app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import load_model

# Load your model with the new path
model_path = 'best_model.keras'
model = load_model(model_path)

# Define the image size your model expects
IMG_SIZE = (224, 224)

# Define class names
class_names = [
    'Corn___Common_Rust',
    'Corn___Gray_Leaf_Spot',
    'Corn___Healthy',
    'Corn___Northern_Leaf_Blight',
    'Corn___Northern_Leaf_Spot',
    'Corn___Phaeosphaeria_Leaf_Spot'
]

# Define prediction function
def predict(image):
    img_array = tf.image.resize(image, IMG_SIZE)
    img_array = tf.expand_dims(img_array, axis=0) / 255.0

    predictions = model.predict(img_array)
    predicted_class = np.argmax(predictions[0])
    confidence = np.max(predictions[0])

    if confidence <= 0.8:
        return "Unknown Object"
    else:
        return {class_names[predicted_class]: float(confidence)}

# Create Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=6),
    title="Maize Leaf Disease Detection",
    description="Upload an image of a maize leaf to classify its disease."
)

# Launch the interface
if __name__ == "__main__":
    interface.launch()