Update app.py

app.py

@@ -3,6 +3,12 @@ import torch
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification, pipeline
 from numpy import exp
 import pandas as  pd
+from PIL import Image
+import urllib.request 
+import uuid
+uid=uuid.uuid4()
+
+
 
 def softmax(vector):
  e = exp(vector)
@@ -15,7 +21,7 @@ models=[
     "arnolfokam/ai-generated-image-detector",
 
 ]
-    
+   
 def aiornot0(image):    
     labels = ["Real", "AI"]
     mod=models[0]
@@ -31,8 +37,7 @@ 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]}"""
@@ -56,8 +61,7 @@ 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]}"""
@@ -81,8 +85,7 @@ 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]}"""
@@ -92,26 +95,45 @@ 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:
+        urllib.request.urlretrieve( 
+            f'{url}', 
+            f"{uid}tmp_im.png")         
+        image = Image.open(f"{uid}tmp_im.png")
+        mes = "Image Loaded"
+    except Exception as e:
+        image=None
+        mes=f"Image not Found<br>Error: {e}"
+    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>""")
-                outp0 = gr.HTML("""""")
+                lab0 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[0]}'>{models[0]}</a></b>""")
                 n_out0=gr.Label(label="Output")
+                outp0 = gr.HTML("""""")
             with gr.Box():
-                lab1 = gr.HTML(f"""<b>Testing on Model: {models[1]}</b>""")
-                outp1 = gr.HTML("""""")
+                lab1 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[1]}'>{models[1]}</a></b>""")
                 n_out1=gr.Label(label="Output")
+                outp1 = gr.HTML("""""")
             with gr.Box():
-                lab2 = gr.HTML(f"""<b>Testing on Model: {models[2]}</b>""")
-                outp2 = gr.HTML("""""")            
+                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("""""")    
+          
+    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])
-app.launch(enable_queue=False)
+    
+app.queue(concurrency_count=20).launch()