---
title: CDIApp
emoji: 🏆
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 5.4.0
app_file: app.py
pinned: false
license: apache-2.0
---

# Plant Disease Classification and Remedy System
![](assets/ss.png)
This is a deep learning project for classifying plant diseases from images. It uses a convolutional neural network trained on a dataset of plant disease images.
 
A deep learning-based system that identifies plant diseases from images and provides remedies using AI.
 
## Features
 
- 🌿 Identifies 38 different plant diseases across various crops
- 🔍 Uses advanced deep learning models for accurate disease detection
- 🤖 Integrates with Gemini AI for providing detailed remedies
- 🎯 Includes Out-of-Distribution detection for reliable predictions
- 🖥️ User-friendly Gradio web interface
 
## System Requirements
 
- Python 3.8+
- CUDA-compatible GPU (optional, for faster inference)
- 4GB+ RAM
 
## Installation
 
1. Clone the repository:
 
2. Install dependencies:
```bash
pip install -r requirements.txt
```
 
3. Set up environment variables:
Create a `.env` file in the root directory and add:
```
GEMINI_API_KEY=your_gemini_api_key_here
```
 
## Project Structure
 
```
├── acfg/               # Application configuration
├── ml/                 # Machine learning modules
│   ├── app/           # Core ML application code
│   └── input/         # Dataset directory
├── service/           # Backend service
└── static/           # Model checkpoints and static files
```
 
## Usage
 
### Running the Web Interface
 
```bash
python app.py
```
Access the interface at `http://localhost:7860`
 
### Training Models
 
1. Train the classification model:
```bash
python train_classifier.py
```
 
2. Train the OOD detection model:
```bash
python train_ood.py
```
 
## Model Architecture
 
- **Classification**: MobileNetV3 Small with custom MLP head
- **OOD Detection**: Custom Autoencoder architecture
- **LLM Integration**: Gemini 1.5 Flash for remedy generation
 
## Dataset
 
The system is trained on a comprehensive dataset of plant disease images, covering:
- Multiple crop types (Apple, Corn, Tomato, etc.)
- Various disease conditions
- Healthy plant samples
 
## Contributing
 
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request