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README.md
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@@ -62,6 +62,12 @@ The frontend is developed using HTML and CSS, with Bootstrap for responsive desi
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- **Dynamic Background:** The background image changes depending on whether the user is predicting for potato or tomato.
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### Outcome
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- **Performance**
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- **Potato Model:** Achieved an accuracy of 98% on the validation set, with strong performance in classifying Early Blight, Late Blight, and Healthy leaves.
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- **Dynamic Background:** The background image changes depending on whether the user is predicting for potato or tomato.
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### Usage
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1. Install the required dependencies using ```pip install -r requirements.txt```.
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2. Download the pre-trained model weights and place them in the `models/` directory.
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3. Run the Flask web application using ```python app.py```.
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4. Access the application in your web browser at `http://localhost:5000`.
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### Outcome
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- **Performance**
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- **Potato Model:** Achieved an accuracy of 98% on the validation set, with strong performance in classifying Early Blight, Late Blight, and Healthy leaves.
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