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import gradio as gr
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification, pipeline
models=[
"Nahrawy/AIorNot",
"arnolfokam/ai-generated-image-detector",
"umm-maybe/AI-image-detector",
]
def aiornot0(image):
labels = ["Real", "AI"]
mod=models[0]
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)
print (outputs)
logits = outputs.logits
print (logits)
prediction = logits.argmax(-1).item()
label = labels[prediction]
return label
def aiornot1(image):
labels = ["Real", "AI"]
mod=models[1]
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)
print (outputs)
logits = outputs.logits
print (logits)
prediction = logits.argmax(-1).item()
label = labels[prediction]
return label
def aiornot2(image):
labels = ["Real", "AI"]
mod=models[2]
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)
print (outputs)
logits = outputs.logits
print (logits)
prediction = logits.argmax(-1).item()
label = labels[prediction]
return label
with gr.Blocks() as app:
with gr.Row():
with gr.Column():
inp = gr.Image()
mod_choose=gr.Number(value=0)
btn = gr.Button()
with gr.Column():
outp0 = gr.Textbox()
outp1 = gr.Textbox()
outp2 = gr.Textbox()
btn.click(aiornot0,[inp],outp0)
btn.click(aiornot1,[inp],outp1)
btn.click(aiornot2,[inp],outp2)
app.launch() |