Update app.py

app.py

@@ -1,24 +1,19 @@
 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):
-    """
-    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']
@@ -27,15 +22,11 @@ def classify_text(text):
 # Create the Gradio interface
 demo = gr.Interface(
     fn=classify_text,
-    inputs=gr.Textbox(lines=2, label="Job Description Text", placeholder="Enter a job description..."),
+    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."
-    ),
+    title="XLM-R ISCO classification with ZeroGPU",
+    description="Classify occupations using a pre-trained XLM-R-ISCO model on Hugging Face Spaces with ZeroGPU"
 )
 
-# Run the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()