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import os
import gradio as gr
from transformers import pipeline
import huggingface_hub
# Load the pre-trained model
classifier = pipeline("text-classification", model="ICILS/xlm-r-icils-ilo", device=0)
# Define the prediction function
def classify_text(text):
"""
Classify the input text into occupational categories using a pre-trained model.
Args:
text (str): Job description text.
Returns:
tuple: (label, score) - The classification label and the associated confidence score.
"""
result = classifier(text)[0]
label = result['label']
score = result['score']
return label, score
# Create the Gradio interface
demo = gr.Interface(
fn=classify_text,
inputs=gr.Textbox(lines=2, label="Job Description Text", placeholder="Enter a job description..."),
outputs=[gr.Textbox(label="ISCO-08 Label"), gr.Number(label="Score")],
title="XLM-R ISCO Classification",
description=(
"Classify job descriptions into occupational categories using a pre-trained XLM-R-ISCO model "
"from Hugging Face Spaces."
),
)
# Run the Gradio app
if __name__ == "__main__":
demo.launch()
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