Update app.py

app.py

@@ -19,7 +19,9 @@ models=[
 pipe0 = pipeline("image-classification", f"{models[0]}")
 pipe1 = pipeline("image-classification", f"{models[1]}")
 pipe2 = pipeline("image-classification", f"{models[2]}")
+ 
 
+fin_sum=[] 
 def image_classifier0(image):
     labels = ["Real","AI"]
     outputs = pipe0(image)
@@ -30,7 +32,8 @@ def image_classifier0(image):
     print (result_test)
     for result in outputs:
         results[result['label']] = result['score']    
-    print (results)    
+    print (results) 
+    fin_sum.append(results)
     return results
 def image_classifier1(image):
     labels = ["Real","AI"]
@@ -42,7 +45,8 @@ def image_classifier1(image):
     print (result_test)    
     for result in outputs:
         results[result['label']] = result['score']
-    print (results)    
+    print (results)
+    fin_sum.append(results)
     return results
 def image_classifier2(image):
     labels = ["Real","AI"]
@@ -54,16 +58,15 @@ def image_classifier2(image):
     print (result_test)    
     for result in outputs:
         results[result['label']] = result['score']
-    print (results)    
+    print (results) 
+    fin_sum.append(results)
     return results
 
 def softmax(vector):
  e = exp(vector)
  return e / e.sum()
 
-    
-
-fin_sum=[]   
+     
 
 def aiornot0(image):    
     labels = ["Real", "AI"]
@@ -220,12 +223,12 @@ with gr.Blocks() as app:
                 
     btn.click(fin_clear,None,fin)
     load_btn.click(load_url,in_url,[inp,mes])
-    btn.click(aiornot0,[inp],[outp0,n_out0]).then(tot_prob,None,fin)
-    btn.click(aiornot1,[inp],[outp1,n_out1]).then(tot_prob,None,fin)
-    btn.click(aiornot2,[inp],[outp2,n_out2]).then(tot_prob,None,fin)
+    btn.click(aiornot0,[inp],[outp0,n_out0]).then(tot_prob,None,fin,show_progress=False)
+    btn.click(aiornot1,[inp],[outp1,n_out1]).then(tot_prob,None,fin,show_progress=False)
+    btn.click(aiornot2,[inp],[outp2,n_out2]).then(tot_prob,None,fin,show_progress=False)
     
-    btn.click(image_classifier0,[inp],[n_out3])
-    btn.click(image_classifier1,[inp],[n_out4])
-    btn.click(image_classifier2,[inp],[n_out5])
+    btn.click(image_classifier0,[inp],[n_out3]).then(tot_prob,None,fin,show_progress=False)
+    btn.click(image_classifier1,[inp],[n_out4]).then(tot_prob,None,fin,show_progress=False)
+    btn.click(image_classifier2,[inp],[n_out5]).then(tot_prob,None,fin,show_progress=False)
 
 app.queue(concurrency_count=20).launch()