Update app.py

app.py

@@ -139,23 +139,18 @@ def generate_response(user_input: str, temperature: float = 0.7, max_tokens: int
         return f"Error: {str(e)}"
 
 
-def chat_fn(user_input, history, temperature, max_tokens):
-    """Chat function for Gradio 6.x with messages format"""
-    if not user_input or not user_input.strip():
-        return "", history or []
+def respond(message, chat_history, temperature, max_tokens):
+    """Chat function - returns tuple format"""
+    if not message or not message.strip():
+        return "", chat_history if chat_history else []
     
-    response = generate_response(user_input, temperature, max_tokens)
+    bot_message = generate_response(message, temperature, max_tokens)
     
-    if history is None:
-        history = []
+    if chat_history is None:
+        chat_history = []
     
-    # Gradio 6.x requires messages format with 'role' and 'content'
-    history = history + [
-        {"role": "user", "content": user_input},
-        {"role": "assistant", "content": response}
-    ]
-    
-    return "", history
+    chat_history.append((message, bot_message))
+    return "", chat_history
 
 
 def feedback_good():
@@ -168,14 +163,14 @@ def feedback_good():
 def feedback_bad():
     if current_conv_id > 0:
         db.update_feedback(current_conv_id, -1)
-        return "👎 Thanks for the feedback. I will try to improve."
+        return "👎 Thanks! I'll try to improve."
     return "No conversation to rate yet."
 
 
-def clear_fn():
+def clear_chat():
     global conversation_history
     conversation_history = []
-    return [], "Conversation cleared."
+    return [], "Chat cleared."
 
 
 def retrain(epochs):
@@ -185,7 +180,7 @@ def retrain(epochs):
     good_convs = db.get_good_conversations()
     
     if not good_convs:
-        return "No approved conversations yet. Rate some responses first!"
+        return "No approved conversations yet. Rate some responses as 'Good' first!"
     
     extra_data = ""
     for conv in good_convs:
@@ -199,7 +194,7 @@ def retrain(epochs):
     tokenizer = trainer.tokenizer
     
     loss = history.history['loss'][-1]
-    return f"✅ Training done! Loss: {loss:.4f}, Used {len(good_convs)} conversations"
+    return f"✅ Training complete! Loss: {loss:.4f}, Used {len(good_convs)} conversations"
 
 
 def get_stats():
@@ -208,9 +203,9 @@ def get_stats():
 
 | Metric | Count |
 |--------|-------|
-| 💬 Conversations | {stats['total']} |
-| 👍 Positive | {stats['positive']} |
-| 👎 Negative | {stats['negative']} |
+| 💬 Total Conversations | {stats['total']} |
+| 👍 Positive Feedback | {stats['positive']} |
+| 👎 Negative Feedback | {stats['negative']} |
 """
 
 
@@ -220,23 +215,22 @@ initialize()
 print("Initialization complete!")
 
 
-# Create Gradio interface for Gradio 6.x
+# Create Gradio interface - NO type parameter
 with gr.Blocks(title="Veda Programming Assistant") as demo:
     
     gr.Markdown("""
     # 🕉️ Veda Programming Assistant
     
-    I can chat, write code, explain concepts, and answer programming questions!
+    I can **chat**, **write code**, **explain concepts**, and **answer questions**!
     """)
     
     with gr.Tabs():
         
         with gr.TabItem("💬 Chat"):
-            # Chatbot with messages format for Gradio 6.x
+            # Simple Chatbot without type parameter
             chatbot = gr.Chatbot(
                 label="Conversation",
-                height=400,
-                type="messages"
+                height=400
             )
             
             with gr.Row():
@@ -273,22 +267,22 @@ with gr.Blocks(title="Veda Programming Assistant") as demo:
             
             # Event handlers
             send_btn.click(
-                chat_fn,
+                respond,
                 inputs=[msg, chatbot, temperature, max_tokens],
                 outputs=[msg, chatbot]
             )
             
             msg.submit(
-                chat_fn,
+                respond,
                 inputs=[msg, chatbot, temperature, max_tokens],
                 outputs=[msg, chatbot]
             )
             
             good_btn.click(feedback_good, outputs=feedback_msg)
             bad_btn.click(feedback_bad, outputs=feedback_msg)
-            clear_btn.click(clear_fn, outputs=[chatbot, feedback_msg])
+            clear_btn.click(clear_chat, outputs=[chatbot, feedback_msg])
             
-            gr.Markdown("### 💡 Example prompts")
+            gr.Markdown("### 💡 Try these examples:")
             gr.Examples(
                 examples=[
                     ["Hello! What can you do?"],
@@ -302,15 +296,20 @@ with gr.Blocks(title="Veda Programming Assistant") as demo:
             )
         
         with gr.TabItem("🎓 Training"):
-            gr.Markdown("### Train on approved conversations")
-            gr.Markdown("Rate responses as 'Good' to add them to training data.")
+            gr.Markdown("""
+            ### Improve the Assistant
+            
+            1. Chat with the assistant
+            2. Rate good responses with 👍
+            3. Click "Retrain Model" to learn from good conversations
+            """)
             
             train_epochs = gr.Slider(
                 minimum=5,
                 maximum=20,
                 value=10,
                 step=1,
-                label="Epochs"
+                label="Training Epochs"
             )
             train_btn = gr.Button("🔄 Retrain Model", variant="primary")
             train_output = gr.Markdown()
@@ -318,20 +317,15 @@ with gr.Blocks(title="Veda Programming Assistant") as demo:
         
         with gr.TabItem("📊 Statistics"):
             stats_out = gr.Markdown()
-            refresh_btn = gr.Button("🔄 Refresh")
+            refresh_btn = gr.Button("🔄 Refresh Statistics")
             refresh_btn.click(get_stats, outputs=stats_out)
-            
-            gr.Markdown("""
-            ### How it works
-            1. Chat with the assistant
-            2. Rate responses as Good 👍 or Bad 👎
-            3. Good responses are saved for training
-            4. Click 'Retrain Model' to improve the assistant
-            """)
     
-    gr.Markdown("---\n**Veda Programming Assistant** - Learning from conversations!")
+    gr.Markdown("""
+    ---
+    **Veda Programming Assistant** - Learning from every conversation!
+    """)
 
 
-# Launch
+# Launch the app
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)