Update README.md

README.md

@@ -4,41 +4,131 @@ emoji: 🌱📸🩺
 colorFrom: green
 colorTo: green
 sdk: gradio
-sdk_version: 4.41.0
+sdk_version: 5.31.0
 app_file: app.py
 pinned: false
-license: afl-3.0
+license: apache-2.0
 ---
 
-# Bean Plant Health Predictor
+# Bean Plant Health ViT Classifier 🌱📸🩺
 
-This application is designed to help farmers quickly identify the health of bean plants by analyzing images of their leaves. The app uses a Vision Transformer (ViT) model to classify images into three categories: **angular_leaf_spot**, **bean_rust**, and **healthy**. This tool can be deployed on a drone for real-time monitoring of crops, enabling timely treatment of diseased plants.
+A web-based agricultural AI tool that helps farmers identify bean plant diseases using computer vision. Built with Vision Transformer (ViT) model and Gradio for real-time crop health monitoring and disease detection.
 
-## Use Case
+![Demo Screenshot](bean-plant-health-logo.png)
 
-Farmers need to monitor the health of their bean plants regularly to prevent the spread of diseases. This app provides a machine learning-based solution to automate the identification of plant diseases, which can be particularly useful when integrated with drone technology.
+## 🚀 Live Demo
 
-## Features
+Try the app: [Bean-Plant-Health-Classifier](https://huggingface.co/spaces/ashish-soni08/Bean-plant-health-ViT-classifier)
 
-- **Image Classification**: Upload an image of a bean leaf, and the app will classify it into one of the following categories:
-  - Angular Leaf Spot
-  - Bean Rust
-  - Healthy
+## ✨ Features
 
-## Model Details
+- **Disease Detection**: Automatically identify angular leaf spot, bean rust, and healthy plants
+- **Real-time Analysis**: Get instant classification results with confidence scores
+- **Drone-Ready**: Designed for integration with drone technology for field monitoring
+- **Clean Interface**: Intuitive web UI built with Gradio for farmers and agricultural professionals
 
-- **Model Used**: [Vision Transformer (ViT) - base-sized model](https://huggingface.co/google/vit-base-patch16-224) fine-tuned on the [Beans dataset](https://huggingface.co/datasets/beans).
-- **Image Processor**: The app uses the `ViTImageProcessor` for preparing images before classification.
-- **Labels**: The possible outcomes are `angular_leaf_spot`, `bean_rust`, and `healthy`.
+## 🛠️ Technology Stack
 
-## How to Use
+- **Backend**: Python, Hugging Face Transformers
+- **Frontend**: Gradio
+- **Model**: [Vision Transformer (ViT-base)](https://huggingface.co/google/vit-base-patch16-224) fine-tuned on [Beans dataset](https://huggingface.co/datasets/beans)
+- **Deployment**: Hugging Face Spaces
 
-1. **Upload an Image**: Click on the image input field and upload a photo of a bean leaf.
-2. **Get Results**: The app will classify the image and display the probabilities for each category.
-3. **Interpret the Results**: The app shows the confidence levels for each label, helping farmers identify whether the plant is healthy or requires treatment.
+## 🏃‍♂️ Quick Start
 
-## Technology Stack
+### Prerequisites
 
-- **Gradio**: Used to create the user interface.
-- **PyTorch**: Utilized for running the model inference.
-- **Hugging Face Transformers**: Provides the pre-trained Vision Transformer model.
+```bash
+Python 3.8+
+pip
+```
+
+### Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/Ashish-Soni08/bean-plant-health-classifier.git
+cd bean-plant-health-vit-classifier
+```
+
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3. Run the application:
+```bash
+python app.py
+```
+
+4. Open your browser and navigate to `http://localhost:7860`
+
+## 📋 Usage
+
+1. **Upload Image**: Click the image input field and upload a photo of a bean leaf
+2. **Get Classification**: The app automatically analyzes the image and provides predictions
+3. **View Results**: Check confidence scores for each disease category to make informed decisions
+
+### Disease Categories
+
+- **Angular Leaf Spot**: Fungal disease causing angular brown spots on leaves
+- **Bean Rust**: Fungal disease creating rust-colored pustules on leaf surface
+- **Healthy**: No visible signs of disease or infection
+
+### Example
+
+**Input Image:**
+```
+[Photo of bean leaf with brown spots]
+```
+
+**Output:**
+```
+Angular Leaf Spot: 87.5%
+Bean Rust: 8.2%
+Healthy: 4.3%
+```
+
+## 🧠 Model Information
+
+This app uses **Vision Transformer (ViT-base)** fine-tuned for agricultural disease detection:
+
+- **Architecture**: Vision Transformer with 16x16 patches, 224x224 input resolution  
+- **Parameters**: ~86.6 million parameters
+- **Training Data**: Beans dataset with 1,034 field images of bean leaves
+- **Classes**: 3 categories (Angular Leaf Spot, Bean Rust, Healthy)
+- **Base Model**: Pre-trained on ImageNet-21k, fine-tuned on beans disease dataset
+
+## 📁 Project Structure
+
+```
+bean-plant-health-vit-classifier/
+├── app.py                 # Main Gradio application
+├── requirements.txt       # Python dependencies
+├── README.md              # Project documentation
+└── images/                # Sample bean leaf images for testing
+```
+
+## 🌾 Agricultural Impact
+
+This tool helps farmers:
+- **Early Disease Detection**: Identify problems before they spread
+- **Reduce Crop Loss**: Take timely action to treat diseased plants
+- **Optimize Treatment**: Focus resources on affected areas only
+- **Scale Monitoring**: Use with drones for large-field surveillance
+
+## 📄 License
+
+This project is licensed under the Apache License 2.0
+
+## 🙏 Acknowledgments
+
+- [Hugging Face](https://huggingface.co/) for the Transformers library and model hosting
+- [Gradio](https://gradio.app/) for the web interface framework
+- [Google Research](https://github.com/google-research/vision_transformer) for the Vision Transformer architecture
+
+## 📞 Contact
+
+Ashish Soni - [email protected]
+
+Project Link: [github](https://github.com/Ashish-Soni08/bean-plant-health-classifier)