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()