This project extracts transcripts from YouTube videos using YouTubeTranscriptAPI or Whisper, then summarizes them using Groq's LLM. The app is built with Streamlit and runs in a Dockerized environment for easy deployment.
β
Extracts YouTube transcripts automatically
β
Uses Whisper for speech-to-text if transcripts are unavailable
β
Summarizes content using Groq LLM
β
Provides a simple Streamlit UI for easy interaction
β
Dockerized for seamless deployment
π youtube-llm-summarizer
βββ π app.py # Streamlit UI for summarization
βββ π summarizer.py # Core logic for transcript extraction & summarization
βββ π Dockerfile # Docker configuration
βββ π docker-compose.yml # Docker Compose setup
βββ π .env # Environment variables
βββ π requirements.txt # Python dependencies
βββ π temp/ # Temporary storage for audio files
git clone https://github.com/ArchitJ6/youtube-llm-summarizer.git
cd youtube-llm-summarizerCreate a .env file in the root directory and add your API keys:
GROQ_API_KEY=your_groq_api_key
docker-compose up --buildThis will:
- Pull the necessary dependencies
- Set up the Streamlit app in a container
- Run the app on http://localhost:8501
- Open http://localhost:8501
- Enter a YouTube video URL
- Click "Summarize"
- View the generated summary
If you prefer to run locally:
pip install -r requirements.txt
streamlit run app.py- Python π
- Streamlit π
- YouTubeTranscriptAPI π₯
- Whisper ποΈ
- Groq LLM π§
- Docker & Docker Compose π³
This project is licensed under the MIT License.