Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,90 @@
+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Load the model
+model_id = "samurai9776/thought-classifier"
+classifier = pipeline(
+    "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
+
+# 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="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", 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(
+        fn=classify_thought,
+        inputs=[ai_input, cx_input],
+        outputs=[prediction, complete_score, incomplete_score]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()