Update app.py

app.py

@@ -12,21 +12,9 @@ model = from_pretrained_keras(
     pretrained_model_name_or_path="fbadine/image-spam-detection"
 )
 
-####### To be removed
-def get_txt_output(arr):
-    txt = f"Image shape: {arr.shape}\n"
-    txt += f"Min: {np.min(arr)}, Max: {np.max(arr)}\n"
-    txt += f"Image hash: {hash(tuple(arr.reshape(-1)))}\n\n"
-
-    return txt
-####### End
-
 # This is the predict function that takes as input an array-like-image and produces
 # the probabilities that this image is either spam or ham
 def predict(image):
-####### To be removed
-    txt_output = get_txt_output(image)
-####### End
     # Resize image
     resized_image = keras.layers.Resizing(
         IMAGE_SIZE[0],
@@ -34,27 +22,20 @@ def predict(image):
         interpolation="bilinear",
         crop_to_aspect_ratio=True
     )(image)
-    resized_image = tf.expand_dims(resized_image, axis=0)
 
-####### To be removed
-    txt_output += get_txt_output(resized_image.numpy())
-####### End
+    # Add the batch axis
+    resized_image = tf.expand_dims(resized_image, axis=0)
     
     # Predict
     pred = model.predict(resized_image)
     prob = float(pred[0][0])
 
-####### To be removed
-    txt_output += f"Probability: {prob}"
-####### End
-
     scoring_output = {
         "Spam": prob,
         "Ham": 1 - prob 
     }
     
-    #return scoring_output
-    return [scoring_output, txt_output]
+    return scoring_output
 
 # Clear Input and outpout
 def clear_inputs_and_outputs():
@@ -88,9 +69,6 @@ if __name__ == "__main__":
             with gr.Column():
                 # Output
                 lbl_output = gr.Label(label="Prediction")
-                ####### To be removed
-                txt_output = gr.TextArea(label="info_out")
-                ####### End
 
         clr_btn.click(
             fn=clear_inputs_and_outputs,
@@ -100,8 +78,7 @@ if __name__ == "__main__":
         prd_btn.click(
             fn=predict,
             inputs=[image_input],
-            #outputs=[lbl_output],
-            outputs=[lbl_output, txt_output],
+            outputs=[lbl_output],
         )
 
         gr.Examples(
@@ -111,8 +88,7 @@ if __name__ == "__main__":
                 os.path.join(os.path.curdir, "examples", "sample2.jpg"),
             ],
             inputs=image_input,
-            #outputs=lbl_output,
-            outputs=[lbl_output, txt_output],
+            outputs=lbl_output,
             fn=predict,
             cache_examples=True,
         )