Update app.py

app.py

@@ -1,98 +1,257 @@
 import gradio as gr
 import torch
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification, pipeline
+#from transformers import pipeline
+import os 
 from numpy import exp
-import pandas as pd
+import pandas as  pd
 from PIL import Image
 import urllib.request 
 import uuid
+uid=uuid.uuid4()
 
-uid = uuid.uuid4()
-models = [
+models=[
     "cmckinle/sdxl-flux-detector",
     "umm-maybe/AI-image-detector",
     "Organika/sdxl-detector",
+    #"arnolfokam/ai-generated-image-detector",
 ]
-results_store = []
+
+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 = ["AI","Real"]
+    outputs = pipe0(image)
+    results = {}
+    result_test={}
+    for idx,result in enumerate(outputs):
+        results[labels[idx]] = outputs[idx]['score']
+    #print (result_test)
+    #for result in outputs:
+    #    results[result['label']] = result['score']    
+    #print (results) 
+    fin_sum.append(results)
+    return results
+def image_classifier1(image):
+    labels = ["AI","Real"]
+    outputs = pipe1(image)
+    results = {}
+    result_test={}
+    for idx,result in enumerate(outputs):
+        results[labels[idx]] = outputs[idx]['score']
+    #print (result_test)    
+    #for result in outputs:
+    #    results[result['label']] = result['score']
+    #print (results)
+    fin_sum.append(results)
+    return results
+def image_classifier2(image):
+    labels = ["AI","Real"]
+    outputs = pipe2(image)
+    results = {}
+    result_test={}
+    for idx,result in enumerate(outputs):
+        results[labels[idx]] = outputs[idx]['score']
+    #print (result_test)    
+    #for result in outputs:
+    #    results[result['label']] = result['score']
+    #print (results) 
+    fin_sum.append(results)
+    return results
 
 def softmax(vector):
-    e = exp(vector)
-    return e / e.sum()
+ e = exp(vector)
+ return e / e.sum()
 
-def aiornot(image, model_index):
-    model_index = int(model_index)  # Convert to integer
-    mod = models[model_index]
-    feature_extractor = AutoFeatureExtractor.from_pretrained(mod)
-    model = AutoModelForImageClassification.from_pretrained(mod)
-    input = feature_extractor(image, return_tensors="pt")
+     
+
+def aiornot0(image):    
+    labels = ["AI", "Real"]
+    mod=models[0]
+    feature_extractor0 = AutoFeatureExtractor.from_pretrained(mod)
+    model0 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor0(image, return_tensors="pt")
     with torch.no_grad():
-        outputs = model(**input)
+        outputs = model0(**input)
         logits = outputs.logits
         probability = softmax(logits)
         px = pd.DataFrame(probability.numpy())
-    
-    if model_index == 2:  # Organika model
-        real_prob, ai_prob = px[0][0], px[1][0]
-        label = "Real" if real_prob > ai_prob else "AI"
-    else:
-        ai_prob, real_prob = px[0][0], px[1][0]
-        label = "AI" if ai_prob > real_prob else "Real"
-    
+    prediction = logits.argmax(-1).item()
+    label = labels[prediction]
     html_out = f"""
     <h1>This image is likely: {label}</h1><br><h3>
-    Probabilities:<br>
-    Real: {real_prob:.4f}<br>
-    AI: {ai_prob:.4f}"""
-    
-    results = {"Real": real_prob, "AI": ai_prob}
-    results_store.append(results)
-    return gr.HTML.update(html_out), results
+ 
+    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']
+    fin_sum.append(results)
+    return gr.HTML.update(html_out),results
+def aiornot1(image):    
+    labels = ["AI", "Real"]
+    mod=models[1]
+    feature_extractor1 = AutoFeatureExtractor.from_pretrained(mod)
+    model1 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor1(image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model1(**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']
+    fin_sum.append(results)
+    return gr.HTML.update(html_out),results    
+def aiornot2(image):    
+    labels = ["Real", "AI"]
+    mod=models[2]
+    feature_extractor2 = AutoFeatureExtractor.from_pretrained(mod)
+    #feature_extractor2 = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
+    model2 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor2(image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model2(**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']
+    fin_sum.append(results)
+        
+    return gr.HTML.update(html_out),results
 
 def load_url(url):
     try:
-        urllib.request.urlretrieve(f'{url}', f"{uid}tmp_im.png")         
+        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
+        image=None
+        mes=f"Image not Found<br>Error: {e}"
+    return image,mes
+
+def tot_prob():
+    try:
+        fin_out = fin_sum[0]["Real"]+fin_sum[1]["Real"]+fin_sum[2]["Real"]+fin_sum[3]["Real"]+fin_sum[4]["Real"]+fin_sum[5]["Real"]
+        fin_out = fin_out/6
+        fin_sub = 1-fin_out
+        out={
+            "Real":f"{fin_out}",
+            "AI":f"{fin_sub}"
+        }
+        #fin_sum.clear()
+        #print (fin_out)
+        return out
+    except Exception as e:
+        pass
+        print (e)
+        return None
+def fin_clear():
+    fin_sum.clear()
+    return None
 
-def calculate_final_prob():
-    if not results_store:
-        return {"Real": "N/A", "AI": "N/A"}
-    fin_out = sum(result["Real"] for result in results_store) / len(results_store)
-    return {
-        "Real": f"{fin_out:.4f}",
-        "AI": f"{1 - fin_out:.4f}"
-    }
+def upd(image):
+    print (image)
+    rand_im = uuid.uuid4()
+    image.save(f"{rand_im}-vid_tmp_proc.png")
+    out = Image.open(f"{rand_im}-vid_tmp_proc.png")
 
-def clear_results():
-    results_store.clear()
-    return gr.HTML.update(value=""), gr.Label.update(value=None)
+    #image.save(f"{rand_im}-vid_tmp_proc.png")
+    #out = os.path.abspath(f"{rand_im}-vid_tmp_proc.png")
+    #out_url = f'https://omnibus_AI_or_Not_dev.hf.space/file={out}'
+    #out_url = f"{rand_im}-vid_tmp_proc.png"
+    return out 
 
+    
 with gr.Blocks() as app:
-    gr.Markdown("""<center><h1>AI Image Detector<br><h4>(Test Demo - accuracy varies by model)</h4></center>""")
+    gr.Markdown("""<center><h1>AI Image Detector<br><h4>(Test Demo - accuracy varies by model)""")
     with gr.Column():
         inp = gr.Image(type='pil')
-        in_url = gr.Textbox(label="Image URL")
+        in_url=gr.Textbox(label="Image URL")
         with gr.Row():
-            load_btn = gr.Button("Load URL")
+            load_btn=gr.Button("Load URL")
             btn = gr.Button("Detect AI")
-        mes = gr.HTML()
-    with gr.Group():
+        mes = gr.HTML("""""")
+    with gr.Group():    
+        with gr.Row():
+            fin=gr.Label(label="Final Probability")       
+        with gr.Row():
+            with gr.Box():
+                lab0 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[0]}'>{models[0]}</a></b>""")
+                nun0 = gr.HTML("""""")
+            with gr.Box():
+                lab1 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[1]}'>{models[1]}</a></b>""")
+                nun1 = gr.HTML("""""")
+            with gr.Box():
+                lab2 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[2]}'>{models[2]}</a></b>""")
+                nun2 = gr.HTML("""""")
+                
         with gr.Row():
-            fin = gr.Label(label="Final Probability")
+            with gr.Box():
+                n_out0=gr.Label(label="Output")
+                outp0 = gr.HTML("""""")
+            with gr.Box():
+                n_out1=gr.Label(label="Output")
+                outp1 = gr.HTML("""""")
+            with gr.Box():
+                n_out2=gr.Label(label="Output")
+                outp2 = gr.HTML("""""")    
         with gr.Row():
-            for i, model in enumerate(models):
-                with gr.Column():
-                    gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{model}'>{model}</a></b>""")
-                    output_html = gr.HTML()
-                    output_label = gr.Label(label="Output")
-                    btn.click(aiornot, inputs=[inp, gr.Number(value=i, visible=False)], outputs=[output_html, output_label])
+            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("""""")    
+    hid_box=gr.Textbox(visible=False)  
+    hid_im = gr.Image(type="pil",visible=False)
+    def echo(inp):
+        return inp
+
+    #inp.change(echo,inp,hid_im).then(upd,hid_im,inp)
+    
+    btn.click(fin_clear,None,fin,show_progress=False)
+    load_btn.click(load_url,in_url,[inp,mes])
+    
+    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(clear_results, outputs=[output_html, output_label], queue=False)
-    btn.click(calculate_final_prob, outputs=fin)
-    load_btn.click(load_url, in_url, [inp, mes])
+    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.launch(show_api=False, max_threads=24)
+app.launch(show_api=False,max_threads=24)