Tushar2005Bhadane/video-agent

🎬 AI Video Assistant

An intelligent meeting analysis tool that transcribes, summarizes, and lets you chat with your video content using RAG (Retrieval-Augmented Generation).

✨ Features

• —🔊 Audio Processing — Supports YouTube URLs and local video/audio files
• —📝 Transcription — Whisper (English) + Sarvam AI Saaras v3 (Hinglish)
• —📋 Summarization — AI-powered meeting summaries
• —✅ Action Items — Automatically extracts action items
• —🔑 Key Decisions — Identifies important decisions made
• —❓ Open Questions — Highlights unresolved questions
• —💬 RAG Chat — Chat with your meeting transcript using natural language

🛠️ Tech Stack

🚀 Getting Started (Local)

Prerequisites

• —Python 3.10+
• —ffmpeg installed on your system

Installation

# Clone the repo
git clone https://github.com/Tushar6405/video-agent.git
cd video-agent

# Create virtual environment
python -m venv .venv
.venv\Scripts\activate  # Windows
source .venv/bin/activate  # Mac/Linux

# Install dependencies
pip install -r requirements.txt

Environment Variables

Create a .env file in the root directory:

MISTRAL_API_KEY=your_mistral_api_key_here
SARVAM_API_KEY=your_sarvam_api_key_here

Get your Sarvam API key at dashboard.sarvam.ai.

Run the app

streamlit run app.py

Then open http://localhost:8501 in your browser.

☁️ Deploy on Streamlit Cloud

1. 1.Push your code to GitHub (make sure app.py and requirements.txt are in the root).

1. 1.Go to [share.streamlit.io](https://share.streamlit.io) and sign in with your GitHub account.

1. 1.Click "New app" and fill in:
2. 2.Repository: Tushar6405/video-agent
3. 3.Branch: main
4. 4.Main file path: app.py

1. 1.Add your secret keys:
2. 2.In the Streamlit Cloud dashboard, go to Settings → Secrets
3. 3.Add the following:

     MISTRAL_API_KEY = "your_mistral_api_key_here"
     SARVAM_API_KEY = "your_sarvam_api_key_here"

1. 1.Add system packages — Streamlit Cloud uses your packages.txt to install system-level dependencies. Make sure packages.txt contains:

   ffmpeg

1. 1.Click "Deploy" — your app will be live at:

   https://your-app-name.streamlit.app

⚠️ Note on Whisper: Streamlit Cloud's free tier has limited RAM (~1GB). Use a smaller Whisper model (tiny or base) for English transcription to avoid memory errors. Hinglish uses Sarvam AI's cloud API so no RAM impact there.

🌐 Transcription Details

Sarvam's Saaras v2.5 is purpose-built for Indian languages and handles mid-sentence switching between Hindi and English (code-mixing) without any drops or accuracy loss.

📁 Project Structure

video-agent/
├── app.py                 # Main Streamlit UI
├── main.py                # CLI entry point
├── requirements.txt       # Python dependencies
├── packages.txt           # System dependencies (ffmpeg)
├── readme.md
├── .gitignore
├── core/
│   ├── transcriber.py     # Whisper (English) + Sarvam (Hinglish)
│   ├── summarizer.py      # LLM summarization
│   ├── extractor.py       # Action items, decisions, questions
│   ├── rag_engine.py      # RAG pipeline
│   └── vector_store.py    # ChromaDB vector store
├── utils/
│   └── audio_processor.py # Audio download & chunking
└── downloades/             # Temporary audio files (gitignored)

💡 Usage

1. 1.Paste a YouTube URL or local file path in the sidebar
2. 2.Select your language (English or Hinglish)
3. 3.Click Analyse
4. 4.View transcript, summary, action items, decisions and questions
5. 5.Chat with your meeting using the RAG chatbot

⚙️ CLI Usage

python main.py

📄 License

MIT License

🙏 Acknowledgements

• —OpenAI Whisper
• —Sarvam AI — Saaras v2.5 for Hinglish transcription
• —LangChain
• —Mistral AI
• —Streamlit