app.py

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()