| import gradio as gr | |
| import requests | |
| from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") | |
| img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' | |
| raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') | |
| def caption(img): | |
| raw_image = Image.open(img).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt") | |
| out = model.generate(**inputs, min_length=30, max_length=1000) | |
| return processor.decode(out[0], skip_special_tokens=True) | |
| def greet(img): | |
| return caption(img) | |
| iface = gr.Interface(fn=greet, inputs=gr.Image(type='filepath'), outputs="text") | |
| iface.launch() |