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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() |