Update README.md

README.md

@@ -12,7 +12,7 @@ This project is a web application developed using Flask that allows users to upl
 - **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
 - **Python:** Core programming language used for model development and Flask backend.
 - **Flask:** Web framework for developing the web application.
@@ -21,7 +21,7 @@ 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.
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+  
 ## Models
 1. **Potato Disease Classification Model**
 - **Classes:**
@@ -46,21 +46,21 @@ Dropout layers to prevent overfitting.
 - **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.
+  **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.
 
-- **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.
+  **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.
 
 ## Outcome
 - **Performance**
--  **Potato Model:** Achieved an accuracy of 95% on the validation set, with strong performance in classifying Early Blight, Late Blight, and Healthy leaves.
--  **Tomato Model:** Achieved an accuracy of 93% on the validation set, effectively distinguishing between Early Blight, Late Blight, and Healthy leaves.
+  **Potato Model:** Achieved an accuracy of 95% on the validation set, with strong performance in classifying Early Blight, Late Blight, and Healthy leaves.
+  **Tomato Model:** Achieved an accuracy of 93% 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.
+  **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.