Update app.py

app.py

@@ -1,4 +1,4 @@
-"""Gradio App - REPLACED with chat interface"""
+"""Gradio App for Veda Programming Assistant - Gradio 6.2.0"""
 
 import gradio as gr
 import tensorflow as tf
@@ -11,17 +11,18 @@ from database import db
 from train import VedaTrainer
 from config import MODEL_DIR
 
-# Global state
+
 model = None
 tokenizer = None
 conversation_history = []
 current_conv_id = -1
 
+
 def initialize():
     """Initialize the assistant"""
     global model, tokenizer
     
-    print("🕉️ Initializing Veda Programming Assistant...")
+    print("Initializing Veda Programming Assistant...")
     
     config_path = os.path.join(MODEL_DIR, "config.json")
     
@@ -47,26 +48,24 @@ def initialize():
         model(dummy)
         model.load_weights(os.path.join(MODEL_DIR, "weights.h5"))
         
-        print("✅ Model loaded!")
+        print("Model loaded!")
     else:
-        print("Training new model (this takes a few minutes)...")
+        print("Training new model...")
         trainer = VedaTrainer()
         trainer.train(epochs=15)
         model = trainer.model
         tokenizer = trainer.tokenizer
-        print("✅ Model trained!")
+        print("Model trained!")
+
 
 def clean_response(text: str) -> str:
     """Clean the response"""
-    # Handle code blocks
     text = text.replace("<CODE>", "\n```python\n")
     text = text.replace("<ENDCODE>", "\n```\n")
     
-    # Remove special tokens
     for token in ["<PAD>", "<UNK>", "<START>", "<END>", "<USER>", "<ASSISTANT>"]:
         text = text.replace(token, "")
     
-    # Clean whitespace
     lines = text.split('\n')
     cleaned = []
     empty_count = 0
@@ -82,34 +81,29 @@ def clean_response(text: str) -> str:
     
     return '\n'.join(cleaned).strip()
 
-def generate_response(user_input: str, temperature: float = 0.7, 
-                     max_tokens: int = 200) -> str:
+
+def generate_response(user_input: str, temperature: float = 0.7, max_tokens: int = 200) -> str:
     """Generate a response"""
     global current_conv_id
     
     if model is None:
-        return "⏳ Model is loading..."
+        return "Model is loading, please wait..."
     
     if not user_input.strip():
         return "Please type a message!"
     
     try:
-        # Build context from history (last 3 exchanges)
         context = ""
         for msg in conversation_history[-3:]:
             context += f"<USER> {msg['user']}\n<ASSISTANT> {msg['assistant']}\n"
         
-        # Add current input
         prompt = context + f"<USER> {user_input}\n<ASSISTANT>"
         
-        # Encode
         tokens = tokenizer.encode(prompt)
         
-        # Truncate if too long
         if len(tokens) > model.max_length - max_tokens:
             tokens = tokens[-(model.max_length - max_tokens):]
         
-        # Generate
         generated = model.generate(
             tokens,
             max_new_tokens=max_tokens,
@@ -119,10 +113,8 @@ def generate_response(user_input: str, temperature: float = 0.7,
             repetition_penalty=1.2
         )
         
-        # Decode
         response = tokenizer.decode(generated)
         
-        # Extract assistant's response
         if "<ASSISTANT>" in response:
             parts = response.split("<ASSISTANT>")
             response = parts[-1].strip()
@@ -132,13 +124,11 @@ def generate_response(user_input: str, temperature: float = 0.7,
         
         response = clean_response(response)
         
-        # Save to history
         conversation_history.append({
             'user': user_input,
             'assistant': response
         })
         
-        # Save to database
         current_conv_id = db.save_conversation(user_input, response)
         
         return response
@@ -146,30 +136,38 @@ def generate_response(user_input: str, temperature: float = 0.7,
     except Exception as e:
         import traceback
         traceback.print_exc()
-        return f"❌ Error: {str(e)}"
+        return f"Error: {str(e)}"
+
 
 def chat(user_input, history, temperature, max_tokens):
     """Chat function for Gradio"""
+    if not user_input.strip():
+        return "", history
+    
     response = generate_response(user_input, temperature, max_tokens)
-    history.append((user_input, response))
+    history = history + [[user_input, response]]
     return "", history
 
+
 def feedback_good():
     if current_conv_id > 0:
         db.update_feedback(current_conv_id, 1)
-        return "👍 Thanks! This helps me improve."
-    return ""
+        return "Thanks for the positive feedback!"
+    return "No conversation to rate yet."
+
 
 def feedback_bad():
     if current_conv_id > 0:
         db.update_feedback(current_conv_id, -1)
-        return "👎 Thanks for the feedback. I'll try to do better."
-    return ""
+        return "Thanks for the feedback. I will try to improve."
+    return "No conversation to rate yet."
+
 
 def clear_conversation():
     global conversation_history
     conversation_history = []
-    return [], ""
+    return [], "Conversation cleared."
+
 
 def retrain(epochs):
     """Retrain with good conversations"""
@@ -192,97 +190,150 @@ 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 done! Loss: {loss:.4f}, Used {len(good_convs)} conversations"
+
 
 def get_stats():
     stats = db.get_stats()
-    return f"""## 📊 Statistics
+    return f"""## Statistics
 
 | Metric | Count |
 |--------|-------|
-| 💬 Conversations | {stats['total']} |
-| 👍 Positive | {stats['positive']} |
-| 👎 Negative | {stats['negative']} |
+| Conversations | {stats['total']} |
+| Positive | {stats['positive']} |
+| Negative | {stats['negative']} |
 """
 
-# Create interface
-def create_app():
-    with gr.Blocks(title="Veda Programming Assistant", theme=gr.themes.Soft()) as app:
-        
-        gr.Markdown("""
-        # 🕉️ Veda Programming Assistant
-        
-        I can **chat**, **write code**, **explain concepts**, and **answer questions**!
-        """)
+
+# Initialize model
+print("Starting initialization...")
+initialize()
+print("Initialization complete!")
+
+
+# Create Gradio interface
+with gr.Blocks(title="Veda Programming Assistant", theme=gr.themes.Soft()) as demo:
+    
+    gr.Markdown("""
+    # Veda Programming Assistant
+    
+    I can chat, write code, explain concepts, and answer programming questions!
+    """)
+    
+    with gr.Tabs():
         
-        with gr.Tabs():
+        with gr.TabItem("Chat"):
+            chatbot = gr.Chatbot(
+                label="Conversation",
+                height=400,
+                type="messages"
+            )
             
-            # Chat Tab
-            with gr.TabItem("💬 Chat"):
-                chatbot = gr.Chatbot(label="Conversation", height=400)
-                
-                with gr.Row():
-                    msg = gr.Textbox(
-                        label="Your message",
-                        placeholder="Ask me anything about programming...",
-                        lines=2,
-                        scale=4
-                    )
-                    send_btn = gr.Button("Send 📤", variant="primary", scale=1)
-                
-                with gr.Row():
-                    temperature = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Creativity")
-                    max_tokens = gr.Slider(50, 400, value=200, step=50, label="Response length")
+            with gr.Row():
+                msg = gr.Textbox(
+                    label="Your message",
+                    placeholder="Ask me anything about programming...",
+                    lines=2,
+                    scale=4
+                )
+                send_btn = gr.Button("Send", variant="primary", scale=1)
+            
+            with gr.Row():
+                temperature = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.5,
+                    value=0.7,
+                    step=0.1,
+                    label="Creativity"
+                )
+                max_tokens = gr.Slider(
+                    minimum=50,
+                    maximum=400,
+                    value=200,
+                    step=50,
+                    label="Response length"
+                )
+            
+            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")
+            
+            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
                 
-                with gr.Row():
-                    good_btn = gr.Button("👍 Good", variant="secondary")
-                    bad_btn = gr.Button("👎 Bad", variant="secondary")
-                    clear_btn = gr.Button("🗑️ Clear", variant="secondary")
+                response = generate_response(user_input, temperature, max_tokens)
                 
-                feedback_msg = gr.Textbox(label="", lines=1)
+                if history is None:
+                    history = []
                 
-                # Events
-                send_btn.click(chat, [msg, chatbot, temperature, max_tokens], [msg, chatbot])
-                msg.submit(chat, [msg, chatbot, temperature, max_tokens], [msg, chatbot])
-                good_btn.click(feedback_good, outputs=feedback_msg)
-                bad_btn.click(feedback_bad, outputs=feedback_msg)
-                clear_btn.click(clear_conversation, outputs=[chatbot, feedback_msg])
+                history = history + [
+                    {"role": "user", "content": user_input},
+                    {"role": "assistant", "content": response}
+                ]
                 
-                gr.Markdown("### 💡 Try these:")
-                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"],
-                        ["What's the difference between list and tuple?"],
-                    ],
-                    inputs=msg
-                )
+                return "", history
+            
+            send_btn.click(
+                chat_fn,
+                inputs=[msg, chatbot, temperature, max_tokens],
+                outputs=[msg, chatbot]
+            )
+            
+            msg.submit(
+                chat_fn,
+                inputs=[msg, chatbot, temperature, max_tokens],
+                outputs=[msg, chatbot]
+            )
             
-            # Training Tab
-            with gr.TabItem("🎓 Training"):
-                gr.Markdown("### Train on your approved conversations")
-                train_epochs = gr.Slider(5, 20, value=10, step=1, label="Epochs")
-                train_btn = gr.Button("🔄 Retrain", variant="primary")
-                train_output = gr.Markdown()
-                train_btn.click(retrain, [train_epochs], train_output)
+            good_btn.click(feedback_good, outputs=feedback_msg)
+            bad_btn.click(feedback_bad, outputs=feedback_msg)
             
-            # Stats Tab
-            with gr.TabItem("📊 Stats"):
-                stats_out = gr.Markdown()
-                refresh_btn = gr.Button("🔄 Refresh")
-                refresh_btn.click(get_stats, outputs=stats_out)
+            def clear_fn():
+                global conversation_history
+                conversation_history = []
+                return [], "Conversation cleared."
+            
+            clear_btn.click(clear_fn, outputs=[chatbot, feedback_msg])
+            
+            gr.Markdown("### Examples")
+            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",
+                ],
+                inputs=msg
+            )
         
-        gr.Markdown("---\n**Veda Programming Assistant** | Learning from every conversation!")
+        with gr.TabItem("Training"):
+            gr.Markdown("### Train on approved conversations")
+            train_epochs = gr.Slider(
+                minimum=5,
+                maximum=20,
+                value=10,
+                step=1,
+                label="Epochs"
+            )
+            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"):
+            stats_out = gr.Markdown()
+            refresh_btn = gr.Button("Refresh Statistics")
+            refresh_btn.click(get_stats, outputs=stats_out)
     
-    return app
+    gr.Markdown("---\nVeda Programming Assistant - Learning from conversations!")
+
 
-# Main
+# Launch the app
 if __name__ == "__main__":
-    initialize()
-    print("\n🚀 Starting...")
-    app = create_app()
-    app.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)