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README.md

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-title: ModelApp
-emoji: 📉
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.38.0
-app_file: app.py
-pinned: false
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-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+🛰️ Satellite Classification Dashboard
+A streamlined deep learning application for classifying satellite images using state-of-the-art neural network models. This interactive Gradio-based dashboard allows users to upload satellite images, select multiple models for classification, and view predictions with confidence scores and visualizations, all hosted on Hugging Face Spaces.
+🌟 Features
+
+Interactive Interface: Built with Gradio for a simple, user-friendly web experience.
+Real-Time Image Classification: Upload PNG, JPG, or JPEG images to classify satellites and space debris.
+Multiple Model Support: Choose from four pre-trained models with varying strengths:
+Custom CNN: Tailored for satellite imagery (95.2% accuracy).
+MobileNetV2: Lightweight and fast (92.8% accuracy, 18ms inference).
+EfficientNetB0: Best accuracy-efficiency balance (96.4% accuracy).
+DenseNet121: Complex pattern recognition (94.7% accuracy).
+
+
+Visualizations: Interactive Plotly charts for confidence comparison and class probability distribution.
+Model Recommendation: Automatically suggests the best model based on confidence and performance metrics.
+Supported Classes: Classifies 11 categories:
+AcrimSat, Aquarius, Aura, Calipso, Cloudsat, CubeSat, Debris, Jason, Sentinel-6, TRMM, Terra
+
+
+
+🎯 Quick Start
+Try the Live Demo
+Visit the Hugging Face Space to use the application directly in your browser: https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
+Local Installation
+
+Clone the repository:git clone https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
+cd Satellite-Classification-Gradio
+
+
+Create a virtual environment (optional but recommended):python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+
+Install dependencies:pip install -r requirements.txt
+
+
+Run the application:python app.py
+
+
+Open your browser and navigate to http://localhost:7860.
+
+📦 Dependencies
+Listed in requirements.txt:
+
+gradio==5.0.2
+tensorflow-cpu==2.15.0
+numpy==1.26.4
+pandas==2.2.2
+plotly==5.22.0
+Pillow==10.4.0
+requests==2.32.3
+protobuf==3.20.3
+
+🎮 How to Use
+
+Upload Image: Upload a satellite image (PNG, JPG, or JPEG).
+Select Models: Choose one or more models (Custom CNN, MobileNetV2, EfficientNetB0, DenseNet121).
+Classify: Click "Classify Image" to get predictions.
+View Results:
+Table of predictions (model, predicted class, confidence, inference time).
+Recommended model based on confidence and performance.
+Confidence comparison bar chart.
+Top 5 class probabilities for the recommended model.
+
+
+
+📊 Model Performance
+
+
+
+Model
+Accuracy
+Precision
+Recall
+F1-Score
+Inference Time
+Model Size
+
+
+
+EfficientNetB0
+96.4%
+96.1%
+96.2%
+96.1%
+35ms
+20.1MB
+
+
+Custom CNN
+95.2%
+94.8%
+95.1%
+94.9%
+45ms
+25.3MB
+
+
+DenseNet121
+94.7%
+94.2%
+94.5%
+94.3%
+52ms
+32.8MB
+
+
+MobileNetV2
+92.8%
+92.1%
+92.5%
+92.3%
+18ms
+8.7MB
+
+
+🎯 Model Selection Guide
+
+Best Overall Accuracy: EfficientNetB0 (96.4%)
+Fastest Inference: MobileNetV2 (18ms)
+Most Lightweight: MobileNetV2 (8.7MB)
+Best Balance: EfficientNetB0 (High accuracy + efficiency)
+Mobile/Edge Deployment: MobileNetV2
+Research/High Accuracy: EfficientNetB0 or DenseNet121
+
+🏗️ Architecture
+Model Sources
+All models are hosted on Hugging Face Model Hub:
+
+Custom CNN: Bhavi23/Custom_CNN
+MobileNetV2: Bhavi23/MobilenetV2
+EfficientNetB0: Bhavi23/EfficientNet_B0
+DenseNet121: Bhavi23/DenseNet
+
+Data Pipeline
+
+Image Upload: Supports PNG, JPG, JPEG formats.
+Preprocessing: Resize to 224x224, normalize to [0,1].
+Model Inference: Multi-model prediction with timing.
+Post-processing: Confidence scoring and model recommendations.
+
+🔧 Technical Details
+
+Input Requirements:
+Image Format: PNG, JPG, JPEG
+Input Size: 224x224x3 (RGB)
+Preprocessing: Automatic resizing and normalization
+
+
+Output Format:
+Class Prediction: One of 11 satellite categories
+Confidence Score: Percentage confidence (0-100%)
+Inference Time: Milliseconds for prediction
+Probability Distribution: Full softmax output for all classes
+
+
+Performance Optimization:
+Model Caching: Models loaded on-demand.
+Efficient Preprocessing: Optimized image pipeline.
+Memory Management: Automatic cleanup of model objects.
+
+
+
+🚢 Deployment
+The application is deployed on Hugging Face Spaces using:
+
+Runtime: Python 3.9
+Framework: Gradio
+Resources: CPU-optimized for inference
+
+Docker Deployment (Optional)
+If needed, use this Dockerfile:
+FROM python:3.9-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+EXPOSE 7860
+CMD ["python", "app.py"]
+
+🤝 Contributing
+We welcome contributions! Please follow these steps:
+
+Fork the repository.
+Create a feature branch (git checkout -b feature/amazing-feature).
+Commit changes (git commit -m 'Add amazing feature').
+Push to branch (git push origin feature/amazing-feature).
+Open a Pull Request.
+
+Development Setup
+# Clone your fork
+git clone https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+# Install dependencies
+pip install -r requirements.txt
+# Run in development mode
+python app.py
+
+📄 License
+This project is licensed under the MIT License - see the LICENSE file for details.
+🙏 Acknowledgments
+
+Dataset: Spark 2021 dataset used for training.
+Frameworks: TensorFlow, Gradio, Plotly.
+Models: Pre-trained architectures from TensorFlow/Keras.
+Hosting: Hugging Face Spaces for deployment.
+
+📞 Support
+
+Issues: GitHub Issues
+Discussions: Hugging Face Discussions
+Email: bhavithrass@gmail.com
+
+🔮 Future Enhancements
+
+Real-time video classification.
+Batch processing for multiple images.
+API endpoint for programmatic access.
+Advanced visualizations with satellite orbit data.
+
+🛠️ Troubleshooting
+If you encounter errors during deployment or runtime, try the following:
+ModuleNotFoundError: No module named 'tensorflow'
+
+Cause: TensorFlow failed to install during the Space build.
+Fix:
+Ensure requirements.txt includes tensorflow-cpu==2.15.0 and protobuf==3.20.3.
+Verify the file is in the repository root:git add requirements.txt
+git commit -m "Update requirements.txt"
+git push
+
+
+Check build logs in the Space’s Settings tab for dependency installation errors.
+Test locally to confirm compatibility:python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+python app.py
+
+
+If the issue persists, use a Dockerfile to enforce Python 3.9:FROM python:3.9-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+EXPOSE 7860
+CMD ["python", "app.py"]
+
+
+Restart the Space after pushing changes.
+
+
+Contact Support: If unresolved, open an issue on Hugging Face or check the Hugging Face Forums.
+
+📊 Usage Statistics
+Track your application usage:
+
+Classifications Performed: Real-time counter.
+Popular Models: Usage analytics.
+Performance Metrics: Response time tracking.
+
+Built with ❤️ using Gradio and TensorFlow
+For more information, visit our Hugging Face Space