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app.py

@@ -1,44 +1,90 @@
 import gradio as gr
 from transformers import pipeline
+import torch
 
-# Load the clean v2 model
+# Load the model
+model_id = "samurai9776/thought-classifier"
 classifier = pipeline(
-    "text-classification", 
-    model="samurai9776/thought-classifier-menu-aware"
+    "text-classification",
+    model=model_id,
+    device=0 if torch.cuda.is_available() else -1
 )
 
 def classify_thought(ai_utterance, cx_utterance):
+    """Classify if the conversation is complete or incomplete"""
+
     if not ai_utterance or not cx_utterance:
         return "Please enter both utterances", 0, 0
-    
+
+    # Combine utterances with [SEP] token
     text = f"{ai_utterance} [SEP] {cx_utterance}"
+
+    # Get prediction
     results = classifier(text)
-    
+
+    # Extract scores
     scores = {r['label']: r['score'] for r in results}
     complete_score = scores.get('COMPLETE', 0)
     incomplete_score = scores.get('INCOMPLETE', 0)
-    
+
+    # Determine prediction
     prediction = "Complete ✓" if complete_score > incomplete_score else "Incomplete ⚠️"
+
     return prediction, complete_score, incomplete_score
 
-with gr.Blocks(title="Thought Classifier v2") as demo:
-    gr.Markdown("# 🤖 Thought Completion Classifier v2")
-    
+# Create Gradio interface
+with gr.Blocks(title="Thought Completion Classifier", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🤖 Thought Completion Classifier
+
+    This model determines if a conversation between an AI assistant and a customer represents a **complete** or **incomplete** thought.
+
+    Enter both utterances below and click "Classify" to see the results.
+    """)
+
     with gr.Row():
         with gr.Column():
-            ai_input = gr.Textbox(label="AI Utterance", placeholder="What sandwich?")
-            cx_input = gr.Textbox(label="Customer Utterance", placeholder="Chicken")
-            classify_btn = gr.Button("Classify", variant="primary")
-            
+            ai_input = gr.Textbox(
+                label="AI Utterance",
+                placeholder="e.g., What else can I get for you?",
+                lines=2
+            )
+            cx_input = gr.Textbox(
+                label="Customer Utterance",
+                placeholder="e.g., That's all for now",
+                lines=2
+            )
+
+            classify_btn = gr.Button("🔍 Classify", variant="primary", size="lg")
+
         with gr.Column():
-            prediction = gr.Textbox(label="Prediction")
-            complete_score = gr.Number(label="Complete Score")
-            incomplete_score = gr.Number(label="Incomplete Score")
-    
+            prediction = gr.Textbox(label="Prediction", interactive=False)
+            with gr.Row():
+                complete_score = gr.Number(label="Complete Score", precision=3, interactive=False)
+                incomplete_score = gr.Number(label="Incomplete Score", precision=3, interactive=False)
+
+    # Examples
+    gr.Examples(
+        examples=[
+            ["Great. Would you like anything else?", "Picking up on app order."],
+            ["What size would you like?", "Large please"],
+            ["Your total is $15.99", "Actually, let me add"],
+            ["Anything else I can get for you?", "No, that's it"],
+            ["How can I help you today?", "I need to place an order"],
+            ["Will that complete your order?", "Yes, that's everything"],
+        ],
+        inputs=[ai_input, cx_input],
+        outputs=[prediction, complete_score, incomplete_score],
+        fn=classify_thought,
+        cache_examples=True,
+    )
+
     classify_btn.click(
-        classify_thought,
+        fn=classify_thought,
         inputs=[ai_input, cx_input],
         outputs=[prediction, complete_score, incomplete_score]
     )
 
-demo.launch()
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()