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import os
import requests
from io import BytesIO
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
def generate_caption(image):
# Load pre-trained models & processors
model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
prompt = "<grounding>An image of"
# Open the uploaded image file
img = Image.open(BytesIO(image))
# Save the image locally and open it again to avoid potential issues with reusing the same PIL object
img.save("temp_image.jpg")
img = Image.open("temp_image.jpg")
inputs = processor(text=prompt, images=img, return_tensors="pt")
# Generate caption
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# Process the generated caption
processed_text, _ = processor.post_process_generation(generated_text)
return processed_text
import gradio as gr
title = 'Image Caption Generator'
description = 'Generate descriptive captions for images.'
examples = [["https://example.com/image1.jpg"]]
article = '<p style="margin:auto;max-width:600px;">This tool generates descriptive captions for given images.</p>'
interface = gr.Interface(fn=generate_caption,
inputs=gr.Image(),
outputs=gr.Textbox(),
title=title, description=description, examples=examples, article=article)
interface.launch() |