Update app.py

app.py

@@ -1,4 +1,4 @@
-"""Gradio App for Veda Programming Assistant - Gradio 6.2.0"""
+"""Gradio App for Veda Programming Assistant - Fixed for Gradio 6.2.0"""
 
 import gradio as gr
 import tensorflow as tf
@@ -139,13 +139,18 @@ def generate_response(user_input: str, temperature: float = 0.7, max_tokens: int
         return f"Error: {str(e)}"
 
 
-def chat(user_input, history, temperature, max_tokens):
+def chat_fn(user_input, history, temperature, max_tokens):
     """Chat function for Gradio"""
-    if not user_input.strip():
-        return "", history
+    if not user_input or not user_input.strip():
+        return "", history or []
     
     response = generate_response(user_input, temperature, max_tokens)
+    
+    if history is None:
+        history = []
+    
     history = history + [[user_input, response]]
+    
     return "", history
 
 
@@ -163,7 +168,7 @@ def feedback_bad():
     return "No conversation to rate yet."
 
 
-def clear_conversation():
+def clear_fn():
     global conversation_history
     conversation_history = []
     return [], "Conversation cleared."
@@ -205,28 +210,27 @@ def get_stats():
 """
 
 
-# Initialize model
+# Initialize model at startup
 print("Starting initialization...")
 initialize()
 print("Initialization complete!")
 
 
-# Create Gradio interface
-with gr.Blocks(title="Veda Programming Assistant", theme=gr.themes.Soft()) as demo:
+# Create Gradio interface - Fixed for Gradio 6.x
+with gr.Blocks(title="Veda Programming Assistant") as demo:
     
     gr.Markdown("""
-    # Veda Programming Assistant
+    # 🕉️ Veda Programming Assistant
     
     I can chat, write code, explain concepts, and answer programming questions!
     """)
     
     with gr.Tabs():
         
-        with gr.TabItem("Chat"):
+        with gr.TabItem("💬 Chat"):
             chatbot = gr.Chatbot(
                 label="Conversation",
-                height=400,
-                type="messages"
+                height=400
             )
             
             with gr.Row():
@@ -255,29 +259,13 @@ with gr.Blocks(title="Veda Programming Assistant", theme=gr.themes.Soft()) as de
                 )
             
             with gr.Row():
-                good_btn = gr.Button("Good Response", variant="secondary")
-                bad_btn = gr.Button("Bad Response", variant="secondary")
-                clear_btn = gr.Button("Clear Chat", variant="secondary")
+                good_btn = gr.Button("👍 Good", variant="secondary")
+                bad_btn = gr.Button("👎 Bad", variant="secondary")
+                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
             
             feedback_msg = gr.Textbox(label="Status", lines=1, interactive=False)
             
-            # Updated chat function for new Gradio format
-            def chat_fn(user_input, history, temperature, max_tokens):
-                if not user_input.strip():
-                    return "", history
-                
-                response = generate_response(user_input, temperature, max_tokens)
-                
-                if history is None:
-                    history = []
-                
-                history = history + [
-                    {"role": "user", "content": user_input},
-                    {"role": "assistant", "content": response}
-                ]
-                
-                return "", history
-            
+            # Event handlers
             send_btn.click(
                 chat_fn,
                 inputs=[msg, chatbot, temperature, max_tokens],
@@ -292,29 +280,25 @@ with gr.Blocks(title="Veda Programming Assistant", theme=gr.themes.Soft()) as de
             
             good_btn.click(feedback_good, outputs=feedback_msg)
             bad_btn.click(feedback_bad, outputs=feedback_msg)
-            
-            def clear_fn():
-                global conversation_history
-                conversation_history = []
-                return [], "Conversation cleared."
-            
             clear_btn.click(clear_fn, outputs=[chatbot, feedback_msg])
             
-            gr.Markdown("### Examples")
+            gr.Markdown("### 💡 Example prompts")
             gr.Examples(
                 examples=[
-                    "Hello! What can you do?",
-                    "What is Python?",
-                    "Write a function to calculate factorial",
-                    "Explain what recursion is",
-                    "How do I read a file in Python?",
-                    "Write a bubble sort algorithm",
+                    ["Hello! What can you do?"],
+                    ["What is Python?"],
+                    ["Write a function to calculate factorial"],
+                    ["Explain what recursion is"],
+                    ["How do I read a file in Python?"],
+                    ["Write a bubble sort algorithm"],
                 ],
                 inputs=msg
             )
         
-        with gr.TabItem("Training"):
+        with gr.TabItem("🎓 Training"):
             gr.Markdown("### Train on approved conversations")
+            gr.Markdown("Rate responses as 'Good' to add them to training data.")
+            
             train_epochs = gr.Slider(
                 minimum=5,
                 maximum=20,
@@ -322,18 +306,26 @@ with gr.Blocks(title="Veda Programming Assistant", theme=gr.themes.Soft()) as de
                 step=1,
                 label="Epochs"
             )
-            train_btn = gr.Button("Retrain Model", variant="primary")
+            train_btn = gr.Button("🔄 Retrain Model", variant="primary")
             train_output = gr.Markdown()
             train_btn.click(retrain, inputs=[train_epochs], outputs=train_output)
         
-        with gr.TabItem("Statistics"):
+        with gr.TabItem("📊 Statistics"):
             stats_out = gr.Markdown()
-            refresh_btn = gr.Button("Refresh Statistics")
+            refresh_btn = gr.Button("🔄 Refresh")
             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("---\nVeda Programming Assistant - Learning from conversations!")
+    gr.Markdown("---\n**Veda Programming Assistant** - Learning from conversations!")
 
 
-# Launch the app
+# Launch
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)