title: E-commerce Product Classifier
emoji: 🛍️
colorFrom: blue
colorTo: green
sdk: docker
app_file: app.py
pinned: false
E-commerce Product Classifier with Grad-CAM
Live Product Image Classification using Deep Learning
Production-ready Flask web application for e-commerce product classification using trained Custom CNN, MobileNetV2, and ResNet50 models with real-time Grad-CAM explainability visualization.
✨ Features
- 3 Trained Models: Custom CNN, MobileNetV2, ResNet50 (all optimized)
- Grad-CAM Visualization: See exactly which image regions influenced predictions
- Real-time Predictions: Upload any image and get instant results
- 9 Product Categories: BABY_PRODUCTS, BEAUTY_HEALTH, CLOTHING_ACCESSORIES_JEWELLERY, ELECTRONICS, GROCERY, HOBBY_ARTS_STATIONERY, HOME_KITCHEN_TOOLS, PET_SUPPLIES, SPORTS_OUTDOOR
- Model Comparison: Side-by-side metrics of all 3 models
- Production-Grade: Thread-safe, auto-cleanup, error handling, logging
- Responsive UI: Works perfectly on desktop, tablet, mobile
- Accessible: WCAG 2.1 compliant (keyboard navigation, screen readers)
🚀 How to Use
- Upload an Image: Drag-and-drop or click to select a product image
- Select Models: Choose which models to run (or run all 3)
- Get Predictions: See confidence scores and Grad-CAM heatmaps
- Analyze Results: View model comparisons and explanations
📊 Model Performance
| Model | Accuracy | Precision | Recall | F1-Score | Size |
|---|---|---|---|---|---|
| Custom CNN | 45.47% | 41.17% | 45.47% | 0.3858 | 8.9 MB |
| MobileNetV2 | 71.76% | 71.13% | 71.76% | 0.7106 | 33 MB |
| ResNet50 | 76.93% | 77.39% | 76.93% | 0.7680 | 333 MB |
🔧 Technical Details
- Framework: Flask (Python backend)
- Models: TensorFlow/Keras (.keras format)
- Input Size: 224×224 pixels
- Classes: 9 product categories
- Explainability: Grad-CAM overlay visualization
- Deployment: Docker on Hugging Face Spaces
📁 Structure
.
├── app.py # Flask backend (production-optimized)
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── README.md # This file
├── templates/
│ └── index.html # HTML template
├── static/
│ ├── css/styles.css # Styling (responsive design)
│ └── js/app.js # Frontend (retry logic, state mgmt)
└── nn_ecommerce_outputs/
├── models/
│ ├── custom_cnn.keras
│ ├── mobilenetv2.keras
│ └── resnet50.keras
├── metadata/
│ ├── class_names.json
│ └── model_manifest.json
└── tables/
└── (CSV files with metrics)
🛠 Production Features
✅ Thread-safe model caching - Safe for concurrent requests
✅ Automatic cleanup - Old generated files cleaned up
✅ Retry logic - Network failures handled gracefully
✅ Input validation - File type & size checks
✅ Structured logging - Debug everything
✅ Error handling - User-friendly messages
✅ Progress tracking - Real-time prediction progress
✅ Accessible UI - WCAG 2.1 compliant
📱 Browser Support
- Chrome/Chromium (latest)
- Firefox (latest)
- Safari (latest)
- Edge (latest)
- Mobile browsers (iOS Safari, Chrome Mobile)
🚨 Troubleshooting
Q: Models not loading?
A: Check that .keras files exist in nn_ecommerce_outputs/models/
Q: Predictions are slow? A: First prediction loads models (normal), subsequent are faster
Q: Image upload fails? A: Check file size < 12 MB and format (JPG, PNG, WEBP, BMP)
Q: Want to see logs? A: Check Space Settings → Logs tab for detailed information
📚 Resources
📄 License
MIT License - Free for academic and commercial use
🤝 About This Project
This is a comprehensive deep learning project for e-commerce product classification featuring:
- Custom CNN trained from scratch
- Transfer learning with MobileNetV2 and ResNet50
- Extensive explainability analysis with Grad-CAM
- Production-ready web deployment
Author: Ashutosh Rajendra Patil
Institution: University of Europe for Applied Sciences
Dataset: Kaggle ecommerce_product_images_18K (18,175 images, 9 categories)
Status: ✅ Production Ready | Python: 3.9+ | TensorFlow: 2.15.0 | Updated: June 2026