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import gradio as gr
from transformers import pipeline
from PIL import Image
# Initialize the pipeline with the image captioning model
caption_pipeline = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
def generate_caption(image):
# The image is received as a PIL Image, so no need for conversion
result = caption_pipeline(image)
caption = result[0]["generated_text"]
return caption
# Setup the Gradio interface
interface = gr.Interface(fn=generate_caption,
inputs=gr.inputs.Image(label="Upload an Image", type="pil"),
outputs=gr.outputs.Textbox(label="Generated Caption"))
interface.launch()
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