Create app.py

app.py

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+import gradio as gr
+from keras.preprocessing import image
+from keras.preprocessing.image import img_to_array
+from keras.models import load_model
+import numpy as np
+
+# Load the pre-trained model from the local path
+model_path = 'Mango.h5'
+model = load_model(model_path)
+
+def predict_disease(image_file, model, all_labels):
+    """
+    Predict the disease from an image using the trained model.
+
+    Parameters:
+    - image_file: image, input image file
+    - model: Keras model, trained convolutional neural network
+    - all_labels: list, list of class labels
+
+    Returns:
+    - str, predicted class label
+    """
+    try:
+        # Load and preprocess the image
+        img = image.load_img(image_file, target_size=(256, 256))
+        img_array = img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+        img_array = img_array / 255.0  # Normalize the image
+
+        # Predict the class
+        predictions = model.predict(img_array)
+        predicted_class = np.argmax(predictions[0])
+
+        # Return the class label
+        return all_labels[predicted_class]
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+
+# List of class labels
+all_labels = ['Mango Anthracrose','Mango Bacterial Cancker','Mango Cutting weevil','Mango Die Back','Mango Gall Midge ','Mango Healthy','Mango powdery mildew','Mango Sooty Mould']
+
+# Define the Gradio interface
+def gradio_predict(image_file):
+    return predict_disease(image_file, model, all_labels)
+
+# Create a Gradio interface
+gr_interface = gr.Interface(
+    fn=gradio_predict,  # Function to call for predictions
+    inputs=gr.Image(type="filepath"),  # Upload image as file path
+    outputs="text",  # Output will be the class label as text
+    title="Plant Disease Predictor",
+    description="Upload an image of a plant to predict the disease.",
+)
+
+# Launch the Gradio app
+gr_interface.launch()