feat: add meeting transcription app scaffolding

- Add Python backend structure with FastAPI for transcription/summarization - Add React UI with audio recording, transcript, and summary views - Configure Tauri to manage Python backend lifecycle - Set up Windows cross-compilation with cargo-xwin - Add Gitea CI workflow for lint, test, and multi-platform builds - Configure ESLint, Prettier, and Vitest for code quality Note: App scaffolding only - Python env and models not yet set up
feat: prep the scaffolding
2026-01-21 20:18:03 -08:00 · 2026-01-21 17:49:25 -08:00
68 changed files with 10230 additions and 32 deletions
@@ -1,6 +1,6 @@
 name: 🐛 Bug Report
 description: Something isn't working as expected? Let us know!
-title: '[BUG] - '
+title: "[BUG] - "
 labels:
  - "status/awaiting triage"
 body:
@@ -50,7 +50,7 @@ body:
      description: The operating system you are using, including the version/build number.
    validations:
      required: true
-# Remove this section for non-web apps.
+  # Remove this section for non-web apps.
  - type: input
    id: browser
    attributes:
@@ -66,4 +66,3 @@ body:
        - No
    validations:
      required: true
-
@@ -2,4 +2,4 @@ blank_issues_enabled: false
 contact_links:
  - name: "Discord"
    url: "https://chat.nhcarrigan.com"
-    about: "Chat with us directly."
+    about: "Chat with us directly."
@@ -1,6 +1,6 @@
 name: 💭 Feature Proposal
 description: Have an idea for how we can improve? Share it here!
-title: '[FEAT] - '
+title: "[FEAT] - "
 labels:
  - "status/awaiting triage"
 body:
@@ -1,6 +1,6 @@
 name: ❓ Other Issue
 description: I have something that is neither a bug nor a feature request.
-title: '[OTHER] - '
+title: "[OTHER] - "
 labels:
  - "status/awaiting triage"
 body:
@@ -0,0 +1,189 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+  workflow_dispatch:
+
+jobs:
+  lint-and-test:
+    name: Lint & Test
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install Linux dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y \
+            libwebkit2gtk-4.1-dev \
+            librsvg2-dev \
+            patchelf \
+            libgtk-3-dev \
+            libayatana-appindicator3-dev
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 22
+          cache: pnpm
+
+      - name: Install frontend dependencies
+        run: pnpm install
+
+      - name: Run ESLint
+        run: pnpm lint
+
+      - name: Run Prettier check
+        run: pnpm format:check
+
+      - name: Build frontend
+        run: pnpm build
+
+      - name: Run frontend tests
+        run: pnpm test
+
+      - name: Setup Rust
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          components: clippy
+
+      - name: Cache Rust dependencies
+        uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            src-tauri/target/
+          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Run Clippy
+        working-directory: src-tauri
+        run: cargo clippy --all-targets --all-features -- -D warnings
+
+      - name: Run Rust tests
+        working-directory: src-tauri
+        run: cargo test
+
+  build-linux:
+    name: Build Linux
+    runs-on: ubuntu-latest
+    needs: lint-and-test
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install Linux dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y \
+            libwebkit2gtk-4.1-dev \
+            librsvg2-dev \
+            patchelf \
+            libgtk-3-dev \
+            libayatana-appindicator3-dev \
+            xdg-utils
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 22
+          cache: pnpm
+
+      - name: Install frontend dependencies
+        run: pnpm install
+
+      - name: Setup Rust
+        uses: dtolnay/rust-toolchain@stable
+
+      - name: Cache Rust dependencies
+        uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            src-tauri/target/
+          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Build Linux
+        run: pnpm build:linux
+
+  build-windows:
+    name: Build Windows (cross-compile)
+    runs-on: ubuntu-latest
+    needs: lint-and-test
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install Linux dependencies for cross-compilation
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y \
+            libwebkit2gtk-4.1-dev \
+            librsvg2-dev \
+            patchelf \
+            libgtk-3-dev \
+            libayatana-appindicator3-dev \
+            clang \
+            lld \
+            llvm \
+            nsis
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 22
+          cache: pnpm
+
+      - name: Install frontend dependencies
+        run: pnpm install
+
+      - name: Setup Rust
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          targets: x86_64-pc-windows-msvc
+
+      - name: Install cargo-xwin
+        run: |
+          curl -fsSL https://github.com/rust-cross/cargo-xwin/releases/download/v0.20.2/cargo-xwin-v0.20.2.x86_64-unknown-linux-musl.tar.gz | tar xz
+          sudo mv cargo-xwin /usr/local/bin/
+
+      - name: Cache Rust dependencies
+        uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cargo/bin/
+            ~/.cargo/registry/index/
+            ~/.cargo/registry/cache/
+            ~/.cargo/git/db/
+            src-tauri/target/
+          key: ${{ runner.os }}-cargo-windows-${{ hashFiles('**/Cargo.lock') }}
+
+      - name: Build Windows
+        run: pnpm build:windows
@@ -2,11 +2,11 @@ name: Security Scan and Upload

 on:
  push:
-    branches: [ main ]
+    branches: [main]
  pull_request:
-    branches: [ main ]
+    branches: [main]
  schedule:
-    - cron: '0 0 * * 1'
+    - cron: "0 0 * * 1"
  workflow_dispatch:

 jobs:
@@ -24,18 +24,18 @@ jobs:
        env:
          DD_URL: ${{ secrets.DD_URL }}
          DD_TOKEN: ${{ secrets.DD_TOKEN }}
-          PRODUCT_NAME: ${{ github.repository }} 
-          PRODUCT_TYPE_ID: 1 
+          PRODUCT_NAME: ${{ github.repository }}
+          PRODUCT_TYPE_ID: 1
        run: |
          sudo apt-get install jq -y > /dev/null
-          
+
          echo "Checking connection to $DD_URL..."
-          
+
          # Check if product exists - capture HTTP code to debug connection issues
          RESPONSE=$(curl --write-out "%{http_code}" --silent --output /tmp/response.json \
            -H "Authorization: Token $DD_TOKEN" \
            "$DD_URL/api/v2/products/?name=$PRODUCT_NAME")
-          
+
          # If response is not 200, print error
          if [ "$RESPONSE" != "200" ]; then
            echo "::error::Failed to query DefectDojo. HTTP Code: $RESPONSE"
@@ -44,7 +44,7 @@ jobs:
          fi

          COUNT=$(cat /tmp/response.json | jq -r '.count')
-          
+
          if [ "$COUNT" = "0" ]; then
            echo "Creating product '$PRODUCT_NAME'..."
            curl -s -X POST "$DD_URL/api/v2/products/" \
@@ -75,7 +75,7 @@ jobs:
          echo "Uploading Trivy results..."
          # Generate today's date in YYYY-MM-DD format
          TODAY=$(date +%Y-%m-%d)
-          
+
          HTTP_CODE=$(curl --write-out "%{http_code}" --output response.txt --silent -X POST "$DD_URL/api/v2/import-scan/" \
            -H "Authorization: Token $DD_TOKEN" \
            -F "active=true" \
@@ -86,7 +86,7 @@ jobs:
            -F "scan_date=$TODAY" \
            -F "auto_create_context=true" \
            -F "file=@trivy-results.json")
-          
+
          if [[ "$HTTP_CODE" != "200" && "$HTTP_CODE" != "201" ]]; then
            echo "::error::Upload Failed with HTTP $HTTP_CODE"
            echo "--- SERVER RESPONSE ---"
@@ -154,7 +154,7 @@ jobs:
        run: |
          echo "Uploading Semgrep results..."
          TODAY=$(date +%Y-%m-%d)
-          
+
          HTTP_CODE=$(curl --write-out "%{http_code}" --output response.txt --silent -X POST "$DD_URL/api/v2/import-scan/" \
            -H "Authorization: Token $DD_TOKEN" \
            -F "active=true" \
@@ -174,4 +174,4 @@ jobs:
             exit 1
          else
             echo "Upload Success!"
-          fi
+          fi
@@ -0,0 +1,54 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+.venv/
+*.egg-info/
+
+# Models (we'll add these to git lfs later)
+models/*.gguf
+models/*.bin
+
+# Tauri
+src-tauri/target/
+src-tauri/WixTools/
+
+# App data
+recordings/
+transcripts/
+summaries/
+
+# Environment
+.env
+*.env.local
+prod.env
@@ -0,0 +1,8 @@
+build/
+.svelte-kit/
+dist/
+src-tauri/target/
+src-tauri/gen/
+node_modules/
+.pnpm-store/
+pnpm-lock.yaml
@@ -0,0 +1,7 @@
+{
+  "semi": true,
+  "singleQuote": false,
+  "tabWidth": 2,
+  "trailingComma": "es5",
+  "printWidth": 100
+}
@@ -0,0 +1,3 @@
+{
+  "recommendations": ["tauri-apps.tauri-vscode", "rust-lang.rust-analyzer"]
+}
@@ -0,0 +1,120 @@
+# Chronara - Local Meeting Transcription & Summarization
+
+A Windows desktop application that transcribes, diarizes, and summarizes meetings using only locally-running models.
+
+## Features
+
+- 🎙️ Real-time audio transcription with speaker diarization (WhisperX)
+- 📝 Intelligent meeting summarization (Llama 3.2)
+- 🖥️ Everything runs locally - no cloud services required
+- 📦 All models bundled - no separate downloads needed
+
+## Tech Stack
+
+- **Transcription**: WhisperX (Whisper + speaker diarization)
+- **Summarization**: Llama 3.2 1B/3B
+- **Backend**: Python with FastAPI
+- **Frontend**: Tauri + React
+- **Model Runtime**: llama-cpp-python
+
+## Project Structure
+
+```
+chronara/
+├── src/
+│   ├── backend/     # Python FastAPI backend
+│   ├── components/  # React components
+│   └── App.tsx      # Main React app
+├── src-tauri/       # Tauri configuration
+├── models/          # Bundled model files
+├── scripts/         # Build and setup scripts
+└── assets/          # Icons, resources
+```
+
+## Development Setup
+
+### Prerequisites
+
+- Node.js 18+ with pnpm
+- Python 3.10+
+- Rust (for Tauri)
+- Windows build tools (for native modules)
+
+### Installation
+
+1. Clone the repository:
+
+```bash
+git clone https://github.com/naomi-lgbt/chronara.git
+cd chronara
+```
+
+2. Install frontend dependencies:
+
+```bash
+pnpm install
+```
+
+3. Install Python dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+4. Download the AI models:
+
+```bash
+python scripts/download_models.py
+```
+
+5. Run in development mode:
+
+```bash
+pnpm tauri:dev
+```
+
+## Building for Production
+
+### Windows
+
+1. Download models if not already done:
+
+```bash
+python scripts/download_models.py
+```
+
+2. Build the Windows executable:
+
+```bash
+python scripts/build_windows.py
+```
+
+The installer will be created in `src-tauri/target/release/bundle/nsis/`.
+
+## Usage
+
+1. **Start Recording**: Click the "Start Recording" button to begin capturing audio
+2. **Real-time Transcription**: Watch as the conversation is transcribed with speaker labels
+3. **Generate Summary**: After recording, click "Generate Summary" for an AI-powered meeting summary
+4. **Export**: Download both the full transcript and summary as text files
+
+## Model Information
+
+### Transcription (WhisperX)
+
+- **Model**: OpenAI Whisper base model with WhisperX enhancements
+- **Features**: Speaker diarization, timestamp alignment
+- **Size**: ~150MB
+
+### Summarization (Llama 3.2)
+
+- **1B Model**: Fast, good for basic summaries (~1.2GB)
+- **3B Model**: Better quality summaries (~2.5GB)
+- **Format**: GGUF quantized models
+
+## Privacy & Security
+
+- All processing happens locally on your machine
+- No audio or text data is sent to external servers
+- Models are bundled with the application
+- Meeting data stays on your device
@@ -1,16 +1,6 @@
-# New Repository Template
+# Chronara

-This template contains all of our basic files for a new GitHub repository. There is also a handy workflow that will create an issue on a new repository made from this template, with a checklist for the steps we usually take in setting up a new repository.
-
-If you're starting a Node.JS project with TypeScript, we have a [specific template](https://github.com/naomi-lgbt/nodejs-typescript-template) for that purpose.
-
-## Readme
-
-Delete all of the above text (including this line), and uncomment the below text to use our standard readme template.
-
-<!-- # Project Name
-
-Project Description
+A meeting transcription and summarisation tool that uses 100% local models.

 ## Live Version

@@ -36,4 +26,4 @@ Copyright held by Naomi Carrigan.

 ## Contact

-We may be contacted through our [Chat Server](http://chat.nhcarrigan.com) or via email at `contact@nhcarrigan.com`. -->
+We may be contacted through our [Chat Server](http://chat.nhcarrigan.com) or via email at `contact@nhcarrigan.com`.
@@ -0,0 +1,40 @@
+#!/bin/bash
+set -e
+
+echo "🔍 Running all checks..."
+echo "========================================"
+
+echo ""
+echo "📦 Installing dependencies..."
+pnpm install
+
+echo ""
+echo "🔎 Running ESLint..."
+pnpm lint
+
+echo ""
+echo "💅 Running Prettier check..."
+pnpm format:check
+
+echo ""
+echo "🏗️  Building frontend..."
+pnpm build
+
+echo ""
+echo "🧪 Running frontend tests..."
+pnpm test
+
+echo ""
+echo "🦀 Running Clippy..."
+cd src-tauri
+cargo clippy --all-targets --all-features -- -D warnings
+
+echo ""
+echo "🧪 Running Rust tests..."
+cargo test
+
+cd ..
+
+echo ""
+echo "========================================"
+echo "✅ All checks passed!"
@@ -0,0 +1,34 @@
+import js from "@eslint/js";
+import tseslint from "typescript-eslint";
+import reactHooks from "eslint-plugin-react-hooks";
+import reactRefresh from "eslint-plugin-react-refresh";
+import prettier from "eslint-config-prettier";
+import globals from "globals";
+
+export default tseslint.config(
+  js.configs.recommended,
+  ...tseslint.configs.recommended,
+  prettier,
+  {
+    languageOptions: {
+      globals: {
+        ...globals.browser,
+        ...globals.node,
+      },
+    },
+  },
+  {
+    files: ["**/*.{ts,tsx}"],
+    plugins: {
+      "react-hooks": reactHooks,
+      "react-refresh": reactRefresh,
+    },
+    rules: {
+      ...reactHooks.configs.recommended.rules,
+      "react-refresh/only-export-components": ["warn", { allowConstantExport: true }],
+    },
+  },
+  {
+    ignores: ["build/", "dist/", "src-tauri/target/", "node_modules/"],
+  }
+);
@@ -0,0 +1,14 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Tauri + React + Typescript</title>
+  </head>
+
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>
@@ -0,0 +1,49 @@
+{
+  "name": "chronara",
+  "private": true,
+  "version": "0.1.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc && vite build",
+    "lint": "eslint .",
+    "lint:fix": "eslint . --fix",
+    "format": "prettier --write .",
+    "format:check": "prettier --check .",
+    "preview": "vite preview",
+    "tauri": "tauri",
+    "tauri:dev": "tauri dev",
+    "build:linux": "tauri build",
+    "build:windows": "tauri build --runner cargo-xwin --target x86_64-pc-windows-msvc",
+    "build:all": "pnpm build:linux && pnpm build:windows",
+    "test": "vitest run",
+    "test:watch": "vitest",
+    "test:coverage": "vitest run --coverage"
+  },
+  "dependencies": {
+    "@tauri-apps/api": "^2",
+    "@tauri-apps/plugin-opener": "^2",
+    "react": "^19.1.0",
+    "react-dom": "^19.1.0"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.19.0",
+    "@tauri-apps/cli": "^2",
+    "@testing-library/jest-dom": "^6.9.1",
+    "@testing-library/react": "^16.3.0",
+    "@types/react": "^19.1.8",
+    "@types/react-dom": "^19.1.6",
+    "@vitejs/plugin-react": "^4.6.0",
+    "eslint": "^9.19.0",
+    "eslint-config-prettier": "^10.1.8",
+    "eslint-plugin-react-hooks": "^5.2.0",
+    "eslint-plugin-react-refresh": "^0.4.20",
+    "globals": "^17.0.0",
+    "jsdom": "^27.4.0",
+    "prettier": "^3.8.0",
+    "typescript": "~5.8.3",
+    "typescript-eslint": "^8.53.0",
+    "vite": "^7.0.4",
+    "vitest": "^4.0.17"
+  }
+}
@@ -0,0 +1,6 @@
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+</svg>
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
@@ -0,0 +1,27 @@
+[project]
+name = "chronara"
+version = "0.1.0"
+description = "Local meeting transcription and summarization app"
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi==0.115.6",
+    "uvicorn==0.34.0",
+    "whisperx==3.1.6",
+    "llama-cpp-python==0.3.4",
+    "pyaudio==0.2.14",
+    "numpy==1.26.4",
+    "torch==2.5.1",
+    "pydantic==2.10.4",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.ruff]
+line-length = 100
+target-version = "py310"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "N", "W", "B", "C90", "D"]
+ignore = ["D100", "D101", "D102", "D103", "D104", "D105", "D106", "D107"]
@@ -0,0 +1,10 @@
+fastapi==0.115.6
+uvicorn==0.134.0
+whisperx==3.1.6
+llama-cpp-python==0.3.4
+pyaudio==0.2.14
+numpy==1.26.4
+torch==2.5.1
+pydantic==2.10.4
+python-multipart==0.0.18
+websockets==14.1
@@ -0,0 +1,128 @@
+"""Build script to create a Windows executable with bundled Python environment.
+
+This script should be run ON WINDOWS, not cross-compiled from Linux.
+"""
+
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+
+def main():
+    """Create a Windows executable bundle for Chronara."""
+    project_root = Path(__file__).parent.parent
+    dist_dir = project_root / "dist-bundle"
+
+    print("🔨 Chronara Windows Build Script")
+    print("=" * 50)
+    print("\n⚠️  NOTE: This script must be run on Windows!")
+    print("    Cross-compilation from Linux is not supported.\n")
+
+    if sys.platform != "win32":
+        print("❌ This script must be run on Windows.")
+        print("   Please run this on a Windows machine or in a Windows VM.")
+        sys.exit(1)
+
+    # Clean previous builds
+    if dist_dir.exists():
+        print("Cleaning previous build...")
+        shutil.rmtree(dist_dir)
+
+    dist_dir.mkdir(exist_ok=True)
+
+    # Step 1: Create Python bundle using PyInstaller
+    print("\n📦 Creating Python bundle...")
+
+    # Create a temporary spec file for PyInstaller
+    spec_content = f"""
+# -*- mode: python ; coding: utf-8 -*-
+
+a = Analysis(
+    ['{project_root / "src" / "backend" / "main.py"}'],
+    pathex=['{project_root / "src"}'],
+    binaries=[],
+    datas=[
+        ('{project_root / "models" / "*.gguf"}', 'models'),
+    ],
+    hiddenimports=['uvicorn', 'fastapi', 'whisperx', 'llama_cpp'],
+    hookspath=[],
+    hooksconfig={{}},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.datas,
+    [],
+    name='chronara-backend',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=False,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)
+"""
+
+    spec_path = project_root / "chronara-backend.spec"
+    spec_path.write_text(spec_content)
+
+    try:
+        subprocess.run([
+            sys.executable,
+            "-m",
+            "PyInstaller",
+            str(spec_path),
+            "--clean",
+            "--noconfirm",
+            "--distpath", str(dist_dir / "backend")
+        ], check=True)
+    except subprocess.CalledProcessError:
+        print("⚠️  PyInstaller not found. Installing...")
+        subprocess.run([sys.executable, "-m", "pip", "install", "pyinstaller"], check=True)
+        subprocess.run([
+            sys.executable,
+            "-m",
+            "PyInstaller",
+            str(spec_path),
+            "--clean",
+            "--noconfirm",
+            "--distpath", str(dist_dir / "backend")
+        ], check=True)
+
+    # Clean up spec file
+    spec_path.unlink()
+
+    # Step 2: Build Tauri app (creates NSIS installer on Windows)
+    print("\n🦀 Building Tauri app with NSIS installer...")
+    subprocess.run(
+        "pnpm tauri build",
+        cwd=project_root,
+        check=True,
+        shell=True
+    )
+
+    print("\n✅ Build complete!")
+    print("\nWindows installer (.exe) location:")
+    print("  src-tauri/target/release/bundle/nsis/Chronara_0.1.0_x64_en-US.exe")
+    print("\nThis installer includes:")
+    print("  - Tauri app with React frontend")
+    print("  - Python backend (bundled)")
+    print("  - Llama model files")
+    print("  - All dependencies")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,90 @@
+"""Download required models for Chronara."""
+
+import os
+import sys
+from pathlib import Path
+from urllib.request import urlretrieve
+
+# Model download URLs
+MODELS = {
+    "llama-3.2-1B": {
+        "url": "https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf",
+        "filename": "llama-3.2-1B-instruct-Q4_K_M.gguf",
+        "size": "1.2GB",
+    },
+    "llama-3.2-3B": {
+        "url": "https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_K_M.gguf",
+        "filename": "llama-3.2-3B-instruct-Q4_K_M.gguf",
+        "size": "2.5GB",
+    },
+}
+
+
+def download_with_progress(url: str, filepath: Path):
+    """Download file with progress bar."""
+
+    def _progress(block_num, block_size, total_size):
+        downloaded = block_num * block_size
+        percent = min(downloaded * 100 / total_size, 100)
+        progress = int(50 * percent / 100)
+        sys.stdout.write(
+            f"\r[{'=' * progress}{' ' * (50 - progress)}] {percent:.1f}%"
+        )
+        sys.stdout.flush()
+
+    print(f"Downloading {filepath.name}...")
+    urlretrieve(url, filepath, reporthook=_progress)
+    print()  # New line after progress bar
+
+
+def main():
+    """Download all required models."""
+    # Get project root
+    project_root = Path(__file__).parent.parent
+    models_dir = project_root / "models"
+    models_dir.mkdir(exist_ok=True)
+
+    print("🤖 Chronara Model Downloader")
+    print("=" * 50)
+
+    # Ask which model to download
+    print("\nWhich Llama model would you like to use?")
+    print("1. Llama 3.2 1B (1.2GB) - Faster, good for basic summaries")
+    print("2. Llama 3.2 3B (2.5GB) - Better quality summaries")
+    print("3. Both models")
+
+    choice = input("\nEnter your choice (1/2/3): ").strip()
+
+    models_to_download = []
+    if choice == "1":
+        models_to_download = ["llama-3.2-1B"]
+    elif choice == "2":
+        models_to_download = ["llama-3.2-3B"]
+    elif choice == "3":
+        models_to_download = ["llama-3.2-1B", "llama-3.2-3B"]
+    else:
+        print("Invalid choice!")
+        return
+
+    # Download selected models
+    for model_name in models_to_download:
+        model_info = MODELS[model_name]
+        filepath = models_dir / model_info["filename"]
+
+        if filepath.exists():
+            print(f"\n✓ {model_name} already downloaded")
+            continue
+
+        print(f"\n📥 Downloading {model_name} ({model_info['size']})...")
+        try:
+            download_with_progress(model_info["url"], filepath)
+            print(f"✓ Downloaded {model_name} successfully!")
+        except Exception as e:
+            print(f"✗ Failed to download {model_name}: {e}")
+
+    print("\n✨ Model download complete!")
+    print("\nNote: WhisperX models will be downloaded automatically on first run.")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,24 @@
+"""Start the Chronara backend server."""
+
+import os
+import sys
+import subprocess
+from pathlib import Path
+
+# Add src directory to Python path
+src_path = Path(__file__).parent.parent / "src"
+sys.path.insert(0, str(src_path))
+
+# Set environment for bundled deployment
+os.environ["CHRONARA_BUNDLED"] = "1"
+
+# Start the FastAPI server
+subprocess.run([
+    sys.executable,
+    "-m",
+    "uvicorn",
+    "backend.main:app",
+    "--host", "127.0.0.1",
+    "--port", "8000",
+    "--reload" if os.environ.get("CHRONARA_DEV") else ""
+])
@@ -0,0 +1,7 @@
+# Generated by Cargo
+# will have compiled files and executables
+/target/
+
+# Generated by Tauri
+# will have schema files for capabilities auto-completion
+/gen/schemas
@@ -0,0 +1,34 @@
+[package]
+name = "chronara"
+version = "0.1.0"
+description = "A meeting transcription and summarisation tool using local AI models"
+authors = ["Naomi Carrigan <nhcarrigan@gmail.com>"]
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[lib]
+# The `_lib` suffix may seem redundant but it is necessary
+# to make the lib name unique and wouldn't conflict with the bin name.
+# This seems to be only an issue on Windows, see https://github.com/rust-lang/cargo/issues/8519
+name = "chronara_lib"
+crate-type = ["staticlib", "cdylib", "rlib"]
+
+[build-dependencies]
+tauri-build = { version = "2", features = [] }
+
+[dependencies]
+tauri = { version = "2", features = [] }
+tauri-plugin-opener = "2"
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
+
+[target.'cfg(windows)'.dependencies]
+windows = { version = "0.62", features = [
+    "Win32_System_Com",
+    "Win32_Foundation",
+    "Win32_Media_Audio",
+    "Win32_Media_Audio_Endpoints",
+    "Win32_System_Threading",
+] }
+
@@ -0,0 +1,3 @@
+fn main() {
+    tauri_build::build()
+}
@@ -0,0 +1,7 @@
+{
+  "$schema": "../gen/schemas/desktop-schema.json",
+  "identifier": "default",
+  "description": "Capability for the main window",
+  "windows": ["main"],
+  "permissions": ["core:default", "opener:default"]
+}
@@ -0,0 +1,68 @@
+use std::process::{Child, Command};
+use std::sync::Mutex;
+use tauri::{Manager, State};
+
+struct PythonBackend {
+    process: Mutex<Option<Child>>,
+}
+
+#[tauri::command]
+fn start_backend(backend: State<PythonBackend>) -> Result<String, String> {
+    let mut process_lock = backend.process.lock().map_err(|e| e.to_string())?;
+
+    if process_lock.is_some() {
+        return Ok("Backend already running".to_string());
+    }
+
+    // Get the resource path for the bundled Python executable
+    let python_cmd = if cfg!(windows) {
+        "python"
+    } else {
+        "python3"
+    };
+
+    // Start the Python backend
+    let child = Command::new(python_cmd)
+        .args(["-m", "uvicorn", "backend.main:app", "--host", "127.0.0.1", "--port", "8000"])
+        .spawn()
+        .map_err(|e| format!("Failed to start backend: {}", e))?;
+
+    *process_lock = Some(child);
+    Ok("Backend started successfully".to_string())
+}
+
+#[tauri::command]
+fn stop_backend(backend: State<PythonBackend>) -> Result<String, String> {
+    let mut process_lock = backend.process.lock().map_err(|e| e.to_string())?;
+
+    if let Some(mut child) = process_lock.take() {
+        child.kill().map_err(|e| format!("Failed to stop backend: {}", e))?;
+        Ok("Backend stopped".to_string())
+    } else {
+        Ok("Backend not running".to_string())
+    }
+}
+
+#[cfg_attr(mobile, tauri::mobile_entry_point)]
+pub fn run() {
+    tauri::Builder::default()
+        .plugin(tauri_plugin_opener::init())
+        .manage(PythonBackend {
+            process: Mutex::new(None),
+        })
+        .invoke_handler(tauri::generate_handler![start_backend, stop_backend])
+        .on_window_event(|window, event| {
+            // Stop backend when window closes
+            if let tauri::WindowEvent::CloseRequested { .. } = event {
+                if let Some(backend) = window.try_state::<PythonBackend>() {
+                    if let Ok(mut process_lock) = backend.process.lock() {
+                        if let Some(mut child) = process_lock.take() {
+                            let _ = child.kill();
+                        }
+                    }
+                }
+            }
+        })
+        .run(tauri::generate_context!())
+        .expect("error while running tauri application");
+}
@@ -0,0 +1,6 @@
+// Prevents additional console window on Windows in release, DO NOT REMOVE!!
+#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]
+
+fn main() {
+    chronara_lib::run()
+}
@@ -0,0 +1,45 @@
+{
+  "$schema": "https://schema.tauri.app/config/2",
+  "productName": "Chronara",
+  "version": "0.1.0",
+  "identifier": "com.nhcarrigan.chronara",
+  "build": {
+    "beforeDevCommand": "pnpm dev",
+    "devUrl": "http://localhost:1420",
+    "beforeBuildCommand": "pnpm build",
+    "frontendDist": "../dist"
+  },
+  "app": {
+    "windows": [
+      {
+        "title": "Chronara - Meeting Transcription",
+        "width": 1200,
+        "height": 800,
+        "minWidth": 800,
+        "minHeight": 600
+      }
+    ],
+    "security": {
+      "csp": null
+    }
+  },
+  "bundle": {
+    "active": true,
+    "targets": ["nsis"],
+    "publisher": "NHCarrigan",
+    "copyright": "Copyright © 2026 Naomi Carrigan",
+    "category": "Productivity",
+    "shortDescription": "Meeting transcription and summarization tool using local AI models",
+    "longDescription": "Chronara provides real-time meeting transcription with speaker diarization and AI-powered summaries, all processed locally for maximum privacy.",
+    "windows": {
+      "nsis": {}
+    },
+    "icon": [
+      "icons/32x32.png",
+      "icons/128x128.png",
+      "icons/128x128@2x.png",
+      "icons/icon.icns",
+      "icons/icon.ico"
+    ]
+  }
+}
@@ -0,0 +1,344 @@
+:root {
+  font-family: Inter, Avenir, Helvetica, Arial, sans-serif;
+  font-size: 16px;
+  line-height: 1.5;
+  font-weight: 400;
+
+  --primary-color: #3b82f6;
+  --primary-hover: #2563eb;
+  --secondary-color: #10b981;
+  --danger-color: #ef4444;
+  --bg-color: #ffffff;
+  --surface-color: #f9fafb;
+  --text-color: #111827;
+  --text-secondary: #6b7280;
+  --border-color: #e5e7eb;
+  --shadow: 0 1px 3px 0 rgba(0, 0, 0, 0.1), 0 1px 2px 0 rgba(0, 0, 0, 0.06);
+
+  color: var(--text-color);
+  background-color: var(--bg-color);
+
+  font-synthesis: none;
+  text-rendering: optimizeLegibility;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+* {
+  box-sizing: border-box;
+  margin: 0;
+  padding: 0;
+}
+
+body {
+  margin: 0;
+  min-height: 100vh;
+}
+
+.container {
+  max-width: 1200px;
+  margin: 0 auto;
+  padding: 2rem;
+  min-height: 100vh;
+  display: flex;
+  flex-direction: column;
+}
+
+/* Header */
+.app-header {
+  text-align: center;
+  margin-bottom: 2rem;
+}
+
+.app-header h1 {
+  font-size: 2.5rem;
+  font-weight: 700;
+  margin-bottom: 0.5rem;
+}
+
+.app-header p {
+  color: var(--text-secondary);
+  font-size: 1.125rem;
+}
+
+/* Warning Banner */
+.warning-banner {
+  background-color: #fef3c7;
+  color: #92400e;
+  padding: 1rem;
+  border-radius: 0.5rem;
+  margin-bottom: 2rem;
+  text-align: center;
+}
+
+/* App Content */
+.app-content {
+  flex: 1;
+  display: flex;
+  flex-direction: column;
+  gap: 2rem;
+}
+
+/* Controls Section */
+.controls-section {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 1.5rem;
+}
+
+/* Audio Recorder */
+.audio-recorder {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 1rem;
+}
+
+.record-button {
+  font-size: 1.25rem;
+  padding: 1rem 2rem;
+  border-radius: 0.75rem;
+  border: 2px solid transparent;
+  background-color: var(--primary-color);
+  color: white;
+  font-weight: 600;
+  cursor: pointer;
+  transition: all 0.2s;
+  box-shadow: var(--shadow);
+}
+
+.record-button:hover {
+  background-color: var(--primary-hover);
+  transform: translateY(-2px);
+  box-shadow:
+    0 4px 6px -1px rgba(0, 0, 0, 0.1),
+    0 2px 4px -1px rgba(0, 0, 0, 0.06);
+}
+
+.record-button.recording {
+  background-color: var(--danger-color);
+}
+
+.record-button.recording:hover {
+  background-color: #dc2626;
+}
+
+.recording-indicator {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  color: var(--danger-color);
+  font-weight: 500;
+}
+
+.pulse {
+  width: 0.75rem;
+  height: 0.75rem;
+  background-color: var(--danger-color);
+  border-radius: 50%;
+  animation: pulse 1.5s infinite;
+}
+
+@keyframes pulse {
+  0% {
+    opacity: 1;
+    transform: scale(1);
+  }
+  50% {
+    opacity: 0.5;
+    transform: scale(1.2);
+  }
+  100% {
+    opacity: 1;
+    transform: scale(1);
+  }
+}
+
+/* Action Buttons */
+.action-buttons {
+  display: flex;
+  gap: 1rem;
+}
+
+.primary-button,
+.secondary-button {
+  padding: 0.75rem 1.5rem;
+  border-radius: 0.5rem;
+  border: none;
+  font-size: 1rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: all 0.2s;
+  box-shadow: var(--shadow);
+}
+
+.primary-button {
+  background-color: var(--secondary-color);
+  color: white;
+}
+
+.primary-button:hover:not(:disabled) {
+  background-color: #059669;
+  transform: translateY(-1px);
+}
+
+.primary-button:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+.secondary-button {
+  background-color: var(--surface-color);
+  color: var(--text-color);
+  border: 1px solid var(--border-color);
+}
+
+.secondary-button:hover {
+  background-color: var(--border-color);
+}
+
+/* Content Grid */
+.content-grid {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 2rem;
+  flex: 1;
+}
+
+@media (max-width: 768px) {
+  .content-grid {
+    grid-template-columns: 1fr;
+  }
+}
+
+/* Transcript Display */
+.transcript-display,
+.summary-display {
+  background-color: var(--surface-color);
+  border: 1px solid var(--border-color);
+  border-radius: 0.75rem;
+  padding: 1.5rem;
+  display: flex;
+  flex-direction: column;
+  max-height: 600px;
+}
+
+.transcript-display h2,
+.summary-display h2 {
+  font-size: 1.25rem;
+  font-weight: 600;
+  margin-bottom: 1rem;
+}
+
+.transcript-segments,
+.summary-content {
+  flex: 1;
+  overflow-y: auto;
+}
+
+.empty-state {
+  color: var(--text-secondary);
+  text-align: center;
+  padding: 2rem;
+}
+
+.segment {
+  margin-bottom: 1rem;
+  padding-bottom: 1rem;
+  border-bottom: 1px solid var(--border-color);
+}
+
+.segment:last-child {
+  border-bottom: none;
+}
+
+.segment-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 0.5rem;
+}
+
+.speaker {
+  font-weight: 600;
+  font-size: 0.875rem;
+}
+
+.timestamp {
+  font-size: 0.75rem;
+  color: var(--text-secondary);
+}
+
+.segment-text {
+  line-height: 1.6;
+}
+
+/* Summary Display */
+.summary-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 1rem;
+}
+
+.download-button {
+  padding: 0.5rem 1rem;
+  border-radius: 0.375rem;
+  border: 1px solid var(--border-color);
+  background-color: var(--bg-color);
+  font-size: 0.875rem;
+  cursor: pointer;
+  transition: all 0.2s;
+}
+
+.download-button:hover {
+  background-color: var(--surface-color);
+}
+
+.summary-text {
+  white-space: pre-wrap;
+  line-height: 1.6;
+}
+
+/* Loading */
+.loading {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  padding: 3rem;
+  gap: 1rem;
+}
+
+.spinner {
+  width: 3rem;
+  height: 3rem;
+  border: 3px solid var(--border-color);
+  border-top-color: var(--primary-color);
+  border-radius: 50%;
+  animation: spin 1s linear infinite;
+}
+
+@keyframes spin {
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+/* Dark Mode */
+@media (prefers-color-scheme: dark) {
+  :root {
+    --bg-color: #111827;
+    --surface-color: #1f2937;
+    --text-color: #f3f4f6;
+    --text-secondary: #9ca3af;
+    --border-color: #374151;
+    --shadow: 0 1px 3px 0 rgba(0, 0, 0, 0.3), 0 1px 2px 0 rgba(0, 0, 0, 0.2);
+  }
+
+  .warning-banner {
+    background-color: #451a03;
+    color: #fbbf24;
+  }
+}
@@ -0,0 +1,189 @@
+import { useState, useEffect, useRef } from "react";
+import { invoke } from "@tauri-apps/api/core";
+import "./App.css";
+import { AudioRecorder } from "./components/AudioRecorder";
+import { TranscriptDisplay } from "./components/TranscriptDisplay";
+import { SummaryDisplay } from "./components/SummaryDisplay";
+
+interface TranscriptSegment {
+  start: number;
+  end: number;
+  text: string;
+  speaker: string;
+}
+
+function App() {
+  const [isRecording, setIsRecording] = useState(false);
+  const [transcriptSegments, setTranscriptSegments] = useState<TranscriptSegment[]>([]);
+  const [summary, setSummary] = useState<string | null>(null);
+  const [isGeneratingSummary, setIsGeneratingSummary] = useState(false);
+  const [backendReady, setBackendReady] = useState(false);
+  const wsRef = useRef<WebSocket | null>(null);
+
+  useEffect(() => {
+    // Start Python backend through Tauri
+    startPythonBackend();
+  }, []);
+
+  const startPythonBackend = async () => {
+    try {
+      // Start backend through Tauri command
+      await invoke("start_backend");
+
+      // Give backend time to start up
+      setTimeout(() => {
+        checkBackendHealth();
+      }, 2000);
+    } catch (error) {
+      console.error("Failed to start backend:", error);
+    }
+  };
+
+  const checkBackendHealth = async () => {
+    try {
+      const response = await fetch("http://localhost:8000/health");
+      if (response.ok) {
+        setBackendReady(true);
+      }
+    } catch (error) {
+      console.error("Backend not ready:", error);
+      // In production, Tauri will start the backend automatically
+    }
+  };
+
+  const handleAudioData = (audioData: ArrayBuffer) => {
+    if (!wsRef.current || wsRef.current.readyState !== WebSocket.OPEN) {
+      // Create WebSocket connection
+      wsRef.current = new WebSocket("ws://localhost:8000/ws/transcribe");
+
+      wsRef.current.onopen = () => {
+        console.log("WebSocket connected");
+        // Send the audio data
+        wsRef.current?.send(audioData);
+      };
+
+      wsRef.current.onmessage = (event) => {
+        const data = JSON.parse(event.data);
+        if (data.type === "transcription" && data.data.segments) {
+          setTranscriptSegments((prev) => [...prev, ...data.data.segments]);
+        }
+      };
+
+      wsRef.current.onclose = () => {
+        console.log("WebSocket disconnected");
+      };
+    } else {
+      // Send audio data through existing connection
+      wsRef.current.send(audioData);
+    }
+  };
+
+  const generateSummary = async () => {
+    if (transcriptSegments.length === 0) return;
+
+    setIsGeneratingSummary(true);
+
+    // Combine all transcript segments into text
+    const fullTranscript = transcriptSegments
+      .map((seg) => `${seg.speaker}: ${seg.text}`)
+      .join("\n");
+
+    try {
+      const response = await fetch("http://localhost:8000/summarize", {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify({ transcript: fullTranscript }),
+      });
+
+      const data = await response.json();
+      setSummary(data.summary);
+    } catch (error) {
+      console.error("Failed to generate summary:", error);
+    } finally {
+      setIsGeneratingSummary(false);
+    }
+  };
+
+  const downloadTranscript = () => {
+    const content = transcriptSegments
+      .map((seg) => `[${formatTime(seg.start)}] ${seg.speaker}: ${seg.text}`)
+      .join("\n");
+
+    const blob = new Blob([content], { type: "text/plain" });
+    const url = URL.createObjectURL(blob);
+    const a = document.createElement("a");
+    a.href = url;
+    a.download = `meeting-transcript-${new Date().toISOString().split("T")[0]}.txt`;
+    a.click();
+    URL.revokeObjectURL(url);
+  };
+
+  const downloadSummary = () => {
+    if (!summary) return;
+
+    const blob = new Blob([summary], { type: "text/plain" });
+    const url = URL.createObjectURL(blob);
+    const a = document.createElement("a");
+    a.href = url;
+    a.download = `meeting-summary-${new Date().toISOString().split("T")[0]}.txt`;
+    a.click();
+    URL.revokeObjectURL(url);
+  };
+
+  const formatTime = (seconds: number) => {
+    const mins = Math.floor(seconds / 60);
+    const secs = Math.floor(seconds % 60);
+    return `${mins}:${secs.toString().padStart(2, "0")}`;
+  };
+
+  return (
+    <main className="container">
+      <header className="app-header">
+        <h1>🎙️ Chronara</h1>
+        <p>Local Meeting Transcription & Summarization</p>
+      </header>
+
+      {!backendReady && (
+        <div className="warning-banner">⚠️ Backend is starting up. This may take a moment...</div>
+      )}
+
+      <div className="app-content">
+        <section className="controls-section">
+          <AudioRecorder
+            onAudioData={handleAudioData}
+            isRecording={isRecording}
+            setIsRecording={setIsRecording}
+          />
+
+          {!isRecording && transcriptSegments.length > 0 && (
+            <div className="action-buttons">
+              <button className="secondary-button" onClick={downloadTranscript}>
+                📄 Download Transcript
+              </button>
+              <button
+                className="primary-button"
+                onClick={generateSummary}
+                disabled={isGeneratingSummary}
+              >
+                ✨ Generate Summary
+              </button>
+            </div>
+          )}
+        </section>
+
+        <div className="content-grid">
+          <TranscriptDisplay segments={transcriptSegments} />
+          <SummaryDisplay
+            summary={summary}
+            isLoading={isGeneratingSummary}
+            onDownload={downloadSummary}
+          />
+        </div>
+      </div>
+    </main>
+  );
+}
+
+export default App;
@@ -0,0 +1,7 @@
+import { describe, it, expect } from "vitest";
+
+describe("App", () => {
+  it("placeholder test", () => {
+    expect(true).toBe(true);
+  });
+});
@@ -0,0 +1 @@
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@@ -0,0 +1 @@
+"""Chronara backend - Local meeting transcription and summarization."""
@@ -0,0 +1,74 @@
+"""Main FastAPI application for Chronara."""
+
+import os
+from pathlib import Path
+
+from fastapi import FastAPI, WebSocket
+from fastapi.middleware.cors import CORSMiddleware
+
+from .models.audio import AudioProcessor
+from .models.llm import LlamaSummarizer
+from .models.transcriber import WhisperXTranscriber
+
+app = FastAPI(title="Chronara API", version="0.1.0")
+
+# Enable CORS for Tauri frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["tauri://localhost", "http://localhost:*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Initialize models
+MODEL_DIR = Path(__file__).parent.parent.parent / "models"
+transcriber = WhisperXTranscriber(model_dir=MODEL_DIR)
+summarizer = LlamaSummarizer(model_dir=MODEL_DIR)
+audio_processor = AudioProcessor()
+
+
+@app.get("/health")
+async def health_check():
+    """Check if the API is running and models are loaded."""
+    return {
+        "status": "healthy",
+        "models": {
+            "whisper": transcriber.is_loaded,
+            "llama": summarizer.is_loaded,
+        },
+    }
+
+
+@app.websocket("/ws/transcribe")
+async def transcribe_audio(websocket: WebSocket):
+    """WebSocket endpoint for real-time audio transcription."""
+    await websocket.accept()
+
+    try:
+        while True:
+            # Receive audio chunk
+            audio_data = await websocket.receive_bytes()
+
+            # Process audio
+            audio_chunk = audio_processor.process_chunk(audio_data)
+
+            # Transcribe if we have enough audio
+            if audio_processor.has_speech(audio_chunk):
+                result = await transcriber.transcribe_chunk(audio_chunk)
+
+                if result:
+                    await websocket.send_json({
+                        "type": "transcription",
+                        "data": result,
+                    })
+
+    except Exception as e:
+        await websocket.close(code=1000, reason=str(e))
+
+
+@app.post("/summarize")
+async def summarize_transcript(transcript: str):
+    """Summarize a meeting transcript."""
+    summary = await summarizer.summarize(transcript)
+    return {"summary": summary}
@@ -0,0 +1 @@
+"""Model modules for Chronara."""
@@ -0,0 +1,73 @@
+"""Audio processing utilities."""
+
+import io
+import wave
+from typing import Optional
+
+import numpy as np
+import pyaudio
+
+
+class AudioProcessor:
+    """Handles audio capture and processing."""
+
+    def __init__(self, sample_rate: int = 16000, channels: int = 1):
+        """Initialize audio processor."""
+        self.sample_rate = sample_rate
+        self.channels = channels
+        self.chunk_size = 1024
+        self.format = pyaudio.paInt16
+
+        # Initialize PyAudio
+        self.audio = pyaudio.PyAudio()
+
+        # Audio buffer for accumulating chunks
+        self.buffer = []
+        self.min_speech_duration = 0.5  # seconds
+
+    def start_recording(self) -> pyaudio.Stream:
+        """Start audio recording stream."""
+        stream = self.audio.open(
+            format=self.format,
+            channels=self.channels,
+            rate=self.sample_rate,
+            input=True,
+            frames_per_buffer=self.chunk_size,
+        )
+        return stream
+
+    def stop_recording(self, stream: pyaudio.Stream) -> None:
+        """Stop audio recording."""
+        stream.stop_stream()
+        stream.close()
+
+    def process_chunk(self, audio_bytes: bytes) -> np.ndarray:
+        """Convert audio bytes to numpy array."""
+        # Convert bytes to numpy array
+        audio_array = np.frombuffer(audio_bytes, dtype=np.int16)
+
+        # Normalize to [-1, 1]
+        audio_float = audio_array.astype(np.float32) / 32768.0
+
+        return audio_float
+
+    def has_speech(self, audio_chunk: np.ndarray, energy_threshold: float = 0.01) -> bool:
+        """Simple voice activity detection based on energy."""
+        # Calculate RMS energy
+        energy = np.sqrt(np.mean(audio_chunk**2))
+
+        # Check if energy exceeds threshold
+        return energy > energy_threshold
+
+    def save_audio(self, audio_data: bytes, filepath: str) -> None:
+        """Save audio data to WAV file."""
+        with wave.open(filepath, "wb") as wf:
+            wf.setnchannels(self.channels)
+            wf.setsampwidth(self.audio.get_sample_size(self.format))
+            wf.setframerate(self.sample_rate)
+            wf.writeframes(audio_data)
+
+    def __del__(self):
+        """Cleanup PyAudio."""
+        if hasattr(self, "audio"):
+            self.audio.terminate()
@@ -0,0 +1,67 @@
+"""Local LLM for meeting summarization using Llama."""
+
+from pathlib import Path
+from typing import Optional
+
+from llama_cpp import Llama
+
+
+class LlamaSummarizer:
+    """Handles meeting summarization using local Llama model."""
+
+    def __init__(self, model_dir: Path, model_size: str = "1B"):
+        """Initialize Llama model."""
+        self.model_dir = model_dir
+        self.is_loaded = False
+
+        model_path = model_dir / f"llama-3.2-{model_size}-instruct-Q4_K_M.gguf"
+
+        try:
+            self.llm = Llama(
+                model_path=str(model_path),
+                n_ctx=8192,  # Context window
+                n_threads=4,  # CPU threads
+                n_gpu_layers=-1,  # Use GPU if available
+                verbose=False,
+            )
+            self.is_loaded = True
+        except Exception as e:
+            print(f"Failed to load Llama model: {e}")
+            self.is_loaded = False
+
+    async def summarize(self, transcript: str) -> Optional[str]:
+        """Generate a meeting summary from transcript."""
+        if not self.is_loaded:
+            return None
+
+        prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are a helpful assistant that creates concise meeting summaries. Focus on:
+- Key decisions made
+- Action items and who owns them
+- Important discussions and their outcomes
+- Next steps
+
+Keep the summary structured and easy to scan.<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+Please summarize this meeting transcript:
+
+{transcript}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+Meeting Summary:
+"""
+
+        try:
+            response = self.llm(
+                prompt,
+                max_tokens=1024,
+                temperature=0.7,
+                top_p=0.9,
+                stop=["<|eot_id|>", "<|end_of_text|>"],
+            )
+
+            return response["choices"][0]["text"].strip()
+
+        except Exception as e:
+            print(f"Summarization error: {e}")
+            return None
@@ -0,0 +1,88 @@
+"""WhisperX transcription with speaker diarization."""
+
+import json
+from pathlib import Path
+from typing import Any, Optional
+
+import numpy as np
+import torch
+import whisperx
+
+
+class WhisperXTranscriber:
+    """Handles audio transcription and speaker diarization using WhisperX."""
+
+    def __init__(self, model_dir: Path, model_size: str = "base"):
+        """Initialize WhisperX with local models."""
+        self.model_dir = model_dir
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.compute_type = "float16" if self.device == "cuda" else "int8"
+        self.is_loaded = False
+
+        try:
+            # Load ASR model
+            self.model = whisperx.load_model(
+                model_size,
+                self.device,
+                compute_type=self.compute_type,
+                download_root=str(model_dir / "whisper"),
+            )
+
+            # Load alignment model
+            self.align_model, self.align_metadata = whisperx.load_align_model(
+                language_code="en",
+                device=self.device,
+                model_dir=str(model_dir / "alignment"),
+            )
+
+            # Load diarization pipeline
+            self.diarize_model = whisperx.DiarizationPipeline(
+                device=self.device,
+                model_name=str(model_dir / "diarization"),
+            )
+
+            self.is_loaded = True
+        except Exception as e:
+            print(f"Failed to load WhisperX models: {e}")
+            self.is_loaded = False
+
+    async def transcribe_chunk(self, audio_chunk: np.ndarray) -> Optional[dict[str, Any]]:
+        """Transcribe an audio chunk with speaker diarization."""
+        if not self.is_loaded:
+            return None
+
+        try:
+            # Transcribe
+            result = self.model.transcribe(
+                audio_chunk,
+                batch_size=16,
+            )
+
+            # Align whisper output
+            result = whisperx.align(
+                result["segments"],
+                self.align_model,
+                self.align_metadata,
+                audio_chunk,
+                self.device,
+            )
+
+            # Diarize
+            diarize_segments = self.diarize_model(audio_chunk)
+            result = whisperx.assign_word_speakers(diarize_segments, result)
+
+            # Format output
+            formatted_result = []
+            for segment in result["segments"]:
+                formatted_result.append({
+                    "start": segment["start"],
+                    "end": segment["end"],
+                    "text": segment["text"],
+                    "speaker": segment.get("speaker", "Unknown"),
+                })
+
+            return {"segments": formatted_result}
+
+        except Exception as e:
+            print(f"Transcription error: {e}")
+            return None
@@ -0,0 +1,83 @@
+import { useRef, useEffect } from "react";
+
+interface AudioRecorderProps {
+  onAudioData: (data: ArrayBuffer) => void;
+  isRecording: boolean;
+  setIsRecording: (recording: boolean) => void;
+}
+
+export function AudioRecorder({ onAudioData, isRecording, setIsRecording }: AudioRecorderProps) {
+  const mediaRecorderRef = useRef<MediaRecorder | null>(null);
+  const streamRef = useRef<MediaStream | null>(null);
+
+  useEffect(() => {
+    return () => {
+      // Cleanup on unmount
+      if (streamRef.current) {
+        streamRef.current.getTracks().forEach((track) => track.stop());
+      }
+    };
+  }, []);
+
+  const startRecording = async () => {
+    try {
+      const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+      streamRef.current = stream;
+
+      const mediaRecorder = new MediaRecorder(stream, {
+        mimeType: "audio/webm",
+      });
+
+      mediaRecorderRef.current = mediaRecorder;
+
+      const chunks: Blob[] = [];
+
+      mediaRecorder.ondataavailable = (event) => {
+        if (event.data.size > 0) {
+          chunks.push(event.data);
+        }
+      };
+
+      mediaRecorder.onstop = async () => {
+        const blob = new Blob(chunks, { type: "audio/webm" });
+        const arrayBuffer = await blob.arrayBuffer();
+        onAudioData(arrayBuffer);
+        chunks.length = 0;
+      };
+
+      // Send data every second for real-time processing
+      mediaRecorder.start(1000);
+      setIsRecording(true);
+    } catch (error) {
+      console.error("Error starting recording:", error);
+      alert("Failed to access microphone");
+    }
+  };
+
+  const stopRecording = () => {
+    if (mediaRecorderRef.current && isRecording) {
+      mediaRecorderRef.current.stop();
+      setIsRecording(false);
+
+      if (streamRef.current) {
+        streamRef.current.getTracks().forEach((track) => track.stop());
+      }
+    }
+  };
+
+  return (
+    <div className="audio-recorder">
+      <button
+        className={`record-button ${isRecording ? "recording" : ""}`}
+        onClick={isRecording ? stopRecording : startRecording}
+      >
+        {isRecording ? "⏹ Stop Recording" : "🎙️ Start Recording"}
+      </button>
+      {isRecording && (
+        <div className="recording-indicator">
+          <span className="pulse"></span> Recording...
+        </div>
+      )}
+    </div>
+  );
+}
@@ -0,0 +1,32 @@
+interface SummaryDisplayProps {
+  summary: string | null;
+  isLoading: boolean;
+  onDownload: () => void;
+}
+
+export function SummaryDisplay({ summary, isLoading, onDownload }: SummaryDisplayProps) {
+  return (
+    <div className="summary-display">
+      <div className="summary-header">
+        <h2>Meeting Summary</h2>
+        {summary && (
+          <button className="download-button" onClick={onDownload}>
+            📥 Download
+          </button>
+        )}
+      </div>
+      <div className="summary-content">
+        {isLoading ? (
+          <div className="loading">
+            <div className="spinner"></div>
+            <p>Generating summary...</p>
+          </div>
+        ) : summary ? (
+          <div className="summary-text">{summary}</div>
+        ) : (
+          <p className="empty-state">Summary will appear here after recording is complete.</p>
+        )}
+      </div>
+    </div>
+  );
+}
@@ -0,0 +1,57 @@
+interface TranscriptSegment {
+  start: number;
+  end: number;
+  text: string;
+  speaker: string;
+}
+
+interface TranscriptDisplayProps {
+  segments: TranscriptSegment[];
+}
+
+export function TranscriptDisplay({ segments }: TranscriptDisplayProps) {
+  const formatTime = (seconds: number) => {
+    const mins = Math.floor(seconds / 60);
+    const secs = Math.floor(seconds % 60);
+    return `${mins}:${secs.toString().padStart(2, "0")}`;
+  };
+
+  const getSpeakerColor = (speaker: string) => {
+    const colors = [
+      "#3b82f6", // blue
+      "#10b981", // green
+      "#f59e0b", // amber
+      "#ef4444", // red
+      "#8b5cf6", // purple
+      "#14b8a6", // teal
+    ];
+    const speakerParts = speaker.split("_");
+    const index = speakerParts[1] ? parseInt(speakerParts[1], 10) % colors.length : 0;
+    return colors[index];
+  };
+
+  return (
+    <div className="transcript-display">
+      <h2>Transcript</h2>
+      <div className="transcript-segments">
+        {segments.length === 0 ? (
+          <p className="empty-state">No transcript yet. Start recording to begin.</p>
+        ) : (
+          segments.map((segment, index) => (
+            <div key={index} className="segment">
+              <div className="segment-header">
+                <span className="speaker" style={{ color: getSpeakerColor(segment.speaker) }}>
+                  {segment.speaker}
+                </span>
+                <span className="timestamp">
+                  {formatTime(segment.start)} - {formatTime(segment.end)}
+                </span>
+              </div>
+              <p className="segment-text">{segment.text}</p>
+            </div>
+          ))
+        )}
+      </div>
+    </div>
+  );
+}
@@ -0,0 +1,9 @@
+import React from "react";
+import ReactDOM from "react-dom/client";
+import App from "./App";
+
+ReactDOM.createRoot(document.getElementById("root") as HTMLElement).render(
+  <React.StrictMode>
+    <App />
+  </React.StrictMode>
+);
@@ -0,0 +1 @@
+/// <reference types="vite/client" />
@@ -0,0 +1,21 @@
+{
+  "compilerOptions": {
+    "target": "ES2020",
+    "useDefineForClassFields": true,
+    "lib": ["ES2020", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "noEmit": true,
+    "jsx": "react-jsx",
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true
+  },
+  "include": ["src"],
+  "references": [{ "path": "./tsconfig.node.json" }]
+}
@@ -0,0 +1,10 @@
+{
+  "compilerOptions": {
+    "composite": true,
+    "skipLibCheck": true,
+    "module": "ESNext",
+    "moduleResolution": "bundler",
+    "allowSyntheticDefaultImports": true
+  },
+  "include": ["vite.config.ts"]
+}
@@ -0,0 +1,32 @@
+import { defineConfig } from "vite";
+import react from "@vitejs/plugin-react";
+
+// @ts-expect-error process is a nodejs global
+const host = process.env.TAURI_DEV_HOST;
+
+// https://vite.dev/config/
+export default defineConfig(async () => ({
+  plugins: [react()],
+
+  // Vite options tailored for Tauri development and only applied in `tauri dev` or `tauri build`
+  //
+  // 1. prevent Vite from obscuring rust errors
+  clearScreen: false,
+  // 2. tauri expects a fixed port, fail if that port is not available
+  server: {
+    port: 1420,
+    strictPort: true,
+    host: host || false,
+    hmr: host
+      ? {
+          protocol: "ws",
+          host,
+          port: 1421,
+        }
+      : undefined,
+    watch: {
+      // 3. tell Vite to ignore watching `src-tauri`
+      ignored: ["**/src-tauri/**"],
+    },
+  },
+}));
@@ -0,0 +1,12 @@
+import { defineConfig } from "vitest/config";
+import react from "@vitejs/plugin-react";
+
+export default defineConfig({
+  plugins: [react()],
+  test: {
+    include: ["src/**/*.{test,spec}.{js,ts,tsx}"],
+    environment: "jsdom",
+    setupFiles: ["./vitest.setup.ts"],
+    globals: true,
+  },
+});
@@ -0,0 +1 @@
+import "@testing-library/jest-dom/vitest";
Author	SHA1	Message	Date
naomi	3c8a46e5a6	feat: add meeting transcription app scaffolding Security Scan and Upload / Security & DefectDojo Upload (pull_request) Successful in 48s Details CI / Lint & Test (pull_request) Successful in 14m18s Details CI / Build Linux (pull_request) Successful in 14m19s Details CI / Build Windows (cross-compile) (pull_request) Failing after 19m39s Details - Add Python backend structure with FastAPI for transcription/summarization - Add React UI with audio recording, transcript, and summary views - Configure Tauri to manage Python backend lifecycle - Set up Windows cross-compilation with cargo-xwin - Add Gitea CI workflow for lint, test, and multi-platform builds - Configure ESLint, Prettier, and Vitest for code quality Note: App scaffolding only - Python env and models not yet set up	2026-01-21 20:18:03 -08:00
naomi	96494a9997	feat: prep the scaffolding	2026-01-21 17:49:25 -08:00
				`@@ -0,0 +1 @@`
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				`@@ -0,0 +1 @@`
				`"""Chronara backend - Local meeting transcription and summarization."""`