Create app.py

app.py

@@ -0,0 +1,16 @@
+import keras.backend as K
+def psnr(y_true, y_pred):
+    return -10*K.log(K.mean(K.flatten((y_true - y_pred))**2)) / np.log(10)
+
+from keras.models import load_model
+model = load_model("C:/Users/owner/Desktop/MyNet.h5", custom_objects={'psnr': psnr, 'val_psnr': psnr})
+
+def predict_input_image(img):
+  #img_4d=img.reshape(-1,180,180,3)
+  img_4d=img.reshape(256,256,3)
+  prediction=model.predict(img_4d)[0]
+  return {flower_classes[i]: float(prediction[i]) for i in range(5)}
+
+image = gr.inputs.Image(shape=(256,256))
+
+gr.Interface(fn=predict_input_image, inputs=image, outputs=image,interpretation='default').launch(debug='True')