Add Hugging Face deployment configuration

README.md

@@ -1,251 +1,27 @@
-# Analytical Finance Chatbot
-
-A full-stack AI-powered chatbot for financial data analysis with an OpenAI-inspired interface. The system analyzes CSV data (holdings and trades) and provides intelligent insights through natural language queries with real-time streaming responses.
-
-![Tech Stack](https://img.shields.io/badge/Next.js-15+-black?style=flat-square&logo=next.js)
-![FastAPI](https://img.shields.io/badge/FastAPI-0.100+-009688?style=flat-square&logo=fastapi)
-![Python](https://img.shields.io/badge/Python-3.10+-3776AB?style=flat-square&logo=python)
-![Tailwind](https://img.shields.io/badge/Tailwind-4.0-38B2AC?style=flat-square&logo=tailwind-css)
-
-## 🌟 Features
-
-### Backend
-- **Intelligent Data Analysis**: Processes financial CSV data (holdings & trades)
-- **Contextual Facts Engine**: Pre-computes analytical facts for accurate responses
-- **LLM-Powered Insights**: Uses Groq's Llama 3.1 for natural language understanding
-- **Streaming Responses**: Real-time Server-Sent Events (SSE) for smooth UX
-- **Conversation Management**: Persistent conversation history and context
-
-### Frontend
-- **OpenAI-Inspired UI**: Clean, minimalist black & white design
-- **Real-Time Streaming**: Live message streaming with visual feedback
-- **Rich Markdown Support**: Tables, code blocks, lists with proper formatting
-- **Responsive Design**: Mobile-friendly with collapsible sidebar
-- **Dark Mode**: Automatic light/dark theme support
-- **Conversation History**: Sidebar with all past conversations
-
-## 🏗️ Architecture
-
-```
-project-proojectloop/
-├── backend/                    # FastAPI backend
-│   ├── main.py                # API endpoints & streaming logic
-│   ├── csv_engine.py          # Data processing & fact extraction
-│   ├── llm_client.py          # Groq LLM integration
-│   └── data/
-│       ├── holdings.csv       # Financial holdings data
-│       └── trades.csv         # Trading data
-│
-└── frontend-next/             # Next.js frontend
-    ├── src/
-    │   ├── app/
-    │   │   ├── layout.js      # Root layout with sidebar
-    │   │   ├── page.js        # Home page
-    │   │   ├── globals.css    # Theme & styling
-    │   │   └── c/[id]/        # Dynamic chat routes
-    │   ├── components/
-    │   │   ├── Sidebar.js     # Conversation list
-    │   │   └── ChatInterface.js  # Main chat UI
-    │   └── lib/
-    │       └── api.js         # API client
-    └── package.json
-```
-
-## 🚀 Quick Start
-
-### Prerequisites
-- Python 3.10+
-- Node.js 18+
-- Groq API Key ([Get one here](https://console.groq.com))
-
-### Backend Setup
-
-1. **Navigate to backend directory**:
-   ```bash
-   cd backend
-   ```
-
-2. **Create virtual environment** (optional but recommended):
-   ```bash
-   python -m venv .venv
-   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
-   ```
-
-3. **Install dependencies**:
-   ```bash
-   pip install fastapi uvicorn pandas groq python-dotenv
-   ```
-
-4. **Configure environment**:
-   Create a `.env` file in the `backend/` directory:
-   ```env
-   GROQ_API_KEY=your_groq_api_key_here
-   DATA_DIR=data
-   ```
-
-5. **Run the server**:
-   ```bash
-   uvicorn main:app --host 0.0.0.0 --port 8000 --reload
-   ```
-
-   Backend will be available at: `http://localhost:8000`
-
-### Frontend Setup
-
-1. **Navigate to frontend directory**:
-   ```bash
-   cd frontend-next
-   ```
-
-2. **Install dependencies**:
-   ```bash
-   npm install
-   ```
-
-3. **Run the development server**:
-   ```bash
-   npm run dev
-   ```
-
-   Frontend will be available at: `http://localhost:3000` (or next available port)
-
-## 📡 API Endpoints
-
-### Core Endpoints
-
-| Method | Endpoint | Description |
-|--------|----------|-------------|
-| `GET` | `/auth/status` | Authentication status |
-| `GET` | `/chat_models` | Available chat models |
-| `POST` | `/chat/new_conversation` | Create new conversation |
-| `GET` | `/conversation/all` | List all conversations |
-| `GET` | `/conversation/{id}` | Get specific conversation |
-| `POST` | `/chat/conversation` | Send message (streaming) |
-| `POST` | `/chat/get_alias` | Generate conversation title |
-
-### Example Request
-
-```bash
-curl -X POST http://localhost:8000/chat/conversation \
-  -H "Content-Type: application/json" \
-  -d '{
-    "conversation_id": "123e4567-e89b-12d3-a456-426614174000",
-    "user_message": [{"type": "text", "text": "Which funds performed better?"}]
-  }'
-```
-
-## 💡 Example Queries
-
-Try these questions with your data:
-
-- "Which funds performed better depending on the yearly Profit and Loss?"
-- "Show me the top 5 portfolios by total holdings"
-- "What is the total quantity for YTUM fund?"
-- "Which portfolio has the most records?"
-- "Compare the performance of Garfield vs Heather funds"
-
-## 🎨 Tech Stack
-
-### Backend
-- **FastAPI**: Modern Python web framework
-- **Pandas**: Data processing and analysis
-- **Groq**: LLM API (Llama 3.1 8B Instant)
-- **Uvicorn**: ASGI server
-
-### Frontend
-- **Next.js 15+**: React framework with App Router
-- **Tailwind CSS v4**: Utility-first styling
-- **React Markdown**: Rich text rendering
-- **Lucide React**: Icon library
-- **Inter Font**: Clean typography
-
-## 🔧 Configuration
-
-### Backend Environment Variables
-
-```env
-GROQ_API_KEY=your_api_key          # Required: Groq API key
-DATA_DIR=data                      # Optional: Data directory path
-```
-
-### Frontend API Configuration
-
-Update `src/lib/api.js` if your backend runs on a different port:
-
-```javascript
-const API_BASE_URL = 'http://localhost:8000';
-```
-
-## 📊 Data Format
-
-The system expects CSV files with the following structure:
-
-### Holdings CSV
-- `PortfolioName`: Fund/portfolio identifier
-- `PL_YTD`: Year-to-date profit/loss
-- `Qty`: Quantity
-- `MV_Base`: Market value
-- Other financial metrics...
-
-### Trades CSV
-- Similar structure with trade-specific fields
-
-## 🛠️ Development
-
-### Running Tests
-
-```bash
-# Backend tests
-cd backend
-python test_api.py
-
-# Frontend (if tests are added)
-cd frontend-next
-npm test
-```
-
-### Building for Production
-
-```bash
-# Frontend production build
-cd frontend-next
-npm run build
-npm start
-```
-
-## 🐛 Troubleshooting
-
-### Backend Issues
-
-**Problem**: `ModuleNotFoundError: No module named 'csv_engine'`
-- **Solution**: Run uvicorn from the `backend/` directory
-
-**Problem**: No CSV files found
-- **Solution**: Ensure CSV files are in `backend/data/` directory
-
-### Frontend Issues
-
-**Problem**: Grey/blurry text in responses
-- **Solution**: Already fixed with `prose-neutral dark:prose-invert` classes
-
-**Problem**: Page reloads on new chat
-- **Solution**: Using `window.history.pushState()` instead of router navigation
-
-**Problem**: Tables not rendering
-- **Solution**: Ensure `react-markdown` and `remark-gfm` are installed
-
-## 📝 License
-
-This project is open source and available under the MIT License.
-
-## 🤝 Contributing
+---
+title: Analytical Finance Chatbot
+emoji: 💹
+colorFrom: black
+colorTo: gray
+sdk: docker
+pinned: false
+license: mit
+app_port: 7860
+---
 
-Contributions are welcome! Please feel free to submit a Pull Request.
+# Analytical Finance Chatbot
 
-## 📧 Support
+An AI-powered financial data analysis chatbot with an OpenAI-inspired interface.
 
-For issues and questions, please open an issue on the repository.
+## Features
+- Real-time streaming responses
+- Financial data analysis (holdings & trades)
+- Clean, modern UI with dark mode
+- Markdown table rendering
 
----
+## Tech Stack
+- Backend: FastAPI + Groq AI
+- Frontend: Next.js + Tailwind CSS
+- Deployment: Docker
 
-**Built with ❤️ using Next.js, FastAPI, and Groq AI**
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference