| 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='Blip Image Captioning Large', |
| description="[Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large)", |
| 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'), |
| theme = gr.themes.Base(primary_hue="teal",secondary_hue="teal",neutral_hue="slate"),) |
| iface.launch() |
