---
title: Tomato Blight Classifier
emoji: 🍅
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 4.26.0
python_version: '3.10'
app_file: app.py
pinned: false
license: mit
short_description: Tomato disease classifier with Gradio
---

# 🍅 Tomato Disease Classifier

A Gradio-based web application that uses a Vision Transformer (ViT) model from Hugging Face to classify tomato leaf diseases in real-time.

## Features

- **Real-time Classification**: Upload tomato leaf images to instantly identify diseases
- **Three Disease Categories**:
  - 🟢 **Healthy**: No disease detected
  - 🔴 **Early Blight**: Fungal disease in early stages (easier to treat)
  - 🔴🔴 **Late Blight**: Advanced fungal disease (more severe, requires immediate action)
- **Confidence Scores**: View the model's confidence level for each prediction
- **Visual Interface**: Easy-to-use web interface powered by Gradio
- **Pre-trained Model**: Uses `nexusbert/tomato-disease-vit` from Hugging Face

## 🚀 Quick Start

Simply upload an image of a tomato leaf and click "Classify Disease" to get instant predictions!

## Installation

1. **Clone or use this space directly on Hugging Face**

2. **Create a virtual environment** (if running locally):
```bash
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

3. **Install dependencies**:
```bash
pip install -r requirements.txt
```

## Usage

Run the application:
```bash
python app.py
```

The application will launch a web interface (typically at `http://localhost:7860`) where you can:
1. Upload an image of a tomato leaf
2. Click "Classify Disease" to get predictions
3. View confidence scores for each disease category
4. See a bar chart showing the prediction distribution

## Model Details

- **Model Name**: `nexusbert/tomato-disease-vit`
- **Architecture**: Vision Transformer (ViT)
- **Task**: Image Classification
- **Input**: RGB images of tomato leaves
- **Output**: Classification scores for disease categories

## How It Works

The classifier uses two approaches:

1. **Pipeline API**: For quick, simple predictions
```python
from transformers import pipeline
classifier = pipeline("image-classification", model="nexusbert/tomato-disease-vit")
results = classifier("path/to/tomato_image.jpg")
```

2. **Direct Model & Processor**: For more detailed control
```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("nexusbert/tomato-disease-vit")
model = AutoModelForImageClassification.from_pretrained("nexusbert/tomato-disease-vit")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
```

## Requirements

- Python 3.8+
- PyTorch
- Transformers
- Gradio
- CUDA (optional, for GPU acceleration)

See `requirements.txt` for complete dependency list.

## Project Structure

```
tomato_blight_classifier/
├── app.py              # Main Gradio application
├── requirements.txt    # Python dependencies
└── README.md          # This file
```

## Performance Notes

- First run will download the model (takes a few minutes)
- Subsequent runs are faster as the model is cached
- GPU recommended for faster inference (CUDA)
- CPU inference is supported but slower

## 🔍 Example Workflow

1. **Prepare**: Take a clear photo of a tomato leaf (both sides work)
2. **Upload**: Click the upload area and select your image
3. **Analyze**: Click "Classify Disease"
4. **Review**: Check the predictions and confidence scores

## Troubleshooting

### Model Download Issues
If the model fails to download, ensure you have internet connection and sufficient disk space (~300MB).

### Memory Issues
If you encounter out-of-memory errors, close other applications or use CPU inference.

### Slow Performance
Enable GPU acceleration by installing PyTorch with CUDA support.

## Model Source

This classifier uses the pre-trained model: **nexusbert/tomato-disease-vit**

[View Model on Hugging Face](https://huggingface.co/nexusbert/tomato-disease-vit)

## References

- [Hugging Face Model Hub](https://huggingface.co/)
- [Gradio Documentation](https://www.gradio.app/)
- [Transformers Library](https://huggingface.co/docs/transformers/)

## License

MIT License - See LICENSE file for details