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import gradio as gr
from transformers import pipeline
from PIL import Image
# Load model
captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
# Inference function
def generate_caption(image):
result = captioner(image)
return result[0]["generated_text"]
# Gradio UI
iface = gr.Interface(
fn=generate_caption,
inputs=gr.Image(type="pil"),
outputs="text",
title="🖼️ Image Caption Generator",
description="Upload an image and the model will describe it in a sentence.",
)
iface.launch() |