app.py

import gradio as gr
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification, pipeline
from numpy import exp
import pandas as  pd

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

    
models=[
    "Nahrawy/AIorNot",
    "umm-maybe/AI-image-detector",
    "arnolfokam/ai-generated-image-detector",

]
    
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>
    Model used: <a href='https://huggingface.co/{mod}'>{mod}</a><br>    
    <br>    
    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']
    return gr.HTML.update(html_out),results
def aiornot1(image):    
    labels = ["Real", "AI"]
    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>
    Model used: <a href='https://huggingface.co/{mod}'>{mod}</a><br>    
    <br>    
    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']
    return gr.HTML.update(html_out),results    
def aiornot2(image):    
    labels = ["AI", "Real"]
    mod=models[2]
    feature_extractor2 = AutoFeatureExtractor.from_pretrained(mod)
    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>
    Model used: <a href='https://huggingface.co/{mod}'>{mod}</a><br>    
    <br>    
    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']
    return gr.HTML.update(html_out),results
    
with gr.Blocks() as app:
    with gr.Column():
        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("""""")
                n_out0=gr.Label(label="Output")
            with gr.Box():
                lab1 = gr.HTML(f"""<b>Testing on Model: {models[1]}</b>""")
                outp1 = gr.HTML("""""")
                n_out1=gr.Label(label="Output")
            with gr.Box():
                lab2 = gr.HTML(f"""<b>Testing on Model: {models[2]}</b>""")
                outp2 = gr.HTML("""""")            
                n_out2=gr.Label(label="Output")
    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)