Create app.py

app.py

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+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+import cv2
+import gradio as gd
+from keras.models import load_model
+model2 = load_model()
+def predict(image):
+    image=cv2.resize(image,(240,240))
+    image=img_to_array(image)/255.0
+    image = np.expand_dims(image, axis=0)  
+    prediction=model2.predict(image)
+    predictions=np.array(prediction)
+    predicted_index=np.argmax(predictions)
+    index_to_class={0:'Disease : Alzheimer  || Type : Moderate_Demented',
+      1:'Disease : Alzheimer || Type : MildDemented',
+     2:'Disease : Alzheimer || Type : VeryMildDemented',
+            3:'Disease : tumor ||  Type : glioma',
+           4:'Disease :tumor ||  Type : meningioma',   
+           5: 'Disease : tumor  || Type : pituitary',
+               6:'Disease : None'}
+    predicted_class_name=index_to_class[predicted_index]
+    return predicted_class_name
+    
+
+headline="BRAIN DISEASE DETECTION "
+a=gd.Interface(predict,inputs=gd.Image(),outputs="text",title=headline)
+a.launch(share=True, debug=False)