Update app.py

app.py

@@ -1,44 +1,44 @@
 # Create wrapper function for proper chat handling
-    def respond(message, history):
-        """Response handler for Gradio messages format"""
-        if not message.strip():
-            return "", history
-        
-        print(f"🔍 Input message: {message}")
-        print(f"🔍 History length: {len(history)}")
-        
-        # Convert messages format to simple tuples for model processing
-        simple_history = []
-        for i in range(0, len(history) - 1, 2):
-            if i + 1 < len(history):
-                user_msg = history[i].get('content', '') if isinstance(history[i], dict) else str(history[i])
-                bot_msg = history[i + 1].get('content', '') if isinstance(history[i + 1], dict) else str(history[i + 1])
-                if user_msg and bot_msg:
-                    simple_history.append([user_msg, bot_msg])
-        
-        print(f"🔍 Simple history: {simple_history}")
-        
-        # Generate response with debugging
-        try:
-            response_generator = creed_ai.generate_response(message, simple_history)
-            final_response = ""
-            
-            for response_chunk in response_generator:
-                final_response = response_chunk
-                # Create new history with the streaming response
-                new_history = history + [
-                    {"role": "user", "content": message},
-                    {"role": "assistant", "content": response_chunk}
-                ]
-                yield "", new_history
-                
-        except Exception as e:
-            print(f"❌ Error in respond: {e}")
-            error_history = history + [
+def respond(message, history):
+    """Response handler for Gradio messages format"""
+    if not message.strip():
+        return "", history
+    
+    print(f"🔍 Input message: {message}")
+    print(f"🔍 History length: {len(history)}")
+    
+    # Convert messages format to simple tuples for model processing
+    simple_history = []
+    for i in range(0, len(history) - 1, 2):
+        if i + 1 < len(history):
+            user_msg = history[i].get('content', '') if isinstance(history[i], dict) else str(history[i])
+            bot_msg = history[i + 1].get('content', '') if isinstance(history[i + 1], dict) else str(history[i + 1])
+            if user_msg and bot_msg:
+                simple_history.append([user_msg, bot_msg])
+    
+    print(f"🔍 Simple history: {simple_history}")
+    
+    # Generate response with debugging
+    try:
+        response_generator = creed_ai.generate_response(message, simple_history)
+        final_response = ""
+        
+        for response_chunk in response_generator:
+            final_response = response_chunk
+            # Create new history with the streaming response
+            new_history = history + [
                 {"role": "user", "content": message},
-                {"role": "assistant", "content": f"🎸 *Creed's brain malfunctioned* Error: {str(e)[:100]}"}
+                {"role": "assistant", "content": response_chunk}
             ]
-            yield "", error_history#!/usr/bin/env python3
+            yield "", new_history
+            
+    except Exception as e:
+        print(f"❌ Error in respond: {e}")
+        error_history = history + [
+            {"role": "user", "content": message},
+            {"role": "assistant", "content": f"🎸 *Creed's brain malfunctioned* Error: {str(e)[:100]}"}
+        ]
+        yield "", error_history#!/usr/bin/env python3
 """
 🎸 Creed Bratton AI - Using phxdev/creed-qwen-0.5b-lora
 The REAL Creed, trained by Mark, not some knockoff prompt engineering
@@ -59,267 +59,267 @@ os.environ['GRADIO_MCP_ENABLED'] = 'true'
 
 # Spaces compatibility
 try:
-    import spaces
-    SPACES_AVAILABLE = True
-    @spaces.GPU
-    def gpu_placeholder():
-        return "GPU satisfied"
+import spaces
+SPACES_AVAILABLE = True
+@spaces.GPU
+def gpu_placeholder():
+    return "GPU satisfied"
 except ImportError:
-    SPACES_AVAILABLE = False
+SPACES_AVAILABLE = False
 
 class CreedBrattonAI:
-    """Real Creed AI using Mark's trained model - GPU optimized"""
+"""Real Creed AI using Mark's trained model - GPU optimized"""
+
+def __init__(self):
+    self.model = None
+    self.tokenizer = None
+    self.model_loaded = False
+    self.loading = False
+    self.device = "cuda" if torch.cuda.is_available() else "cpu"
     
-    def __init__(self):
-        self.model = None
-        self.tokenizer = None
-        self.model_loaded = False
-        self.loading = False
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        
-        print(f"🎸 Initializing Creed AI")
-        print(f"🖥️ Device detected: {self.device}")
-        if torch.cuda.is_available():
-            print(f"🚀 GPU: {torch.cuda.get_device_name()}")
-            print(f"💾 GPU Memory: {torch.cuda.get_device_properties(0).total_memory // 1024**3} GB")
+    print(f"🎸 Initializing Creed AI")
+    print(f"🖥️ Device detected: {self.device}")
+    if torch.cuda.is_available():
+        print(f"🚀 GPU: {torch.cuda.get_device_name()}")
+        print(f"💾 GPU Memory: {torch.cuda.get_device_properties(0).total_memory // 1024**3} GB")
+    
+    # Load model with proper GPU detection
+    self.load_model()
+
+def load_model(self):
+    """Load the model with GPU optimization when available"""
+    if self.loading or self.model_loaded:
+        return
         
-        # Load model with proper GPU detection
-        self.load_model()
+    self.loading = True
     
-    def load_model(self):
-        """Load the model with GPU optimization when available"""
-        if self.loading or self.model_loaded:
-            return
-            
-        self.loading = True
+    try:
+        print(f"🧠 Loading Creed's consciousness on {self.device}...")
         
-        try:
-            print(f"🧠 Loading Creed's consciousness on {self.device}...")
-            
-            # Load model and tokenizer
-            model_name = "phxdev/creed-qwen-0.5b-lora"
-            
-            print("📦 Loading tokenizer...")
-            self.tokenizer = AutoTokenizer.from_pretrained(
+        # Load model and tokenizer
+        model_name = "phxdev/creed-qwen-0.5b-lora"
+        
+        print("📦 Loading tokenizer...")
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_name,
+            trust_remote_code=True,
+            padding_side="left"
+        )
+        
+        # Fix pad token issue
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.unk_token or "[PAD]"
+            print(f"🔧 Set pad_token to: {self.tokenizer.pad_token}")
+        
+        # Add Creed's custom tokens
+        custom_tokens = ["<thinking>", "<conspiracy>", "<tangent>"]
+        print(f"🎸 Adding Creed's custom tokens: {custom_tokens}")
+        
+        num_added_tokens = self.tokenizer.add_tokens(custom_tokens)
+        print(f"✅ Added {num_added_tokens} custom tokens")
+        
+        print(f"🤖 Loading model on {self.device}...")
+        
+        # Load model with proper device handling
+        if self.device == "cuda":
+            self.model = AutoModelForCausalLM.from_pretrained(
                 model_name,
+                torch_dtype=torch.float16,  # Use float16 for GPU efficiency
+                device_map="auto",  # Auto device mapping for GPU
                 trust_remote_code=True,
-                padding_side="left"
+                low_cpu_mem_usage=True
             )
+            # Explicitly ensure model is on CUDA
+            if self.model.device.type != "cuda":
+                print("🔧 Explicitly moving model to CUDA...")
+                self.model = self.model.to(self.device)
+        else:
+            self.model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                torch_dtype=torch.float32,  # Use float32 for CPU
+                device_map=None,
+                trust_remote_code=True,
+                low_cpu_mem_usage=True
+            )
+            self.model = self.model.to("cpu")
+        
+        # Resize embeddings for custom tokens
+        if num_added_tokens > 0:
+            print(f"🔧 Resizing model embeddings for {num_added_tokens} custom tokens")
+            self.model.resize_token_embeddings(len(self.tokenizer))
+        
+        self.model.eval()
+        
+        # Verify device placement
+        model_device = next(self.model.parameters()).device
+        print(f"🎯 Model is actually on: {model_device}")
+        
+        self.model_loaded = True
+        self.loading = False
+        print(f"✅ Creed's consciousness loaded on {model_device}!")
+        
+        # GPU memory info
+        if self.device == "cuda" and torch.cuda.is_available():
+            print(f"🔥 GPU Memory Used: {torch.cuda.memory_allocated() // 1024**2} MB")
+            print(f"📊 GPU Memory Cached: {torch.cuda.memory_reserved() // 1024**2} MB")
+        
+    except Exception as e:
+        print(f"❌ Error loading Creed model: {e}")
+        print("🔄 Falling back to base model...")
+        try:
+            base_model = "Qwen/Qwen2.5-0.5B-Instruct"
+            self.tokenizer = AutoTokenizer.from_pretrained(base_model)
             
-            # Fix pad token issue
+            # Fix pad token for fallback too
             if self.tokenizer.pad_token is None:
                 self.tokenizer.pad_token = self.tokenizer.unk_token or "[PAD]"
-                print(f"🔧 Set pad_token to: {self.tokenizer.pad_token}")
-            
-            # Add Creed's custom tokens
-            custom_tokens = ["<thinking>", "<conspiracy>", "<tangent>"]
-            print(f"🎸 Adding Creed's custom tokens: {custom_tokens}")
-            
-            num_added_tokens = self.tokenizer.add_tokens(custom_tokens)
-            print(f"✅ Added {num_added_tokens} custom tokens")
-            
-            print(f"🤖 Loading model on {self.device}...")
             
-            # Load model with proper device handling
             if self.device == "cuda":
                 self.model = AutoModelForCausalLM.from_pretrained(
-                    model_name,
-                    torch_dtype=torch.float16,  # Use float16 for GPU efficiency
-                    device_map="auto",  # Auto device mapping for GPU
-                    trust_remote_code=True,
-                    low_cpu_mem_usage=True
+                    base_model,
+                    torch_dtype=torch.float16,
+                    device_map="auto"
                 )
-                # Explicitly ensure model is on CUDA
                 if self.model.device.type != "cuda":
-                    print("🔧 Explicitly moving model to CUDA...")
                     self.model = self.model.to(self.device)
             else:
                 self.model = AutoModelForCausalLM.from_pretrained(
-                    model_name,
-                    torch_dtype=torch.float32,  # Use float32 for CPU
-                    device_map=None,
-                    trust_remote_code=True,
-                    low_cpu_mem_usage=True
+                    base_model,
+                    torch_dtype=torch.float32,
+                    device_map=None
                 )
                 self.model = self.model.to("cpu")
             
-            # Resize embeddings for custom tokens
-            if num_added_tokens > 0:
-                print(f"🔧 Resizing model embeddings for {num_added_tokens} custom tokens")
-                self.model.resize_token_embeddings(len(self.tokenizer))
-            
             self.model.eval()
-            
-            # Verify device placement
-            model_device = next(self.model.parameters()).device
-            print(f"🎯 Model is actually on: {model_device}")
-            
             self.model_loaded = True
-            self.loading = False
-            print(f"✅ Creed's consciousness loaded on {model_device}!")
-            
-            # GPU memory info
-            if self.device == "cuda" and torch.cuda.is_available():
-                print(f"🔥 GPU Memory Used: {torch.cuda.memory_allocated() // 1024**2} MB")
-                print(f"📊 GPU Memory Cached: {torch.cuda.memory_reserved() // 1024**2} MB")
-            
-        except Exception as e:
-            print(f"❌ Error loading Creed model: {e}")
-            print("🔄 Falling back to base model...")
-            try:
-                base_model = "Qwen/Qwen2.5-0.5B-Instruct"
-                self.tokenizer = AutoTokenizer.from_pretrained(base_model)
-                
-                # Fix pad token for fallback too
-                if self.tokenizer.pad_token is None:
-                    self.tokenizer.pad_token = self.tokenizer.unk_token or "[PAD]"
-                
-                if self.device == "cuda":
-                    self.model = AutoModelForCausalLM.from_pretrained(
-                        base_model,
-                        torch_dtype=torch.float16,
-                        device_map="auto"
-                    )
-                    if self.model.device.type != "cuda":
-                        self.model = self.model.to(self.device)
-                else:
-                    self.model = AutoModelForCausalLM.from_pretrained(
-                        base_model,
-                        torch_dtype=torch.float32,
-                        device_map=None
-                    )
-                    self.model = self.model.to("cpu")
-                
-                self.model.eval()
-                self.model_loaded = True
-                model_device = next(self.model.parameters()).device
-                print(f"✅ Fallback model loaded on {model_device}")
-            except Exception as fallback_error:
-                print(f"❌ Fallback also failed: {fallback_error}")
-            self.loading = False
+            model_device = next(self.model.parameters()).device
+            print(f"✅ Fallback model loaded on {model_device}")
+        except Exception as fallback_error:
+            print(f"❌ Fallback also failed: {fallback_error}")
+        self.loading = False
+
+@spaces.GPU if SPACES_AVAILABLE else lambda func: func
+def generate_response_gpu(self, conversation: str) -> str:
+    """Generate response using the loaded model with proper device handling"""
     
-    @spaces.GPU if SPACES_AVAILABLE else lambda func: func
-    def generate_response_gpu(self, conversation: str) -> str:
-        """Generate response using the loaded model with proper device handling"""
-        
-        if not self.model_loaded:
-            return "❌ Model not loaded"
+    if not self.model_loaded:
+        return "❌ Model not loaded"
+    
+    try:
+        print(f"🔍 Input conversation length: {len(conversation)}")
+        print(f"🔍 Input sample: {conversation[:200]}...")
+        
+        # Ensure model is on the correct device
+        if self.device == "cuda" and self.model.device.type != "cuda":
+            print(f"🔄 Moving model from {self.model.device} to {self.device}")
+            self.model = self.model.to(self.device)
+        
+        # Tokenize input with attention mask
+        inputs = self.tokenizer(
+            conversation, 
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=1024,  # Reduce max length to avoid issues
+            add_special_tokens=True
+        )
         
-        try:
-            print(f"🔍 Input conversation length: {len(conversation)}")
-            print(f"🔍 Input sample: {conversation[:200]}...")
-            
-            # Ensure model is on the correct device
-            if self.device == "cuda" and self.model.device.type != "cuda":
-                print(f"🔄 Moving model from {self.model.device} to {self.device}")
-                self.model = self.model.to(self.device)
-            
-            # Tokenize input with attention mask
-            inputs = self.tokenizer(
-                conversation, 
-                return_tensors="pt",
-                padding=True,
-                truncation=True,
-                max_length=1024,  # Reduce max length to avoid issues
-                add_special_tokens=True
+        print(f"🔍 Tokenized input_ids shape: {inputs['input_ids'].shape}")
+        print(f"🔍 First few tokens: {inputs['input_ids'][0][:10]}")
+        
+        # Move inputs to same device as model
+        input_ids = inputs['input_ids'].to(self.device)
+        attention_mask = inputs['attention_mask'].to(self.device)
+        
+        print(f"🔍 Model device: {self.model.device}, Input device: {input_ids.device}")
+        
+        # Generate response with proper attention mask
+        with torch.no_grad():
+            outputs = self.model.generate(
+                input_ids=input_ids,
+                attention_mask=attention_mask,  # Pass attention mask
+                max_new_tokens=100,  # Reduce to debug
+                do_sample=True,
+                temperature=0.7,  # Lower temperature
+                top_p=0.9,
+                top_k=50,
+                repetition_penalty=1.1,
+                pad_token_id=self.tokenizer.pad_token_id,
+                eos_token_id=self.tokenizer.eos_token_id,
+                use_cache=True
             )
-            
-            print(f"🔍 Tokenized input_ids shape: {inputs['input_ids'].shape}")
-            print(f"🔍 First few tokens: {inputs['input_ids'][0][:10]}")
-            
-            # Move inputs to same device as model
-            input_ids = inputs['input_ids'].to(self.device)
-            attention_mask = inputs['attention_mask'].to(self.device)
-            
-            print(f"🔍 Model device: {self.model.device}, Input device: {input_ids.device}")
-            
-            # Generate response with proper attention mask
-            with torch.no_grad():
-                outputs = self.model.generate(
-                    input_ids=input_ids,
-                    attention_mask=attention_mask,  # Pass attention mask
-                    max_new_tokens=100,  # Reduce to debug
-                    do_sample=True,
-                    temperature=0.7,  # Lower temperature
-                    top_p=0.9,
-                    top_k=50,
-                    repetition_penalty=1.1,
-                    pad_token_id=self.tokenizer.pad_token_id,
-                    eos_token_id=self.tokenizer.eos_token_id,
-                    use_cache=True
-                )
-            
-            print(f"🔍 Generated output shape: {outputs.shape}")
-            print(f"🔍 Generated tokens: {outputs[0][-20:]}")  # Last 20 tokens
-            
-            # Decode response
-            full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-            input_text = self.tokenizer.decode(input_ids[0], skip_special_tokens=True)
-            response = full_response[len(input_text):].strip()
-            
-            print(f"🔍 Raw response: {response[:200]}...")
-            
-            cleaned_response = self._clean_response(response)
-            print(f"🔍 Cleaned response: {cleaned_response[:200]}...")
-            
-            return cleaned_response
-            
-        except Exception as e:
-            print(f"❌ Generation error: {e}")
-            import traceback
-            traceback.print_exc()
-            return f"🎸 *Creed scratches his head* Something weird happened... {str(e)[:100]}"
-    
-    def generate_response(self, message: str, history: List[Dict]) -> Iterator[str]:
-        """Generate response using the trained Creed model - works with messages format"""
-        
-        if not self.model_loaded:
-            if self.loading:
-                yield "🧠 Creed's consciousness is still loading... give me a moment..."
-                return
-            else:
-                yield "❌ Something went wrong loading Creed's mind. Try refreshing the page."
-                return
         
-        try:
-            # Convert messages format to simple history
-            formatted_history = []
-            if history:
-                for msg in history:
-                    if msg.get('role') == 'user':
-                        user_msg = msg.get('content', '')
-                        # Look for the next assistant message
-                        assistant_msg = ""
-                        formatted_history.append([user_msg, assistant_msg])
-                    elif msg.get('role') == 'assistant':
-                        # Update the last entry with assistant response
-                        if formatted_history:
-                            formatted_history[-1][1] = msg.get('content', '')
-            
-            # Format the conversation
-            conversation = self._format_conversation(message, formatted_history)
-            
-            # Generate response using GPU function
-            response = self.generate_response_gpu(conversation)
-            
-            # Stream the response word by word for effect
-            words = response.split()
-            current_response = ""
-            
-            for word in words:
-                current_response += word + " "
-                time.sleep(0.05)
-                yield current_response.strip()
-                
-        except Exception as e:
-            print(f"❌ Error generating response: {e}")
-            yield f"🎸 *Creed scratches his head* Something weird happened in my brain... {str(e)[:100]}"
-    
-    def _format_conversation(self, message: str, history: List[List[str]]) -> str:
-        """Format the conversation for the model with proper system prompt"""
+        print(f"🔍 Generated output shape: {outputs.shape}")
+        print(f"🔍 Generated tokens: {outputs[0][-20:]}")  # Last 20 tokens
+        
+        # Decode response
+        full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        input_text = self.tokenizer.decode(input_ids[0], skip_special_tokens=True)
+        response = full_response[len(input_text):].strip()
+        
+        print(f"🔍 Raw response: {response[:200]}...")
         
-        # Comprehensive Creed system prompt
-        system_prompt = """You are Creed Bratton from The Office. You embody his complete personality and speaking patterns.
+        cleaned_response = self._clean_response(response)
+        print(f"🔍 Cleaned response: {cleaned_response[:200]}...")
+        
+        return cleaned_response
+        
+    except Exception as e:
+        print(f"❌ Generation error: {e}")
+        import traceback
+        traceback.print_exc()
+        return f"🎸 *Creed scratches his head* Something weird happened... {str(e)[:100]}"
+
+def generate_response(self, message: str, history: List[Dict]) -> Iterator[str]:
+    """Generate response using the trained Creed model - works with messages format"""
+    
+    if not self.model_loaded:
+        if self.loading:
+            yield "🧠 Creed's consciousness is still loading... give me a moment..."
+            return
+        else:
+            yield "❌ Something went wrong loading Creed's mind. Try refreshing the page."
+            return
+    
+    try:
+        # Convert messages format to simple history
+        formatted_history = []
+        if history:
+            for msg in history:
+                if msg.get('role') == 'user':
+                    user_msg = msg.get('content', '')
+                    # Look for the next assistant message
+                    assistant_msg = ""
+                    formatted_history.append([user_msg, assistant_msg])
+                elif msg.get('role') == 'assistant':
+                    # Update the last entry with assistant response
+                    if formatted_history:
+                        formatted_history[-1][1] = msg.get('content', '')
+        
+        # Format the conversation
+        conversation = self._format_conversation(message, formatted_history)
+        
+        # Generate response using GPU function
+        response = self.generate_response_gpu(conversation)
+        
+        # Stream the response word by word for effect
+        words = response.split()
+        current_response = ""
+        
+        for word in words:
+            current_response += word + " "
+            time.sleep(0.05)
+            yield current_response.strip()
+            
+    except Exception as e:
+        print(f"❌ Error generating response: {e}")
+        yield f"🎸 *Creed scratches his head* Something weird happened in my brain... {str(e)[:100]}"
+
+def _format_conversation(self, message: str, history: List[List[str]]) -> str:
+    """Format the conversation for the model with proper system prompt"""
+    
+    # Comprehensive Creed system prompt
+    system_prompt = """You are Creed Bratton from The Office. You embody his complete personality and speaking patterns.
 
 CORE IDENTITY:
 - Former member of The Grass Roots (1960s rock band)
@@ -359,554 +359,554 @@ RESPONSE GUIDELINES:
 Remember: You're not trying to be helpful in a traditional sense - you're being Creed Bratton.
 
 """
-        
-        # Add conversation history
-        conversation = system_prompt
-        for user_msg, creed_msg in history[-4:]:  # Keep recent context
-            if user_msg and creed_msg:  # Only add complete exchanges
-                conversation += f"Human: {user_msg}\n"
-                conversation += f"Creed: {creed_msg}\n"
-        
-        # Add current message
-        conversation += f"Human: {message}\n"
-        conversation += "Creed:"
-        
-        return conversation
     
-    def _clean_response(self, response: str) -> str:
-        """Clean up the model response and format custom tokens"""
-        
-        # Remove common artifacts
-        response = response.replace("Human:", "").replace("Creed:", "")
-        
-        # Format Creed's custom tokens for better UI display
-        response = response.replace("<thinking>", "🤔 *thinking* ")
-        response = response.replace("</thinking>", "")
-        response = response.replace("<conspiracy>", "🕵️ *conspiracy mode* ")
-        response = response.replace("</conspiracy>", "")
-        response = response.replace("<tangent>", "🌀 *tangent* ")
-        response = response.replace("</tangent>", "")
-        
-        # Remove excessive whitespace
-        response = " ".join(response.split())
-        
-        # Ensure it ends properly
-        if response and not response.endswith(('.', '!', '?', '...', '*')):
-            response += "."
-        
-        return response
+    # Add conversation history
+    conversation = system_prompt
+    for user_msg, creed_msg in history[-4:]:  # Keep recent context
+        if user_msg and creed_msg:  # Only add complete exchanges
+            conversation += f"Human: {user_msg}\n"
+            conversation += f"Creed: {creed_msg}\n"
     
-    def creed_wisdom_tool(self, topic: str = "life") -> str:
-        """MCP tool: Get Creed's wisdom on a topic"""
-        if not self.model_loaded:
-            return "🧠 Creed's consciousness is still loading..."
-        
-        prompt = f"Give me your wisdom about {topic}."
-        
-        # Generate a one-shot response
-        final_response = ""
-        for response in self.generate_response(prompt, []):
-            final_response = response
-        
-        return final_response
+    # Add current message
+    conversation += f"Human: {message}\n"
+    conversation += "Creed:"
     
-    def cleanup_gpu_memory(self):
-        """Clean up GPU memory if using CUDA"""
-        if self.device == "cuda" and torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            print(f"🧹 GPU Memory cleaned. Current: {torch.cuda.memory_allocated() // 1024**2} MB")
+    return conversation
+
+def _clean_response(self, response: str) -> str:
+    """Clean up the model response and format custom tokens"""
     
-    def creed_story_tool(self, situation: str = "mysterious") -> str:
-        """MCP tool: Get a Creed story"""
-        if not self.model_loaded:
-            return "🧠 Creed's consciousness is still loading..."
-        
-        prompt = f"Tell me a {situation} story from your past."
-        
-        # Generate a one-shot response
-        final_response = ""
-        for response in self.generate_response(prompt, []):
-            final_response = response
-        
-        return final_response
+    # Remove common artifacts
+    response = response.replace("Human:", "").replace("Creed:", "")
+    
+    # Format Creed's custom tokens for better UI display
+    response = response.replace("<thinking>", "🤔 *thinking* ")
+    response = response.replace("</thinking>", "")
+    response = response.replace("<conspiracy>", "🕵️ *conspiracy mode* ")
+    response = response.replace("</conspiracy>", "")
+    response = response.replace("<tangent>", "🌀 *tangent* ")
+    response = response.replace("</tangent>", "")
+    
+    # Remove excessive whitespace
+    response = " ".join(response.split())
+    
+    # Ensure it ends properly
+    if response and not response.endswith(('.', '!', '?', '...', '*')):
+        response += "."
+    
+    return response
 
-def main():
-    """Initialize and launch the real Creed AI with modern styling"""
-    
-    print("🎸 Initializing REAL Creed Bratton AI...")
-    print("📡 Loading Mark's trained model: phxdev/creed-qwen-0.5b-lora")
-    
-    # Initialize Creed AI
-    creed_ai = CreedBrattonAI()
-    
-    # Test the model with a simple input
-    if creed_ai.model_loaded:
-        print("🧪 Testing model with simple input...")
-        test_response = creed_ai.generate_response_gpu("Hello, test.")
-        print(f"🧪 Test response: {test_response}")
-        
-        if "Woah:" in test_response or len(test_response) > 100 and any(ord(c) > 127 for c in test_response):
-            print("❌ Model is outputting garbage! Trying fallback...")
-            # Force fallback to base model
-            creed_ai.model_loaded = False
-            creed_ai.load_model()
-    
-    if SPACES_AVAILABLE:
-        gpu_placeholder()
-        print("✅ Spaces GPU compatibility enabled")
-    
-    # Memory status if GPU available
-    if torch.cuda.is_available() and creed_ai.model_loaded:
-        print(f"🎯 Model device verification: {next(creed_ai.model.parameters()).device}")
-        print(f"🔥 Final GPU Memory: {torch.cuda.memory_allocated() // 1024**2} MB allocated")
-        print(f"📊 GPU Memory Reserved: {torch.cuda.memory_reserved() // 1024**2} MB reserved")
-    
-    # Modern glassmorphism CSS
-    modern_css = """
-    /* Creed AI - Modern Glassmorphism Design */
-    :root {
-        --primary-gradient: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-        --secondary-gradient: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
-        --glass-bg: rgba(255, 255, 255, 0.08);
-        --glass-border: rgba(255, 255, 255, 0.18);
-        --text-primary: #ffffff;
-        --text-secondary: rgba(255, 255, 255, 0.8);
-        --accent-purple: #8b5cf6;
-        --accent-blue: #3b82f6;
-        --shadow-glow: 0 8px 32px rgba(139, 92, 246, 0.3);
-    }
+def creed_wisdom_tool(self, topic: str = "life") -> str:
+    """MCP tool: Get Creed's wisdom on a topic"""
+    if not self.model_loaded:
+        return "🧠 Creed's consciousness is still loading..."
+    
+    prompt = f"Give me your wisdom about {topic}."
+    
+    # Generate a one-shot response
+    final_response = ""
+    for response in self.generate_response(prompt, []):
+        final_response = response
+    
+    return final_response
+
+def cleanup_gpu_memory(self):
+    """Clean up GPU memory if using CUDA"""
+    if self.device == "cuda" and torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        print(f"🧹 GPU Memory cleaned. Current: {torch.cuda.memory_allocated() // 1024**2} MB")
+
+def creed_story_tool(self, situation: str = "mysterious") -> str:
+    """MCP tool: Get a Creed story"""
+    if not self.model_loaded:
+        return "🧠 Creed's consciousness is still loading..."
+    
+    prompt = f"Tell me a {situation} story from your past."
+    
+    # Generate a one-shot response
+    final_response = ""
+    for response in self.generate_response(prompt, []):
+        final_response = response
+    
+    return final_response
 
-    /* Main container with animated background */
-    .gradio-container {
-        min-height: 100vh !important;
-        background: var(--primary-gradient) !important;
-        background-attachment: fixed !important;
-        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
-        color: var(--text-primary) !important;
-        padding: 20px !important;
-        position: relative !important;
-        overflow-x: hidden !important;
-    }
+def main():
+"""Initialize and launch the real Creed AI with modern styling"""
 
-    .gradio-container::before {
-        content: '';
-        position: fixed;
-        top: 0;
-        left: 0;
-        width: 100%;
-        height: 100%;
-        background: 
-            radial-gradient(circle at 20% 80%, rgba(139, 92, 246, 0.3) 0%, transparent 50%),
-            radial-gradient(circle at 80% 20%, rgba(59, 130, 246, 0.3) 0%, transparent 50%),
-            radial-gradient(circle at 40% 40%, rgba(167, 139, 250, 0.2) 0%, transparent 50%);
-        pointer-events: none;
-        z-index: -1;
-    }
+print("🎸 Initializing REAL Creed Bratton AI...")
+print("📡 Loading Mark's trained model: phxdev/creed-qwen-0.5b-lora")
 
-    /* Floating particles animation */
-    .gradio-container::after {
-        content: '';
-        position: fixed;
-        top: 0;
-        left: 0;
-        width: 100%;
-        height: 100%;
-        background-image: 
-            radial-gradient(2px 2px at 20px 30px, rgba(255, 255, 255, 0.3), transparent),
-            radial-gradient(2px 2px at 40px 70px, rgba(139, 92, 246, 0.4), transparent),
-            radial-gradient(1px 1px at 90px 40px, rgba(59, 130, 246, 0.3), transparent);
-        background-size: 120px 120px;
-        animation: float 20s ease-in-out infinite;
-        pointer-events: none;
-        z-index: -1;
-    }
+# Initialize Creed AI
+creed_ai = CreedBrattonAI()
 
-    @keyframes float {
-        0%, 100% { transform: translateY(0px) rotate(0deg); }
-        50% { transform: translateY(-20px) rotate(180deg); }
+# Test the model with a simple input
+if creed_ai.model_loaded:
+    print("🧪 Testing model with simple input...")
+    test_response = creed_ai.generate_response_gpu("Hello, test.")
+    print(f"🧪 Test response: {test_response}")
+    
+    if "Woah:" in test_response or len(test_response) > 100 and any(ord(c) > 127 for c in test_response):
+        print("❌ Model is outputting garbage! Trying fallback...")
+        # Force fallback to base model
+        creed_ai.model_loaded = False
+        creed_ai.load_model()
+
+if SPACES_AVAILABLE:
+    gpu_placeholder()
+    print("✅ Spaces GPU compatibility enabled")
+
+# Memory status if GPU available
+if torch.cuda.is_available() and creed_ai.model_loaded:
+    print(f"🎯 Model device verification: {next(creed_ai.model.parameters()).device}")
+    print(f"🔥 Final GPU Memory: {torch.cuda.memory_allocated() // 1024**2} MB allocated")
+    print(f"📊 GPU Memory Reserved: {torch.cuda.memory_reserved() // 1024**2} MB reserved")
+
+# Modern glassmorphism CSS
+modern_css = """
+/* Creed AI - Modern Glassmorphism Design */
+:root {
+    --primary-gradient: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    --secondary-gradient: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
+    --glass-bg: rgba(255, 255, 255, 0.08);
+    --glass-border: rgba(255, 255, 255, 0.18);
+    --text-primary: #ffffff;
+    --text-secondary: rgba(255, 255, 255, 0.8);
+    --accent-purple: #8b5cf6;
+    --accent-blue: #3b82f6;
+    --shadow-glow: 0 8px 32px rgba(139, 92, 246, 0.3);
+}
+
+/* Main container with animated background */
+.gradio-container {
+    min-height: 100vh !important;
+    background: var(--primary-gradient) !important;
+    background-attachment: fixed !important;
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
+    color: var(--text-primary) !important;
+    padding: 20px !important;
+    position: relative !important;
+    overflow-x: hidden !important;
+}
+
+.gradio-container::before {
+    content: '';
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: 
+        radial-gradient(circle at 20% 80%, rgba(139, 92, 246, 0.3) 0%, transparent 50%),
+        radial-gradient(circle at 80% 20%, rgba(59, 130, 246, 0.3) 0%, transparent 50%),
+        radial-gradient(circle at 40% 40%, rgba(167, 139, 250, 0.2) 0%, transparent 50%);
+    pointer-events: none;
+    z-index: -1;
+}
+
+/* Floating particles animation */
+.gradio-container::after {
+    content: '';
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background-image: 
+        radial-gradient(2px 2px at 20px 30px, rgba(255, 255, 255, 0.3), transparent),
+        radial-gradient(2px 2px at 40px 70px, rgba(139, 92, 246, 0.4), transparent),
+        radial-gradient(1px 1px at 90px 40px, rgba(59, 130, 246, 0.3), transparent);
+    background-size: 120px 120px;
+    animation: float 20s ease-in-out infinite;
+    pointer-events: none;
+    z-index: -1;
+}
+
+@keyframes float {
+    0%, 100% { transform: translateY(0px) rotate(0deg); }
+    50% { transform: translateY(-20px) rotate(180deg); }
+}
+
+/* Header styling */
+.header {
+    background: var(--glass-bg) !important;
+    backdrop-filter: blur(20px) !important;
+    border: 1px solid var(--glass-border) !important;
+    border-radius: 24px !important;
+    padding: 32px !important;
+    margin-bottom: 24px !important;
+    text-align: center !important;
+    box-shadow: var(--shadow-glow) !important;
+    position: relative !important;
+    overflow: hidden !important;
+}
+
+.header::before {
+    content: '';
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    height: 1px;
+    background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.6), transparent);
+}
+
+.header h1 {
+    font-size: 36px !important;
+    font-weight: 700 !important;
+    background: linear-gradient(135deg, #ffffff 0%, #a855f7 50%, #3b82f6 100%) !important;
+    -webkit-background-clip: text !important;
+    -webkit-text-fill-color: transparent !important;
+    background-clip: text !important;
+    margin: 0 0 12px 0 !important;
+    text-shadow: 0 0 30px rgba(168, 85, 247, 0.5) !important;
+}
+
+.header p {
+    font-size: 16px !important;
+    color: var(--text-secondary) !important;
+    margin: 0 !important;
+    font-weight: 500 !important;
+}
+
+/* Info boxes with glass effect */
+.info-box {
+    background: rgba(255, 255, 255, 0.06) !important;
+    backdrop-filter: blur(16px) !important;
+    border: 1px solid rgba(255, 255, 255, 0.12) !important;
+    border-radius: 16px !important;
+    padding: 20px !important;
+    margin: 16px 0 !important;
+    color: var(--text-secondary) !important;
+    font-size: 14px !important;
+    line-height: 1.6 !important;
+    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1) !important;
+}
+
+.status-box {
+    background: rgba(16, 185, 129, 0.1) !important;
+    backdrop-filter: blur(16px) !important;
+    border: 1px solid rgba(16, 185, 129, 0.3) !important;
+    border-radius: 16px !important;
+    padding: 16px 20px !important;
+    margin: 16px 0 !important;
+    color: #10b981 !important;
+    font-weight: 600 !important;
+    box-shadow: 0 4px 20px rgba(16, 185, 129, 0.2) !important;
+}
+
+/* Chat area styling */
+.chat-area {
+    background: var(--glass-bg) !important;
+    backdrop-filter: blur(20px) !important;
+    border: 1px solid var(--glass-border) !important;
+    border-radius: 20px !important;
+    margin: 16px 0 !important;
+    overflow: hidden !important;
+    box-shadow: var(--shadow-glow) !important;
+}
+
+/* Tools section */
+.tools-area {
+    background: var(--glass-bg) !important;
+    backdrop-filter: blur(20px) !important;
+    border: 1px solid var(--glass-border) !important;
+    border-radius: 20px !important;
+    padding: 28px !important;
+    margin: 24px 0 !important;
+    box-shadow: var(--shadow-glow) !important;
+}
+
+.tools-title {
+    font-size: 22px !important;
+    font-weight: 600 !important;
+    color: var(--text-primary) !important;
+    margin: 0 0 20px 0 !important;
+    padding-bottom: 12px !important;
+    border-bottom: 1px solid rgba(255, 255, 255, 0.2) !important;
+    background: linear-gradient(135deg, #ffffff 0%, #a855f7 100%) !important;
+    -webkit-background-clip: text !important;
+    -webkit-text-fill-color: transparent !important;
+}
+
+/* Form elements */
+.gradio-textbox input,
+.gradio-textbox textarea {
+    background: rgba(255, 255, 255, 0.08) !important;
+    backdrop-filter: blur(10px) !important;
+    border: 1px solid rgba(255, 255, 255, 0.16) !important;
+    color: var(--text-primary) !important;
+    border-radius: 12px !important;
+    padding: 12px 16px !important;
+    transition: all 0.3s ease !important;
+    font-size: 14px !important;
+}
+
+.gradio-textbox input:focus,
+.gradio-textbox textarea:focus {
+    border-color: var(--accent-purple) !important;
+    outline: none !important;
+    box-shadow: 0 0 0 2px rgba(139, 92, 246, 0.3) !important;
+    background: rgba(255, 255, 255, 0.12) !important;
+}
+
+.gradio-textbox input::placeholder,
+.gradio-textbox textarea::placeholder {
+    color: rgba(255, 255, 255, 0.5) !important;
+}
+
+/* Labels */
+.gradio-container label {
+    color: var(--text-secondary) !important;
+    font-weight: 500 !important;
+    font-size: 14px !important;
+    margin-bottom: 6px !important;
+    display: block !important;
+}
+
+/* Buttons */
+.gradio-container button {
+    background: linear-gradient(135deg, var(--accent-purple) 0%, var(--accent-blue) 100%) !important;
+    color: var(--text-primary) !important;
+    border: none !important;
+    border-radius: 12px !important;
+    padding: 12px 24px !important;
+    font-weight: 600 !important;
+    cursor: pointer !important;
+    transition: all 0.3s ease !important;
+    box-shadow: 0 4px 15px rgba(139, 92, 246, 0.4) !important;
+    backdrop-filter: blur(10px) !important;
+    min-height: 44px !important;
+    display: flex !important;
+    align-items: center !important;
+    justify-content: center !important;
+}
+
+.gradio-container button:hover {
+    transform: translateY(-2px) !important;
+    box-shadow: 0 8px 25px rgba(139, 92, 246, 0.6) !important;
+    background: linear-gradient(135deg, #9333ea 0%, #2563eb 100%) !important;
+}
+
+.gradio-container button:active {
+    transform: translateY(0px) !important;
+}
+
+/* Send button specific styling */
+.gradio-container .gr-button {
+    background: linear-gradient(135deg, var(--accent-purple) 0%, var(--accent-blue) 100%) !important;
+    border: 1px solid rgba(255, 255, 255, 0.2) !important;
+    color: white !important;
+    font-weight: 600 !important;
+    text-transform: none !important;
+    letter-spacing: 0.5px !important;
+}
+
+/* Chatbot specific styling */
+.gradio-chatbot {
+    background: transparent !important;
+    border: none !important;
+}
+
+/* Footer */
+.footer {
+    text-align: center !important;
+    padding: 28px !important;
+    color: var(--text-secondary) !important;
+    background: var(--glass-bg) !important;
+    backdrop-filter: blur(20px) !important;
+    border: 1px solid var(--glass-border) !important;
+    border-radius: 20px !important;
+    margin-top: 32px !important;
+    box-shadow: var(--shadow-glow) !important;
+}
+
+/* Scrollbar styling */
+::-webkit-scrollbar {
+    width: 8px;
+}
+
+::-webkit-scrollbar-track {
+    background: rgba(255, 255, 255, 0.05);
+    border-radius: 4px;
+}
+
+::-webkit-scrollbar-thumb {
+    background: linear-gradient(135deg, var(--accent-purple), var(--accent-blue));
+    border-radius: 4px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: linear-gradient(135deg, #9333ea, #2563eb);
+}
+
+/* Responsive design */
+@media (max-width: 768px) {
+    .gradio-container {
+        padding: 12px !important;
     }
-
-    /* Header styling */
+    
     .header {
-        background: var(--glass-bg) !important;
-        backdrop-filter: blur(20px) !important;
-        border: 1px solid var(--glass-border) !important;
-        border-radius: 24px !important;
-        padding: 32px !important;
-        margin-bottom: 24px !important;
-        text-align: center !important;
-        box-shadow: var(--shadow-glow) !important;
-        position: relative !important;
-        overflow: hidden !important;
-    }
-
-    .header::before {
-        content: '';
-        position: absolute;
-        top: 0;
-        left: 0;
-        right: 0;
-        height: 1px;
-        background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.6), transparent);
+        padding: 20px !important;
+        border-radius: 16px !important;
     }
-
+    
     .header h1 {
-        font-size: 36px !important;
-        font-weight: 700 !important;
-        background: linear-gradient(135deg, #ffffff 0%, #a855f7 50%, #3b82f6 100%) !important;
-        -webkit-background-clip: text !important;
-        -webkit-text-fill-color: transparent !important;
-        background-clip: text !important;
-        margin: 0 0 12px 0 !important;
-        text-shadow: 0 0 30px rgba(168, 85, 247, 0.5) !important;
+        font-size: 28px !important;
     }
-
-    .header p {
-        font-size: 16px !important;
-        color: var(--text-secondary) !important;
-        margin: 0 !important;
-        font-weight: 500 !important;
-    }
-
-    /* Info boxes with glass effect */
-    .info-box {
-        background: rgba(255, 255, 255, 0.06) !important;
-        backdrop-filter: blur(16px) !important;
-        border: 1px solid rgba(255, 255, 255, 0.12) !important;
+    
+    .tools-area,
+    .chat-area {
         border-radius: 16px !important;
         padding: 20px !important;
-        margin: 16px 0 !important;
-        color: var(--text-secondary) !important;
-        font-size: 14px !important;
-        line-height: 1.6 !important;
-        box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1) !important;
-    }
-
-    .status-box {
-        background: rgba(16, 185, 129, 0.1) !important;
-        backdrop-filter: blur(16px) !important;
-        border: 1px solid rgba(16, 185, 129, 0.3) !important;
-        border-radius: 16px !important;
-        padding: 16px 20px !important;
-        margin: 16px 0 !important;
-        color: #10b981 !important;
-        font-weight: 600 !important;
-        box-shadow: 0 4px 20px rgba(16, 185, 129, 0.2) !important;
-    }
-
-    /* Chat area styling */
-    .chat-area {
-        background: var(--glass-bg) !important;
-        backdrop-filter: blur(20px) !important;
-        border: 1px solid var(--glass-border) !important;
-        border-radius: 20px !important;
-        margin: 16px 0 !important;
-        overflow: hidden !important;
-        box-shadow: var(--shadow-glow) !important;
-    }
-
-    /* Tools section */
-    .tools-area {
-        background: var(--glass-bg) !important;
-        backdrop-filter: blur(20px) !important;
-        border: 1px solid var(--glass-border) !important;
-        border-radius: 20px !important;
-        padding: 28px !important;
-        margin: 24px 0 !important;
-        box-shadow: var(--shadow-glow) !important;
-    }
-
-    .tools-title {
-        font-size: 22px !important;
-        font-weight: 600 !important;
-        color: var(--text-primary) !important;
-        margin: 0 0 20px 0 !important;
-        padding-bottom: 12px !important;
-        border-bottom: 1px solid rgba(255, 255, 255, 0.2) !important;
-        background: linear-gradient(135deg, #ffffff 0%, #a855f7 100%) !important;
-        -webkit-background-clip: text !important;
-        -webkit-text-fill-color: transparent !important;
-    }
-
-    /* Form elements */
-    .gradio-textbox input,
-    .gradio-textbox textarea {
-        background: rgba(255, 255, 255, 0.08) !important;
-        backdrop-filter: blur(10px) !important;
-        border: 1px solid rgba(255, 255, 255, 0.16) !important;
-        color: var(--text-primary) !important;
-        border-radius: 12px !important;
-        padding: 12px 16px !important;
-        transition: all 0.3s ease !important;
-        font-size: 14px !important;
-    }
-
-    .gradio-textbox input:focus,
-    .gradio-textbox textarea:focus {
-        border-color: var(--accent-purple) !important;
-        outline: none !important;
-        box-shadow: 0 0 0 2px rgba(139, 92, 246, 0.3) !important;
-        background: rgba(255, 255, 255, 0.12) !important;
-    }
-
-    .gradio-textbox input::placeholder,
-    .gradio-textbox textarea::placeholder {
-        color: rgba(255, 255, 255, 0.5) !important;
-    }
-
-    /* Labels */
-    .gradio-container label {
-        color: var(--text-secondary) !important;
-        font-weight: 500 !important;
-        font-size: 14px !important;
-        margin-bottom: 6px !important;
-        display: block !important;
-    }
-
-    /* Buttons */
-    .gradio-container button {
-        background: linear-gradient(135deg, var(--accent-purple) 0%, var(--accent-blue) 100%) !important;
-        color: var(--text-primary) !important;
-        border: none !important;
-        border-radius: 12px !important;
-        padding: 12px 24px !important;
-        font-weight: 600 !important;
-        cursor: pointer !important;
-        transition: all 0.3s ease !important;
-        box-shadow: 0 4px 15px rgba(139, 92, 246, 0.4) !important;
-        backdrop-filter: blur(10px) !important;
-        min-height: 44px !important;
-        display: flex !important;
-        align-items: center !important;
-        justify-content: center !important;
-    }
-
-    .gradio-container button:hover {
-        transform: translateY(-2px) !important;
-        box-shadow: 0 8px 25px rgba(139, 92, 246, 0.6) !important;
-        background: linear-gradient(135deg, #9333ea 0%, #2563eb 100%) !important;
-    }
-
-    .gradio-container button:active {
-        transform: translateY(0px) !important;
-    }
-
-    /* Send button specific styling */
-    .gradio-container .gr-button {
-        background: linear-gradient(135deg, var(--accent-purple) 0%, var(--accent-blue) 100%) !important;
-        border: 1px solid rgba(255, 255, 255, 0.2) !important;
-        color: white !important;
-        font-weight: 600 !important;
-        text-transform: none !important;
-        letter-spacing: 0.5px !important;
-    }
-
-    /* Chatbot specific styling */
-    .gradio-chatbot {
-        background: transparent !important;
-        border: none !important;
-    }
-
-    /* Footer */
-    .footer {
-        text-align: center !important;
-        padding: 28px !important;
-        color: var(--text-secondary) !important;
-        background: var(--glass-bg) !important;
-        backdrop-filter: blur(20px) !important;
-        border: 1px solid var(--glass-border) !important;
-        border-radius: 20px !important;
-        margin-top: 32px !important;
-        box-shadow: var(--shadow-glow) !important;
-    }
-
-    /* Scrollbar styling */
-    ::-webkit-scrollbar {
-        width: 8px;
-    }
-
-    ::-webkit-scrollbar-track {
-        background: rgba(255, 255, 255, 0.05);
-        border-radius: 4px;
-    }
-
-    ::-webkit-scrollbar-thumb {
-        background: linear-gradient(135deg, var(--accent-purple), var(--accent-blue));
-        border-radius: 4px;
-    }
-
-    ::-webkit-scrollbar-thumb:hover {
-        background: linear-gradient(135deg, #9333ea, #2563eb);
     }
+}
+"""
 
-    /* Responsive design */
-    @media (max-width: 768px) {
-        .gradio-container {
-            padding: 12px !important;
-        }
-        
-        .header {
-            padding: 20px !important;
-            border-radius: 16px !important;
-        }
-        
-        .header h1 {
-            font-size: 28px !important;
-        }
-        
-        .tools-area,
-        .chat-area {
-            border-radius: 16px !important;
-            padding: 20px !important;
-        }
-    }
-    """
-    
-    # Create wrapper function for proper chat handling
-    def respond(message, history):
-        """Response handler for Gradio messages format"""
-        for response_chunk in creed_ai.generate_response(message, history):
-            # Update the history with the current response in messages format
-            updated_history = history + [
-                {"role": "user", "content": message},
-                {"role": "assistant", "content": response_chunk}
-            ]
-            yield "", updated_history
-    
-    # Create the interface with modern theme
-    with gr.Blocks(
-        title="🎸 Creed Bratton AI",
-        css=modern_css,
-        theme=gr.themes.Base()  # Use base theme for better CSS control
-    ) as demo:
-        
-        # Modern header
-        gr.HTML(f"""
-        <div class="header">
-            <h1>🎸 Creed Bratton AI</h1>
-            <p>Powered by phxdev/creed-qwen-0.5b-lora • Running on {'🚀 GPU' if creed_ai.device == 'cuda' else '🖥️ CPU'}</p>
-        </div>
-        """)
-        
-        # Model info with glass styling
+# Create wrapper function for proper chat handling
+def respond(message, history):
+    """Response handler for Gradio messages format"""
+    for response_chunk in creed_ai.generate_response(message, history):
+        # Update the history with the current response in messages format
+        updated_history = history + [
+            {"role": "user", "content": message},
+            {"role": "assistant", "content": response_chunk}
+        ]
+        yield "", updated_history
+
+# Create the interface with modern theme
+with gr.Blocks(
+    title="🎸 Creed Bratton AI",
+    css=modern_css,
+    theme=gr.themes.Base()  # Use base theme for better CSS control
+) as demo:
+    
+    # Modern header
+    gr.HTML(f"""
+    <div class="header">
+        <h1>🎸 Creed Bratton AI</h1>
+        <p>Powered by phxdev/creed-qwen-0.5b-lora • Running on {'🚀 GPU' if creed_ai.device == 'cuda' else '🖥️ CPU'}</p>
+    </div>
+    """)
+    
+    # Model info with glass styling
+    gr.HTML("""
+    <div class="info-box">
+        <strong>Model:</strong> phxdev/creed-qwen-0.5b-lora<br>
+        <strong>Base:</strong> Qwen 0.5B + LoRA fine-tuning<br>
+        <strong>Tokens:</strong> &lt;thinking&gt;, &lt;conspiracy&gt;, &lt;tangent&gt;
+    </div>
+    """)
+    
+    # MCP status
+    if os.environ.get('GRADIO_MCP_ENABLED'):
         gr.HTML("""
-        <div class="info-box">
-            <strong>Model:</strong> phxdev/creed-qwen-0.5b-lora<br>
-            <strong>Base:</strong> Qwen 0.5B + LoRA fine-tuning<br>
-            <strong>Tokens:</strong> &lt;thinking&gt;, &lt;conspiracy&gt;, &lt;tangent&gt;
+        <div class="status-box">
+            ✓ MCP Server Active • Available as tool for Claude Desktop
         </div>
         """)
-        
-        # MCP status
-        if os.environ.get('GRADIO_MCP_ENABLED'):
-            gr.HTML("""
-            <div class="status-box">
-                ✓ MCP Server Active • Available as tool for Claude Desktop
-            </div>
-            """)
-        
-        # Main chat interface with glass styling
-        with gr.Row(elem_classes="chat-area"):
-            chatbot = gr.Chatbot(
-                type='messages',  # Use messages format (modern)
-                height=550,
-                show_copy_button=True,
-                show_share_button=False,
-                avatar_images=["👤", "🎸"],
-                bubble_full_width=False,
-                show_label=False,
-                placeholder="🎸 Creed is ready...",
-                container=False
+    
+    # Main chat interface with glass styling
+    with gr.Row(elem_classes="chat-area"):
+        chatbot = gr.Chatbot(
+            type='messages',  # Use messages format (modern)
+            height=550,
+            show_copy_button=True,
+            show_share_button=False,
+            avatar_images=["👤", "🎸"],
+            bubble_full_width=False,
+            show_label=False,
+            placeholder="🎸 Creed is ready...",
+            container=False
+        )
+    
+    # Input with explicit send button
+    with gr.Row():
+        with gr.Column(scale=7):
+            msg = gr.Textbox(
+                placeholder="Ask Creed anything...",
+                container=False,
+                submit_btn=False,  # Disable built-in submit
+                stop_btn=False
             )
+        with gr.Column(scale=1, min_width=100):
+            send_btn = gr.Button("Send", variant="primary", size="lg")
+    
+    # Wire up the chat - both Enter key and Send button
+    msg.submit(
+        respond,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        show_progress="hidden"
+    )
+    
+    send_btn.click(
+        respond,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        show_progress="hidden"
+    )
+    
+    # MCP Tools section with glass styling
+    with gr.Row(elem_classes="tools-area"):
+        gr.HTML('<div class="tools-title">🛠️ MCP Tools</div>')
         
-        # Input with explicit send button
         with gr.Row():
-            with gr.Column(scale=7):
-                msg = gr.Textbox(
-                    placeholder="Ask Creed anything...",
-                    container=False,
-                    submit_btn=False,  # Disable built-in submit
-                    stop_btn=False
+            with gr.Column():
+                wisdom_topic = gr.Textbox(
+                    label="Wisdom Topic",
+                    placeholder="life, business, relationships..."
                 )
-            with gr.Column(scale=1, min_width=100):
-                send_btn = gr.Button("Send", variant="primary", size="lg")
-        
-        # Wire up the chat - both Enter key and Send button
-        msg.submit(
-            respond,
-            inputs=[msg, chatbot],
-            outputs=[msg, chatbot],
-            show_progress="hidden"
-        )
-        
-        send_btn.click(
-            respond,
-            inputs=[msg, chatbot],
-            outputs=[msg, chatbot],
-            show_progress="hidden"
-        )
-        
-        # MCP Tools section with glass styling
-        with gr.Row(elem_classes="tools-area"):
-            gr.HTML('<div class="tools-title">🛠️ MCP Tools</div>')
-            
-            with gr.Row():
-                with gr.Column():
-                    wisdom_topic = gr.Textbox(
-                        label="Wisdom Topic",
-                        placeholder="life, business, relationships..."
-                    )
-                    wisdom_output = gr.Textbox(
-                        label="Creed's Response", 
-                        interactive=False,
-                        lines=3
-                    )
-                    wisdom_btn = gr.Button("Ask Creed", variant="primary")
-                    
-                with gr.Column():
-                    story_situation = gr.Textbox(
-                        label="Story Request",
-                        placeholder="Tell me about..."
-                    )
-                    story_output = gr.Textbox(
-                        label="Creed's Story",
-                        interactive=False,
-                        lines=3
-                    )
-                    story_btn = gr.Button("Get Story", variant="primary")
-        
-        # Wire up the tools
-        wisdom_btn.click(
-            creed_ai.creed_wisdom_tool,
-            inputs=[wisdom_topic],
-            outputs=[wisdom_output]
-        )
-        
-        story_btn.click(
-            creed_ai.creed_story_tool,
-            inputs=[story_situation],
-            outputs=[story_output]
-        )
-        
-        # Modern footer
-        gr.HTML("""
-        <div class="footer">
-            <strong>Creed Bratton AI</strong><br>
-            Model: phxdev/creed-qwen-0.5b-lora • Trained by Mark Scott<br>
-            <em>"Sometimes a guy's gotta ride the bull, am I right?"</em>
-        </div>
-        """)
+                wisdom_output = gr.Textbox(
+                    label="Creed's Response", 
+                    interactive=False,
+                    lines=3
+                )
+                wisdom_btn = gr.Button("Ask Creed", variant="primary")
+                
+            with gr.Column():
+                story_situation = gr.Textbox(
+                    label="Story Request",
+                    placeholder="Tell me about..."
+                )
+                story_output = gr.Textbox(
+                    label="Creed's Story",
+                    interactive=False,
+                    lines=3
+                )
+                story_btn = gr.Button("Get Story", variant="primary")
     
-    # Launch with modern styling and public sharing
-    print("🚀 Launching Real Creed AI with modern glassmorphism design...")
+    # Wire up the tools
+    wisdom_btn.click(
+        creed_ai.creed_wisdom_tool,
+        inputs=[wisdom_topic],
+        outputs=[wisdom_output]
+    )
     
-    demo.launch(
-        ssr_mode=False,
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True,  # Create public link
-        show_error=True
+    story_btn.click(
+        creed_ai.creed_story_tool,
+        inputs=[story_situation],
+        outputs=[story_output]
     )
+    
+    # Modern footer
+    gr.HTML("""
+    <div class="footer">
+        <strong>Creed Bratton AI</strong><br>
+        Model: phxdev/creed-qwen-0.5b-lora • Trained by Mark Scott<br>
+        <em>"Sometimes a guy's gotta ride the bull, am I right?"</em>
+    </div>
+    """)
+
+# Launch with modern styling and public sharing
+print("🚀 Launching Real Creed AI with modern glassmorphism design...")
+
+demo.launch(
+    ssr_mode=False,
+    server_name="0.0.0.0",
+    server_port=7860,
+    share=True,  # Create public link
+    show_error=True
+)
 
 if __name__ == "__main__":
-    main()
+main()