Update app.py

app.py

@@ -19,6 +19,9 @@ models=[
     "Nahrawy/AIorNot",
     "umm-maybe/AI-image-detector",
     "arnolfokam/ai-generated-image-detector",
+    "Binyamin/Hybrid_1",
+    "HuggingSara/model_soups",
+    "psyne/AIResnetClone",
 
 ]
     
@@ -95,6 +98,79 @@ def aiornot2(image):
         results[labels[idx]] = px[idx][0]
     #results[labels['label']] = result['score']
     return gr.HTML.update(html_out),results
+def aiornot3(image):    
+    labels = ["Real", "AI"]
+    mod=models[3]
+    feature_extractor3 = AutoFeatureExtractor.from_pretrained(mod)
+    model3 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor3(image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model3(**input)
+        logits = outputs.logits
+        probability = softmax(logits)
+        px = pd.DataFrame(probability.numpy())
+    prediction = logits.argmax(-1).item()
+    label = labels[prediction]
+    html_out = f"""
+    <h1>This image is likely: {label}</h1><br><h3>
+ 
+    Probabilites:<br>
+    Real: {px[0][0]}<br>
+    AI: {px[1][0]}"""
+    results = {}
+    for idx,result in enumerate(px):
+        results[labels[idx]] = px[idx][0]
+    #results[labels['label']] = result['score']
+    return gr.HTML.update(html_out),results
+def aiornot4(image):    
+    labels = ["Real", "AI"]
+    mod=models[4]
+    feature_extractor4 = AutoFeatureExtractor.from_pretrained(mod)
+    model4 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor4(image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model4(**input)
+        logits = outputs.logits
+        probability = softmax(logits)
+        px = pd.DataFrame(probability.numpy())
+    prediction = logits.argmax(-1).item()
+    label = labels[prediction]
+    html_out = f"""
+    <h1>This image is likely: {label}</h1><br><h3>
+  
+    Probabilites:<br>
+    Real: {px[0][0]}<br>
+    AI: {px[1][0]}"""
+    results = {}
+    for idx,result in enumerate(px):
+        results[labels[idx]] = px[idx][0]
+    #results[labels['label']] = result['score']
+    return gr.HTML.update(html_out),results    
+def aiornot5(image):    
+    labels = ["AI", "Real"]
+    mod=models[5]
+    feature_extractor5 = AutoFeatureExtractor.from_pretrained(mod)
+    model5 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor5(image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model5(**input)
+        logits = outputs.logits
+        probability = softmax(logits)
+        px = pd.DataFrame(probability.numpy())
+    prediction = logits.argmax(-1).item()
+    label = labels[prediction]
+    html_out = f"""
+    <h1>This image is likely: {label}</h1><br><h3>
+  
+    Probabilites:<br>
+    Real: {px[1][0]}<br>
+    AI: {px[0][0]}"""
+
+    results = {}
+    for idx,result in enumerate(px):
+        results[labels[idx]] = px[idx][0]
+    #results[labels['label']] = result['score']
+    return gr.HTML.update(html_out),results    
 def load_url(url):
     try:
         urllib.request.urlretrieve( 
@@ -128,9 +204,25 @@ with gr.Blocks() as app:
             with gr.Box():
                 lab2 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[2]}'>{models[2]}</a></b>""")
                 n_out2=gr.Label(label="Output")
-                outp2 = gr.HTML("""""")            
+                outp2 = gr.HTML("""""")    
+        with gr.Row():
+            with gr.Box():
+                lab3 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[3]}'>{models[3]}</a></b>""")
+                n_out3=gr.Label(label="Output")
+                outp3 = gr.HTML("""""")
+            with gr.Box():
+                lab4 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[4]}'>{models[4]}</a></b>""")
+                n_out4=gr.Label(label="Output")
+                outp4 = gr.HTML("""""")
+            with gr.Box():
+                lab5 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[5]}'>{models[5]}</a></b>""")
+                n_out5=gr.Label(label="Output")
+                outp5 = gr.HTML("""""")                    
     load_btn.click(load_url,in_url,[inp,mes])
     btn.click(aiornot0,[inp],[outp0,n_out0])
     btn.click(aiornot1,[inp],[outp1,n_out1])
     btn.click(aiornot2,[inp],[outp2,n_out2])
+    btn.click(aiornot3,[inp],[outp3,n_out3])
+    btn.click(aiornot4,[inp],[outp4,n_out4])
+    btn.click(aiornot5,[inp],[outp5,n_out5])    
 app.launch()