README.md

metadata

title: GP-Tea Skin Analysis
emoji: 🔬
colorFrom: green
colorTo: blue
sdk: docker
app_port: 7860

GP-Tea Skin Analysis API

An AI-powered skin condition analysis service that provides medical insights from skin images using deep learning.

Features

• Image Classification: Advanced skin condition detection using TensorFlow EfficientNetV2S
• Medical Analysis: Comprehensive skin health assessment with confidence scoring
• Real-time Processing: Fast inference with optimized model performance
• REST API: Easy integration with mobile and web applications

---

Repository Structure

api_image/
├── app/
│   ├── main.py                # FastAPI application with JSON endpoints
│   ├── predict.py             # Image preprocessing and prediction logic
│   ├── model_loader.py        # Loads the trained EfficientNetV2S model
│   ├── model/                 # Local model storage (auto-downloaded if missing)
│   │   └── efficientnetv2s.h5 # Pretrained model file
│   └── uploads/               # Temporary upload directory (auto-created)
├── test_images/               # Sample images for testing the API
├── requirements.txt           # Python dependencies
├── Dockerfile                 # Docker setup for deployment
└── README.md                  # Project documentation (this file)

---

Features

• JSON API Endpoints: Clean REST API for Flutter integration
• CORS Enabled: Ready for mobile app cross-origin requests
• Multi-class Classification: Supports medical image classification
• Confidence Scores: Returns probability percentages for predictions
• File Upload Support: Handles image file uploads with validation
• Error Handling: Proper HTTP status codes and error messages
• Health Check: Monitoring endpoint for service status
• Temperature Scaling: Calibrated probabilities for reliable predictions
• Automatic Cleanup: Temporary files are automatically removed

---

Model Details

• Base Model: EfficientNetV2S (20.5 million parameters)
• Dataset: HAM10000
• Calibration: Temperature Scaling (optimal T=2.77)
• Performance:
  • Accuracy: 0.88
  • Macro F1-score: 0.80
  • Expected Calibration Error (ECE): 0.022 (after T-scaling)

For full technical details, see the Model Technical Information section in the app.

---

Model Download from Hugging Face

The model file efficientnetv2s.h5 is hosted on Hugging Face Hub and is automatically downloaded the first time the app runs.

How it works:

• Expected local path: app/model/efficientnetv2s.h5
• On startup, app/model_loader.py checks for the file
• If missing, it is downloaded via huggingface_hub and saved to app/model/
• Subsequent runs load the local copy

Note for Docker users: When running inside a container, the downloaded model is stored inside the container’s filesystem. Mount a volume if you need to persist it on the host.

Manual download link (optional): https://huggingface.co/Miguel764/efficientnetv2s-skin-cancer-classifier

---

Installation & Usage

1) Install dependencies

cd Image_classification/api_image
pip install -r requirements.txt

Requirements: Python 3.8 and TensorFlow 2.10.0 (GPU recommended). (very important!!!!! Must be Python 3.8)

2) Run the API server

  uvicorn app.main:app --host 0.0.0.0 --port 8003 --reload

The API will be available at http://localhost:8003

Access the API documentation at http://localhost:8003/docs

3) Test the API

Use the sample images in test_images/ folder to test the endpoints.

---

API Endpoints

Health Check

• GET /health - Service health status

Image Classification

• POST /api/classify - Upload and classify medical image

Example Request (cURL):

curl -X POST "http://localhost:8000/api/classify" \
     -H "accept: application/json" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@test_images/sample.jpg"

Example Response:

{
  "success": true,
  "prediction": {
    "top_prediction": {
      "label": "Melanoma",
      "confidence": 0.95,
      "confidence_percent": "95.00%"
    },
    "all_predictions": [
      {
        "label": "Melanoma",
        "confidence": 0.95,
        "confidence_percent": "95.00%"
      },
      {
        "label": "Benign Nevus",
        "confidence": 0.03,
        "confidence_percent": "3.00%"
      }
    ]
  }
}

---

Flutter Integration

Base URL Configuration

class ApiConfig {
  static const String imageApiUrl = "http://localhost:8000";
}

---

Docker Deployment

Build and run the API in a container:

docker build -t medical-image-api .
docker run -p 8000:8000 medical-image-api

---

Error Handling

The API returns consistent error responses:

{
  "detail": "File must be an image"
}

Common HTTP status codes:

• 200 - Success
• 400 - Bad Request (invalid file, missing data)
• 422 - Unprocessable Entity (validation error)
• 500 - Internal Server Error (classification failed)

---

Technical Notes

• Backend Only: No web interface, pure JSON API
• CORS Enabled: Ready for mobile app integration
• File Validation: Checks file type and validity
• Temporary Storage: Uploaded files are automatically cleaned up
• Model Auto-Download: Model downloads from Hugging Face on first run
• Python 3.8 Compatible: Uses TensorFlow 2.10.0 for compatibility