Update README.md

README.md

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-Here’s an updated `README.md` for your `MAAS` project, reflecting the expanded functionality and project structure that now includes the RAG-based chat system in addition to PageSpeed insights and Gemini-based analysis.
-
----
-
-# MAAS API (Metrics & AI-Assisted Suggestions)
-
-A professional FastAPI application that offers two core services:
-
-1. **PageSpeed Performance Reports** – Using Google's PageSpeed Insights and Gemini AI for analysis and recommendations.
-2. **RAG-Powered Chat System** – Retrieval-Augmented Generation (RAG) chat sessions with document ingestion, vectorstore indexing (FAISS), and persistent chat history (MongoDB).
-
-## ✨ Features
-
-* 🔍 PageSpeed Insights integration for web performance metrics
-* 🤖 Gemini AI–powered optimization report generation
-* 📚 Document ingestion and chunked embedding with FAISS
-* 💬 RAG-based conversational system per user and chat session
-* 📄 Clean modular FastAPI architecture
-* 🛠️ Configuration via environment variables
-* 🔐 Secure, with input validation and API key protection
-* 📈 Built-in health check, detailed logging, and auto-generated API docs
-
----
-
-## 🗂 Project Structure
-
-```
-MAAS/
-├── app/
-│   ├── rag/                        # RAG module for document ingestion and chat
-│   │   ├── db.py
-│   │   ├── embedding.py
-│   │   ├── routes.py               # RAG API endpoints
-│   │   ├── schemas.py
-│   │   └── utils.py
-│   │   └── chat_history.py         # chat history persistence
-
-│   ├── config.py                   # Environment & settings
-│   ├── main.py                     # FastAPI app instance & routers
-│   ├── models.py                   # Pydantic models
-│   ├── run_server.py               # Server runner
-│   └── services.py                 # PageSpeed + Gemini logic
-├── Dockerfile                      # Optional containerization
-├── requirements.txt                # Dependencies
-└── README.md                       # You're reading it
-```
-
----
-
-## 🚀 Getting Started
-
-### 1. Install Dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-### 2. Create a `.env` file
-
-```env
-PAGESPEED_API_KEY=your_pagespeed_api_key_here
-GEMINI_API_KEY=your_gemini_api_key_here
-MONGO_URI=mongodb://localhost:27017
-HOST=0.0.0.0
-PORT=8000
-DEBUG=True
-```
-
-## 2. Create & Activate Virtual Environment
-
-### Windows (PowerShell)
-```powershell
-# Create venv folder named `.venv`
-python -m venv .venv
-
-# Activate it
-.venv\Scripts\Activate.ps1
-````
-
-### Linux / macOS (bash/zsh)
-
-```bash
-# Create venv folder named .venv
-python3 -m venv .venv
-
-# Activate it
-source .venv/bin/activate
-```
-
-> **Tip:** On macOS you may need to run `chmod +x .venv/bin/activate` first if you get a permission error.
-
-### 3. Install Dependencies
-
-```bash
-pip install --upgrade pip
-pip install -r requirements.txt
-```
-
-## 4. Run the Application
-
-```bash
-# Option 1: Using the script
-python run_server.py
-
-# Option 2: Directly with uvicorn
-uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
-```
-
-## 📘 API Overview
-
-### 🔗 General
-
-| Method | Endpoint  | Description                    |
-| ------ | --------- | ------------------------------ |
-| GET    | `/`       | Welcome + links to docs/health |
-| GET    | `/health` | Health check and uptime        |
-
 ---
-
-### 🧠 PageSpeed + Gemini Endpoints
-
-| Method | Endpoint               | Description                       |
-| ------ | ---------------------- | --------------------------------- |
-| POST   | `/pagespeed`           | Fetch raw PageSpeed Insights JSON |
-| POST   | `/generate-report`     | Generate AI optimization report   |
-| POST   | `/generate-priorities` | Rank optimizations by priority    |
-
+title: MAAS
+emoji: 👀
+colorFrom: indigo
+colorTo: purple
+sdk: docker
+pinned: false
 ---
 
-### 📚 RAG Chat System Endpoints
-
-| Method | Endpoint                        | Description                                |
-| ------ | ------------------------------- | ------------------------------------------ |
-| POST   | `/rag/ingest/{user_id}`         | Ingest documents and store FAISS index     |
-| POST   | `/rag/chat/create/{user_id}`    | Start a new chat session (returns chat ID) |
-| POST   | `/rag/chat/{user_id}/{chat_id}` | Ask a question in an existing chat session |
-
----
-
-## 📎 RAG Workflow
-
-1. **Ingest Documents**
-
-   * POST `/rag/ingest/{user_id}`
-   * Body: `{"documents": ["doc 1 text", "doc 2 text", ...]}`
-
-2. **Create Chat**
-
-   * POST `/rag/chat/create/{user_id}`
-   * Response: `chat_id`
-
-3. **Ask Questions**
-
-   * POST `/rag/chat/{user_id}/{chat_id}`
-   * Body: `{"question": "What does the document say about X?"}`
-
----
-
-## 🛠 Example Usage (Python)
-
-```python
-import requests
-
-# Ingest docs
-requests.post("http://localhost:8000/rag/ingest/user123", json={
-    "documents": ["The capital of France is Paris.", "Python is a programming language."]
-})
-
-# Create chat
-res = requests.post("http://localhost:8000/rag/chat/create/user123")
-chat_id = res.json()["chat_id"]
-
-# Chat
-requests.post(f"http://localhost:8000/rag/chat/user123/{chat_id}", json={
-    "question": "What is the capital of France?"
-})
-```
-
----
-
-## 📄 API Docs
-
-Once the app is running:
-
-* Swagger UI: [http://localhost:8000/docs](http://localhost:8000/docs)
-* ReDoc: [http://localhost:8000/redoc](http://localhost:8000/redoc)
-
----
-
-## 🛡️ Error Handling
-
-* `400 Bad Request`: Invalid input
-* `404 Not Found`: Unknown endpoint or missing user/chat/doc
-* `500 Internal Server Error`: API or service errors
-
----
-
-## 🧪 Development Tips
-
-* Use `DEBUG=True` in `.env` for auto-reload and verbose logs
-* Modify `CORS` policy in `main.py` before production
-* Use `logger` calls to trace errors or logic flows
-
----
-
-## 🌍 API Key Setup
-
-### PageSpeed Insights
-
-1. [Google Cloud Console](https://console.cloud.google.com/)
-2. Enable the API, generate a key
-
-### Gemini AI
-
-1. [Google AI Studio](https://makersuite.google.com/)
-2. Create API Key
-
-Add both to your `.env`.
-
----
-
-## 📦 Docker Support
-
-Basic Dockerfile is included. To build and run:
-
-```bash
-docker build -t maas-api .
-docker run -p 8000:8000 --env-file .env maas-api
-```
-
----
-
-## 🤝 Contributing
-
-1. Follow existing modular structure
-2. Document all new endpoints clearly
-3. Test edge cases (e.g., malformed docs or bad chat IDs)
-4. Use logging for traceability
-5. Create clear, typed Pydantic schemas
-
----
-
-## 🔗 Repository
-
-[https://github.com/Hammadwakeel/MAAS](https://github.com/Hammadwakeel/MAAS-FastAPI.git)
-
----
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference