Update app.py

app.py

@@ -11,33 +11,10 @@ def softmax(vector):
     
 models=[
     "Nahrawy/AIorNot",
-    "arnolfokam/ai-generated-image-detector",
     "umm-maybe/AI-image-detector",
-]
-'''
-pipe0 = pipeline("image-classification", f"{models[0]}")
-pipe1 = pipeline("image-classification", f"{models[1]}")
-pipe2 = pipeline("image-classification", f"{models[2]}")
+    "arnolfokam/ai-generated-image-detector",
 
-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 aiornot0(image):    
     labels = ["Real", "AI"]
@@ -54,8 +31,7 @@ def aiornot0(image):
     label = labels[prediction]
     html_out = f"""
     <h1>This image is likely: {label}</h1><br><h3>
-    Model used: <a href='https://huggingface.co/{mod}'>{mod}</a><br>    
-    <br>    
+ 
     Probabilites:<br>
     Real: {px[0][0]}<br>
     AI: {px[1][0]}"""
@@ -79,8 +55,7 @@ def aiornot1(image):
     label = labels[prediction]
     html_out = f"""
     <h1>This image is likely: {label}</h1><br><h3>
-    Model used: <a href='https://huggingface.co/{mod}'>{mod}</a><br>    
-    <br>    
+  
     Probabilites:<br>
     Real: {px[0][0]}<br>
     AI: {px[1][0]}"""
@@ -104,8 +79,7 @@ def aiornot2(image):
     label = labels[prediction]
     html_out = f"""
     <h1>This image is likely: {label}</h1><br><h3>
-    Model used: <a href='https://huggingface.co/{mod}'>{mod}</a><br>    
-    <br>    
+  
     Probabilites:<br>
     Real: {px[1][0]}<br>
     AI: {px[0][0]}"""
@@ -115,29 +89,38 @@ def aiornot2(image):
         results[labels[idx]] = px[idx][0]
     #results[labels['label']] = result['score']
     return gr.HTML.update(html_out),results
-    
+def load_url(url):
+    try:
+        image = Image.open(url)
+        mes = "Image Loaded"
+    except Exception:
+        mes="Image not Found"
+    return image,mes
 with gr.Blocks() as app:
-    with gr.Column():
+    with gr.Row():
+        with gr.Column():
+            in_url=gr.Textbox(label="Image URL")
+            with gr.Row():
+                load_btn=gr.Button("Load URL")
+                btn = gr.Button("Detect AI")
+            mes = gr.HTML("""""")
         inp = gr.Pil()
-        btn = gr.Button()
     with gr.Group():        
         with gr.Row():
             with gr.Box():
-                lab0 = gr.HTML(f"""<b>Testing on Model: {models[0]}</b>""")
+                lab0 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[0]}'>{models[0]}</a></b>""")
                 outp0 = gr.HTML("""""")
                 n_out0=gr.Label(label="Output")
             with gr.Box():
-                lab1 = gr.HTML(f"""<b>Testing on Model: {models[1]}</b>""")
+                lab1 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[1]}'>{models[1]}</a></b>""")
                 outp1 = gr.HTML("""""")
                 n_out1=gr.Label(label="Output")
             with gr.Box():
-                lab2 = gr.HTML(f"""<b>Testing on Model: {models[2]}</b>""")
+                lab2 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[2]}'>{models[2]}</a></b>""")
                 outp2 = gr.HTML("""""")            
                 n_out2=gr.Label(label="Output")
+    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(image_classifier0,[inp],n_out0)
-    #btn.click(image_classifier1,[inp],n_out1)
-    #btn.click(image_classifier2,[inp],n_out2)    
-app.launch()
+app.launch(enable_queue=False)