generated from nhcarrigan/template
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
This commit is contained in:
@@ -1,4 +0,0 @@
|
||||
[target.x86_64-pc-windows-gnu]
|
||||
linker = "x86_64-w64-mingw32-gcc"
|
||||
ar = "x86_64-w64-mingw32-ar"
|
||||
rustflags = ["-C", "link-arg=-lws2_32", "-C", "link-arg=-lbcrypt", "-C", "link-arg=-lole32", "-C", "link-arg=-luuid"]
|
||||
@@ -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 $DD_TOKEN" \
|
||||
-F "active=true" \
|
||||
@@ -174,4 +174,4 @@ jobs:
|
||||
exit 1
|
||||
else
|
||||
echo "Upload Success!"
|
||||
fi
|
||||
fi
|
||||
|
||||
+30
@@ -22,3 +22,33 @@ dist-ssr
|
||||
*.njsproj
|
||||
*.sln
|
||||
*.sw?
|
||||
|
||||
# Python
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$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
|
||||
}
|
||||
+120
@@ -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
|
||||
@@ -26,4 +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`.
|
||||
|
||||
@@ -1,89 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Colors for output
|
||||
RED='\033[0;31m'
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
BLUE='\033[0;34m'
|
||||
NC='\033[0m' # No Color
|
||||
|
||||
echo -e "${BLUE}🚀 Building Chronara for all platforms...${NC}"
|
||||
|
||||
# Function to run a build and check its status
|
||||
run_build() {
|
||||
local target=$1
|
||||
local desc=$2
|
||||
|
||||
echo -e "\n${YELLOW}Building: ${desc}${NC}"
|
||||
|
||||
if pnpm tauri build --target "$target"; then
|
||||
echo -e "${GREEN}✓ ${desc} build succeeded${NC}"
|
||||
return 0
|
||||
else
|
||||
echo -e "${RED}✗ ${desc} build failed${NC}"
|
||||
return 1
|
||||
fi
|
||||
}
|
||||
|
||||
# Ensure we're using the correct Node version
|
||||
source /home/naomi/.nvm/nvm.sh
|
||||
nvm use 24.11.1
|
||||
|
||||
# Install dependencies if needed
|
||||
echo -e "${YELLOW}Installing dependencies...${NC}"
|
||||
pnpm install
|
||||
|
||||
# Build frontend first
|
||||
echo -e "${YELLOW}Building frontend...${NC}"
|
||||
pnpm build
|
||||
|
||||
# Track if any builds fail
|
||||
failed=0
|
||||
|
||||
# Linux builds (native in WSL)
|
||||
echo -e "\n${BLUE}Building Linux targets...${NC}"
|
||||
run_build "x86_64-unknown-linux-gnu" "Linux AppImage/Deb/RPM" || failed=1
|
||||
|
||||
# Windows build (cross-compile from WSL)
|
||||
echo -e "\n${BLUE}Building Windows target...${NC}"
|
||||
|
||||
# Check if Windows target is installed
|
||||
if ! rustup target list --installed | grep -q "x86_64-pc-windows-gnu"; then
|
||||
echo -e "${YELLOW}Installing Windows target...${NC}"
|
||||
rustup target add x86_64-pc-windows-gnu
|
||||
fi
|
||||
|
||||
# Check if full mingw-w64 toolchain is installed
|
||||
if ! command -v x86_64-w64-mingw32-gcc &> /dev/null || ! command -v x86_64-w64-mingw32-dlltool &> /dev/null; then
|
||||
echo -e "${RED}Windows cross-compilation tools are missing!${NC}"
|
||||
echo -e "${YELLOW}Please install the full mingw-w64 toolchain:${NC}"
|
||||
echo -e "${YELLOW} sudo apt-get update${NC}"
|
||||
echo -e "${YELLOW} sudo apt-get install -y gcc-mingw-w64-x86-64 g++-mingw-w64-x86-64 mingw-w64-tools${NC}"
|
||||
echo -e "${RED}Skipping Windows build...${NC}"
|
||||
SKIP_WINDOWS=1
|
||||
fi
|
||||
|
||||
# Set up environment for Windows cross-compilation
|
||||
export CARGO_TARGET_X86_64_PC_WINDOWS_GNU_LINKER="x86_64-w64-mingw32-gcc"
|
||||
export CC_x86_64_pc_windows_gnu="x86_64-w64-mingw32-gcc"
|
||||
export CXX_x86_64_pc_windows_gnu="x86_64-w64-mingw32-g++"
|
||||
export AR_x86_64_pc_windows_gnu="x86_64-w64-mingw32-ar"
|
||||
|
||||
if [ -z "$SKIP_WINDOWS" ]; then
|
||||
run_build "x86_64-pc-windows-gnu" "Windows NSIS" || failed=1
|
||||
fi
|
||||
|
||||
# Summary
|
||||
echo -e "\n${YELLOW}========================================${NC}"
|
||||
if [ $failed -eq 0 ]; then
|
||||
echo -e "${GREEN}✨ All builds completed successfully!${NC}"
|
||||
echo -e "${GREEN}Build outputs:${NC}"
|
||||
echo -e "${GREEN} Linux: src-tauri/target/release/bundle/appimage/chronara_*.AppImage${NC}"
|
||||
echo -e "${GREEN} Linux: src-tauri/target/release/bundle/deb/chronara_*.deb${NC}"
|
||||
echo -e "${GREEN} Linux: src-tauri/target/release/bundle/rpm/chronara-*.rpm${NC}"
|
||||
echo -e "${GREEN} Windows: src-tauri/target/x86_64-pc-windows-gnu/release/bundle/nsis/Chronara_*.exe${NC}"
|
||||
exit 0
|
||||
else
|
||||
echo -e "${RED}❌ Some builds failed. Check the output above for details.${NC}"
|
||||
exit 1
|
||||
fi
|
||||
Executable
+40
@@ -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!"
|
||||
+33
-2
@@ -1,3 +1,34 @@
|
||||
import nhcarriganConfig from "@nhcarrigan/eslint-config";
|
||||
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 [...nhcarriganConfig];
|
||||
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/"],
|
||||
}
|
||||
);
|
||||
|
||||
+22
-6
@@ -7,11 +7,18 @@
|
||||
"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",
|
||||
"tauri:build": "tauri build",
|
||||
"build:all": "./build-all.sh"
|
||||
"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",
|
||||
@@ -20,14 +27,23 @@
|
||||
"react-dom": "^19.1.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@nhcarrigan/eslint-config": "2.0.0",
|
||||
"@nhcarrigan/typescript-config": "4.0.0",
|
||||
"@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": "^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",
|
||||
"vite": "^7.0.4"
|
||||
"typescript-eslint": "^8.53.0",
|
||||
"vite": "^7.0.4",
|
||||
"vitest": "^4.0.17"
|
||||
}
|
||||
}
|
||||
|
||||
Generated
+880
-1536
File diff suppressed because it is too large
Load Diff
@@ -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 ""
|
||||
])
|
||||
@@ -3,8 +3,5 @@
|
||||
"identifier": "default",
|
||||
"description": "Capability for the main window",
|
||||
"windows": ["main"],
|
||||
"permissions": [
|
||||
"core:default",
|
||||
"opener:default"
|
||||
]
|
||||
"permissions": ["core:default", "opener:default"]
|
||||
}
|
||||
|
||||
+58
-4
@@ -1,14 +1,68 @@
|
||||
// Learn more about Tauri commands at https://tauri.app/develop/calling-rust/
|
||||
use std::process::{Child, Command};
|
||||
use std::sync::Mutex;
|
||||
use tauri::{Manager, State};
|
||||
|
||||
struct PythonBackend {
|
||||
process: Mutex<Option<Child>>,
|
||||
}
|
||||
|
||||
#[tauri::command]
|
||||
fn greet(name: &str) -> String {
|
||||
format!("Hello, {}! You've been greeted from Rust!", name)
|
||||
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())
|
||||
.invoke_handler(tauri::generate_handler![greet])
|
||||
.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");
|
||||
}
|
||||
|
||||
@@ -12,9 +12,11 @@
|
||||
"app": {
|
||||
"windows": [
|
||||
{
|
||||
"title": "Chronara",
|
||||
"width": 800,
|
||||
"height": 600
|
||||
"title": "Chronara - Meeting Transcription",
|
||||
"width": 1200,
|
||||
"height": 800,
|
||||
"minWidth": 800,
|
||||
"minHeight": 600
|
||||
}
|
||||
],
|
||||
"security": {
|
||||
@@ -23,23 +25,12 @@
|
||||
},
|
||||
"bundle": {
|
||||
"active": true,
|
||||
"targets": ["app", "deb", "rpm", "appimage", "nsis"],
|
||||
"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.",
|
||||
"linux": {
|
||||
"deb": {
|
||||
"depends": []
|
||||
},
|
||||
"rpm": {
|
||||
"release": "1"
|
||||
},
|
||||
"appimage": {
|
||||
"bundleMediaFramework": true
|
||||
}
|
||||
},
|
||||
"windows": {
|
||||
"nsis": {}
|
||||
},
|
||||
|
||||
+304
-76
@@ -1,116 +1,344 @@
|
||||
.logo.vite:hover {
|
||||
filter: drop-shadow(0 0 2em #747bff);
|
||||
}
|
||||
|
||||
.logo.react:hover {
|
||||
filter: drop-shadow(0 0 2em #61dafb);
|
||||
}
|
||||
:root {
|
||||
font-family: Inter, Avenir, Helvetica, Arial, sans-serif;
|
||||
font-size: 16px;
|
||||
line-height: 24px;
|
||||
line-height: 1.5;
|
||||
font-weight: 400;
|
||||
|
||||
color: #0f0f0f;
|
||||
background-color: #f6f6f6;
|
||||
--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;
|
||||
-webkit-text-size-adjust: 100%;
|
||||
}
|
||||
|
||||
* {
|
||||
box-sizing: border-box;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0;
|
||||
min-height: 100vh;
|
||||
}
|
||||
|
||||
.container {
|
||||
margin: 0;
|
||||
padding-top: 10vh;
|
||||
max-width: 1200px;
|
||||
margin: 0 auto;
|
||||
padding: 2rem;
|
||||
min-height: 100vh;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
/* 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;
|
||||
}
|
||||
|
||||
.logo {
|
||||
height: 6em;
|
||||
padding: 1.5em;
|
||||
will-change: filter;
|
||||
transition: 0.75s;
|
||||
}
|
||||
|
||||
.logo.tauri:hover {
|
||||
filter: drop-shadow(0 0 2em #24c8db);
|
||||
}
|
||||
|
||||
.row {
|
||||
/* App Content */
|
||||
.app-content {
|
||||
flex: 1;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
flex-direction: column;
|
||||
gap: 2rem;
|
||||
}
|
||||
|
||||
a {
|
||||
font-weight: 500;
|
||||
color: #646cff;
|
||||
text-decoration: inherit;
|
||||
/* Controls Section */
|
||||
.controls-section {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
gap: 1.5rem;
|
||||
}
|
||||
|
||||
a:hover {
|
||||
color: #535bf2;
|
||||
/* Audio Recorder */
|
||||
.audio-recorder {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
gap: 1rem;
|
||||
}
|
||||
|
||||
h1 {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
input,
|
||||
button {
|
||||
border-radius: 8px;
|
||||
border: 1px solid transparent;
|
||||
padding: 0.6em 1.2em;
|
||||
font-size: 1em;
|
||||
font-weight: 500;
|
||||
font-family: inherit;
|
||||
color: #0f0f0f;
|
||||
background-color: #ffffff;
|
||||
transition: border-color 0.25s;
|
||||
box-shadow: 0 2px 2px rgba(0, 0, 0, 0.2);
|
||||
}
|
||||
|
||||
button {
|
||||
.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);
|
||||
}
|
||||
|
||||
button:hover {
|
||||
border-color: #396cd8;
|
||||
}
|
||||
button:active {
|
||||
border-color: #396cd8;
|
||||
background-color: #e8e8e8;
|
||||
.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);
|
||||
}
|
||||
|
||||
input,
|
||||
button {
|
||||
outline: none;
|
||||
.record-button.recording {
|
||||
background-color: var(--danger-color);
|
||||
}
|
||||
|
||||
#greet-input {
|
||||
margin-right: 5px;
|
||||
.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 {
|
||||
color: #f6f6f6;
|
||||
background-color: #2f2f2f;
|
||||
--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);
|
||||
}
|
||||
|
||||
a:hover {
|
||||
color: #24c8db;
|
||||
}
|
||||
|
||||
input,
|
||||
button {
|
||||
color: #ffffff;
|
||||
background-color: #0f0f0f98;
|
||||
}
|
||||
button:active {
|
||||
background-color: #0f0f0f69;
|
||||
.warning-banner {
|
||||
background-color: #451a03;
|
||||
color: #fbbf24;
|
||||
}
|
||||
}
|
||||
|
||||
+174
-36
@@ -1,49 +1,187 @@
|
||||
import { useState } from "react";
|
||||
import reactLogo from "./assets/react.svg";
|
||||
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 [greetMsg, setGreetMsg] = useState("");
|
||||
const [name, setName] = useState("");
|
||||
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);
|
||||
|
||||
async function greet() {
|
||||
// Learn more about Tauri commands at https://tauri.app/develop/calling-rust/
|
||||
setGreetMsg(await invoke("greet", { name }));
|
||||
}
|
||||
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">
|
||||
<h1>Welcome to Tauri + React</h1>
|
||||
<header className="app-header">
|
||||
<h1>🎙️ Chronara</h1>
|
||||
<p>Local Meeting Transcription & Summarization</p>
|
||||
</header>
|
||||
|
||||
<div className="row">
|
||||
<a href="https://vite.dev" target="_blank">
|
||||
<img src="/vite.svg" className="logo vite" alt="Vite logo" />
|
||||
</a>
|
||||
<a href="https://tauri.app" target="_blank">
|
||||
<img src="/tauri.svg" className="logo tauri" alt="Tauri logo" />
|
||||
</a>
|
||||
<a href="https://react.dev" target="_blank">
|
||||
<img src={reactLogo} className="logo react" alt="React logo" />
|
||||
</a>
|
||||
{!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>
|
||||
<p>Click on the Tauri, Vite, and React logos to learn more.</p>
|
||||
|
||||
<form
|
||||
className="row"
|
||||
onSubmit={(e) => {
|
||||
e.preventDefault();
|
||||
greet();
|
||||
}}
|
||||
>
|
||||
<input
|
||||
id="greet-input"
|
||||
onChange={(e) => setName(e.currentTarget.value)}
|
||||
placeholder="Enter a name..."
|
||||
/>
|
||||
<button type="submit">Greet</button>
|
||||
</form>
|
||||
<p>{greetMsg}</p>
|
||||
</main>
|
||||
);
|
||||
}
|
||||
|
||||
@@ -0,0 +1,7 @@
|
||||
import { describe, it, expect } from "vitest";
|
||||
|
||||
describe("App", () => {
|
||||
it("placeholder test", () => {
|
||||
expect(true).toBe(true);
|
||||
});
|
||||
});
|
||||
@@ -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>
|
||||
);
|
||||
}
|
||||
+1
-1
@@ -5,5 +5,5 @@ import App from "./App";
|
||||
ReactDOM.createRoot(document.getElementById("root") as HTMLElement).render(
|
||||
<React.StrictMode>
|
||||
<App />
|
||||
</React.StrictMode>,
|
||||
</React.StrictMode>
|
||||
);
|
||||
|
||||
+12
-4
@@ -1,13 +1,21 @@
|
||||
{
|
||||
"extends": "@nhcarrigan/typescript-config",
|
||||
"compilerOptions": {
|
||||
"target": "ES2020",
|
||||
"useDefineForClassFields": true,
|
||||
"lib": ["ES2020", "DOM", "DOM.Iterable"],
|
||||
"module": "ESNext",
|
||||
"skipLibCheck": true,
|
||||
"moduleResolution": "bundler",
|
||||
"allowImportingTsExtensions": true,
|
||||
"resolveJsonModule": true,
|
||||
"isolatedModules": true,
|
||||
"noEmit": true,
|
||||
"lib": ["ES2020", "DOM", "DOM.Iterable"],
|
||||
"jsx": "react-jsx"
|
||||
"jsx": "react-jsx",
|
||||
"strict": true,
|
||||
"noUnusedLocals": true,
|
||||
"noUnusedParameters": true,
|
||||
"noFallthroughCasesInSwitch": true
|
||||
},
|
||||
"include": ["src"],
|
||||
"references": [{ "path": "./tsconfig.node.json" }]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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";
|
||||
Reference in New Issue
Block a user