🎥 SmartNotes: Transform YouTube Videos into Intelligent Notes

SmartNotes harnesses the power of cutting-edge AI to revolutionize how we learn from video content. By combining Meta's LLaMA3-70B (via Groq's lightning-fast inference engine) with OpenAI's Whisper, SmartNotes transforms YouTube videos into comprehensive, structured notes that capture the essence of any content.

🚀 Key Features

Advanced AI Integration

• Dual-Engine Transcription: Seamlessly switches between native YouTube transcripts and Whisper ASR for maximum accuracy
• State-of-the-Art Summarization: Leverages LLaMA3-70B through Groq's ultra-efficient API for nuanced content understanding
• Multi-Language Support: Process content in 5 different languages with native-quality results

User Experience

• Interactive Video Preview: Rich thumbnail integration for content verification
• Conversation History: Track and review your note-generation history
• Streamlined Interface: Built with Streamlit for a smooth, intuitive user experience

🛠️ Technical Architecture

graph LR
    A[YouTube Video] --> B[Transcript Extraction]
    B --> C[LLaMA3 Processing]
    C --> D[Note Generation]
    B --> |Fallback| E[Whisper ASR]
    E --> C

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🔧 Installation

1. Clone & Navigate:

   git clone https://github.com/oussemabenhassena5/SmartNotes-with-Llama-and-whisper.git
   cd SmartNotes

2. Set Up Environment:

   python -m venv venv
   source venv/bin/activate  # Windows: `venv\Scripts\activate`
   pip install -r requirements.txt

3. Configure API Keys:

   # .env file
   GROQ_API_KEY=your_groq_api_key_here

4. Launch Application:

   streamlit run app.py

💡 Usage Example

from smartnotes import VideoProcessor

# Initialize processor
processor = VideoProcessor(video_url="https://youtube.com/watch?v=example")

# Generate notes
notes = processor.generate_notes(
    language="en",
    format="detailed"
)

# Access structured content
print(notes.summary)
print(notes.key_points)

🔬 Technical Deep Dive

AI Model Integration

• LLaMA3-70B: Leveraging one of the most advanced open-source language models for precise content understanding
• Whisper ASR: State-of-the-art speech recognition with robust noise handling
• Custom Prompting Engine: Sophisticated prompt engineering for optimal AI performance

Performance Optimization

• Efficient token management for processing long videos
• Smart caching system for rapid repeated access
• Parallel processing for simultaneous video and transcript handling

🤝 Contributing

Your expertise can help make SmartNotes even better! Here's how to contribute:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📚 Tech Stack

• Frontend: Streamlit
• AI Models: LLaMA3-70B, OpenAI Whisper
• APIs: Groq Inference API, YouTube Data API
• Core: Python 3.9+
• Dependencies: PyTube, youtube-transcript-api

🙏 Acknowledgements

Built with gratitude using these amazing technologies:

• Streamlit - For the sleek UI
• YouTube Transcript API - For reliable transcript extraction
• PyTube - For YouTube integration
• OpenAI Whisper - For powerful ASR capabilities
• LLaMA - For state-of-the-art language processing

📝 License

Distributed under the MIT License. See LICENSE for more information.

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Built with ❤️ by OUSSEMA

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