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README.md

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+---
+title: Land Cover Classification
+emoji: 🌍
+colorFrom: green
+colorTo: blue
+sdk: docker
+app_file: app.py
+pinned: false
+---
+
+# Land Cover Classification (Hugging Face Space)
+
+A web-based land cover classification system using a pretrained ResNet50 model and the EuroSAT dataset. This Space features a modern Streamlit interface for easy image upload, prediction, and visualization of land type probabilities.
+
+## 🚀 Demo
+
+[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-spaces-sm.svg)](https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME)
+
+## Features
+
+- **Deep Learning Model**: Pretrained ResNet50 fine-tuned on EuroSAT satellite imagery.
+- **User-Friendly Web App**: Built with Streamlit for interactive image upload and real-time predictions.
+- **Class Visualization**: Displays prediction confidence for each land cover class.
+- **Modular Code**: Clean separation of model, data processing, and visualization logic.
+
+## How to Use
+
+1. **Upload a satellite image** (PNG, JPG, JPEG, TIFF).
+2. **Preview the image** and click "Run Classification".
+3. **View the predicted land cover class** and confidence scores.
+
+## Land Cover Classes
+
+| Index | Class Name             |
+|-------|------------------------|
+| 0     | AnnualCrop             |
+| 1     | Forest                 |
+| 2     | HerbaceousVegetation   |
+| 3     | Highway                |
+| 4     | Industrial             |
+| 5     | Pasture                |
+| 6     | PermanentCrop          |
+| 7     | Residential            |
+| 8     | River                  |
+| 9     | SeaLake                |
+
+## Project Structure
+
+```
+.
+├── app.py                # Streamlit web app
+├── model_handler.py      # Model loading and prediction logic
+├── data_processor.py     # Data preprocessing utilities
+├── config.py             # Configuration (class names, paths)
+├── requirements.txt      # Python dependencies
+├── models/               # Model files (see below)
+├── assets/               # Static images for the app
+├── Dockerfile            # Docker configuration for Hugging Face Spaces
+└── README.md             # This file
+```
+
+## Model Files
+
+The following files must be present in the `models/` directory:
+- `ResNet50_eurosat.h5`
+- `model.weights.best.keras`
+- `class_indices.npy`
+
+If you fork this Space, upload these files via the Hugging Face web interface if they are too large for git.
+
+## Deploying on Hugging Face Spaces
+
+### Docker Space Deployment
+
+1. **Create a new Space on Hugging Face:**
+   - Go to [Hugging Face Spaces](https://huggingface.co/spaces).
+   - Click **"Create new Space"**.
+   - Choose **SDK: Docker**.
+
+2. **Push your code:**
+   - Push your entire project (including the `Dockerfile`) to the Space.
+   - If your model files are too large for git, upload them via the Hugging Face web interface after the initial push.
+
+3. **Wait for the build:**
+   - Hugging Face will build and run your Docker container.
+   - Your Streamlit app will be available at the Space URL.
+
+## License
+
+MIT License. See [LICENSE](LICENSE) for details.
+
+---
+
+*This Space is powered by [Streamlit](https://streamlit.io/) and [Hugging Face Spaces](https://huggingface.co/spaces).*