Spaces:
Sleeping
Sleeping
File size: 8,519 Bytes
6364b8e 050a6c5 436d80d 050a6c5 06b2f35 050a6c5 06b2f35 050a6c5 977f529 d19e84e 75a5505 050a6c5 d19e84e 050a6c5 e2346d7 436d80d 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 06b2f35 050a6c5 06b2f35 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 46a0a15 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 5ca31bc 050a6c5 46a0a15 050a6c5 d19e84e 050a6c5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 |
import gradio as gr
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification, pipeline
#from transformers import pipeline
import os
from numpy import exp
import pandas as pd
from PIL import Image
import urllib.request
import uuid
uid=uuid.uuid4()
models=[
"cmckinle/sdxl-flux-detector",
"umm-maybe/AI-image-detector",
"Organika/sdxl-detector",
#"arnolfokam/ai-generated-image-detector",
]
pipe0 = pipeline("image-classification", f"{models[0]}")
pipe1 = pipeline("image-classification", f"{models[1]}")
pipe2 = pipeline("image-classification", f"{models[2]}")
fin_sum=[]
def image_classifier0(image):
labels = ["AI","Real"]
outputs = pipe0(image)
results = {}
result_test={}
for idx,result in enumerate(outputs):
results[labels[idx]] = outputs[idx]['score']
#print (result_test)
#for result in outputs:
# results[result['label']] = result['score']
#print (results)
fin_sum.append(results)
return results
def image_classifier1(image):
labels = ["AI","Real"]
outputs = pipe1(image)
results = {}
result_test={}
for idx,result in enumerate(outputs):
results[labels[idx]] = outputs[idx]['score']
#print (result_test)
#for result in outputs:
# results[result['label']] = result['score']
#print (results)
fin_sum.append(results)
return results
def image_classifier2(image):
labels = ["AI","Real"]
outputs = pipe2(image)
results = {}
result_test={}
for idx,result in enumerate(outputs):
results[labels[idx]] = outputs[idx]['score']
#print (result_test)
#for result in outputs:
# results[result['label']] = result['score']
#print (results)
fin_sum.append(results)
return results
def softmax(vector):
e = exp(vector)
return e / e.sum()
def aiornot0(image):
labels = ["AI", "Real"]
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[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 aiornot1(image):
labels = ["AI", "Real"]
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[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 aiornot2(image):
labels = ["Real", "AI"]
mod=models[2]
feature_extractor2 = AutoFeatureExtractor.from_pretrained(mod)
#feature_extractor2 = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
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[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 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_sum[3]["Real"]+fin_sum[4]["Real"]+fin_sum[5]["Real"]
fin_out = fin_out/6
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)
return None
def fin_clear():
fin_sum.clear()
return None
def upd(image):
print (image)
rand_im = uuid.uuid4()
image.save(f"{rand_im}-vid_tmp_proc.png")
out = Image.open(f"{rand_im}-vid_tmp_proc.png")
#image.save(f"{rand_im}-vid_tmp_proc.png")
#out = os.path.abspath(f"{rand_im}-vid_tmp_proc.png")
#out_url = f'https://omnibus_AI_or_Not_dev.hf.space/file={out}'
#out_url = f"{rand_im}-vid_tmp_proc.png"
return out
with gr.Blocks() as app:
gr.Markdown("""<center><h1>AI Image Detector<br><h4>(Test Demo - accuracy varies by model)""")
with gr.Column():
inp = gr.Image(type='pil')
in_url=gr.Textbox(label="Image URL")
with gr.Row():
load_btn=gr.Button("Load URL")
btn = gr.Button("Detect AI")
mes = gr.HTML("""""")
with gr.Group():
with gr.Row():
fin=gr.Label(label="Final Probability")
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>""")
nun0 = gr.HTML("""""")
with gr.Box():
lab1 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co./{models[1]}'>{models[1]}</a></b>""")
nun1 = gr.HTML("""""")
with gr.Box():
lab2 = gr.HTML(f"""<b>Testing on Model: <a href='https://huggingface.co./{models[2]}'>{models[2]}</a></b>""")
nun2 = gr.HTML("""""")
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():
with gr.Box():
n_out3=gr.Label(label="Output")
outp3 = gr.HTML("""""")
with gr.Box():
n_out4=gr.Label(label="Output")
outp4 = gr.HTML("""""")
with gr.Box():
n_out5=gr.Label(label="Output")
outp5 = gr.HTML("""""")
hid_box=gr.Textbox(visible=False)
hid_im = gr.Image(type="pil",visible=False)
def echo(inp):
return inp
#inp.change(echo,inp,hid_im).then(upd,hid_im,inp)
btn.click(fin_clear,None,fin,show_progress=False)
load_btn.click(load_url,in_url,[inp,mes])
btn.click(aiornot0,[inp],[outp0,n_out0]).then(tot_prob,None,fin,show_progress=False)
btn.click(aiornot1,[inp],[outp1,n_out1]).then(tot_prob,None,fin,show_progress=False)
btn.click(aiornot2,[inp],[outp2,n_out2]).then(tot_prob,None,fin,show_progress=False)
btn.click(image_classifier0,[inp],[n_out3]).then(tot_prob,None,fin,show_progress=False)
btn.click(image_classifier1,[inp],[n_out4]).then(tot_prob,None,fin,show_progress=False)
btn.click(image_classifier2,[inp],[n_out5]).then(tot_prob,None,fin,show_progress=False)
app.launch(show_api=False,max_threads=24) |