| from PIL import Image | |
| import os | |
| import requests | |
| import torch | |
| from torchvision import transforms | |
| from torchvision.transforms.functional import InterpolationMode | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| import gradio as gr | |
| from models.blip_vqa import blip_vqa | |
| image_size_vq = 480 | |
| transform_vq = transforms.Compose([ | |
| transforms.Resize((image_size_vq,image_size_vq),interpolation=InterpolationMode.BICUBIC), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) | |
| ]) | |
| model_url_vq = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth' | |
| model_vq = blip_vqa(pretrained=model_url_vq, image_size=480, vit='base') | |
| model_vq.eval() | |
| model_vq = model_vq.to(device) | |
| def inference(raw_image, question, mnlen, mxlen, token): | |
| if token != os.environ["TOKEN"]: | |
| return "Rong token" | |
| image_vq = transform_vq(raw_image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| answer = model_vq(image_vq, question, train=False, inference='generate', mina_len=mnlen, maxa_len=mxlen) | |
| return 'answer: '+answer[0] | |
| inputs = [gr.Image(type='pil'), | |
| gr.Textbox(lines=2, label="Question"), | |
| gr.Number(value=1, label="Min length", precision=0), | |
| gr.Number(value=10, label="Max length", precision=0), | |
| gr.Textbox(lines=1, label="Auth token")] | |
| outputs = gr.outputs.Textbox(label="Output") | |
| title = "BLIP" | |
| description = "Gradio endpoint for spuun's BLIP (Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research)). To use it you need to obtain a token from me :) Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.12086' target='_blank'>BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation</a> | <a href='https://github.com/salesforce/BLIP' target='_blank'>Github Repo</a></p>" | |
| gr.Interface(inference, inputs, outputs, title=title, description=description, article=article).launch() |