Create app.py

app.py

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+import gradio as gr
+import cv2
+from keras.models import load_model
+
+my_model=load_model('Liver_model.h5',compile=True)
+class_num={0:'Healthy',1:'Un-Healthy'}
+
+def Predict(Image):
+  Image=cv2.resize(Image,(224,224))
+  class_no=my_model.predict(Image.reshape(1,224,224,3)).argmax()
+  class_name=class_num.get(class_no)
+  return class_name
+
+
+interface=gr.Interface(fn=Predict,inputs='image',outputs=[gr.components.Textbox(label="Class Name")],
+                       title="This Space predict the liver of Chicken is healthy or un-healthy",
+                       examples=[['Test1.jpg'],['Test2.jpeg'],['Test3.jpeg']]) 
+interface.launch(debug=True)
+
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