Create app.py

app.py

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+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()