| import gradio as gr | |
| import requests | |
| from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| import time | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") | |
| def caption(img, min_len, max_len): | |
| raw_image = Image.open(img).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt") | |
| out = model.generate(**inputs, min_length=min_len, max_length=max_len) | |
| return processor.decode(out[0], skip_special_tokens=True) | |
| def greet(img, min_len, max_len): | |
| start = time.time() | |
| result = caption(img, min_len, max_len) | |
| end = time.time() | |
| total_time = str(end - start) | |
| result = result + '\n' + total_time + ' seconds' | |
| return result | |
| iface = gr.Interface(fn=greet, | |
| title='Image Captioning with BLIP', | |
| description="Generate captions for images using the BLIP model.", | |
| inputs=[gr.Image(type='filepath', label='Image'), | |
| gr.Slider(label='Minimum Length', minimum=1, maximum=1000, value=30), | |
| gr.Slider(label='Maximum Length', minimum=1, maximum=1000, value=100)], | |
| outputs=gr.Textbox(label='Caption')) | |
| iface.launch() |