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import gradio as gr
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
import time
Image.MAX_IMAGE_PIXELS = None # disable pillow’s size limit
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
def caption(img, min_new, max_new):
raw_image = Image.open(img).convert('RGB')
raw_image.thumbnail((1024, 1024))
inputs = processor(raw_image, return_tensors="pt")
out = model.generate(
**inputs,
min_new_tokens=min_new,
max_new_tokens=max_new
)
return processor.decode(out[0], skip_special_tokens=True)
def greet(img, min_new, max_new):
if img is None:
return "❌ Please upload an image."
start = time.time()
try:
result = caption(img, min_new, max_new)
except Exception as e:
return f"⚠️ Error: {e}"
elapsed = time.time() - start
return f"{result}\n⏱ Took {elapsed:.2f} seconds"
iface = gr.Interface(
fn=greet,
title='BLIP Image Captioning (large)',
description="Uses Salesforce/blip-image-captioning-large on CPU.",
inputs=[
gr.Image(type='filepath', label='Image'),
gr.Slider(label='Min New Tokens', minimum=1, maximum=50, value=5),
gr.Slider(label='Max New Tokens', minimum=1, maximum=100, value=20),
],
outputs=gr.Textbox(label='Caption'),
theme=gr.themes.Base(primary_hue="teal", secondary_hue="teal", neutral_hue="slate"),
)
iface.launch()
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