📝 Fix Hugging Face README config

README.md

@@ -0,0 +1,136 @@
+---
+title: "CropGuard: Leaf Disease Detector"
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: "4.14.0"
+app_file: app.py
+pinned: false
+---
+
+# CropGuard: Leaf Disease Detector
+
+**CropGuard** is a lightweight, deployable machine learning app that detects **leaf diseases** in **Potato**, **Tomato**, and **Grape** plants from user-uploaded or captured images.
+
+Built using **PyTorch**, **Gradio**, **Docker**, and **Hugging Face Spaces**, it provides the following capabilities:
+
+- Upload or capture a leaf image
+- Predict plant health status
+- Identify likely disease (if any)
+- Visualize model attention using **GradCAM++** heatmaps
+- Provide quick disease information and treatment suggestions
+
+---
+
+## Project Structure
+
+```
+CropGuard/
+├── app.py                # Gradio app
+├── Dockerfile            # Docker container definition
+├── requirements.txt      # Python dependencies
+├── notebooks/            # Step-by-step project development
+│   ├── 01_data_preprocessing.ipynb
+│   ├── 02_model_training.ipynb
+│   ├── 03_model_validation.ipynb
+│   └── 04_gradcam_visualization.ipynb
+├── src/                  # Source code (organized into modules)
+│   ├── app/
+│   ├── data/
+│   ├── model/
+│   └── utils/
+├── sample_images/        # Few test images (optional for demo)
+├── disease_info.json     # Disease descriptions
+└── README.md             # (this file)
+```
+
+---
+
+## Notebooks Overview
+
+| Notebook | Purpose |
+|:-|:-|
+| `01_data_preprocessing.ipynb` | Download PlantVillage dataset, clean and split into train/val/test sets |
+| `02_model_training.ipynb` | Set up data augmentation, train MobileNetV2 model, monitor training curves |
+| `03_model_validation.ipynb` | Evaluate model performance, generate metrics, confusion matrix |
+| `04_gradcam_visualization.ipynb` | Generate GradCAM++ heatmaps to visualize model focus |
+
+---
+
+## How to Run Locally
+
+1. **Clone the repo:**
+
+```bash
+git clone https://github.com/YOUR_USERNAME/CropGuard.git
+cd CropGuard
+```
+
+2. **Create a virtual environment:**
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+```
+
+3. **Install dependencies:**
+
+```bash
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+4. **Launch the app:**
+
+```bash
+python app.py
+```
+
+It will be available at [http://localhost:7860](http://localhost:7860).
+
+---
+
+## How to Build and Run with Docker
+
+```bash
+docker build -t cropguard-app .
+docker run -p 7860:7860 cropguard-app
+```
+
+---
+
+## Web Deployment
+
+Easily deployable on:
+
+- Hugging Face Spaces
+- DockerHub
+
+---
+
+## Sample Images
+
+We provide a few **sample leaf images** in the `sample_images/` directory so users can test the model even without their own images.
+
+---
+
+## License
+
+MIT License.
+
+---
+
+## Acknowledgments
+
+- **Dataset:** [PlantVillage Dataset](https://www.kaggle.com/datasets/mohitsingh1804/plantvillage)
+- **Base Model:** [MobileNetV2 (pretrained on ImageNet)](https://arxiv.org/abs/1801.04381)
+- **Visualization:** GradCAM++
+
+---
+
+## Author
+
+Made by **[Arka Mitra](https://github.com/mitraarka27)** © 2025.
+
+---
+