This Streamlit application helps you in summarizing YouTube videos, making it easier to digest content efficiently. Additionally, it allows users to pose follow-up questions related to the video, and the application generates pertinent responses, enhancing engagement and understanding.

1. Create and activate virtual environment

python3 -m venv .venv
source .venv

2. Download all dependencies

pip install -r requirements.txt

3. Run application

streamlit run main.py 

1. Enter Youtube video link in left side bar.

2. Using Youtube Transcription api application will fetch transcriptions of Youtube video.

3. Fetched transcriptions are sent to Mistral-7B-instruction model for summarization.

4. Embeddings of transcriptions are generated using all-MiniLM-L6-v2 model

5. Generated embeddings are stored in Qdrant in memory vector database.

6. When you follow question relevant similar Youtube video context is fetched from vector database and that context is sent to Mistral-7B-instruction model along with question.

7. Mistral-7B-instruction uses fetched background context and gives answer of asked question.
   
   

   Limitations

   1. Youtube videos larger that 30 minutes length are not supported.
   2. Youtube video only in english language are only supported.