Update README.md

README.md

@@ -1,4 +1,3 @@
----
 title: Satellite Classification Dashboard
 emoji: 🛰️
 colorFrom: blue
@@ -7,69 +6,55 @@ sdk: streamlit
 sdk_version: 1.47.0
 app_file: app.py
 pinned: false
----
-
-# 🛰️ Satellite Classification Dashboard
+preload_from_hub:
+  - Bhavi23/Custom_CNN/best_multimodal_model.keras
+  - Bhavi23/MobilenetV2/multi_input_model_v1.keras
+  - Bhavi23/EfficientNet_B0/efficientnet_model.keras
+  - Bhavi23/DenseNet/densenet_model.keras
 
+🛰️ Satellite Classification Dashboard
 A comprehensive deep learning application for classifying satellite images using multiple state-of-the-art neural network models. This interactive dashboard allows you to compare different models, analyze their performance, and classify satellite images in real-time.
 
-## 🌟 Features
-
-### 🏠 **Dashboard Overview**
-- Interactive web interface built with Streamlit
-- Multi-page navigation system
-- Real-time image classification
-- Comprehensive model comparison
-- Performance analytics and visualizations
-
-### 🤖 **Multiple Model Support**
-- **Custom CNN**: Tailored architecture for satellite imagery (95.2% accuracy)
-- **MobileNetV2**: Lightweight model optimized for speed (92.8% accuracy, 18ms inference)
-- **EfficientNetB0**: Best accuracy-efficiency balance (96.4% accuracy)
-- **DenseNet121**: Complex pattern recognition capabilities (94.7% accuracy)
-
-### 🎯 **Classification Categories**
+🌟 Features
+🏠 Dashboard Overview
+Interactive web interface built with Streamlit
+Multi-page navigation system
+Real-time image classification
+Comprehensive model comparison
+Performance analytics and visualizations
+🤖 Multiple Model Support
+Custom CNN: Tailored architecture for satellite imagery (95.2% accuracy)
+MobileNetV2: Lightweight model optimized for speed (92.8% accuracy, 18ms inference)
+EfficientNetB0: Best accuracy-efficiency balance (96.4% accuracy)
+DenseNet121: Complex pattern recognition capabilities (94.7% accuracy)
+🎯 Classification Categories
 The system can classify 11 different types of satellites and space objects:
-- AcrimSat
-- Aquarius
-- Aura
-- Calipso
-- Cloudsat
-- CubeSat
-- Debris
-- Jason
-- Sentinel-6
-- TRMM
-- Terra
-
-## 🚀 Quick Start
-
-### Try the Live Demo
-Visit the [Hugging Face Space](https://huggingface.co/spaces/YOUR_USERNAME/satellite-classification-dashboard) to use the application directly in your browser.
-
-### Local Installation
-
-1. **Clone the repository**
-```bash
+
+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.
+
+Local Installation
+Clone the repository
 git clone https://github.com/YOUR_USERNAME/satellite-classification-dashboard.git
 cd satellite-classification-dashboard
-```
-
-2. **Install dependencies**
-```bash
+Install dependencies
 pip install -r requirements.txt
-```
-
-3. **Run the application**
-```bash
+Run the application
 streamlit run app.py
-```
-
-4. **Open your browser** and navigate to `http://localhost:8501`
-
-## 📦 Dependencies
-
-```txt
+Open your browser and navigate to http://localhost:8501
+📦 Dependencies
 streamlit>=1.28.0
 tensorflow>=2.13.0
 numpy>=1.24.0
@@ -77,123 +62,96 @@ pandas>=2.0.0
 plotly>=5.15.0
 Pillow>=10.0.0
 requests>=2.31.0
-```
-
-## 🎮 How to Use
-
-### 1. **Home Page**
-- Overview of the system capabilities
-- Class distribution visualization
-- Quick navigation to other features
-
-### 2. **Model Comparison**
-- Side-by-side performance metrics
-- Interactive charts comparing accuracy, speed, and model size
-- Model selection recommendations
-
-### 3. **Image Classification**
-- Upload satellite images (PNG, JPG, JPEG)
-- Select one or multiple models for prediction
-- View confidence scores and detailed probabilities
-- Get model recommendations based on performance
-
-### 4. **Performance Analytics**
-- Detailed performance breakdowns
-- Efficiency metrics analysis
-- Comprehensive model comparison tables
-
-### 5. **About Models**
-- Technical specifications for each model
-- Strengths and use cases
-- Model architecture details
-
-## 📊 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
+🎮 How to Use
+1. Home Page
+Overview of the system capabilities
+Class distribution visualization
+Quick navigation to other features
+2. Model Comparison
+Side-by-side performance metrics
+Interactive charts comparing accuracy, speed, and model size
+Model selection recommendations
+3. Image Classification
+Upload satellite images (PNG, JPG, JPEG)
+Select one or multiple models for prediction
+View confidence scores and detailed probabilities
+Get model recommendations based on performance
+4. Performance Analytics
+Detailed performance breakdowns
+Efficiency metrics analysis
+Comprehensive model comparison tables
+5. About Models
+Technical specifications for each model
+Strengths and use cases
+Model architecture details
+📊 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
-1. **Image Upload**: Supports PNG, JPG, JPEG formats
-2. **Preprocessing**: Resize to 224x224, normalize to [0,1]
-3. **Model Inference**: Multi-model prediction with timing
-4. **Post-processing**: Confidence scoring and 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 are cached using `@st.cache_resource`
-- **Lazy Loading**: Models loaded only when needed
-- **Efficient Preprocessing**: Optimized image pipeline
-- **Memory Management**: Automatic cleanup of model objects
-
-## 🚢 Deployment
-
-### Hugging Face Spaces
+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 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 are cached using @st.cache_resource
+Lazy Loading: Models loaded only when needed
+Efficient Preprocessing: Optimized image pipeline
+Memory Management: Automatic cleanup of model objects
+🚢 Deployment
+Hugging Face Spaces
 The application is deployed on Hugging Face Spaces using:
-- **Runtime**: Python 3.9
-- **Framework**: Streamlit
-- **Resources**: CPU-optimized for inference
 
-### Docker Deployment
-```dockerfile
+Runtime: Python 3.9
+Framework: Streamlit
+Resources: CPU-optimized for inference
+Docker Deployment
 FROM python:3.9-slim
 
 WORKDIR /app
 COPY requirements.txt .
-RUN pip install -r requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
 
 COPY . .
 EXPOSE 8501
 
 CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
-```
-
-## 🤝 Contributing
-
+🤝 Contributing
 We welcome contributions! Please follow these steps:
 
-1. **Fork the repository**
-2. **Create a feature branch** (`git checkout -b feature/amazing-feature`)
-3. **Commit changes** (`git commit -m 'Add amazing feature'`)
-4. **Push to branch** (`git push origin feature/amazing-feature`)
-5. **Open a Pull Request**
-
-### Development Setup
-```bash
+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://github.com/YOUR_USERNAME/satellite-classification-dashboard.git
 
@@ -206,45 +164,33 @@ pip install -r requirements.txt
 
 # Run in development mode
 streamlit run app.py --logger.level=debug
-```
-
-## 📄 License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-## 🙏 Acknowledgments
-
-- **Dataset**: Satellite imagery dataset used for training
-- **Frameworks**: TensorFlow, Streamlit, Plotly
-- **Models**: Pre-trained architectures from TensorFlow/Keras
-- **Hosting**: Hugging Face Spaces for deployment
-
-## 📞 Support
-
-- **Issues**: [GitHub Issues](https://github.com/YOUR_USERNAME/satellite-classification-dashboard/issues)
-- **Discussions**: [GitHub Discussions](https://github.com/YOUR_USERNAME/satellite-classification-dashboard/discussions)
-- **Email**: your.email@example.com
-
-## 🔮 Future Enhancements
-
-- [ ] **Real-time Video Classification**
-- [ ] **Batch Processing** for multiple images
-- [ ] **Model Fine-tuning Interface**
-- [ ] **Custom Model Upload**
-- [ ] **Export Predictions** (CSV/JSON)
-- [ ] **API Endpoint** for programmatic access
-- [ ] **Mobile App** version
-- [ ] **Advanced Visualization** with satellite orbit data
-
-## 📊 Usage Statistics
-
+📄 License
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+🙏 Acknowledgments
+Dataset: Satellite imagery dataset used for training
+Frameworks: TensorFlow, Streamlit, Plotly
+Models: Pre-trained architectures from TensorFlow/Keras
+Hosting: Hugging Face Spaces for deployment
+📞 Support
+Issues: GitHub Issues
+Discussions: GitHub Discussions
+Email: your.email@example.com
+🔮 Future Enhancements
+ Real-time Video Classification
+ Batch Processing for multiple images
+ Model Fine-tuning Interface
+ Custom Model Upload
+ Export Predictions (CSV/JSON)
+ API Endpoint for programmatic access
+ Mobile App version
+ Advanced Visualization with satellite orbit data
+📊 Usage Statistics
 Track your application usage:
-- **Classifications Performed**: Real-time counter
-- **Popular Models**: Usage analytics
-- **Performance Metrics**: Response time tracking
-
----
 
-**Built with ❤️ using Streamlit and TensorFlow**
+Classifications Performed: Real-time counter
+Popular Models: Usage analytics
+Performance Metrics: Response time tracking
+Built with ❤️ using Streamlit and TensorFlow
 
-*For more information, visit our [Hugging Face Space](https://huggingface.co/spaces/YOUR_USERNAME/satellite-classification-dashboard)*
+For more information, visit our Hugging Face Space