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import gradio as gr
from transformers import AutoProcessor, BlipForConditionalGeneration
import torch
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/elon.jpg', 'elon.png')
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/pentagon.jpg', 'pentagon.jpg')
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/horns.jpg', 'horns.jpg')
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/waves.png', 'waves.jpg')
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/radcliffe.jpg', 'radcliffe.jpg')
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/australia.jpg', 'australia.jpg')
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/yeti.jpeg', 'yeti.jpg')
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/pollock.jpg', 'pollock.jpg')
torch.hub.download_url_to_file('https://static01.nytimes.com/newsgraphics/2023-06-08-disinfo-ai-detector/7343d4ca746b7965141e230d94dd4f5f564f2bfb/_assets/man.png', 'man.png')
blip_processor_large = AutoProcessor.from_pretrained("umm-maybe/image-generator-identifier")
blip_model_large = BlipForConditionalGeneration.from_pretrained("umm-maybe/image-generator-identifier")
device = "cuda" if torch.cuda.is_available() else "cpu"
blip_model_large.to(device)
def generate_caption(processor, model, image):
inputs = processor(images=image, return_tensors="pt").to(device)
generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_caption
def generate_captions(image):
caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image)
return caption_blip_large
examples = [["elon.jpg"], ["pentagon.jpg"], ["horns.jpg"], ["waves.jpg"], ["radcliffe.jpg"], ["australia.jpg"], ["yeti.jpg"], ["pollock.jpg"], ["man.png"]]
title = "Generator Identification via Image Captioning"
description = "Gradio Demo to illustrate the use of a fine-tuned BLIP image captioning model to identify synthetic images. To use it, simply upload your image and click 'submit', or click one of the examples to load them."
interface = gr.Interface(fn=generate_captions,
inputs="image",
outputs="textbox",
examples=examples,
title=title,
description=description)
interface.launch() |