app.py

import gradio as gr
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import os 
from numpy import exp
import pandas as pd
from PIL import Image
import urllib.request 
import uuid

uid = uuid.uuid4()

models = [
    "cmckinle/sdxl-flux-detector",
    "umm-maybe/AI-image-detector",
    "Organika/sdxl-detector",
]

fin_sum = []

def softmax(vector):
    e = exp(vector)
    return e / e.sum()

def aiornot(image, model_index):    
    labels = ["AI", "Real"]
    mod = models[model_index]
    feature_extractor = AutoFeatureExtractor.from_pretrained(mod)
    model = AutoModelForImageClassification.from_pretrained(mod)
    input = feature_extractor(image, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model(**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>
    Probabilities:<br>
    Real: {px[1][0]}<br>
    AI: {px[0][0]}"""
    
    results = {}
    for idx, result in enumerate(px):
        results[labels[idx]] = px[idx][0]
    
    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 = sum([result["Real"] for result in fin_sum]) / len(fin_sum)
        fin_sub = 1 - fin_out
        out = {
            "Real": f"{fin_out:.2%}",
            "AI": f"{fin_sub:.2%}"
        }
        return out
    except Exception as e:
        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)</h4></center>""")
    inp = gr.Image(type='pil')
    in_url = gr.Textbox(label="Image URL")
    load_btn = gr.Button("Load URL")
    btn = gr.Button("Detect AI")
    mes = gr.HTML("""""")
    
    fin = gr.Label(label="Final Probability")       
    outp0 = gr.HTML("""""")
    outp1 = gr.HTML("""""")
    outp2 = gr.HTML("""""")

    load_btn.click(load_url, in_url, [inp, mes])
    
    btn.click(fin_clear, None, fin, show_progress=False)
    btn.click(lambda img: aiornot(img, 0), inp, [outp0]).then(tot_prob, None, fin, show_progress=False)
    btn.click(lambda img: aiornot(img, 1), inp, [outp1]).then(tot_prob, None, fin, show_progress=False)
    btn.click(lambda img: aiornot(img, 2), inp, [outp2]).then(tot_prob, None, fin, show_progress=False)

app.launch(show_api=False, max_threads=24)