Update app.py

Added debugging info

app.py

@@ -12,9 +12,21 @@ 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],
@@ -24,16 +36,25 @@ def predict(image):
     )(image)
     resized_image = tf.expand_dims(resized_image, axis=0)
 
+####### To be removed
+    txt_output += get_txt_output(resized_image.numpy())
+####### End
+    
     # 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
+    return [scoring_output, txt_output]
 
 # Clear Input and outpout
 def clear_inputs_and_outputs():
@@ -67,9 +88,9 @@ if __name__ == "__main__":
             with gr.Column():
                 # Output
                 lbl_output = gr.Label(label="Prediction")
-                #plt_output = gr.Plot(
-                #    label="Prediction", show_label=False
-                #)
+                ####### To be removed
+                txt_output = gr.TextArea(label="info_out")
+                ####### End
 
         clr_btn.click(
             fn=clear_inputs_and_outputs,
@@ -79,7 +100,8 @@ if __name__ == "__main__":
         prd_btn.click(
             fn=predict,
             inputs=[image_input],
-            outputs=[lbl_output],
+            #outputs=[lbl_output],
+            outputs=[lbl_output, txt_output],
         )
 
         gr.Examples(