---
title: Plant Disease Prediction API
emoji: 🌱
colorFrom: green
colorTo: blue
sdk: docker
pinned: false
license: apache-2.0
app_port: 7860
---

# Plant Disease Prediction API 🌱

A FastAPI-based web service that predicts plant diseases from leaf images using a deep learning model trained on plant pathology data.

## Features

- **38 Disease Classes**: Supports detection of various diseases across multiple plant species including Apple, Corn, Grape, Tomato, Potato, and more
- **Batch Processing**: Process up to 10 images simultaneously
- **High Accuracy**: Uses a pre-trained CNN model for reliable disease classification
- **Easy Integration**: RESTful API with comprehensive documentation
- **Model Pre-warming**: Fast inference with pre-loaded model

## Supported Plants & Diseases

### Apple

- Apple Scab, Black Rot, Cedar Apple Rust, Healthy

### Corn (Maize)

- Cercospora Leaf Spot, Common Rust, Northern Leaf Blight, Healthy

### Tomato

- Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites, Target Spot, Yellow Leaf Curl Virus, Mosaic Virus, Healthy

### Grape

- Black Rot, Esca (Black Measles), Leaf Blight, Healthy

### Potato

- Early Blight, Late Blight, Healthy

### Other Plants

- Blueberry, Cherry, Orange, Peach, Bell Pepper, Raspberry, Soybean, Squash, Strawberry

## API Endpoints

### `POST /predict`

Upload one or more images to get disease predictions.

**Request:**

- Content-Type: `multipart/form-data`
- Body: Image files (jpg, png, bmp, gif, tiff, webp)

**Response:**

```json
{
  "success": true,
  "results": [
    {
      "predicted_class": "Tomato___Late_blight",
      "clean_class_name": "Tomato - Late blight",
      "confidence": 0.95,
      "all_predictions": {
        "Apple - Apple scab": 0.001,
        "Tomato - Late blight": 0.95
        // ... other classes
      }
    }
  ],
  "message": "Successfully processed 1 image(s)"
}
```

### `GET /health`

Check API and model status.

### `GET /classes`

Get all supported disease classes.

### `GET /`

API information and status.

## Usage Examples

### Python

```python
import requests

# Single image prediction
with open("leaf_image.jpg", "rb") as f:
    files = {"files": ("leaf_image.jpg", f, "image/jpeg")}
    response = requests.post("https://your-space-url/predict", files=files)
    result = response.json()
    print(f"Predicted: {result['results'][0]['clean_class_name']}")
    print(f"Confidence: {result['results'][0]['confidence']:.2%}")
```

### cURL

```bash
curl -X POST "https://your-space-url/predict" \
     -F "files=@leaf_image.jpg"
```

### JavaScript

```javascript
const formData = new FormData();
formData.append("files", fileInput.files[0]);

fetch("/predict", {
  method: "POST",
  body: formData,
})
  .then((response) => response.json())
  .then((data) => console.log(data));
```

## Model Information

- **Architecture**: Convolutional Neural Network (CNN)
- **Input Size**: 300x300 RGB images
- **Training Data**: PlantVillage dataset
- **Classes**: 38 plant disease categories
- **Framework**: TensorFlow/Keras

## Performance

- **Batch Size**: Up to 10 images per request
- **Response Time**: ~1-3 seconds per image
- **Memory Usage**: ~2GB for model loading
- **Accuracy**: >95% on validation set

## Limitations

- Best results with clear, well-lit leaf images
- Works optimally with images similar to training data
- Single leaf per image recommended
- Supported image formats: JPG, PNG, BMP, GIF, TIFF, WEBP

## Environment Variables

- `HF_MODEL_REPO`: Hugging Face model repository (default: "yasyn14/smart-leaf-model")
- `HF_MODEL_FILENAME`: Model file name (default: "best_model_32epochs.keras")
- `HF_HOME`: Cache directory for models (default: "/tmp/huggingface")

## License

Apache 2.0 License

## Support

For issues and questions, please visit the [model repository](https://huggingface.co/yasyn14/smart-leaf-model) or create an issue in this space.

---

_Built with FastAPI, TensorFlow, and 🤗 Transformers_