🎥 YouTube Content Assistant

A powerful Streamlit application that allows users to analyze and interact with YouTube video content through natural language questions.

🌟 Features

• YouTube Video Processing: Input any valid YouTube URL to analyze its content
• Audio Transcription: Automatically transcribes video content using Whisper AI
• Interactive Q&A: Ask questions about the video content using advanced RAG (Retrieval-Augmented Generation)
• GPU Acceleration: Utilizes CUDA for faster processing when available
• User-Friendly Interface: Clean and intuitive Streamlit interface

🚀 Installation

1. Clone the repository:

git clone https://github.com/codewithdark-git/TalkTube.git
cd TalkTube

2. Install the required dependencies:

pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the root directory with necessary API keys and configurations.

📦 Dependencies

• streamlit
• streamlit-extras
• yt-dlp
• whisper
• torch
• python-dotenv
• (other dependencies as specified in requirements.txt)

🎮 Usage

1. Run the Streamlit app:

streamlit run app.py

2. Enter a YouTube URL in the input field
3. Wait for the video to be processed and transcribed
4. Ask questions about the video content in the chat interface

💡 How It Works

1. Video Processing: The app downloads the audio from YouTube videos using yt-dlp
2. Transcription: Uses OpenAI's Whisper model to transcribe the audio content
3. Question Answering: Implements RAG (Retrieval-Augmented Generation) to provide accurate answers based on the video content

🛠️ Technical Details

• Built with Streamlit for the web interface
• Uses Whisper AI for accurate speech-to-text transcription
• Implements advanced RAG techniques for question answering
• Supports both CPU and GPU processing
• Handles various YouTube URL formats

⚠️ Notes

• Processing time depends on video length and available computing resources
• GPU acceleration significantly improves transcription speed
• Internet connection required for YouTube video download and processing