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| from gradio import Interface, Image, Label | |
| import tensorflow as tf | |
| # Load your TensorFlow model | |
| model = tf.keras.models.load_model("mangoleaf.h5") | |
| # Define your class names if needed | |
| class_names = ['Anthracnose', 'Bacterial Canker', 'Cutting Weevil', 'Die Back', 'Gall Midge','Healthy','Powdery Mildew','Sooty Mould'] | |
| # Function to make predictions | |
| def classify_image(image): | |
| # Preprocess the image | |
| img = tf.image.resize(image, (224, 224)) | |
| img = tf.expand_dims(img, 0) # Add batch dimension | |
| # Make prediction | |
| prediction = model.predict(img) | |
| predicted_class = class_names[prediction.argmax()] | |
| return predicted_class | |
| # Gradio interface | |
| image = Image() # Remove the `shape` argument | |
| label = Label() | |
| # Create interface | |
| interface = Interface(classify_image, image, label, | |
| title="Mango leaf disease detection", | |
| description="Upload an image of a leaf.").launch() | |