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| import gradio as gr | |
| import torch | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification, pipeline | |
| import os | |
| from numpy import exp | |
| import pandas as pd | |
| from PIL import Image | |
| import urllib.request | |
| import uuid | |
| uid = uuid.uuid4() | |
| # Reordered models as requested | |
| models = [ | |
| "umm-maybe/AI-image-detector", | |
| "Organika/sdxl-detector", | |
| "cmckinle/sdxl-flux-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 softmax(vector): | |
| e = exp(vector - vector.max()) # for numerical stability | |
| return e / e.sum() | |
| def image_classifier0(image): | |
| labels = ["AI", "Real"] | |
| outputs = pipe0(image) | |
| results = {} | |
| for idx, result in enumerate(outputs): | |
| results[labels[idx]] = float(outputs[idx]['score']) # Convert to float | |
| fin_sum.append(results) | |
| return results | |
| def image_classifier1(image): | |
| labels = ["AI", "Real"] | |
| outputs = pipe1(image) | |
| results = {} | |
| for idx, result in enumerate(outputs): | |
| results[labels[idx]] = float(outputs[idx]['score']) # Convert to float | |
| fin_sum.append(results) | |
| return results | |
| def image_classifier2(image): | |
| labels = ["AI", "Real"] | |
| outputs = pipe2(image) | |
| results = {} | |
| for idx, result in enumerate(outputs): | |
| results[labels[idx]] = float(outputs[idx]['score']) # Convert to float | |
| fin_sum.append(results) | |
| return results | |
| 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) # Apply softmax on 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> | |
| Probabilities:<br> | |
| Real: {float(px[1][0])}<br> | |
| AI: {float(px[0][0])}""" | |
| results = { | |
| "Real": float(px[1][0]), | |
| "AI": float(px[0][0]) | |
| } | |
| 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) # Apply softmax on 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> | |
| Probabilities:<br> | |
| Real: {float(px[1][0])}<br> | |
| AI: {float(px[0][0])}""" | |
| results = { | |
| "Real": float(px[1][0]), | |
| "AI": float(px[0][0]) | |
| } | |
| 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) # Apply softmax on 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> | |
| Probabilities:<br> | |
| Real: {float(px[1][0])}<br> | |
| AI: {float(px[0][0])}""" | |
| results = { | |
| "Real": float(px[1][0]), | |
| "AI": float(px[0][0]) | |
| } | |
| 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 = sum([result["Real"] for result in fin_sum]) / len(fin_sum) | |
| fin_sub = 1 - fin_out | |
| out = { | |
| "Real": f"{fin_out}", | |
| "AI": f"{fin_sub}" | |
| } | |
| return out | |
| except Exception as e: | |
| print(e) | |
| return None | |
| def fin_clear(): | |
| fin_sum.clear() | |
| return None | |
| def upd(image): | |
| rand_im = uuid.uuid4() | |
| image.save(f"{rand_im}-vid_tmp_proc.png") | |
| out = Image.open(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", visible=False) | |
| with gr.Row(): | |
| # Updated model names | |
| with gr.Box(): | |
| lab0 = gr.HTML(f"""<b>Testing on Original 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 SDXL Fine Tuned 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 SDXL and Flux Fine Tuned 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("""""") | |
| 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_out0]).then(tot_prob, None, fin, show_progress=False) | |
| btn.click(image_classifier1, [inp], [n_out1]).then(tot_prob, None, fin, show_progress=False) | |
| btn.click(image_classifier2, [inp], [n_out2]).then(tot_prob, None, fin, show_progress=False) | |
| app.launch(show_api=False, max_threads=24) | |