Update app.py

app.py

@@ -2,23 +2,22 @@
 
 import gradio as gr
 import torch
+import os
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# ✅ Use your pushed model from the Hugging Face Hub
+# ✅ Load token from secret environment variable
+HF_TOKEN = os.getenv("HF_TOKEN")
 model_id = "Ozziejoe/eemm-deberta-v3-small"
 
-# ✅ Label names
 label_names = [
     "Cognition", "Affect", "Self", "Motivation", "Attention", "OB", "Context",
     "Social", "Physical", "Psych"
 ]
 
-# ✅ Load tokenizer and model from Hub
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForSequenceClassification.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=HF_TOKEN)
+model = AutoModelForSequenceClassification.from_pretrained(model_id, use_auth_token=HF_TOKEN)
 model.eval()
 
-# ✅ Classification function
 def classify(text):
     with torch.no_grad():
         inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
@@ -27,14 +26,14 @@ def classify(text):
         labels = [label_names[i] for i, p in enumerate(probs) if p > 0.5]
         return ", ".join(labels) if labels else "No domain confidently predicted."
 
-# ✅ Gradio interface
 demo = gr.Interface(
     fn=classify,
     inputs=gr.Textbox(label="Enter a question"),
     outputs=gr.Textbox(label="Predicted domains"),
     title="EEMM Multi-Label Classifier",
-    description="Classifies a question into psychological domains.",
+    description="Classifies a question into multiple psychological domains.",
     allow_flagging="never"
 )
 
-demo.launch(share=True, server_name="0.0.0.0")
+if __name__ == "__main__":
+    demo.launch(share=True, server_name="0.0.0.0")