from keras.models import load_model
import cv2
import gradio as gr


intestine_model=load_model('Intestine_model.h5')
intestine_class_name={0:'Healthy',1:'Intestine have ulcer button'}

def predict_intestine(img):
  img=cv2.resize(img,(224,224))
  prediction=intestine_model.predict(img.reshape(1,224,224,3)).argmax()
  return intestine_class_name[prediction]


interface=gr.Interface(fn=predict_intestine,inputs='image',outputs=[gr.components.Textbox(label='Result')])

interface.launch(debug=True)