Create app.py

app.py

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+from keras.models import load_model
+import cv2
+import gradio as gr
+
+
+intestine_model=load_model('/content/drive/MyDrive/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)
+
+
+
+
+
+
+
+
+