README.md

metadata

title: Video Transcript Chatbot
emoji: 🎥
colorFrom: yellow
colorTo: indigo
sdk: gradio
python_version: '3.10'

Video Transcript Chatbot

A beginner-friendly Gradio app that turns any YouTube video into a conversational chatbot using LangChain and Hugging Face Inference API.

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Features

• Dynamic Video Input: Paste a full YouTube URL or raw video ID.
• Embedding Model Selection: Pick any HF embedding model (default: sentence-transformers/all-MiniLM-L6-v2).
• LLM Model Selection: Choose any HF text-generation model (default: meta-llama/Llama-3.1-8B-Instruct).
• Secure Token Entry: You must enter your own HF API token at runtime—no hard-coded defaults.
• Conversational Memory: Multi-turn chat history is preserved.
• Retrieval-Augmented Generation: Uses FAISS + transcript context to ground answers.

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Prerequisites

• Python 3.8+
• Hugging Face API Token with Inference access:
  https://huggingface.co/settings/tokens
• Git (for cloning the repo)

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Installation

1. Clone the repo

   git clone https://github.com/<your-username>/yt-rag-chatbot.git
   cd yt-rag-chatbot
   

2. (Optional) Create a virtual environment

   python -m venv venv
   source venv/bin/activate    # macOS/Linux
   venv\Scripts\activate       # Windows
   

3. Install dependencies

   python -m venv venv
   source venv/bin/activate    # macOS/Linux
   venv\Scripts\activate       # Windows
   

Usage

1. Start the app:

   python app.py
   

2. Open your browser at the local URL (e.g. http://127.0.0.1:7860)
3. Use the UI:

• YouTube Video URL or ID: Paste your link/ID.

• Embedding Model: Leave default or enter another HF embedding model.

• LLM Model: Enter your desired HF LLM repo.

• Your HF API Token: Paste your token (input hidden).

• Click Initialize Chat to load and index the transcript.

• Ask questions in the chat window to interact with the video content.

Customization

• Default Models: Edit the default values for embedding_model_input and llm_model_input in app.py.

• Retrieval Size: Change the k value in the retriever configuration:

  retriever = vector_store.as_retriever(search_kwargs={'k': 4})