Update app.py

app.py

@@ -3,7 +3,6 @@ import spaces
 import torch
 from transformers import pipeline
 
-# Don't load model globally - ZeroGPU doesn't allow CUDA operations outside @spaces.GPU
 classifier = None
 
 def get_classifier():
@@ -13,36 +12,44 @@ def get_classifier():
         classifier = pipeline(
             "text-classification",
             model="MatteoFasulo/xlm-roberta-xstance",
-            device=0  # This will work inside @spaces.GPU context
+            device=0
         )
     return classifier
 
-# Prediction function - all CUDA ops happen here
 @spaces.GPU
 def predict_stance(question, comment):
     if not question.strip() or not comment.strip():
         return "⚠️ Please provide both a question and a comment.", None
     
-    # Get classifier (lazy loading inside GPU context)
-    model = get_classifier()
-    
-    # Get prediction
-    result = model({"text": question, "text_pair": comment})
-    label = result[0]['label']
-    score = result[0]['score']
-    
-    # Format the output
-    if label == "FAVOR":
-        emoji = "✅"
-        color = "green"
-        explanation = "The comment **supports** the political question."
-    else:
-        emoji = "❌"
-        color = "red"
-        explanation = "The comment **opposes** the political question."
-    
-    # Create formatted output
-    output = f"""
+    try:
+        # Get classifier
+        model = get_classifier()
+        
+        # Get prediction
+        result = model({"text": question, "text_pair": comment})
+        
+        if isinstance(result, list) and len(result) > 0:
+            prediction = result[0]
+            label = prediction['label']
+            score = prediction['score']
+        elif isinstance(result, dict):
+            label = result.get('label', 'Unknown')
+            score = result.get('score', 0.0)
+        else:
+            return "⚠️ Unexpected model output format.", None
+        
+        # Format the output
+        if "FAVOR" in label.upper():
+            emoji = "✅"
+            color = "green"
+            explanation = "The comment **supports** the political question."
+        else:
+            emoji = "❌"
+            color = "red"
+            explanation = "The comment **opposes** the political question."
+        
+        # Create formatted output
+        output = f"""
 ### {emoji} Prediction: **{label}**
 
 <div style="padding: 10px; border-left: 4px solid {color}; background-color: #f5f5f5; margin: 10px 0;">
@@ -53,12 +60,21 @@ def predict_stance(question, comment):
 
 ---
 *💡 Tip: Try questions in German, French, Italian, or English!*
-    """
-    
-    return output, {"FAVOR": score if label == "FAVOR" else 1-score,
-                    "AGAINST": score if label == "AGAINST" else 1-score}
+        """
+        
+        # Calculate confidence distribution
+        if "FAVOR" in label.upper():
+            confidence_dist = {"FAVOR": score, "AGAINST": 1 - score}
+        else:
+            confidence_dist = {"AGAINST": score, "FAVOR": 1 - score}
+        
+        return output, confidence_dist
+        
+    except Exception as e:
+        error_msg = f"❌ Error during prediction: {str(e)}"
+        print(f"Detailed error: {e}")
+        return error_msg, None
 
-# Create Gradio interface
 with gr.Blocks(title="Multilingual Stance Detection", theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
     # 🌍 Multilingual Political Stance Detection
@@ -106,13 +122,17 @@ with gr.Blocks(title="Multilingual Stance Detection", theme=gr.themes.Soft()) as
         label="📝 Try these examples"
     )
     
-    # Handle submission
     submit_btn.click(
         fn=predict_stance,
         inputs=[question_input, comment_input],
         outputs=[output_text, confidence_plot]
     )
+    
+    comment_input.submit(
+        fn=predict_stance,
+        inputs=[question_input, comment_input],
+        outputs=[output_text, confidence_plot]
+    )
 
-# Launch the app
 if __name__ == "__main__":
     demo.launch()