app.py

import gradio as gr
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification, pipeline
#from transformers import pipeline
import os 
from numpy import exp
import pandas as  pd
from PIL import Image
import urllib.request 
import uuid
uid=uuid.uuid4()

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]}")
 

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()

     

def aiornot0(image):    
    labels = ["Real", "AI"]
    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 = model0(**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 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("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")         
        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

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
with gr.Blocks() as app:
    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")
        with gr.Row():
            load_btn=gr.Button("Load URL")
            btn = gr.Button("Detect AI")
        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():
            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():
            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)            
    
    def upd(image):
        rand_im = uuid.uuid4()
        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_url 
    #inp.change(upd,inp,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(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.queue(concurrency_count=60).launch()