app.py

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')