Update app.py

app.py

@@ -8,27 +8,41 @@ import urllib.request
 import uuid
 uid=uuid.uuid4()
 
-pipe = pipeline("image-classification", "umm-maybe/AI-image-detector")
+models=[
+    "Nahrawy/AIorNot",
+    "umm-maybe/AI-image-detector",
+    "arnolfokam/ai-generated-image-detector",
+]
+
+pipe0 = pipeline("image-classification", f"{models[0]}")
+pipe1 = pipeline("image-classification", f"{models[1]}")
+pipe2 = pipeline("image-classification", f"{models[2]}")
 
-def image_classifier(image):
-    outputs = pipe(image)
+def image_classifier0(image):
+    outputs = pipe0(image)
+    results = {}
+    for result in outputs:
+        results[result['label']] = result['score']
+    return results
+def image_classifier1(image):
+    outputs = pipe1(image)
+    results = {}
+    for result in outputs:
+        results[result['label']] = result['score']
+    return results
+def image_classifier2(image):
+    outputs = pipe2(image)
     results = {}
     for result in outputs:
         results[result['label']] = result['score']
     return results
-
 
 def softmax(vector):
  e = exp(vector)
  return e / e.sum()
 
     
-models=[
-    "Nahrawy/AIorNot",
-    "umm-maybe/AI-image-detector",
-    "arnolfokam/ai-generated-image-detector",
 
-]
 fin_sum=[]   
 def aiornot0(image):    
     labels = ["Real", "AI"]
@@ -172,11 +186,25 @@ with gr.Blocks() as app:
             with gr.Box():
                 n_out2=gr.Label(label="Output")
                 outp2 = gr.HTML("""""")    
-          
+        with gr.Row():
+            with gr.Box():
+                n_out3=gr.Label(label="Output")
+                outp3 = gr.HTML("""""")
+            with gr.Box():
+                n_out4=gr.Label(label="Output")
+                outp4 = gr.HTML("""""")
+            with gr.Box():
+                n_out5=gr.Label(label="Output")
+                outp5 = gr.HTML("""""")    
+                
     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(image_classifier0,[inp],[n_out3])
+    btn.click(image_classifier1,[inp],[n_out4])
+    btn.click(image_classifier2,[inp],[n_out5])
+
 app.queue(concurrency_count=20).launch()