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import gradio as gr |
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import sys |
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from BLIP.models.blip import blip_decoder |
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from PIL import Image |
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import requests |
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import torch |
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from torchvision import transforms |
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from torchvision.transforms.functional import InterpolationMode |
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from urllib.parse import urlparse |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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image_size = 384 |
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transform = transforms.Compose([ |
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transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC), |
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transforms.ToTensor(), |
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transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) |
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]) |
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model_url = "https://technionmail-my.sharepoint.com/personal/snoamr_campus_technion_ac_il/_layouts/15/download.aspx?share=EZxgXQaBXGREgDsQiaTcwAAB0z8jQA_hgAnwwPQDt8Dgew" |
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model = blip_decoder(pretrained=model_url, image_size=384, vit='base') |
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model.eval() |
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model = model.to(device) |
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def inference(raw_image): |
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image = transform(raw_image).unsqueeze(0).to(device) |
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with torch.no_grad(): |
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caption = model.generate(image, sample=False, num_beams=1, max_length=60, min_length=5) |
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return caption[0] |
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inputs = [gr.Image(type='pil', interactive=False),] |
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outputs = gr.outputs.Textbox(label="Caption") |
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title = "FuseCap" |
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description = "Gradio demo for FuseCap: Leveraging Large Language Models to Fuse Visual Data into Enriched Image Captions. This demo features a BLIP-based model, trained using FuseCap." |
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article = "place holder" |
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gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['birthday_dog.jpeg']]).launch(enable_queue=True) |
