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- title: Satellite Classification Dashboardemoji: 🛰️colorFrom: bluecolorTo: purplesdk: gradiosdk_version: "5.0.2"app_file: app.pypinned: false
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- Satellite Classification Dashboard
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- A Gradio-based application for classifying satellite images using pre-trained deep learning models. Upload a PNG, JPG, or JPEG image, select one or more models (Custom CNN, MobileNetV2, EfficientNetB0, DenseNet121), and view predictions with confidence scores and visualizations.
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- Quick Start
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-
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- Live Demo: Visit https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio.
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- Local Setup:git clone https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
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- cd Satellite-Classification-Gradio
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- python -m venv venv
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- source venv/bin/activate # On Windows: venv\Scripts\activate
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- pip install -r requirements.txt
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- python app.py
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-
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- Open http://localhost:7860.
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-
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- Dependencies
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- Listed in requirements.txt:
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-
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- gradio==5.0.2
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- tensorflow-cpu==2.15.0
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- h5py==3.10.0
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- numpy==1.26.4
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- pandas==2.2.2
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- plotly==5.22.0
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- Pillow==10.4.0
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- requests==2.32.3
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- protobuf==3.20.3
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-
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- Troubleshooting
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- Missing configuration in README
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-
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- Cause: The README.md lacks proper YAML front matter or is not detected.
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- Fix:
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- Save this README.md as README.md (case-sensitive) in the repository root.
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- Ensure YAML syntax is correct (2-space indentation, quoted sdk_version).
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- Push to repository:git add README.md
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- git commit -m "Fix YAML front matter in README.md"
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- git push
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-
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-
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- Restart the Space in the Settings tab.
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- If the error persists, create a new Space to avoid caching issues.
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-
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- Error loading <model>: Unable to load model. Filepath is not an hdf5 file (or h5py is not available) or SavedModel
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- Cause: The model file is invalid, or h5py is missing.
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- Fix:
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- Ensure requirements.txt includes h5py==3.10.0.
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- Verify the model URL (e.g., https://huggingface.co/Bhavi23/Custom_CNN/resolve/main/best_multimodal_model.keras):curl -I https://huggingface.co/Bhavi23/Custom_CNN/resolve/main/best_multimodal_model.keras
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- Test the model file locally:wget https://huggingface.co/Bhavi23/Custom_CNN/resolve/main/best_multimodal_model.keras
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- python -c "import tensorflow as tf; model = tf.keras.models.load_model('best_multimodal_model.keras')"
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- If invalid, check the Hugging Face repository or contact the model owner.
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- Alternatively, include model files in the repository:mkdir models
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- mv best_multimodal_model.keras models/
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- git add models/best_multimodal_model.keras
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- git commit -m "Add Custom CNN model file"
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- git push
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-
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-
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- Use a Dockerfile for consistency:FROM python:3.9-slim
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- WORKDIR /app
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- COPY requirements.txt .
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- RUN pip install --no-cache-dir -r requirements.txt
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- COPY . .
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- EXPOSE 7860
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- CMD ["python", "app.py"]
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-
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- ModuleNotFoundError: No module named 'tensorflow'
 
 
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- Cause: TensorFlow failed to install.
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- Fix:
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- Verify requirements.txt includes tensorflow-cpu==2.15.0.
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- Check build logs in the Space’s Settings tab.
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- Test locally:python -m venv venv
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- source venv/bin/activate
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  pip install -r requirements.txt
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- python app.py
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- Support
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-
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- Issues: Hugging Face Discussions
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- Email: bhavithrass@gmail.com
 
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+ ---
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+ title: Satellite Classification Dashboard
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+ emoji: "🛰️"
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+ colorFrom: blue
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+ colorTo: purple
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+ sdk: gradio
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+ sdk_version: "5.0.2"
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+ app_file: app.py
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+ pinned: false
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+ ---
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+ # 🛰️ Satellite Classification Dashboard
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ A Gradio-based application for classifying satellite images using pre-trained deep learning models.
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+ ---
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+ ## 🔍 Features
 
 
 
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+ - Upload a PNG, JPG, or JPEG satellite image.
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+ - Choose from 4 pretrained models:
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+ - ✅ Custom CNN
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+ - ✅ MobileNetV2
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+ - ✅ EfficientNetB0
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+ - ✅ DenseNet121
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+ - Get predictions with confidence scores.
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+ - View visualizations of model outputs.
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+ ---
 
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+ ## 🚀 Live Demo
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+ 👉 [Launch the App](https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ ## 🛠️ Local Setup
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+ ```bash
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+ git clone https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
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+ cd Satellite-Classification-Gradio
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+ # Set up a virtual environment
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+ python -m venv venv
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+ source venv/bin/activate # On Windows: venv\Scripts\activate
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+ # Install dependencies
 
 
 
 
 
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  pip install -r requirements.txt
 
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+ # Run the app
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+ python app.py