Update README.md

README.md

@@ -1,21 +1,21 @@
 ---
 license: gpl-3.0
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-# Potato & Tomato Disease Classification Web Application
 
+# Potato & Tomato Disease Classification Web Application
 
-### Overview
 This project is a web application developed using Flask that allows users to upload images of potato or tomato leaves and receive predictions regarding potential diseases. The application utilizes two deep learning models: one trained to classify potato leaf diseases and another for tomato leaf diseases. Both models were trained using convolutional neural networks (CNNs) and implemented using PyTorch.
 
+## Features
+
+- Image Upload: Users can upload images of potato or tomato leaves.
+- Disease Prediction: The application predicts whether the leaf is healthy or affected by specific diseases.
+- Dynamic Background: The background image of the web page dynamically changes based on whether the user selects potato or tomato.
+- Probability Display: The probability of the predicted class is displayed as a percentage.
 
-### Key Features
-- **Image Upload:** Users can upload images of potato or tomato leaves.
-- **Disease Prediction:** The application predicts whether the leaf is healthy or affected by specific diseases.
-- **Dynamic Background:** The background image of the web page dynamically changes based on whether the user selects potato or tomato.
-- **Probability Display:** The probability of the predicted class is displayed as a percentage.
 
-  
-### Technologies Used
+## Technologies
+
 - **Python:** Core programming language used for model development and Flask backend.
 - **Flask:** Web framework for developing the web application.
 - **PyTorch:** Deep learning framework used to develop and train the models.
@@ -23,59 +23,41 @@ This project is a web application developed using Flask that allows users to upl
 - **PIL (Pillow):** For image processing.
 - **OpenCV:** For image display and preprocessing.
 - **Torchvision:** For image transformation utilities.
+## Models
 
-  
-### Models
 - **Potato Disease Classification Model**
-   - **Classes:**
-Potato Early Blight,
-Potato Late Blight,
-Potato Healthy
-   - **Techniques Used:**
-      -  Convolutional layers for feature extraction.
-      -  Batch normalization and max pooling for enhanced training stability and performance.
-      -  Dropout layers to prevent overfitting.
 
-- **Tomato Disease Classification Model**
-   - **Classes:**
-    Tomato Early Blight,
-    Tomato Late Blight,
-    Tomato Healthy
+  - **Classes:** Potato Early Blight, Potato Late Blight, Potato Healthy
    - **Techniques Used:**
-      -  Similar architecture to the potato model with appropriate adjustments for tomato disease classification.
-      -  Batch normalization, max pooling, and dropout layers are also used here.
+     - Convolutional layers for feature extraction.
+     - Batch normalization and max pooling for enhanced training stability and performance.
+     - Dropout layers to prevent overfitting.
 
+- **Tomato Disease Classification Model**
 
-### Web Application
-- **Backend**
-The backend of the application is powered by Flask. It loads the trained models, handles image uploads, processes images, and returns predictions to the frontend.
-
-  - **Model Loading:** Both models (potato and tomato) are loaded at the start of the application to minimize latency during prediction.
-  - **Prediction Logic:** Depending on the selected plant type (potato or tomato), the corresponding model is used to predict the disease class.
-  - **Dynamic Background:** The background image on the frontend changes based on the selected plant type, enhancing user experience.
-
-- **Frontend**
-The frontend is developed using HTML and CSS, with Bootstrap for responsive design.
+  -  **Classes:** Tomato Early Blight, Tomato Late Blight, Tomato Healthy
+  - **Techniques Used:**
+    - Similar architecture to the potato model with appropriate adjustments for tomato disease classification.
+    - Batch normalization, max pooling, and dropout layers are also used here.
+## Usage
 
-  - **File Upload Interface:** Users can upload an image of a leaf.
-  - **Prediction Display:** After processing, the application displays the predicted disease class and the associated probability.
-  - **Dynamic Background:** The background image changes depending on whether the user is predicting for potato or tomato.
+- Install the required dependencies using `pip install -r requirements.txt`.
+- Download the pre-trained model weights and place them in the `models/` directory.
+- Run the Flask web application using `python app.py`.
+- Access the application in your web browser at `http://localhost:5000`.
 
 
-### Usage
-1. Install the required dependencies using ```pip install -r requirements.txt```.
-2. Download the pre-trained model weights and place them in the `models/` directory.
-3. Run the Flask web application using ```python app.py```.
-4. Access the application in your web browser at `http://localhost:5000`.
+## Outcome
 
-### Outcome
 - **Performance**
   - **Potato Model:** Achieved an accuracy of 98% on the validation set, with strong performance in classifying Early Blight, Late Blight, and Healthy leaves.
   - **Tomato Model:** Achieved an accuracy of 97% on the validation set, effectively distinguishing between Early Blight, Late Blight, and Healthy leaves.
 - **Benefits**
   - **Disease Detection:** Helps farmers and agriculturists detect diseases in potato and tomato plants early, potentially preventing crop losses.
-  - **User-Friendly Interface:** The web application provides a simple interface for non-technical users to diagnose plant diseases.
+   - **User-Friendly Interface:** The web application provides a simple interface for non-technical users to diagnose plant diseases.
+
+## App
+![App Screenshot](https://huggingface.co/datasets/hassaanik/Plant_Disease_App_Images/blob/main/Potato.png)
+
 
-### WebApp
-![Uploading Screenshot (234).png…](https://huggingface.co/hassaanik/Plant_Disease_Classification/blob/main/Webapp/Screenshot%20(234).png)
-![Uploading Screenshot (235).png…](https://huggingface.co/hassaanik/Plant_Disease_Classification/blob/main/Webapp/Screenshot%20(235).png)
+![App Screenshot](https://huggingface.co/datasets/hassaanik/Plant_Disease_App_Images/blob/main/Tomato.png)