Update README.md

README.md

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 ---
 title: EcoSphereAI
-emoji: 👀
-colorFrom: gray
-colorTo: gray
+emoji: 🚀
+colorFrom: red
+colorTo: red
 sdk: docker
+app_port: 8501
+tags:
+- streamlit
 pinned: false
-license: apache-2.0
 short_description: An AI-powered platform for sustainable network management.
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# EcoSphereAI
+
+EcoSphereAI is a Streamlit-based application leveraging the power of Gemini and traditional machine learning models to provide actionable insights for optimizing network infrastructure, resource allocation, and sustainability efforts.  It offers a suite of AI-powered tools designed to address various aspects of network management, from energy optimization to disaster preparedness and predictive maintenance.
+
+## Features
+
+* **Interactive Dashboard:** Visualize key metrics, node locations, and regional analysis for a comprehensive overview of your network. Upload your own datasets for custom analysis.
+* **AI-Powered Tools:** Leverage nine specialized AI tools, each designed for a specific task:
+    * **Energy & CO₂ Optimizer:** Predicts energy usage and carbon emissions, offering optimization strategies.
+    * **Maintenance Forecaster:** Predicts potential maintenance issues based on historical data and node characteristics.
+    * **Disaster Assessor:** Assesses disaster risk levels based on environmental factors and infrastructure vulnerability.
+    * **Traffic Forecaster:** Predicts future traffic load to inform capacity planning and resource allocation.
+    * **Procurement Planner:** Optimizes procurement decisions by predicting costs, delivery times, and required quantities.
+    * **Connectivity Insights:** Provides region-specific connectivity insights and recommendations.
+    * **Deployment Strategist:** Plans network deployments by predicting costs and timelines.
+    * **Network Node Monitor:** Monitors node performance, predicts data usage, peak usage, and downtime events.
+    * **Sustainability Tracker:** Tracks and reports on key sustainability metrics, providing recommendations for improvement.
+* **Gemini Integration:**  Each AI tool integrates with Google's Gemini for advanced natural language processing, providing insightful and actionable recommendations based on predictions.
+* **Session Management:** Save and review past sessions for each AI tool, enabling tracking and analysis of historical predictions and insights.
+* **Ticketing System:** Built-in ticketing system for reporting issues, providing feedback, and requesting support.
+* **User Authentication:** Secure user login and signup functionality with password validation.
+* **Customizable User Profiles:** Update user information, including full name, username, password, and avatar.
+
+
+## Installation
+
+1. Clone the repository: `git clone https://github.com/mmfarabi/EcoSphereAI.git`
+2. Navigate to the project directory: `cd EcoSphereAI`
+3. Install the required packages: `pip install -r requirements.txt`
+4. Set up your Gemini API key:
+    1. Obtain a Gemini API key from [https://ai.google.dev/gemini-api/docs/api-key](https://ai.google.dev/gemini-api/docs/api-key)
+    2. Replace `"gemini_api_key"` in the code with your actual Gemini API key.
+5. Run the app: `streamlit run app.py`
+
+## Usage
+
+1. **Login/Signup:** Create an account or log in with your credentials.
+2. **Dashboard:** Explore the main dashboard for an overview of your network.
+3. **AI Tools:** Navigate to the desired AI tool using the sidebar.
+4. **Input Data:** Provide the required input data for the selected tool.
+5. **Predict:** Click the "Predict" button to generate predictions and insights.
+6. **Sessions:** Review past sessions and download data.
+7. **Tickets:** Submit tickets for issues or feedback.
+8. **Settings:** Manage your user profile and settings.
+
+
+## Technologies Used
+
+* **Streamlit:** For building the interactive web application.
+* **Gemini:**  For advanced natural language processing and generation of insights.
+* **FLAML:** For automated machine learning model training and selection.
+* **Scikit-learn, XGBoost, LightGBM, CatBoost:** Machine learning libraries used for model training.
+* **Pandas:** For data manipulation and analysis.
+* **Joblib:** For saving and loading machine learning models.
+* **Plotly, Folium, Streamlit-folium:** For data visualization.
+* **Pillow:** For image processing.
+* **SQLite:** For database management.
+* **Other:** `numpy`, `ray[tune]`, `fsspec`
+
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit pull requests or open issues.
+
+## License
+
+Apache License Version 2.0