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import gradio as gr
from transformers import pipeline
from PIL import Image
import requests
from transformers import BlipProcessor, BlipForConditionalGeneration
# Initialize the pipeline
pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
# Initialize processor and model
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
def image_caption(image, text_prompt=None):
# Conditional image captioning if text prompt is provided
if text_prompt:
inputs = processor(image, text_prompt, return_tensors="pt")
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
else:
# Unconditional image captioning
inputs = processor(image, return_tensors="pt")
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
return caption
# Define the Gradio interface
image_input = gr.Image(type="pil", label="Upload an Image")
text_input = gr.Textbox(lines=1, placeholder="Optional: Enter text prompt", label="Text Prompt")
output = gr.Textbox(label="Generated Caption")
gr.Interface(
fn=image_caption,
inputs=[image_input, text_input],
outputs=output,
title="Image Captioning with BLIP",
description="Upload an image and get a generated caption. Optionally, provide a text prompt for conditional captioning."
).launch()
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