A comprehensive AI-powered public speaking analysis platform that provides real-time feedback on your presentation skills, body language, and vocal delivery.

• Real-time video recording with camera and microphone
• Instant AI-powered feedback on your speaking performance
• Live interview simulation with AI-generated questions
• Real-time audio responses using text-to-speech

• Posture & Physical Presence: Analyzes stance, gestures, and body language
• Eye Contact: Evaluates connection with audience and gaze patterns
• Vocal Delivery: Assesses pace, volume, clarity, and tonal variation
• Content & Structure: Reviews message clarity and logical flow
• Nervousness/Comfort: Identifies stress indicators and comfort levels

• Upload pre-recorded videos (.mov, .mp4)
• Detailed analysis with timestamped feedback
• Actionable recommendations for improvement
• Professional assessment summary

• Clean, responsive interface built with React and TypeScript
• Real-time video preview with mirror effect
• Progress indicators and status updates
• Tabbed navigation for different features

• React 19 with TypeScript
• Vite for fast development and building
• React Router for navigation
• Three.js with React Three Fiber for 3D visualizations
• Socket.IO for real-time communication

• Flask (Python) web server
• Google Gemini AI for video analysis and interview questions
• FFmpeg for video/audio processing
• Flask-SocketIO for WebSocket support
• Flask-CORS for cross-origin requests

• Google Gemini 2.0 Flash for video analysis
• Google Gemini 2.5 Flash for interview question generation
• Google Gemini TTS for text-to-speech responses
• Custom prompts for specialized public speaking evaluation

• Node.js (v18 or higher)
• Python 3.10+
• FFmpeg installed on your system
• Google Gemini API key

1. Clone the repository

   git clone https://github.com/yourusername/speakcheck.git
   cd speakcheck

2. Install frontend dependencies

   npm install

3. Install backend dependencies

   cd backend
   pip install flask flask-cors flask-socketio google-genai ffmpeg-python

4. Set up environment variables Create a .env file in the backend directory:

   GEMINI_API_KEY=your_gemini_api_key_here

5. Start the development servers

   Backend (Terminal 1):

   cd backend
   python app.py

   The Flask server will run on http://127.0.0.1:5000

   Frontend (Terminal 2):

   npm run dev

   The React app will run on http://localhost:5173

1. Navigate to the "live" tab
2. Grant camera and microphone permissions
3. Click "Record" to start recording your speech
4. Click "Stop" when finished
5. Click "Upload" to send for analysis
6. Click "End Live" to get comprehensive feedback

1. Navigate to the "upload" tab
2. Click or drag & drop your video file (.mov, .mp4)
3. Click "Upload" to start analysis
4. Wait for AI processing to complete
5. Review detailed feedback with timestamps

1. Navigate to the "pitch" tab
2. Record your pitch or presentation
3. Upload for specialized business pitch analysis
4. Receive targeted feedback for investor presentations

• POST /upload - Upload video for analysis
• POST /uploadInterview - Upload interview video with AI questions
• GET /feedback - Retrieve analysis results
• GET /question - Check for new AI questions
• GET /audio - Stream AI-generated audio responses
• POST /check/<filename> - Process specific video file

speakcheck/
├── backend/                 # Flask server
│   ├── app.py              # Main Flask application
│   ├── check.py            # Video analysis logic
│   ├── interview.py        # Interview question generation
│   └── feedback.json       # Analysis results
├── src/
│   ├── components/         # Reusable React components
│   │   ├── fileUploader.tsx
│   │   ├── videoDisplay.tsx
│   │   └── dynamicAvatar/
│   ├── pages/              # Main application pages
│   │   ├── home.tsx        # Main dashboard
│   │   ├── liveNew.tsx     # Live recording interface
│   │   ├── recordSpeech.tsx # Pitch recording
│   │   └── interReview.tsx # Interview review
│   └── types/              # TypeScript type definitions
├── uploads/                # Uploaded video files
└── public/                 # Static assets

The application uses custom CSS classes. Main styling can be found in:

• src/App.css - Global styles
• src/index.css - Base styles
• Component-specific styles in each component

Modify the analysis prompts in:

• backend/check.py - Main analysis prompt
• backend/interview.py - Interview question generation

This project is licensed under the MIT License - see the LICENSE file for details.

• Google Gemini AI for powerful video analysis capabilities
• React and Vite teams for excellent development tools
• FFmpeg for robust video processing
• The open-source community for inspiration and libraries

Made with ❤️ for better public speaking