| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| processor = BlipProcessor.from_pretrained("noamrot/FuseCap") | |
| model = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap").to(device) | |
| def inference(raw_image): | |
| text = "a picture of " | |
| inputs = processor(raw_image, text, return_tensors="pt").to(device) | |
| out = model.generate(**inputs) | |
| caption = processor.decode(out[0], skip_special_tokens=True) | |
| return caption | |
| inputs = [gr.Image(type='pil', interactive=False),] | |
| outputs = gr.outputs.Textbox(label="Caption") | |
| description = "Gradio demo for FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions. This demo features a BLIP-based model, trained using FuseCap." | |
| examples = [["surfer.jpg"], ["bike.jpg"]] | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2305.17718' target='_blank'>FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions</a>" | |
| iface = gr.Interface(fn=inference, | |
| inputs="image", | |
| outputs="text", | |
| title="FuseCap", | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| enable_queue=True) | |
| iface.launch() | |