Configure Docker and README for MuscleGrowthAI Hugging Face Space.

Point CONFIG_PATH at muscle_growth_config.yaml and update Space card metadata.

Co-authored-by: Cursor <cursoragent@cursor.com>

Dockerfile

@@ -62,9 +62,7 @@ RUN --mount=type=cache,target=/home/user/.cache/pip,uid=1000,gid=1000 \
 COPY --chown=user multi_llm_chatbot_backend/ ./
 
 # ---- Top-level configuration files (config.yaml + persona definitions) ----
-COPY --chown=user cybersecurity_config.yaml ./cybersecurity_config.yaml
-COPY --chown=user phd_config.yaml ./phd_config.yaml
-COPY --chown=user undergrad_config.yaml ./undergrad_config.yaml
+COPY --chown=user muscle_growth_config.yaml ./muscle_growth_config.yaml
 COPY --chown=user personas/ ./personas/
 
 # ---- Frontend bundle ------------------------------------------------------

README.md

@@ -10,494 +10,34 @@ app_port: 7860
 
 # MuscleGrowthAI
 
-An AI-powered bodybuilding advisor panel that helps gym-goers build personalized hypertrophy plans — programming, nutrition, recovery, form, and progress tracking — from a panel of specialized fitness AI advisors. Built on Neon AI's Collaborative Conversational AI (CCAI) framework.
+An AI-powered bodybuilding advisor panel that helps gym-goers build personalized muscle-building plans. Get guidance on hypertrophy programming, nutrition, recovery, form, and progress tracking from five specialized fitness AI advisors. Built on Neon AI's Collaborative Conversational AI (CCAI) framework.
 
 ## Hugging Face Spaces deployment
 
-This Space ships as a single Docker image built from the repository root [`Dockerfile`](Dockerfile). The container does three things:
+This Space ships as a single Docker image built from the repository root [`Dockerfile`](Dockerfile). The container:
 
-1. Builds the React frontend (CRA) at image-build time with `REACT_APP_API_URL=""` so every `fetch` issues a relative URL.
-2. Serves the bundled SPA from FastAPI at `/`, with the API exposed on `/api/...`, `/auth/...`, etc., on the same `:7860` origin.
-3. Persists user data (auth, profiles, chat sessions, onboarding, canvas state) in **SQLite via `aiosqlite`** at `${DATA_DIR}/muscle_growth_panel.db`. Mount a Hugging Face Storage Bucket at `/data` to make the database survive Space rebuilds — there is **no MongoDB**, **no Atlas**, no third-party data plane (the persistence pattern follows [`CU-Student-AIProject-Helper`](https://github.com/NeonClary/CU-Student-AIProject-Helper)).
+1. Builds the React frontend at image-build time with `REACT_APP_API_URL=""` so every `fetch` uses relative URLs.
+2. Serves the bundled SPA from FastAPI at `/`, with the API on `/api/...`, `/auth/...`, etc., on the same `:7860` origin.
+3. Persists user data in **SQLite via `aiosqlite`** at `${DATA_DIR}/`. Mount a Hugging Face Storage Bucket at `/data` to survive Space rebuilds.
 
 ### Required Space secrets
 
 | Secret | Purpose |
 |--------|---------|
-| `JWT_SECRET_KEY` | Signs auth tokens. Set this to a long random string. |
-| `GEMINI_API_KEY` | Powers the default Gemini provider (model: `gemini-2.5-flash`). |
-| `OPENAI_API_KEY` | Optional — only required if you switch to the OpenAI provider. |
-| `VLLM_API_KEY` | Optional — only required if you point the orchestrator/advisors at a Neon vLLM endpoint. |
+| `JWT_SECRET_KEY` | Signs auth tokens. Set to a long random string. |
+| `GEMINI_API_KEY` | Powers the default Gemini provider (`gemini-2.5-flash`). |
 
+### Optional secrets
 
+| Secret | Purpose |
+|--------|---------|
+| `OPENAI_API_KEY` | Only if switching to the OpenAI provider. |
+| `VLLM_API_KEY` | Only if pointing at a Neon vLLM endpoint. |
 
-## Features
-
-- **Multiple AI Advisor Personas**: Chat with 10+ specialized advisors including Methodologist, Theorist, Pragmatist, and more
-- **Document Upload & Analysis**: Upload PDFs, Word documents, and text files for context-aware advice
-- **Intelligent Document Retrieval (RAG)**: Advanced semantic search through your uploaded documents
-- **Multi-LLM Backend**: Supports both Gemini API and local Ollama models
-- **User Authentication**: Secure user accounts with persistent chat sessions
-- **Chat Session Management**: Save, load, and manage multiple conversation threads
-- **Export Capabilities**: Export chats and summaries in TXT, PDF, and DOCX formats
-- **Real-time Chat Interface**: Modern, responsive UI with advisor-specific styling
-
-## Architecture
-
-### Frontend (React)
-- **Technology**: React 18 with modern hooks and functional components
-- **Styling**: CSS custom properties with dark/light theme support
-- **State Management**: React Context and hooks
-- **Authentication**: JWT-based authentication with persistent sessions
-
-### Backend (FastAPI)
-- **Framework**: FastAPI with automatic API documentation
-- **Database**: MongoDB for user data and chat sessions
-- **Vector Database**: ChromaDB for document storage and semantic search
-- **LLM Integration**: Support for Gemini API and Ollama models
-- **Document Processing**: PDF, DOCX, and text file extraction with intelligent chunking
-- **Authentication**: JWT tokens with bcrypt password hashing
-
-## Quick Start (Docker)
-### Prerequisites
-- **Docker**
-
-### Instructions
-1. **Clone the repository**
-```bash
-git clone https://github.com/NeonGeckoCom/CCAI-Demo.git
-cd CCAI-Demo
-```
-
-1. **Configure via `.env` file**
-Create a `.env` file in the root of the project with:
-```
-GEMINI_API_KEY=<valid Gemini API key>
-JWT_SECRET_KEY=<Generated UUID>
-REACT_APP_API_URL=http://localhost:8000
-CORS_ORIGINS=http://localhost:3000
-```
-> `REACT_APP_API_URL` and `CORS_ORIGINS` must match the real addresses used if accessing the demo from a remote host.
-
-2. **Build and Run Containers**
-```bash
-docker compose up -d
-```
-
-3. **Access the application**
-   - Frontend: `http://localhost:3000`
-   - Backend API: `http://localhost:8000`
-
-## Local Installation Prerequisites
-- **Python 3.8+** (3.9+ recommended)
-- **Node.js 16+** and npm
-- **MongoDB** (Community Edition)
-- **Git**
-
-## Installation Guide
-
-### Step 1: Clone the Repository
-
-```bash
-git clone https://github.com/NeonGeckoCom/CCAI-Demo.git
-cd CCAI-Demo
-```
-
-### Step 2: MongoDB Setup
-
-#### Option A: Local MongoDB Installation
-
-**On Windows:**
-1. Download MongoDB Community Server from [mongodb.com](https://www.mongodb.com/try/download/community)
-2. Install with default settings
-3. MongoDB will run as a Windows Service automatically
-
-**On macOS:**
-```bash
-# Using Homebrew
-brew tap mongodb/brew
-brew install mongodb-community
-brew services start mongodb/brew/mongodb-community
-```
-
-**On Linux (Ubuntu/Debian):**
-```bash
-# Import MongoDB public GPG key
-wget -qO - https://www.mongodb.org/static/pgp/server-6.0.asc | sudo apt-key add -
-
-# Create list file
-echo "deb [ arch=amd64,arm64 ] https://repo.mongodb.org/apt/ubuntu focal/mongodb-org/6.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-6.0.list
-
-# Install MongoDB
-sudo apt-get update
-sudo apt-get install -y mongodb-org
-
-# Start MongoDB
-sudo systemctl start mongod
-sudo systemctl enable mongod
-```
-
-#### Option B: MongoDB Atlas (Cloud)
-1. Create a free account at [MongoDB Atlas](https://www.mongodb.com/atlas)
-2. Create a new cluster
-3. Get your connection string
-4. Skip the local MongoDB setup
-
-### Step 3: Ollama Installation (for Local LLM Support)
-
-#### Install Ollama
-
-**On Windows:**
-1. Download Ollama from [ollama.ai](https://ollama.ai)
-2. Run the installer
-3. Ollama will start automatically
-
-**On macOS:**
-```bash
-# Using Homebrew
-brew install ollama
-
-# Or download from ollama.ai
-```
-
-**On Linux:**
-```bash
-# Install Ollama
-curl -fsSL https://ollama.ai/install.sh | sh
-
-# Start Ollama service
-sudo systemctl start ollama
-sudo systemctl enable ollama
-```
-
-#### Download Required Models
-
-Once Ollama is installed, download the recommended models:
-
-```bash
-# Download the default model (recommended for development)
-ollama pull llama3.2:1b
-
-# Optional: Download larger, more capable models
-ollama pull llama3.2:3b
-ollama pull mistral:7b
-
-# Verify installation
-ollama list
-```
-
-**Note**: The `llama3.2:1b` model is small (~1.3GB) and fast, perfect for development. For production, consider larger models for better quality.
-
-### Step 4: Backend Setup
-
-1. **Navigate to the backend directory:**
-```bash
-cd multi_llm_chatbot_backend
-```
-
-2. **Create a Python virtual environment:**
-```bash
-# Create virtual environment
-python -m venv venv
-
-# Activate virtual environment
-# On Windows:
-venv\Scripts\activate
-# On macOS/Linux:
-source venv/bin/activate
-```
-
-3. **Install Python dependencies:**
-```bash
-pip install -r requirements.txt
-```
-
-4. **Set up environment variables:**
-Create a `.env` file in the `multi_llm_chatbot_backend` directory:
-
-```env
-# MongoDB Configuration
-MONGODB_CONNECTION_STRING=mongodb://localhost:27017
-MONGODB_DATABASE_NAME=phd_advisor
-
-# JWT Configuration
-JWT_SECRET_KEY=your-super-secret-jwt-key-change-this-in-production-please-make-it-long-and-random
-
-# Gemini API Configuration (Optional - for cloud LLM)
-GEMINI_API_KEY=your_gemini_api_key_here
-GEMINI_MODEL=gemini-2.0-flash
-
-# Ollama Configuration (for local LLM)
-OLLAMA_BASE_URL=http://localhost:11434
-
-# Application Settings
-CORS_ORIGINS=http://localhost:3000
-```
-
-**Getting a Gemini API Key (Optional):**
-1. Go to [Google AI Studio](https://makersuite.google.com/app/apikey)
-2. Create a new API key
-3. Add it to your `.env` file
-
-5. **Start the backend server:**
-```bash
-uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
-```
-
-The API will be available at `http://localhost:8000` with interactive docs at `http://localhost:8000/docs`
-
-### Step 5: Frontend Setup
-
-1. **Navigate to the frontend directory:**
-```bash
-cd ../phd-advisor-frontend
-```
-
-2. **Install dependencies:**
-```bash
-npm install
-```
-
-3. **Start the development server:**
-```bash
-npm start
-```
-
-The application will open at `http://localhost:3000`
-
-## Quick Start Guide
-
-### First Time Setup Checklist
-
-1. MongoDB is running (check with `mongosh` or MongoDB Compass)
-2. Ollama is running with models downloaded (`ollama list`)
-3. Backend is running on port 8000
-4. Frontend is running on port 3000
-5. Create your first user account
-
-### Basic Usage
-
-1. **Create an Account:**
-   - Open `http://localhost:3000`
-   - Click "Sign Up"
-   - Fill in your details
-
-2. **Start Your First Chat:**
-   - Click "New Chat"
-   - Ask a question like "I need help with my research methodology"
-   - Get responses from multiple advisor personas
-
-3. **Upload Documents:**
-   - Click the upload button in the chat
-   - Upload a PDF, DOCX, or TXT file
-   - Ask questions about your document
-
-4. **Manage Chats:**
-   - Save important conversations
-   - Switch between different chat sessions
-   - Export chats in various formats
-
-## 🔧 Configuration
-
-### Environment Variables Reference
-
-| Variable | Description | Default | Required |
-|----------|-------------|---------|----------|
-| `MONGODB_CONNECTION_STRING` | MongoDB connection URL | `mongodb://localhost:27017` | Yes |
-| `MONGODB_DATABASE_NAME` | Database name | `phd_advisor` | Yes |
-| `JWT_SECRET_KEY` | Secret key for JWT tokens | - | Yes |
-| `GEMINI_API_KEY` | Google Gemini API key | - | No |
-| `GEMINI_MODEL` | Gemini model to use | `gemini-2.0-flash` | No |
-| `OLLAMA_BASE_URL` | Ollama server URL | `http://localhost:11434` | No |
-
-### Switching Between LLM Providers
-
-The application supports two LLM providers:
-
-1. **Ollama (Local, Free):**
-   - Ensure Ollama is running
-   - Models run locally on your machine
-   - No API costs, complete privacy
-
-2. **Gemini (Cloud, Paid):**
-   - Requires API key
-   - Higher quality responses
-   - Faster response times
-
-Switch providers using the API:
-```bash
-curl -X POST "http://localhost:8000/switch-provider" \
-  -H "Content-Type: application/json" \
-  -d '{"provider": "ollama"}'
-```
-
-## API Documentation
-
-### Authentication Endpoints
-- `POST /auth/signup` - Create new user account
-- `POST /auth/login` - Login with email/password
-- `GET /auth/me` - Get current user profile
-
-### Chat Endpoints
-- `POST /chat-stream` - Get streaming responses from all advisors (NDJSON)
-- `POST /chat/{persona_id}` - Chat with specific advisor
-- `POST /reply-to-advisor` - Reply to specific advisor message
-
-### Document Management
-- `POST /upload-document` - Upload PDF, DOCX, or TXT files
-- `GET /uploaded-files` - List uploaded files
-- `GET /document-stats` - Get document statistics
-
-### Session Management
-- `GET /context` - Get current session context
-- `POST /reset-session` - Reset current session
-- `GET /session-stats` - Get session statistics
-
-### Export & Summary
-- `GET /export-chat` - Export chat (txt, pdf, docx)
-- `GET /chat-summary` - Generate chat summary
-
-Full API documentation is available at `http://localhost:8000/docs` when the server is running.
-
-## Troubleshooting
-
-### Common Issues
-
-**Backend won't start:**
-```bash
-# Check if port 8000 is already in use
-netstat -an | grep :8000
-
-# Check Python virtual environment is activated
-which python  # Should point to your venv
-
-# Check all dependencies are installed
-pip list
-```
-
-**MongoDB connection issues:**
-```bash
-# Test MongoDB connection
-mongosh
-
-# Check if MongoDB service is running
-# Windows: Check Services app
-# macOS: brew services list | grep mongodb
-# Linux: systemctl status mongod
-```
-
-**Ollama not working:**
-```bash
-# Check if Ollama is running
-curl http://localhost:11434/api/tags
-
-# Check downloaded models
-ollama list
-
-# Test model directly
-ollama run llama3.2:1b "Hello"
-```
-
-**Frontend won't connect to backend:**
-- Verify backend is running on port 8000
-- Check CORS settings in backend `.env`
-- Check browser developer console for errors
-
-### Performance Tips
-
-1. **For faster local LLM responses:**
-   - Use smaller models like `llama3.2:1b` for development
-   - Ensure sufficient RAM (8GB+ recommended)
-   - Use SSD storage for better model loading
-
-2. **For better document search:**
-   - Upload focused, relevant documents
-   - Use clear, descriptive filenames
-   - Break large documents into smaller sections
-
-3. **For production deployment:**
-   - Use larger, more capable models
-   - Consider GPU acceleration for Ollama
-   - Use MongoDB Atlas for cloud database
-   - Set up proper authentication and HTTPS
-
-## Development
-
-### Running Tests
-
-```bash
-# Backend tests
-cd multi_llm_chatbot_backend
-python -m pytest app/tests/
-
-# Test specific functionality
-python app/tests/test_rag_system.py
-python app/tests/debug_rag.py
-```
-
-### Project Structure
-
-```
-phd-advisor-panel/
-├── multi_llm_chatbot_backend/
-│   ├── app/
-│   │   ├── api/routes/          # API route handlers
-│   │   ├── core/                # Core business logic
-│   │   ├── llm/                 # LLM client implementations
-│   │   ├── models/              # Data models and schemas
-│   │   ├── utils/               # Utility functions
-│   │   └── tests/               # Test files
-│   ├── requirements.txt
-│   └── .env
-├── phd-advisor-frontend/
-│   ├── src/
-│   │   ├── components/          # React components
-│   │   ├── pages/               # Page components
-│   │   ├── styles/              # CSS files
-│   │   └── utils/               # Frontend utilities
-│   ├── package.json
-│   └── public/
-└── README.md
-```
-
-### Adding New Advisor Personas
-
-1. Edit `app/models/default_personas.py`
-2. Add your persona configuration
-3. Restart the backend server
-4. The new persona will be available in chat
-
-### Extending Document Support
-
-1. Add new file type to `app/utils/document_extractor.py`
-2. Update the upload endpoint in `app/api/routes/documents.py`
-3. Test with sample files
-
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch (`git checkout -b feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add amazing feature'`)
-4. Push to the branch (`git push origin feature/amazing-feature`)
-5. Open a Pull Request
-
-## Support
-
-- Check the [API Documentation](http://localhost:8000/docs)
-- Report bugs by opening an issue
-- Request features by opening an issue
-- Contact the development team
-
-## Acknowledgments
-
-- Built with [FastAPI](https://fastapi.tiangolo.com/) and [React](https://reactjs.org/)
-- Powered by [Ollama](https://ollama.ai/) for local LLM support
-- Uses [ChromaDB](https://www.trychroma.com/) for vector storage
-- Document processing with [PyPDF2](https://pypdf2.readthedocs.io/) and [python-docx](https://python-docx.readthedocs.io/)
-
-## Copyright
-
-© 2025 University of Colorado Boulder. All rights reserved.
+## Advisors
 
-This project is developed and maintained by the University of Colorado Boulder for academic and research purposes.
+1. **Hypertrophy Coach** — splits, sets/reps, programming
+2. **Nutrition Strategist** — protein, macros, fueling
+3. **Recovery Specialist** — rest, mobility, post-workout care
+4. **Form & Safety Coach** — technique, breathing, injury prevention
+5. **Program Planner** — scheduling, tracking, progression