Update README.md

README.md

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-# AI Co-Scientist: Multi-Agent Scientific Research Framework
-
-## Overview
-
-AI Co-Scientist is a comprehensive multi-agent AI system designed for scientific research and hypothesis generation. Inspired by the framework described in "Towards an AI Co-Scientist," this system leverages specialized LLM-powered agents to generate, evaluate, and refine scientific hypotheses across multiple disciplines.
-
-The development of this repository was inspired by the paper "Towards an AI Co-Scientist," which can be found [here](https://arxiv.org/pdf/2502.18864).
-
-## 🧩 System Architecture
-
-The system implements multiple specialized agents, each with a specific role in the scientific research process:
-
-1. **Base Agent** - Parent class for all agents, handles core LLM interactions
-2. **Generation Agent** - Generates initial scientific hypotheses from multiple perspectives
-3. **Reflection Agent** - Acts as a peer reviewer, critically evaluating hypotheses
-4. **Ranking Agent** - Compares and scores hypotheses using tournament-style evaluation
-5. **Evolution Agent** - Refines and improves promising hypotheses
-6. **Proximity Agent** - Ensures hypotheses remain relevant to research goals
-7. **Meta-Review Agent** - Synthesizes research findings into comprehensive reports
-8. **Supervisor Agent** - Coordinates the entire multi-agent system workflow
-
-## 🚀 Getting Started
-
-### Prerequisites
-
-- Python 3.8 or higher
-- OpenAI API key
-
-### Installation
-
-1. Clone the repository:
-```
-git clone https://github.com/yourusername/ai-coscientist.git
-cd ai-coscientist
-```
-
-2. Create and activate a virtual environment:
-```
-python -m venv venv
-# On Windows
-venv\Scripts\activate
-# On macOS/Linux
-source venv/bin/activate
-```
-
-3. Install dependencies:
-```
-pip install -r requirements.txt
-```
-
-4. Create a `.env` file in the project root directory and add your OpenAI API key:
-```
-OPENAI_API_KEY=your_api_key_here
-```
-
-### Usage
-
-#### Command Line Interface
-
-Run the system from the command line:
-
-```
-python -m src.main --goal "To investigate the relationship between microbiome diversity and autoimmune disorders in urban populations" --iterations 3 --output ./results
-```
-
-Options:
-- `--goal` or `-g`: Research goal to pursue
-- `--model` or `-m`: LLM model to use (default: GPT-4o)
-- `--temp` or `-t`: Temperature for LLM generation (default: 0.4)
-- `--iterations` or `-i`: Number of refinement iterations (default: 3)
-- `--output` or `-o`: Output directory for results
-- `--verbose` or `-v`: Enable verbose logging
-
-#### Python API
-
-```python
-from src import AICoScientist
-
-# Initialize the system
-acs = AICoScientist()
-
-# Run full workflow
-results = acs.run_full_workflow(
-    research_goal="To investigate the relationship between microbiome diversity and autoimmune disorders in urban populations",
-    iterations=3,
-    output_dir="./results"
-)
-
-# Access results
-top_hypothesis = results["hypotheses"][0]["hypothesis"]
-print(f"Top hypothesis: {top_hypothesis}")
-print(f"Executive summary: {results['report']['executive_summary']}")
-```
-## Output
-![image](https://github.com/user-attachments/assets/d41a43fd-a272-4a37-a2e3-f2b8f5d674d1)
-
-
-## 📂 Project Structure
-
-```
-ACS - AI CoScientist/
-├── .env                  # Environment variables (create this file)
-├── requirements.txt      # Project dependencies
-├── README.md            # Project documentation
-└── src/                 # Source code
-    ├── __init__.py      # Package initialization
-    ├── main.py          # Main application entry point
-    ├── agents/          # Agent implementations
-    ├── tools/           # Tool implementations
-    ├── utils/           # Utility functions
-    └── config/          # Configuration files
-```
-
-## 🔧 Configuration
-
-The system's behavior can be configured through parameters in `src/config/config.py` or by passing a custom configuration dictionary to the `AICoScientist` constructor.
-
-Key configuration options:
-- `AGENT_DEFAULT_MODEL`: Default LLM model to use (e.g., "gpt-4o-2024-05-13")
-- `AGENT_DEFAULT_TEMPERATURE`: Default temperature for LLM generation
-- `MAX_ITERATIONS`: Maximum number of iterations for hypothesis refinement
-- `MAX_TOKENS`: Maximum number of tokens for LLM responses
-
-## 🔍 Features
-
-- **Multi-Agent Collaboration**: Specialized agents working together to generate and refine scientific hypotheses
-- **Iterative Refinement**: Hypotheses are continuously improved through multiple refinement cycles
-- **Quality Evaluation**: Rigorous evaluation of hypotheses for scientific validity and relevance
-- **Comprehensive Reporting**: Detailed research reports with executive summaries and ranked hypotheses
-- **Tool Integration**: Scientific search, reasoning, and citation tools to enhance the research process
-
-## 📝 License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## 🙏 Acknowledgements
-
-- Inspired by the paper "Towards an AI Co-Scientist"
-- Built with OpenAI's LLM technologies
-- Leverages the LangChain framework
+---
+title: AI Co-Scientist
+emoji: 🔬
+colorFrom: blue
+colorTo: green
+sdk: gradio  # Or streamlit if run_ui.py uses Streamlit
+app_file: run_ui.py # Tells Spaces to run this file
+pinned: false
+# python_version: 3.9 # Optional: specify if needed, check repo's Python 3.8+
+---