::Path>>(
-    img: &Tensor,
-    p: P,
-    h: usize,
-    w: usize,
-) -> Result<()> {
-    let p = p.as_ref();
-    let (channel, height, width) = img.dims3()?;
-    if channel != 3 {
-        candle_core::bail!("save_image expects an input of shape (3, height, width)")
-    }
-    let img = img.permute((1, 2, 0))?.flatten_all()?;
-    let pixels = img.to_vec1::<u8>()?;
-    let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
-        match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {
-            Some(image) => image,
-            None => candle_core::bail!("error saving image {p:?}"),
-        };
-    let image = image::DynamicImage::from(image);
-    let image = image.resize_to_fill(w as u32, h as u32, image::imageops::FilterType::CatmullRom);
-    image.save(p).map_err(candle_core::Error::wrap)?;
-    Ok(())
-}
-
-/// Loads the safetensors files for a model from the hub based on a json index file.
-pub fn hub_load_safetensors(
-    repo: &hf_hub::api::sync::ApiRepo,
-    json_file: &str,
-) -> Result<Vec<std::path::PathBuf>> {
-    let json_file = repo.get(json_file).map_err(candle_core::Error::wrap)?;
-    let json_file = std::fs::File::open(json_file)?;
-    let json: serde_json::Value =
-        serde_json::from_reader(&json_file).map_err(candle_core::Error::wrap)?;
-    let weight_map = match json.get("weight_map") {
-        None => candle_core::bail!("no weight map in {json_file:?}"),
-        Some(serde_json::Value::Object(map)) => map,
-        Some(_) => candle_core::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(candle_core::Error::wrap))
-        .collect::<Result<Vec<_>>>()?;
-    Ok(safetensors_files)
-}
-
-pub fn hub_load_local_safetensors<P: AsRef<std::path::Path>>(
-    path: P,
-    json_file: &str,
-) -> Result<Vec<std::path::PathBuf>> {
-    let path = path.as_ref();
-    let jsfile = std::fs::File::open(path.join(json_file))?;
-    let json: serde_json::Value = serde_json::from_reader(&jsfile).map_err(candle_core::Error::wrap)?;
-    let weight_map = match json.get("weight_map") {
-        None => candle_core::bail!("no weight map in {json_file:?}"),
-        Some(serde_json::Value::Object(map)) => map,
-        Some(_) => candle_core::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)
-}

crates/inference-engine/tests/model_tests.rs

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

crates/inference-engine/tests/text_generation_tests.rs

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

crates/inference-engine/tests/token_output_stream_tests.rs

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

crates/leptos-app/src/app.rs

@@ -5,6 +5,25 @@ use leptos_router::{
     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")]
@@ -12,25 +31,7 @@ use std::collections::VecDeque;
 #[cfg(feature = "hydrate")]
 use uuid::Uuid;
 #[cfg(feature = "hydrate")]
-use js_sys::Date;
-#[cfg(feature = "hydrate")]
 use web_sys::{HtmlInputElement, KeyboardEvent, SubmitEvent};
-#[cfg(feature = "hydrate")]
-use futures_util::StreamExt;
-#[cfg(feature = "hydrate")]
-use async_openai_wasm::{
-    types::{
-        ChatCompletionRequestAssistantMessageArgs, ChatCompletionRequestSystemMessageArgs,
-        ChatCompletionRequestUserMessageArgs, CreateChatCompletionRequestArgs, Model as OpenAIModel,
-    },
-    Client,
-};
-#[cfg(feature = "hydrate")]
-use async_openai_wasm::config::OpenAIConfig;
-#[cfg(feature = "hydrate")]
-use async_openai_wasm::types::{Role, FinishReason};
-#[cfg(feature = "hydrate")]
-use leptos::task::spawn_local;
 
 #[cfg(feature = "hydrate")]
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -43,11 +44,15 @@ pub struct Message {
 
 #[cfg(feature = "hydrate")]
 #[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct MessageContent(pub either::Either<String, Vec<std::collections::HashMap<String, MessageInnerContent>>>);
+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>>);
+pub struct MessageInnerContent(
+    pub either::Either<String, std::collections::HashMap<String, String>>,
+);
 
 #[cfg(feature = "hydrate")]
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -62,27 +67,40 @@ 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");
-    
+    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);
-            
+            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);
+                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");
+                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);
+            leptos::logging::log!(
+                "[DEBUG_LOG] fetch_available_models: Failed to fetch models: {:?}",
+                e
+            );
             Err(format!("Failed to fetch models: {}", e))
         }
     }
@@ -150,7 +168,7 @@ fn ChatInterface() -> impl IntoView {
     {
         ChatInterfaceImpl()
     }
-    
+
     #[cfg(not(feature = "hydrate"))]
     {
         view! {
@@ -252,7 +270,7 @@ fn ChatInterfaceImpl() -> impl IntoView {
 
             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);
 
@@ -267,17 +285,17 @@ fn ChatInterfaceImpl() -> impl IntoView {
             // 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) => {
@@ -335,7 +353,11 @@ fn ChatInterfaceImpl() -> impl IntoView {
                                 }
                             }
                             Err(e) => {
-                                leptos::logging::log!("[DEBUG_LOG] send_message: Stream error after {} chunks: {:?}", chunks_received, 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(),
@@ -364,7 +386,10 @@ fn ChatInterfaceImpl() -> impl IntoView {
                     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);
+                    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(),
@@ -404,7 +429,8 @@ fn ChatInterfaceImpl() -> impl IntoView {
     };
 
     let messages_list = move || {
-        messages.get()
+        messages
+            .get()
             .into_iter()
             .map(|message| {
                 let role_class = match message.role.as_str() {
@@ -439,7 +465,7 @@ fn ChatInterfaceImpl() -> impl IntoView {
             <h1>"Chat Interface"</h1>
             <div class="model-selector">
                 <label for="model-select">"Model: "</label>
-                <select 
+                <select
                     id="model-select"
                     on:change=on_model_change
                     prop:value=selected_model

crates/leptos-app/src/lib.rs

@@ -10,10 +10,10 @@ pub fn hydrate() {
 
 #[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};
-    use crate::app::*;
 
     let conf = get_configuration(None).unwrap();
     let leptos_options = conf.leptos_options;

crates/leptos-app/src/main.rs

@@ -1,12 +1,11 @@
-
 #[cfg(feature = "ssr")]
 #[tokio::main]
 async fn main() {
     use axum::Router;
     use leptos::logging::log;
     use leptos::prelude::*;
-    use leptos_axum::{generate_route_list, LeptosRoutes};
     use leptos_app::app::*;
+    use leptos_axum::{generate_route_list, LeptosRoutes};
 
     let conf = get_configuration(None).unwrap();
     let addr = conf.leptos_options.site_addr;

crates/llama-runner/src/lib.rs

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

crates/llama-runner/src/llama_api.rs

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

crates/llama-runner/src/llama_cli.rs

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

crates/llama-runner/src/main.rs

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

crates/predict-otron-9000/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "predict-otron-9000"
-version = "0.1.0"
+version = "0.1.1"
 edition = "2024"
 
 [[bin]]

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

@@ -1,7 +1,9 @@
 use serde::{Deserialize, Serialize};
 use std::env;
+use tracing::info;
+use tracing::log::error;
 
-#[derive(Debug, Clone, Deserialize, Serialize)]
+#[derive(Serialize, Deserialize, Clone, Debug)]
 #[serde(rename_all = "camelCase")]
 pub struct ServerConfig {
     #[serde(default = "default_server_host")]
@@ -10,14 +12,16 @@ pub struct ServerConfig {
     pub server_port: u16,
     pub server_mode: ServerMode,
     #[serde(default)]
-    pub services: Services,
+    pub services: Option<Services>,
 }
 
 fn default_server_host() -> String {
     "127.0.0.1".to_string()
 }
 
-fn default_server_port() -> u16 { 8080 }
+fn default_server_port() -> u16 {
+    8080
+}
 
 #[derive(Debug, Clone, Deserialize, Serialize, PartialEq)]
 #[serde(rename_all = "PascalCase")]
@@ -34,17 +38,15 @@ impl Default for ServerMode {
 
 #[derive(Debug, Clone, Deserialize, Serialize)]
 pub struct Services {
-    #[serde(default = "inference_service_url")]
-    pub inference_url: String,
-    #[serde(default = "embeddings_service_url")]
-    pub embeddings_url: String,
+    pub inference_url: Option<String>,
+    pub embeddings_url: Option<String>,
 }
 
 impl Default for Services {
     fn default() -> Self {
         Self {
-            inference_url: inference_service_url(),
-            embeddings_url: embeddings_service_url(),
+            inference_url: None,
+            embeddings_url: None,
         }
     }
 }
@@ -63,7 +65,7 @@ impl Default for ServerConfig {
             server_host: "127.0.0.1".to_string(),
             server_port: 8080,
             server_mode: ServerMode::Standalone,
-            services: Services::default(),
+            services: Some(Services::default()),
         }
     }
 }
@@ -73,21 +75,19 @@ impl ServerConfig {
     /// Falls back to default (Local mode) if not set or invalid
     pub fn from_env() -> Self {
         match env::var("SERVER_CONFIG") {
-            Ok(config_str) => {
-                match serde_json::from_str::<ServerConfig>(&config_str) {
-                    Ok(config) => {
-                        tracing::info!("Loaded server configuration: {:?}", config);
-                        config
-                    }
-                    Err(e) => {
-                        tracing::warn!(
-                            "Failed to parse SERVER_CONFIG environment variable: {}. Using default configuration.", 
-                            e
-                        );
-                        ServerConfig::default()
-                    }
+            Ok(config_str) => match serde_json::from_str::<ServerConfig>(&config_str) {
+                Ok(config) => {
+                    tracing::info!("Loaded server configuration: {:?}", config);
+                    config
                 }
-            }
+                Err(e) => {
+                    tracing::warn!(
+                        "Failed to parse SERVER_CONFIG environment variable: {}. Using default configuration.",
+                        e
+                    );
+                    ServerConfig::default()
+                }
+            },
             Err(_) => {
                 tracing::info!("SERVER_CONFIG not set, Standalone mode active");
                 ServerConfig::default()
@@ -96,18 +96,52 @@ impl ServerConfig {
     }
 
     /// Check if the server should run in high availability mode
-    pub fn is_high_availability(&self) -> bool {
-        self.server_mode == ServerMode::HighAvailability
+    pub fn is_high_availability(&self) -> Result<bool, std::io::Error> {
+        if self.server_mode == ServerMode::HighAvailability {
+            let services_well_defined: bool = self.clone().services.is_some();
+
+            let inference_url_well_defined: bool =
+                services_well_defined && self.clone().services.unwrap().inference_url.is_some();
+
+            let embeddings_well_defined: bool =
+                services_well_defined && self.clone().services.unwrap().embeddings_url.is_some();
+
+            let is_well_defined_for_ha =
+                services_well_defined && inference_url_well_defined && embeddings_well_defined;
+
+            if !is_well_defined_for_ha {
+                let config_string = serde_json::to_string_pretty(&self).unwrap();
+                error!(
+                    "HighAvailability mode configured but services not well defined! \n## Config Used:\n {}",
+                    config_string
+                );
+                let err = std::io::Error::new(
+                    std::io::ErrorKind::Other,
+                    "HighAvailability mode configured but services not well defined!",
+                );
+                return Err(err);
+            }
+        }
+
+        Ok(self.server_mode == ServerMode::HighAvailability)
     }
 
     /// Get the inference service URL for proxying
-    pub fn inference_url(&self) -> &str {
-        &self.services.inference_url
+    pub fn inference_url(&self) -> Option<String> {
+        if self.services.is_some() {
+            self.services.clone()?.inference_url
+        } else {
+            None
+        }
     }
 
     /// Get the embeddings service URL for proxying
-    pub fn embeddings_url(&self) -> &str {
-        &self.services.embeddings_url
+    pub fn embeddings_url(&self) -> Option<String> {
+        if self.services.is_some() {
+            self.services.clone()?.embeddings_url
+        } else {
+            None
+        }
     }
 }
 
@@ -119,7 +153,7 @@ mod tests {
     fn test_default_config() {
         let config = ServerConfig::default();
         assert_eq!(config.server_mode, ServerMode::Standalone);
-        assert!(!config.is_high_availability());
+        assert!(!config.is_high_availability().unwrap());
     }
 
     #[test]
@@ -134,23 +168,26 @@ mod tests {
 
         let config: ServerConfig = serde_json::from_str(config_json).unwrap();
         assert_eq!(config.server_mode, ServerMode::HighAvailability);
-        assert!(config.is_high_availability());
-        assert_eq!(config.inference_url(), "http://inference-service:8080");
-        assert_eq!(config.embeddings_url(), "http://embeddings-service:8080");
+        assert!(config.is_high_availability().unwrap());
+        assert_eq!(
+            config.inference_url().unwrap(),
+            "http://inference-service:8080"
+        );
+        assert_eq!(
+            config.embeddings_url().unwrap(),
+            "http://embeddings-service:8080"
+        );
     }
 
     #[test]
     fn test_local_mode_config() {
         let config_json = r#"{
-            "serverMode": "Local"
+            "serverMode": "Standalone"
         }"#;
 
         let config: ServerConfig = serde_json::from_str(config_json).unwrap();
         assert_eq!(config.server_mode, ServerMode::Standalone);
-        assert!(!config.is_high_availability());
-        // Should use default URLs
-        assert_eq!(config.inference_url(), "http://inference-service:8080");
-        assert_eq!(config.embeddings_url(), "http://embeddings-service:8080");
+        assert!(!config.is_high_availability().unwrap());
     }
 
     #[test]
@@ -164,17 +201,26 @@ mod tests {
         }"#;
 
         let config: ServerConfig = serde_json::from_str(config_json).unwrap();
-        assert_eq!(config.inference_url(), "http://custom-inference:9000");
-        assert_eq!(config.embeddings_url(), "http://custom-embeddings:9001");
+        assert_eq!(
+            config.inference_url().unwrap(),
+            "http://custom-inference:9000"
+        );
+        assert_eq!(
+            config.embeddings_url().unwrap(),
+            "http://custom-embeddings:9001"
+        );
     }
 
     #[test]
-    fn test_minimal_high_availability_config() {
+    fn test_minimal_high_availability_config_error() {
         let config_json = r#"{"serverMode": "HighAvailability"}"#;
         let config: ServerConfig = serde_json::from_str(config_json).unwrap();
-        assert!(config.is_high_availability());
-        // Should use default URLs
-        assert_eq!(config.inference_url(), "http://inference-service:8080");
-        assert_eq!(config.embeddings_url(), "http://embeddings-service:8080");
+
+        let is_high_availability = config.is_high_availability();
+
+        assert!(is_high_availability.is_err());
+        // // Should use default URLs
+        // assert_eq!(config.inference_url().unwrap(), "http://inference-service:8080");
+        // assert_eq!(config.embeddings_url().unwrap(), "http://embeddings-service:8080");
     }
-}
+}

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

@@ -1,7 +1,9 @@
 mod config;
 mod middleware;
 mod proxy;
+mod standalone;
 
+use crate::standalone::create_standalone_router;
 use axum::response::IntoResponse;
 use axum::routing::get;
 use axum::{Router, http::Uri, response::Html, serve};
@@ -11,6 +13,7 @@ use middleware::{MetricsLayer, MetricsLoggerFuture, MetricsStore};
 use proxy::create_proxy_router;
 use rust_embed::Embed;
 use std::env;
+use std::path::Component::ParentDir;
 use tokio::net::TcpListener;
 use tower_http::classify::ServerErrorsFailureClass::StatusCode;
 use tower_http::cors::{Any, CorsLayer};
@@ -49,33 +52,19 @@ async fn main() {
     let default_host = server_config.server_host.clone();
     let default_port = server_config.server_port;
 
-    // Create router based on server mode
-    let service_router = if server_config.clone().is_high_availability() {
-        tracing::info!("Running in HighAvailability mode - proxying to external services");
-        tracing::info!("  Inference service URL: {}", server_config.inference_url());
-        tracing::info!(
-            "  Embeddings service URL: {}",
-            server_config.embeddings_url()
-        );
-
-        // Use proxy router that forwards requests to external services
-        create_proxy_router(server_config.clone())
-    } else {
-        tracing::info!("Running in Standalone mode - using embedded services");
-
-        // Create unified router by merging embeddings and inference routers (existing behavior)
-        let embeddings_router = embeddings_engine::create_embeddings_router();
-
-        // Create AppState with correct model configuration
-        let app_state = AppState::default();
-
-        // Get the inference router directly from the inference engine
-        let inference_router = inference_engine::create_router(app_state);
-
-        // Merge the local routers
-        Router::new()
-            .merge(embeddings_router)
-            .merge(inference_router)
+    let service_router = match server_config.clone().is_high_availability() {
+        Ok(is_ha) => {
+            if is_ha {
+                log_config(server_config.clone());
+                create_proxy_router(server_config.clone())
+            } else {
+                log_config(server_config.clone());
+                create_standalone_router(server_config)
+            }
+        }
+        Err(error) => {
+            panic!("{}", error);
+        }
     };
 
     // Create CORS layer
@@ -124,5 +113,25 @@ async fn main() {
     serve(listener, app).await.unwrap();
 }
 
+fn log_config(config: ServerConfig) {
+    match config.is_high_availability() {
+        Ok(is_high) => {
+            if is_high {
+                tracing::info!("Running in HighAvailability mode - proxying to external services");
+                tracing::info!("Inference service URL: {}", config.inference_url().unwrap());
+                tracing::info!(
+                    "Embeddings service URL: {}",
+                    config.embeddings_url().unwrap()
+                );
+            } else {
+                tracing::info!("Running in Standalone mode");
+            }
+        }
+        Err(error) => {
+            panic!("{}", error);
+        }
+    }
+}
+
 // Chat completions handler that properly uses the inference server crate's error handling
 // This function is no longer needed as we're using the inference_engine router directly

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

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

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

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

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

@@ -1,10 +1,10 @@
 use axum::{
+    Router,
     body::Body,
     extract::{Request, State},
     http::{HeaderMap, Method, StatusCode, Uri},
     response::{IntoResponse, Response},
     routing::{get, post},
-    Router,
 };
 use reqwest::Client;
 use serde_json::Value;
@@ -47,10 +47,16 @@ async fn proxy_chat_completions(
     headers: HeaderMap,
     body: Body,
 ) -> Result<Response, StatusCode> {
-    let target_url = format!("{}/v1/chat/completions", proxy_client.config.inference_url());
-    
+    let target_url = format!(
+        "{}/v1/chat/completions",
+        proxy_client
+            .config
+            .inference_url()
+            .expect("Invalid Configuration")
+    );
+
     tracing::info!("Proxying chat completions request to: {}", target_url);
-    
+
     // Extract body as bytes
     let body_bytes = match axum::body::to_bytes(body, usize::MAX).await {
         Ok(bytes) => bytes,
@@ -63,7 +69,9 @@ async fn proxy_chat_completions(
     // Check if this is a streaming request
     let is_streaming = if let Ok(body_str) = String::from_utf8(body_bytes.to_vec()) {
         if let Ok(json) = serde_json::from_str::<Value>(&body_str) {
-            json.get("stream").and_then(|v| v.as_bool()).unwrap_or(false)
+            json.get("stream")
+                .and_then(|v| v.as_bool())
+                .unwrap_or(false)
         } else {
             false
         }
@@ -72,7 +80,8 @@ async fn proxy_chat_completions(
     };
 
     // Forward the request
-    let mut req_builder = proxy_client.client
+    let mut req_builder = proxy_client
+        .client
         .post(&target_url)
         .body(body_bytes.to_vec());
 
@@ -85,8 +94,7 @@ async fn proxy_chat_completions(
 
     match req_builder.send().await {
         Ok(response) => {
-            let mut resp_builder = Response::builder()
-                .status(response.status());
+            let mut resp_builder = Response::builder().status(response.status());
 
             // Forward response headers
             for (name, value) in response.headers().iter() {
@@ -99,14 +107,12 @@ async fn proxy_chat_completions(
             if is_streaming {
                 // For streaming, we need to forward the response as-is
                 match response.bytes().await {
-                    Ok(body) => {
-                        resp_builder
-                            .header("content-type", "text/plain; charset=utf-8")
-                            .header("cache-control", "no-cache")
-                            .header("connection", "keep-alive")
-                            .body(Body::from(body))
-                            .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)
-                    }
+                    Ok(body) => resp_builder
+                        .header("content-type", "text/plain; charset=utf-8")
+                        .header("cache-control", "no-cache")
+                        .header("connection", "keep-alive")
+                        .body(Body::from(body))
+                        .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR),
                     Err(e) => {
                         tracing::error!("Failed to read streaming response body: {}", e);
                         Err(StatusCode::INTERNAL_SERVER_ERROR)
@@ -115,11 +121,9 @@ async fn proxy_chat_completions(
             } else {
                 // For non-streaming, forward the JSON response
                 match response.bytes().await {
-                    Ok(body) => {
-                        resp_builder
-                            .body(Body::from(body))
-                            .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)
-                    }
+                    Ok(body) => resp_builder
+                        .body(Body::from(body))
+                        .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR),
                     Err(e) => {
                         tracing::error!("Failed to read response body: {}", e);
                         Err(StatusCode::INTERNAL_SERVER_ERROR)
@@ -139,10 +143,16 @@ async fn proxy_models(
     State(proxy_client): State<ProxyClient>,
     headers: HeaderMap,
 ) -> Result<Response, StatusCode> {
-    let target_url = format!("{}/v1/models", proxy_client.config.inference_url());
-    
+    let target_url = format!(
+        "{}/v1/models",
+        proxy_client
+            .config
+            .inference_url()
+            .expect("Invalid Configuration Detected")
+    );
+
     tracing::info!("Proxying models request to: {}", target_url);
-    
+
     let mut req_builder = proxy_client.client.get(&target_url);
 
     // Forward relevant headers
@@ -154,8 +164,7 @@ async fn proxy_models(
 
     match req_builder.send().await {
         Ok(response) => {
-            let mut resp_builder = Response::builder()
-                .status(response.status());
+            let mut resp_builder = Response::builder().status(response.status());
 
             // Forward response headers
             for (name, value) in response.headers().iter() {
@@ -165,11 +174,9 @@ async fn proxy_models(
             }
 
             match response.bytes().await {
-                Ok(body) => {
-                    resp_builder
-                        .body(Body::from(body))
-                        .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)
-                }
+                Ok(body) => resp_builder
+                    .body(Body::from(body))
+                    .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR),
                 Err(e) => {
                     tracing::error!("Failed to read models response body: {}", e);
                     Err(StatusCode::INTERNAL_SERVER_ERROR)
@@ -189,10 +196,16 @@ async fn proxy_embeddings(
     headers: HeaderMap,
     body: Body,
 ) -> Result<Response, StatusCode> {
-    let target_url = format!("{}/v1/embeddings", proxy_client.config.embeddings_url());
-    
+    let target_url = format!(
+        "{}/v1/embeddings",
+        proxy_client
+            .config
+            .embeddings_url()
+            .expect("Invalid Configuration Detected")
+    );
+
     tracing::info!("Proxying embeddings request to: {}", target_url);
-    
+
     // Extract body as bytes
     let body_bytes = match axum::body::to_bytes(body, usize::MAX).await {
         Ok(bytes) => bytes,
@@ -203,7 +216,8 @@ async fn proxy_embeddings(
     };
 
     // Forward the request
-    let mut req_builder = proxy_client.client
+    let mut req_builder = proxy_client
+        .client
         .post(&target_url)
         .body(body_bytes.to_vec());
 
@@ -216,8 +230,7 @@ async fn proxy_embeddings(
 
     match req_builder.send().await {
         Ok(response) => {
-            let mut resp_builder = Response::builder()
-                .status(response.status());
+            let mut resp_builder = Response::builder().status(response.status());
 
             // Forward response headers
             for (name, value) in response.headers().iter() {
@@ -227,11 +240,9 @@ async fn proxy_embeddings(
             }
 
             match response.bytes().await {
-                Ok(body) => {
-                    resp_builder
-                        .body(Body::from(body))
-                        .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)
-                }
+                Ok(body) => resp_builder
+                    .body(Body::from(body))
+                    .map_err(|_| StatusCode::INTERNAL_SERVER_ERROR),
                 Err(e) => {
                     tracing::error!("Failed to read embeddings response body: {}", e);
                     Err(StatusCode::INTERNAL_SERVER_ERROR)
@@ -250,7 +261,7 @@ fn should_forward_header(header_name: &str) -> bool {
     match header_name.to_lowercase().as_str() {
         "content-type" | "content-length" | "authorization" | "user-agent" | "accept" => true,
         "host" | "connection" | "upgrade" => false, // Don't forward connection-specific headers
-        _ => true, // Forward other headers by default
+        _ => true,                                  // Forward other headers by default
     }
 }
 
@@ -259,7 +270,7 @@ fn should_forward_response_header(header_name: &str) -> bool {
     match header_name.to_lowercase().as_str() {
         "content-type" | "content-length" | "cache-control" | "connection" => true,
         "server" | "date" => false, // Don't forward server-specific headers
-        _ => true, // Forward other headers by default
+        _ => true,                  // Forward other headers by default
     }
 }
 
@@ -290,14 +301,20 @@ mod tests {
             server_host: "127.0.0.1".to_string(),
             server_port: 8080,
             server_mode: ServerMode::HighAvailability,
-            services: Services {
-                inference_url: "http://test-inference:8080".to_string(),
-                embeddings_url: "http://test-embeddings:8080".to_string(),
-            },
+            services: Some(Services {
+                inference_url: Some("http://test-inference:8080".to_string()),
+                embeddings_url: Some("http://test-embeddings:8080".to_string()),
+            }),
         };
 
         let proxy_client = ProxyClient::new(config);
-        assert_eq!(proxy_client.config.inference_url(), "http://test-inference:8080");
-        assert_eq!(proxy_client.config.embeddings_url(), "http://test-embeddings:8080");
+        assert_eq!(
+            proxy_client.config.inference_url().unwrap().as_str(),
+            "http://test-inference:8080"
+        );
+        assert_eq!(
+            proxy_client.config.embeddings_url().unwrap().as_str(),
+            "http://test-embeddings:8080"
+        );
     }
-}
+}

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

@@ -0,0 +1,19 @@
+use crate::config::ServerConfig;
+use axum::Router;
+use inference_engine::AppState;
+
+pub fn create_standalone_router(server_config: ServerConfig) -> Router {
+    // Create unified router by merging embeddings and inference routers (existing behavior)
+    let embeddings_router = embeddings_engine::create_embeddings_router();
+
+    // Create AppState with correct model configuration
+    let app_state = AppState::default();
+
+    // Get the inference router directly from the inference engine
+    let inference_router = inference_engine::create_router(app_state);
+
+    // Merge the local routers
+    Router::new()
+        .merge(embeddings_router)
+        .merge(inference_router)
+}

scripts/build_all_platforms.sh

@@ -0,0 +1,389 @@
+#!/bin/bash
+
+# Cross-platform build script for predict-otron-9000
+# Builds all workspace crates for common platforms
+
+set -euo pipefail
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+# Configuration
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
+BUILD_DIR="${PROJECT_ROOT}/build"
+TIMESTAMP=$(date +%Y%m%d_%H%M%S)
+
+# Supported platforms
+PLATFORMS=(
+    "x86_64-unknown-linux-gnu"
+    "x86_64-pc-windows-msvc"
+    "x86_64-apple-darwin"
+    "aarch64-apple-darwin"
+    "aarch64-unknown-linux-gnu"
+)
+
+# Main binaries to build
+MAIN_BINARIES=(
+    "predict-otron-9000"
+    "embeddings-engine"
+)
+
+# Inference engine binaries (with bin feature)
+INFERENCE_BINARIES=(
+    "gemma_inference"
+    "llama_inference"
+)
+
+# Other workspace binaries
+OTHER_BINARIES=(
+    "helm-chart-tool"
+)
+
+print_header() {
+    echo -e "${BLUE}================================${NC}"
+    echo -e "${BLUE}$1${NC}"
+    echo -e "${BLUE}================================${NC}"
+}
+
+print_info() {
+    echo -e "${GREEN}[INFO]${NC} $1"
+}
+
+print_warn() {
+    echo -e "${YELLOW}[WARN]${NC} $1"
+}
+
+print_error() {
+    echo -e "${RED}[ERROR]${NC} $1"
+}
+
+check_dependencies() {
+    print_header "Checking Dependencies"
+    
+    # Check rust
+    if ! command -v cargo >/dev/null 2>&1; then
+        print_error "Rust/Cargo is not installed"
+        exit 1
+    fi
+    
+    # Check cargo-leptos for WASM frontend
+    if ! command -v cargo-leptos >/dev/null 2>&1; then
+        print_warn "cargo-leptos not found. Installing..."
+        cargo install cargo-leptos
+    fi
+    
+    print_info "All dependencies available"
+}
+
+install_targets() {
+    print_header "Installing Rust Targets"
+    
+    for platform in "${PLATFORMS[@]}"; do
+        print_info "Installing target: $platform"
+        rustup target add "$platform" || {
+            print_warn "Failed to install target $platform (may not be available on this host)"
+        }
+    done
+    
+    # Add WASM target for leptos
+    print_info "Installing wasm32-unknown-unknown target for Leptos"
+    rustup target add wasm32-unknown-unknown
+}
+
+create_build_dirs() {
+    print_header "Setting up Build Directory"
+    
+    rm -rf "$BUILD_DIR"
+    mkdir -p "$BUILD_DIR"
+    
+    for platform in "${PLATFORMS[@]}"; do
+        mkdir -p "$BUILD_DIR/$platform"
+    done
+    
+    mkdir -p "$BUILD_DIR/web"
+    print_info "Build directories created"
+}
+
+build_leptos_app() {
+    print_header "Building Leptos Web Frontend"
+    
+    cd "$PROJECT_ROOT/crates/leptos-app"
+    
+    # Build the WASM frontend
+    print_info "Building WASM frontend with cargo-leptos..."
+    cargo leptos build --release || {
+        print_error "Failed to build Leptos WASM frontend"
+        return 1
+    }
+    
+    # Copy built assets to build directory
+    if [ -d "target/site" ]; then
+        cp -r target/site/* "$BUILD_DIR/web/"
+        print_info "Leptos frontend built and copied to $BUILD_DIR/web/"
+    else
+        print_error "Leptos build output not found at target/site"
+        return 1
+    fi
+    
+    cd "$PROJECT_ROOT"
+}
+
+get_platform_features() {
+    local platform="$1"
+    local features=""
+    
+    case "$platform" in
+        *-apple-darwin)
+            # macOS uses Metal but routes to CPU for Gemma stability
+            features=""
+            ;;
+        *-unknown-linux-gnu|*-pc-windows-msvc)
+            # Linux and Windows can use CUDA if available
+            features=""
+            ;;
+        *)
+            features=""
+            ;;
+    esac
+    
+    echo "$features"
+}
+
+build_binary_for_platform() {
+    local binary_name="$1"
+    local platform="$2"
+    local package_name="$3"
+    local additional_args="$4"
+    
+    print_info "Building $binary_name for $platform"
+    
+    local features=$(get_platform_features "$platform")
+    local feature_flag=""
+    if [ -n "$features" ]; then
+        feature_flag="--features $features"
+    fi
+    
+    # Build command
+    local build_cmd="cargo build --release --target $platform --bin $binary_name"
+    
+    if [ -n "$package_name" ]; then
+        build_cmd="$build_cmd --package $package_name"
+    fi
+    
+    if [ -n "$additional_args" ]; then
+        build_cmd="$build_cmd $additional_args"
+    fi
+    
+    if [ -n "$feature_flag" ]; then
+        build_cmd="$build_cmd $feature_flag"
+    fi
+    
+    print_info "Running: $build_cmd"
+    
+    if eval "$build_cmd"; then
+        # Copy binary to build directory
+        local target_dir="target/$platform/release"
+        local binary_file="$binary_name"
+        
+        # Add .exe extension for Windows
+        if [[ "$platform" == *-pc-windows-msvc ]]; then
+            binary_file="$binary_name.exe"
+        fi
+        
+        if [ -f "$target_dir/$binary_file" ]; then
+            cp "$target_dir/$binary_file" "$BUILD_DIR/$platform/"
+            print_info "✓ $binary_name built and copied for $platform"
+        else
+            print_error "Binary not found: $target_dir/$binary_file"
+            return 1
+        fi
+    else
+        print_error "Failed to build $binary_name for $platform"
+        return 1
+    fi
+}
+
+build_for_platform() {
+    local platform="$1"
+    print_header "Building for $platform"
+    
+    local failed_builds=()
+    
+    # Build main binaries
+    for binary in "${MAIN_BINARIES[@]}"; do
+        if ! build_binary_for_platform "$binary" "$platform" "$binary" ""; then
+            failed_builds+=("$binary")
+        fi
+    done
+    
+    # Build inference engine binaries with bin feature
+    for binary in "${INFERENCE_BINARIES[@]}"; do
+        if ! build_binary_for_platform "$binary" "$platform" "inference-engine" "--features bin"; then
+            failed_builds+=("$binary")
+        fi
+    done
+    
+    # Build other workspace binaries
+    for binary in "${OTHER_BINARIES[@]}"; do
+        if ! build_binary_for_platform "$binary" "$platform" "$binary" ""; then
+            failed_builds+=("$binary")
+        fi
+    done
+    
+    if [ ${#failed_builds[@]} -eq 0 ]; then
+        print_info "✓ All binaries built successfully for $platform"
+    else
+        print_warn "Some builds failed for $platform: ${failed_builds[*]}"
+    fi
+}
+
+create_archives() {
+    print_header "Creating Release Archives"
+    
+    cd "$BUILD_DIR"
+    
+    for platform in "${PLATFORMS[@]}"; do
+        if [ -d "$platform" ] && [ -n "$(ls -A "$platform" 2>/dev/null)" ]; then
+            local archive_name="predict-otron-9000-${platform}-${TIMESTAMP}"
+            
+            print_info "Creating archive for $platform"
+            
+            # Create platform-specific directory with all files
+            mkdir -p "$archive_name"
+            cp -r "$platform"/* "$archive_name/"
+            
+            # Add web assets to each platform archive
+            if [ -d "web" ]; then
+                mkdir -p "$archive_name/web"
+                cp -r web/* "$archive_name/web/"
+            fi
+            
+            # Create README for the platform
+            cat > "$archive_name/README.txt" << EOF
+Predict-Otron-9000 - Platform: $platform
+Build Date: $(date)
+========================================
+
+Binaries included:
+$(ls -1 "$platform")
+
+Web Frontend:
+- Located in the 'web' directory
+- Serve with any static file server on port 8788 or configure your server
+
+Usage:
+1. Start the main server: ./predict-otron-9000
+2. Start embeddings service: ./embeddings-engine  
+3. Access web interface at http://localhost:8080 (served by main server)
+
+For more information, visit: https://github.com/geoffsee/predict-otron-9000
+EOF
+            
+            # Create tar.gz archive
+            tar -czf "${archive_name}.tar.gz" "$archive_name"
+            rm -rf "$archive_name"
+            
+            print_info "✓ Created ${archive_name}.tar.gz"
+        else
+            print_warn "No binaries found for $platform, skipping archive"
+        fi
+    done
+    
+    cd "$PROJECT_ROOT"
+}
+
+generate_build_report() {
+    print_header "Build Report"
+    
+    echo "Build completed at: $(date)"
+    echo "Build directory: $BUILD_DIR"
+    echo ""
+    echo "Archives created:"
+    ls -la "$BUILD_DIR"/*.tar.gz 2>/dev/null || echo "No archives created"
+    echo ""
+    echo "Platform directories:"
+    for platform in "${PLATFORMS[@]}"; do
+        if [ -d "$BUILD_DIR/$platform" ]; then
+            echo "  $platform:"
+            ls -la "$BUILD_DIR/$platform" | sed 's/^/    /'
+        fi
+    done
+    
+    if [ -d "$BUILD_DIR/web" ]; then
+        echo ""
+        echo "Web frontend assets:"
+        ls -la "$BUILD_DIR/web" | head -10 | sed 's/^/    /'
+        if [ $(ls -1 "$BUILD_DIR/web" | wc -l) -gt 10 ]; then
+            echo "    ... and $(( $(ls -1 "$BUILD_DIR/web" | wc -l) - 10 )) more files"
+        fi
+    fi
+}
+
+main() {
+    print_header "Predict-Otron-9000 Cross-Platform Build Script"
+    
+    cd "$PROJECT_ROOT"
+    
+    check_dependencies
+    install_targets
+    create_build_dirs
+    
+    # Build Leptos web frontend first
+    build_leptos_app
+    
+    # Build for each platform
+    for platform in "${PLATFORMS[@]}"; do
+        build_for_platform "$platform"
+    done
+    
+    create_archives
+    generate_build_report
+    
+    print_header "Build Complete!"
+    print_info "All artifacts are available in: $BUILD_DIR"
+}
+
+# Handle command line arguments
+case "${1:-}" in
+    --help|-h)
+        echo "Usage: $0 [options]"
+        echo ""
+        echo "Cross-platform build script for predict-otron-9000"
+        echo ""
+        echo "Options:"
+        echo "  --help, -h          Show this help message"
+        echo "  --platforms         Show supported platforms"
+        echo "  --clean             Clean build directory before building"
+        echo ""
+        echo "Supported platforms:"
+        for platform in "${PLATFORMS[@]}"; do
+            echo "  - $platform"
+        done
+        echo ""
+        echo "Prerequisites:"
+        echo "  - Rust toolchain with rustup"
+        echo "  - cargo-leptos (will be installed if missing)"
+        echo "  - Platform-specific toolchains for cross-compilation"
+        echo ""
+        exit 0
+        ;;
+    --platforms)
+        echo "Supported platforms:"
+        for platform in "${PLATFORMS[@]}"; do
+            echo "  - $platform"
+        done
+        exit 0
+        ;;
+    --clean)
+        print_info "Cleaning build directory..."
+        rm -rf "$BUILD_DIR"
+        print_info "Build directory cleaned"
+        ;;
+esac
+
+main "$@"

scripts/build_cli.sh

@@ -0,0 +1,19 @@
+#!/usr/bin/env sh
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+
+TEMP_DIR="$SCRIPT_DIR/temp"
+
+mkdir -p "$TEMP_DIR"
+
+cp "$SCRIPT_DIR/cli.ts" "$TEMP_DIR/cli.ts"
+cp "$SCRIPT_DIR/../package.json" "$TEMP_DIR/package.json"
+
+(
+cd "$TEMP_DIR"
+bun i
+bun build ./cli.ts --compile --outfile "$SCRIPT_DIR/cli"
+)
+
+rm -rf "$TEMP_DIR"

scripts/run_llama.sh

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