Update README.md

README.md

@@ -11,155 +11,3 @@ license: mit
 short_description: An advanced AI research assistant powered by Google's Gemini
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-
-🧠 Gemini AI Research Agent
-An advanced AI research assistant powered by Google's Gemini 1.5 Flash model, designed to handle complex, multi-faceted questions that require deep analysis, research, and reasoning.
-🎯 Key Features
-Core Capabilities
-    • Complex Research: Multi-source fact-checking and cross-referencing 
-    • Mathematical Reasoning: Step-by-step problem solving and calculations 
-    • Multi-modal Analysis: Processing images, videos, and audio content 
-    • Data Interpretation: Analysis of tables, charts, and statistical data 
-    • Creative Problem Solving: Innovative approaches to unusual questions 
-    • Conversation Context: Maintains context across multiple exchanges 
-Specialized For
-    • Academic research questions 
-    • Historical fact verification 
-    • Mathematical and logical puzzles 
-    • Data analysis and interpretation 
-    • Multi-step reasoning problems 
-    • Creative and unusual challenges 
-🚀 Quick Start
-Prerequisites
-    • Python 3.8 or higher 
-    • Google AI Studio API key (Gemini API access) 
-Installation
-    1. Clone the repository: 
-git clone <repository-url>
-cd gemini-ai-agent
-    2. Install dependencies: 
-pip install -r requirements.txt
-    3. Set up environment variables: 
-# Create a .env file or set environment variable
-export GEMINI_API_KEY="your-gemini-api-key-here"
-    4. Run the application: 
-python main.py
-    5. Access the interface: Open your browser to http://localhost:7860 
-📁 Project Structure
-gemini-ai-agent/
-├── main.py                 # Application entry point
-├── requirements.txt        # Python dependencies
-├── README.md              # This file
-├── .env.example           # Environment variables template
-└── src/
-    ├── __init__.py        # Package initialization
-    ├── agent.py           # Core Gemini AI agent
-    ├── config.py          # Configuration management
-    ├── ui.py              # Gradio user interface
-    └── utils.py           # Utility functions
-🔧 Configuration
-Environment Variables
-    • GEMINI_API_KEY: Your Google AI Studio API key (required) 
-Agent Settings
-You can modify the agent's behavior in src/config.py:
-    • model_name: Gemini model to use (default: "gemini-1.5-flash") 
-    • max_tokens: Maximum response length (default: 2048) 
-    • temperature: Response creativity (default: 0.7) 
-    • max_history_length: Conversation memory length (default: 10) 
-💡 Usage Examples
-Research Questions
-"How many studio albums were published by Mercedes Sosa between 2000 and 2009?"
-Mathematical Problems
-"Given this table defining * on the set S = {a, b, c, d, e}, provide the subset of S 
-involved in any possible counter-examples that prove * is not commutative."
-Creative Challenges
-".rewsna eht sa 'tfel' drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI"
-Data Analysis
-"The attached Excel file contains sales data. What were the total sales from food 
-(not including drinks)?"
-🌐 Web Interface
-The Gradio interface provides:
-    • Question Input: Main text area for complex questions 
-    • Context Field: Additional context or constraints 
-    • Response Display: Formatted AI responses 
-    • Agent Status: Real-time agent information 
-    • Example Questions: Pre-built examples to get started 
-    • Conversation Management: Clear history and refresh options 
-🧪 Testing
-Run the test suite:
-pytest tests/
-For development with auto-reloading:
-pytest --watch tests/
-🚀 Deployment
-Local Development
-python main.py
-Production Deployment
-For production deployment, consider:
-    • Using a WSGI server like Gunicorn 
-    • Setting up reverse proxy with Nginx 
-    • Configuring SSL certificates 
-    • Setting up monitoring and logging 
-Docker Deployment
-FROM python:3.9-slim
-
-WORKDIR /app
-COPY requirements.txt .
-RUN pip install -r requirements.txt
-
-COPY . .
-EXPOSE 7860
-
-CMD ["python", "main.py"]
-🛠️ Development
-Code Style
-This project uses:
-    • Black for code formatting 
-    • Flake8 for linting 
-    • Type hints for better code documentation 
-Format code:
-black src/ main.py
-flake8 src/ main.py
-Adding New Features
-    1. Create feature branch: git checkout -b feature/new-feature 
-    2. Implement changes with tests 
-    3. Run tests: pytest 
-    4. Format code: black . 
-    5. Submit pull request 
-📊 Performance
-Benchmarks
-    • Average response time: 2-5 seconds 
-    • Complex questions: 5-15 seconds 
-    • Memory usage: ~100-200MB 
-    • Concurrent users: Up to 10 (depending on API limits) 
-Optimization Tips
-    • Use context field efficiently 
-    • Break complex questions into parts 
-    • Clear conversation history for better performance 
-    • Monitor API usage and rate limits 
-🔒 Security
-API Key Protection
-    • Never commit API keys to version control 
-    • Use environment variables for sensitive data 
-    • Rotate API keys regularly 
-    • Monitor API usage for anomalies 
-Input Validation
-    • All user inputs are sanitized 
-    • Length limits enforced 
-    • Malicious content detection 
-    • Rate limiting implemented 
-🤝 Contributing
-    1. Fork the repository 
-    2. Create a feature branch 
-    3. Make your changes 
-    4. Add tests if applicable 
-    5. Run the test suite 
-    6. Submit a pull request 
-📝 License
-This project is licensed under the MIT License.
-🙏 Acknowledgments
-    • Google AI for the Gemini API 
-    • Gradio team for the excellent UI framework 
-    • Open source community for various dependencies 
-