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import gradio as gr
from transformers import pipeline
# Pretrained vision-to-text model
captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
def generate_caption(image):
result = captioner(image)[0]['generated_text']
return result
# Gradio UI
demo = gr.Interface(
fn=generate_caption,
inputs=gr.Image(type="filepath"),
outputs=gr.Textbox(label="Generated Caption"),
title="Mini Image Captioner",
description="Upload an image and get a natural language caption (Vision + LLM)"
)
if __name__ == "__main__":
demo.launch()
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