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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",
"arnolfokam/ai-generated-image-detector",
"umm-maybe/AI-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]}"""
return gr.HTML.update(html_out)
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]}"""
return gr.HTML.update(html_out)
def aiornot2(image):
labels = ["Real", "AI"]
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[0][0]}<br>
AI: {px[1][0]}"""
return gr.HTML.update(html_out)
with gr.Blocks() as app:
with gr.Column():
inp = gr.Image()
btn = gr.Button()
with gr.Group():
with gr.Row():
with gr.Box():
lab0 = gr.HTML(f"""Testing on Model: {models[0]}""")
outp0 = gr.HTML("""""")
with gr.Box():
lab1 = gr.HTML(f"""Testing on Model: {models[1]}""")
outp1 = gr.HTML("""""")
with gr.Box():
lab2 = gr.HTML(f"""Testing on Model: {models[2]}""")
outp2 = gr.HTML("""""")
btn.click(aiornot0,[inp],outp0)
btn.click(aiornot1,[inp],outp1)
btn.click(aiornot2,[inp],outp2)
app.launch() |