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app.py

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+from tensorflow import keras
+import gradio as gr
+model = keras.models.load_model('potatoes2.h5')
+class_names = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
+
+def predict_input_image(img):
+  img_4d=img.reshape(-1,256,256,1)
+  prediction=model.predict(img_4d)[0]
+  return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
+
+
+image = gr.inputs.Image(shape=(256,256))
+label = gr.outputs.Label(num_top_classes=len(class_names))
+
+gr.Interface(fn=predict_input_image, inputs=image, outputs=label,interpretation='default').launch(debug='True')
+