Update app.py

app.py

@@ -1,17 +1,30 @@
 import os
 import gradio as gr
 import spaces
+from transformers import pipeline
 import torch
 
 zero = torch.Tensor([0]).cuda()
 print(zero.device) # <-- 'cpu' 🤔
 
+token = os.getenv("HF_TOKEN")
+# gr.load("models/ICILS/xlm-r-icils-ilo", hf_token=token).launch()
+
+# Load the pre-trained model
+classifier = pipeline("text-classification", model="ICILS/xlm-r-icils-ilo", hf_token=token)
+
+# Define the prediction function
 @spaces.GPU
-def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
+def classify_text(text):
+    return classifier(text)[0]
 
-# demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-# demo.launch()
-token = os.getenv("HF_TOKEN")
-gr.load("models/ICILS/xlm-r-icils-ilo", hf_token=token).launch()
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs=gr.Text(),
+    title="XLM-R ISCO classification with ZeroGPU",
+    description="Classify occupations using a pre-trained XLM-R-ISCO model on Hugging Face Spaces with ZeroGPU"
+)
+
+demo.launch()