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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",
]
'''
pipe0 = pipeline("image-classification", f"{models[0]}")
pipe1 = pipeline("image-classification", f"{models[1]}")
pipe2 = pipeline("image-classification", f"{models[2]}")
def image_classifier0(image):
outputs = pipe0(image)
results = {}
for result in outputs:
results[result['label']] = result['score']
return results
def image_classifier1(image):
outputs = pipe1(image)
results = {}
for result in outputs:
results[result['label']] = result['score']
return results
def image_classifier2(image):
outputs = pipe2(image)
results = {}
for result in outputs:
results[result['label']] = result['score']
return results
'''
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])
#btn.click(image_classifier0,[inp],n_out0)
#btn.click(image_classifier1,[inp],n_out1)
#btn.click(image_classifier2,[inp],n_out2)
app.launch() |