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import gradio as gr
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)
return e / e.sum()
models=[
"Nahrawy/AIorNot",
"umm-maybe/AI-image-detector",
"arnolfokam/ai-generated-image-detector",
]
fin_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[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 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>
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 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>
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 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_out = fin_out/3
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)
with gr.Blocks() as app:
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()
with gr.Group():
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>""")
with gr.Box():
lab1 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[1]}'>{models[1]}</a></b>""")
with gr.Box():
lab2 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co/{models[2]}'>{models[2]}</a></b>""")
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():
fin=gr.Label(label="Final Probability")
load_btn.click(load_url,in_url,[inp,mes])
btn.click(aiornot0,[inp],[outp0,n_out0]).then(tot_prob,None,fin)
btn.click(aiornot1,[inp],[outp1,n_out1]).then(tot_prob,None,fin)
btn.click(aiornot2,[inp],[outp2,n_out2]).then(tot_prob,None,fin)
app.queue(concurrency_count=20).launch() |