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import os
import gradio as gr
import spaces
from transformers import pipeline
import huggingface_hub
# Login to Hugging Face Hub
token = os.getenv("HF_TOKEN")
huggingface_hub.login(token=token)
# Load the pre-trained model
classifier = pipeline("text-classification", model="ICILS/xlm-r-icils-ilo", device=0)
# Define the prediction function
@spaces.GPU
def classify_text(text):
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 with ZeroGPU",
description="Classify occupations using a pre-trained XLM-R-ISCO model on Hugging Face Spaces with ZeroGPU"
)
if __name__ == "__main__":
demo.launch()