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README.md

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 title: README
-emoji: 📈
-colorFrom: purple
+emoji: 🌍
+colorFrom: blue
 colorTo: green
 sdk: streamlit
 pinned: false
+license: mit
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-Edit this `README.md` markdown file to author your organization card.
+
+# EcoIdentify by EcoClimSolutions
+
+## Overview
+
+EcoIdentify is an environmental project developed by EcoClimSolutions to help identify and classify waste materials using artificial intelligence. This web application leverages machine learning models to categorize waste into various classes, such as cardboard, glass, metal, paper, plastic, and trash.
+
+## Features
+
+- **Waste Classification:** Upload an image of waste, and EcoIdentify will predict its category using a pre-trained machine learning model.
+  
+- **User-Friendly Interface:** The web application provides a simple and intuitive interface, making it easy for users to upload images and receive instant predictions.
+
+- **Educational Resources:** Explore information about waste categories and learn more about recycling and responsible waste disposal.
+
+## Getting Started
+
+1. Clone the repository:
+
+   ```bash
+   git clone https://github.com/ecoclimsolutions/EcoIdentify.git
+   cd EcoIdentify
+   ```
+
+2. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the application:
+
+   ```bash
+   streamlit run app.py
+   ```
+
+4. Open your browser and navigate to `http://localhost:8501` to use EcoIdentify.
+
+## Usage
+
+1. Select the image upload option: "Upload image from device" or "Upload image via link."
+2. Upload an image or provide an image link.
+3. Click the "Predict" button to classify the waste.
+4. View the predicted waste category and additional information.
+
+## Contributing
+
+We welcome contributions from the community. If you'd like to contribute to EcoIdentify, please follow our [contribution guidelines](CONTRIBUTING.md).
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).
+
+## Acknowledgments
+
+- EcoIdentify uses machine learning models trained on a diverse dataset of waste images.
+- Special thanks to contributors and the open-source community.
+
+For more information, visit [EcoClimSolutions](https://ecoclimsolutions.wordpress.com).
+
+
+
+
+Edit this `README.md` markdown file to author your organization card.