Update README.md

README.md

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-# 🎥 YouTube RAG Q&A System
-
-> **Ask any question about any YouTube video — powered by Retrieval-Augmented Generation**
-
-[![Python](https://img.shields.io/badge/Python-3.10+-blue.svg)](https://python.org)
-[![Groq](https://img.shields.io/badge/LLM-Groq%20LLaMA%203.3-orange.svg)](https://groq.com)
-[![FAISS](https://img.shields.io/badge/Vector%20DB-FAISS-green.svg)](https://github.com/facebookresearch/faiss)
-[![Gradio](https://img.shields.io/badge/UI-Gradio%204.x-purple.svg)](https://gradio.app)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
-
 ---
-
-## What it does
-
-Paste a YouTube URL → the system fetches the transcript, chunks it, embeds it into a local FAISS vector database, and lets you have a grounded conversation with the video content via Groq's blazing-fast LLaMA 3.3-70B model.
-
-**All processing is free and open-source.** No OpenAI key needed.
-
----
-
-## Architecture
-
-```
-YouTube URL
-    │
-    ▼
-YouTubeTranscriptApi          ← free, no API key
-    │  raw transcript text
-    ▼
-RecursiveCharacterTextSplitter  ← LangChain text splitter
-    │  ~500-char chunks with 50-char overlap
-    ▼
-SentenceTransformer            ← all-MiniLM-L6-v2, runs on CPU
-    │  384-dim normalised embeddings
-    ▼
-FAISS IndexFlatIP              ← cosine similarity, in-memory
-    │
-    │◄── user query (embedded on-the-fly)
-    │    top-4 most similar chunks retrieved
-    ▼
-Groq LLaMA-3.3-70B-Versatile  ← 128K context, ~800 tok/s
-    │  grounded, factual answer
-    ▼
-Gradio UI                      ← two-tab chat interface
-```
-
----
-
-## Quickstart (Google Colab)
-
-1. Open `youtube_rag_qa.py` in Colab (or copy-paste the cells).
-2. Add your Groq API key to **Colab Secrets** (🔑 icon in left sidebar) as `GROQ_API_KEY`.  
-   Get a free key at [console.groq.com](https://console.groq.com).
-3. Run the install cell, then the main cell.
-4. Click the Gradio **public link** that appears.
-
----
-
-## Local setup
-
-```bash
-git clone https://github.com/YOUR_USERNAME/youtube-rag-qa.git
-cd youtube-rag-qa
-
-python -m venv .venv
-source .venv/bin/activate      # Windows: .venv\Scripts\activate
-
-pip install -r requirements.txt
-
-export GROQ_API_KEY="gsk_..."  # or create a .env file
-
-python youtube_rag_qa.py
-```
-
-Open `http://localhost:7860` in your browser.
-
----
-
-## Tech stack
-
-| Component | Library | Why |
-|-----------|---------|-----|
-| Transcript | `youtube-transcript-api` | Free, no YouTube API key |
-| Embeddings | `sentence-transformers` | Local, CPU-friendly, 384-dim |
-| Vector DB | `faiss-cpu` | Fast similarity search, in-memory |
-| Text split | `langchain-text-splitters` | Robust recursive splitting |
-| LLM | `groq` + LLaMA-3.3-70B | ~800 tok/s, free tier, 128K context |
-| UI | `gradio` 4.x | Fast prototyping, public sharing link |
-
----
-
-## Deployment options
-
-### Option A — Hugging Face Spaces (recommended for portfolios)
-1. Create a new Space → Gradio SDK.
-2. Upload `youtube_rag_qa.py` as `app.py` and `requirements.txt`.
-3. Add `GROQ_API_KEY` in Space Settings → Repository secrets.
-4. Free, permanent URL, no server management.
-
-### Option B — Vercel + FastAPI backend
-1. Wrap the RAG functions in a FastAPI app (`/api/process`, `/api/chat`).
-2. Build a Next.js frontend calling those endpoints.
-3. Deploy API on Railway/Render (free tier), frontend on Vercel.
-
-### Option C — Docker + any cloud
-```dockerfile
-FROM python:3.11-slim
-WORKDIR /app
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-COPY youtube_rag_qa.py .
-CMD ["python", "youtube_rag_qa.py"]
-```
-
----
-
-## Student resources you can leverage
-
-| Resource | How to use |
-|----------|-----------|
-| **GitHub Student Developer Pack** | Free Namecheap domain + hosting credits |
-| **Azure for Students** | $100 credit — deploy on Azure Container Apps |
-| **Hugging Face** | Free Spaces hosting (always free) |
-| **Groq** | Free API tier (generous rate limits) |
-| **MongoDB Atlas** | Free 512 MB cluster — persist chat history |
-
----
-
-## Roadmap (upgrade ideas)
-
-- [ ] Persistent FAISS index saved to disk between sessions
-- [ ] Multi-video support — query across several videos at once
-- [ ] Timestamp citations — show where in the video the answer comes from
-- [ ] Streaming LLM responses (Groq supports SSE streaming)
-- [ ] Chat history saved per session to MongoDB Atlas
-- [ ] Docker + GitHub Actions CI/CD
-
----
-
-## License
-
-MIT — free to use, modify, and deploy.
+title: RAG Q&A System
+emoji: 🤖
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: "1.40.0"
+app_file: app.py
+pinned: false
+---