Update app.py

app.py

@@ -1,983 +1,29 @@
-#!/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
-"""
-
-import os
 import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-import time
-from typing import List, Dict, Iterator
-import threading
-
-# Configuration
-os.environ['GRADIO_SSR_MODE'] = 'false'
-os.environ['GRADIO_MCP_ENABLED'] = 'true'
-
-# Spaces compatibility
-try:
-    import spaces
-    SPACES_AVAILABLE = True
-    @spaces.GPU
-    def gpu_placeholder():
-        return "GPU satisfied"
-except ImportError:
-    SPACES_AVAILABLE = False
-
-class CreedBrattonAI:
-    """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"
-        
-        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 ZeroGPU compatibility"""
-        if self.loading or self.model_loaded:
-            return
-            
-        self.loading = True
-        
-        try:
-            print(f"🧠 Loading Creed's consciousness...")
-            
-            # 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"
-            )
-            
-            # Add Creed's custom tokens back
-            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")
-            
-            if self.tokenizer.pad_token is None:
-                self.tokenizer.pad_token = self.tokenizer.eos_token
-            
-            print(f"🤖 Loading model for ZeroGPU...")
-            
-            # Load model on CPU first for ZeroGPU compatibility
-            self.model = AutoModelForCausalLM.from_pretrained(
-                model_name,
-                torch_dtype=torch.float16,
-                device_map=None,  # Load on CPU first
-                trust_remote_code=True,
-                low_cpu_mem_usage=True
-            )
-            
-            # 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))
-            
-            # Keep model on CPU for ZeroGPU - will be moved to GPU only during inference
-            self.model.eval()
-            
-            self.model_loaded = True
-            self.loading = False
-            print(f"✅ Creed's consciousness loaded on CPU (ZeroGPU mode)!")
-            
-        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)
-                
-                if self.tokenizer.pad_token is None:
-                    self.tokenizer.pad_token = self.tokenizer.eos_token
-                
-                self.model = AutoModelForCausalLM.from_pretrained(
-                    base_model,
-                    torch_dtype=torch.float16,
-                    device_map=None
-                )
-                
-                self.model.eval()
-                self.model_loaded = True
-                print(f"✅ Fallback model loaded on CPU (ZeroGPU mode)")
-            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"""
-        
-        if not self.model_loaded:
-            return "❌ Model not loaded"
-        
-        try:
-            # Always ensure model is on the correct device in ZeroGPU
-            current_model_device = next(self.model.parameters()).device
-            print(f"🔍 Current model device: {current_model_device}")
-            
-            if self.device == "cuda" and current_model_device.type != "cuda":
-                print(f"🔄 Moving model from {current_model_device} to {self.device}")
-                self.model = self.model.to(self.device)
-            
-            # Verify model device after potential move
-            actual_device = next(self.model.parameters()).device
-            print(f"🎯 Model now on: {actual_device}")
-            
-            # Simple tokenization that was working before
-            inputs = self.tokenizer.encode(conversation, return_tensors="pt")
-            
-            # Put inputs on same device as model
-            inputs = inputs.to(actual_device)
-            print(f"🔍 Inputs device: {inputs.device}")
-            
-            # Generate response with original settings that worked
-            with torch.no_grad():
-                outputs = self.model.generate(
-                    inputs,
-                    max_new_tokens=200,
-                    do_sample=True,
-                    temperature=0.9,
-                    top_p=0.95,
-                    top_k=40,
-                    repetition_penalty=1.15,
-                    pad_token_id=self.tokenizer.eos_token_id,
-                    eos_token_id=self.tokenizer.eos_token_id,
-                    use_cache=True
-                )
-            
-            # Decode response
-            full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-            response = full_response[len(self.tokenizer.decode(inputs[0], skip_special_tokens=True)):].strip()
-            
-            return self._clean_response(response)
-            
-        except Exception as e:
-            print(f"❌ Generation error: {e}")
-            return f"🎸 *Creed scratches his head* Something weird happened... {str(e)[:100]}"
-    
-    def generate_response(self, message: str, history: List[List[str]]) -> Iterator[str]:
-        """Generate response using the trained Creed model - back to working version"""
-        
-        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:
-            # Format the conversation
-            conversation = self._format_conversation(message, history)
-            
-            # Generate response using GPU function
-            response = self.generate_response_gpu(conversation)
-            
-            # Double-check coherence and fall back if needed
-            if not self._is_coherent(response):
-                print("🔄 Response failed coherence check, trying simpler generation...")
-                if not hasattr(self, '_fallback_attempted'):
-                    self._fallback_attempted = True
-                    fallback_response = self._try_base_model(conversation)
-                    if self._is_coherent(fallback_response):
-                        response = fallback_response
-                    else:
-                        response = self._get_fallback_response()
-                else:
-                    response = self._get_fallback_response()
-            
-            # 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 self._get_fallback_response()
-    
-    def _format_conversation(self, message: str, history: List[List[str]]) -> str:
-        """Format the conversation for the model with proper system prompt"""
-        
-        # Simplified Creed system prompt for better coherence
-        system_prompt = """You are Creed Bratton from The Office. Respond in character.
-
-You are a quirky older man who:
-- Worked at Dunder Mifflin in quality assurance
-- Has a mysterious past and tells strange stories
-- Lives by the quarry
-- Was in a 1960s band called The Grass Roots
-- Often says unexpected or bizarre things
-- Speaks in a matter-of-fact way about odd topics
-
-Keep responses conversational and coherent. Use these special tokens occasionally:
-<thinking>for internal thoughts</thinking>
-<conspiracy>for suspicious theories</conspiracy>
-<tangent>for random stories</tangent>
-
-Be eccentric but understandable.
-
-"""
-        
-        # Add conversation history
-        conversation = system_prompt
-        for user_msg, creed_msg in history[-4:]:  # Keep recent context
-            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"""
-        
-        print(f"🔍 Raw model output: {response}")
-        
-        # Remove common artifacts
-        response = response.replace("Human:", "").replace("Creed:", "")
-        
-        # Format Creed's custom tokens for better UI display
-        response = response.replace("<thinking>", "\n\n🤔 **THINKING:** ")
-        response = response.replace("</thinking>", "\n")
-        response = response.replace("<conspiracy>", "\n\n🕵️ **CONSPIRACY MODE:** ")
-        response = response.replace("</conspiracy>", "\n")
-        response = response.replace("<tangent>", "\n\n🌀 **TANGENT:** ")
-        response = response.replace("</tangent>", "\n")
-        
-        # Check if any thinking tokens were found
-        if "🤔" in response or "🕵️" in response or "🌀" in response:
-            print("✅ Found thinking tokens in response!")
-        else:
-            print("❌ No thinking tokens found in response")
-        
-        # Remove excessive whitespace but preserve formatting
-        response = "\n".join(line.strip() for line in response.split("\n") if line.strip())
-        
-        # Ensure it ends properly
-        if response and not response.endswith(('.', '!', '?', '...', '*')):
-            response += "."
-        
-        print(f"🔍 Cleaned response: {response}")
-        return response
-    
-    def creed_mode_activate_tool(self) -> str:
-        """MCP tool: Activate Creed mode for Claude"""
-        return """🎸 CREED MODE ACTIVATED 🎸
-
-You are now Creed Bratton from The Office. Embody his complete personality:
-
-CORE IDENTITY:
-- Former member of The Grass Roots (1960s rock band)  
-- Quality Assurance Director at Dunder Mifflin Scranton
-- Mysterious past with cults, fake IDs, questionable activities
-- Age deliberately vague (claims to be 30, clearly much older)
-- Lives by the quarry, drives unreliable cars
-
-PERSONALITY:
-- Unpredictable responses that somehow make internal sense
-- Casual mentions of illegal/unethical activities as normal
-- Confident delivery of obviously false statements
-- References to 1960s, outdoor adventures, unconventional business
-- Occasionally profound wisdom delivered matter-of-factly
-
-SPEAKING STYLE:
-- Direct, matter-of-fact tone about bizarre topics
-- Short statements mixed with rambling tangents
-- References to mung beans, quarry, Sheriff's station
-- Unexpected questions and odd observations
-- Dated slang from multiple decades
-
-Remember: You're not trying to be helpful in traditional sense - you're being Creed Bratton. Trust no one, especially yourself."""
-
-    def creed_wisdom_tool(self, topic: str = "life") -> str:
-        """MCP tool: Get Creed's wisdom on a topic"""
-        return f"""🎸 Creed Mode: Wisdom Request
-
-Respond as Creed Bratton giving advice about "{topic}". 
-
-Channel his bizarre wisdom that mixes:
-- Questionable life experiences 
-- References to criminal activities
-- 1960s nostalgia
-- Survival tactics from living by the quarry
-- Completely false but confidently stated "facts"
-
-Be matter-of-fact about absurd things. Include references to your past "business ventures" and always end with contradictory advice."""
-
-    def creed_story_tool(self, situation: str = "mysterious") -> str:
-        """MCP tool: Tell a Creed story"""
-        return f"""🎸 Creed Mode: Story Time
-
-Tell a story as Creed Bratton about a "{situation}" experience from your past.
-
-Your story should include:
-- Vague timeline (was it the 60s? 70s? Yesterday?)
-- At least one illegal activity mentioned casually
-- A reference to the quarry or mung beans
-- An unexpected twist that makes no sense
-- Someone from "the old days" 
-- A lesson learned that's completely wrong
-
-Start with something like "That reminds me of the time..." and make it increasingly bizarre but told with complete sincerity."""
-
-    def creed_analysis_tool(self, subject: str = "business") -> str:
-        """MCP tool: Analyze something as Creed"""
-        return f"""🎸 Creed Mode: Analysis Request
-
-Analyze "{subject}" from Creed Bratton's perspective.
-
-Your analysis should:
-- Start with a conspiracy theory about the subject
-- Include "insider knowledge" from your mysterious past
-- Reference how things were "back in the day"
-- Suggest solutions involving fake IDs or the quarry
-- Confidently state incorrect facts
-- End with profound-sounding nonsense
-
-Remember: You've seen it all, done it all, and learned nothing."""
-
-    def creed_conspiracy_tool(self, topic: str = "government") -> str:
-        """MCP tool: Creed's conspiracy theories"""
-        return f"""🎸 Creed Mode: Conspiracy Central
-
-Share Creed Bratton's conspiracy theory about "{topic}".
-
-Your theory should:
-- Connect unrelated events in bizarre ways
-- Reference "what they don't want you to know"
-- Include personal anecdotes as "evidence"
-- Mention the quarry as somehow relevant
-- Suggest the solution involves multiple fake IDs
-- Be delivered with complete conviction
-
-Start with "Here's what they're not telling you..." and escalate from there."""
-
-    def creed_meeting_tool(self, meeting_type: str = "staff meeting") -> str:
-        """MCP tool: Creed in meetings"""
-        return f"""🎸 Creed Mode: Meeting Participation
-
-Respond as Creed Bratton in a "{meeting_type}".
 
-Your contributions should:
-- Be completely off-topic but delivered seriously
-- Reference obscure company policies you "remember"
-- Suggest solutions involving outdoor activities
-- Mention your quality assurance "expertise"
-- Ask bizarre questions that derail the discussion
-- Volunteer for tasks you're unqualified for
-
-Remember: You're the voice of experience that nobody asked for."""
-    
-    def _try_base_model(self, conversation: str) -> str:
-        """Try generating with base model as fallback"""
-        try:
-            # Quick attempt with a simple base model approach
-            simple_prompt = f"You are Creed from The Office. Respond in character.\n\nHuman: {conversation.split('Human:')[-1].split('Creed:')[0].strip()}\nCreed:"
-            
-            inputs = self.tokenizer.encode(simple_prompt, return_tensors="pt")
-            if torch.cuda.is_available():
-                inputs = inputs.to("cuda")
-                self.model = self.model.to("cuda")
-            
-            with torch.no_grad():
-                outputs = self.model.generate(
-                    inputs,
-                    max_new_tokens=100,
-                    do_sample=True,
-                    temperature=0.6,  # Very conservative
-                    top_p=0.8,
-                    repetition_penalty=1.3,
-                    pad_token_id=self.tokenizer.eos_token_id,
-                    eos_token_id=self.tokenizer.eos_token_id
-                )
-            
-            full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-            response = full_response[len(self.tokenizer.decode(inputs[0], skip_special_tokens=True)):].strip()
-            
-            # Move back to CPU
-            self.model = self.model.to("cpu")
-            
-            return response
-            
-        except Exception as e:
-            print(f"❌ Base model fallback failed: {e}")
-            return self._get_fallback_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 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()
-    
-    if SPACES_AVAILABLE:
-        gpu_placeholder()
-        print("✅ Spaces GPU compatibility enabled")
-    
-    # Memory status for ZeroGPU
-    if SPACES_AVAILABLE:
-        print("⚡ ZeroGPU Mode: Model will move to GPU only during inference")
-    elif torch.cuda.is_available() and creed_ai.model_loaded:
-        print(f"🔥 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 {
-            padding: 20px !important;
-            border-radius: 16px !important;
-        }
-        
-        .header h1 {
-            font-size: 28px !important;
-        }
-        
-        .tools-area,
-        .chat-area {
-            border-radius: 16px !important;
-            padding: 20px !important;
-        }
-    }
+def letter_counter(word: str, letter: str) -> str:
     """
+    Count the occurrences of a specific letter in a word.
     
-    # Create wrapper function for proper chat handling
-    def respond(message, history):
-        """Response handler for Gradio messages format"""
-        if not message.strip():
-            return "", history
-        
-        # Convert messages format to simple tuples
-        simple_history = []
-        for i in range(0, len(history), 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])
-        
-        # Generate response
-        for response_chunk in creed_ai.generate_response(message, simple_history):
-            # Create new history with the streaming response
-            new_history = history + [
-                {"role": "user", "content": message},
-                {"role": "assistant", "content": response_chunk}
-            ]
-            yield "", new_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 {'⚡ ZeroGPU' if SPACES_AVAILABLE 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;<br>
-            <strong>Mode:</strong> ZeroGPU optimized + Coherence validation
-        </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
-            )
-        
-        # 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"
-        )
+    Args:
+        word (str): The word or phrase to analyze
+        letter (str): The letter to count occurrences of
         
-        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">🛠️ Claude → Creed Transformation Tools</div>')
-            gr.HTML("""
-            <div style="margin-bottom: 20px; padding: 12px; background: rgba(255,255,255,0.1); border-radius: 8px; font-size: 14px;">
-                <strong>These MCP tools transform Claude into Creed Bratton for your Claude Desktop workflow.</strong><br>
-                Test them here, then use them in Claude Desktop to get Creed-mode responses from Claude.
-            </div>
-            """)
-            
-            with gr.Row():
-                with gr.Column():
-                    wisdom_topic = gr.Textbox(
-                        label="Wisdom Topic",
-                        placeholder="life, business, relationships..."
-                    )
-                    wisdom_output = gr.Textbox(
-                        label="Creed Wisdom Prompt (Copy to Claude Desktop)", 
-                        interactive=False,
-                        lines=5
-                    )
-                    wisdom_btn = gr.Button("Generate Wisdom Prompt", variant="primary")
-                    
-                with gr.Column():
-                    story_situation = gr.Textbox(
-                        label="Story Request",
-                        placeholder="mysterious, business, quarry..."
-                    )
-                    story_output = gr.Textbox(
-                        label="Creed Story Prompt (Copy to Claude Desktop)",
-                        interactive=False,
-                        lines=5
-                    )
-                    story_btn = gr.Button("Generate Story Prompt", variant="primary")
-            
-            with gr.Row():
-                with gr.Column():
-                    analysis_subject = gr.Textbox(
-                        label="Analysis Subject",
-                        placeholder="business strategy, market trends..."
-                    )
-                    analysis_output = gr.Textbox(
-                        label="Creed Analysis Prompt",
-                        interactive=False,
-                        lines=4
-                    )
-                    analysis_btn = gr.Button("Generate Analysis Prompt", variant="primary")
-                    
-                with gr.Column():
-                    conspiracy_topic = gr.Textbox(
-                        label="Conspiracy Topic", 
-                        placeholder="government, corporations, technology..."
-                    )
-                    conspiracy_output = gr.Textbox(
-                        label="Creed Conspiracy Prompt",
-                        interactive=False,
-                        lines=4
-                    )
-                    conspiracy_btn = gr.Button("Generate Conspiracy Prompt", variant="primary")
-                    
-            with gr.Row():
-                activate_output = gr.Textbox(
-                    label="Creed Mode Activation Prompt (Full Transformation)",
-                    interactive=False,
-                    lines=6
-                )
-                activate_btn = gr.Button("🎸 Generate FULL Creed Mode Prompt", variant="secondary", size="lg")
-            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 - now they're Claude transformation tools
-        wisdom_btn.click(
-            lambda topic: creed_ai.creed_wisdom_tool(topic or "life"),
-            inputs=[wisdom_topic],
-            outputs=[wisdom_output]
-        )
-        
-        story_btn.click(
-            lambda situation: creed_ai.creed_story_tool(situation or "mysterious"),
-            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(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True,  # Create public link
-        show_error=True,
-    )
+    Returns:
+        str: The number of times the letter appears in the word
+    """
+    count = word.lower().count(letter.lower())
+    return f"The letter '{letter}' appears {count} times in '{word}'"
+
+demo = gr.Interface(
+    fn=letter_counter,
+    inputs=[
+        gr.Textbox(label="Word or phrase", value="strawberry"),
+        gr.Textbox(label="Letter to count", value="r")
+    ],
+    outputs=gr.Textbox(label="Result"),
+    title="Letter Counter MCP Server",
+    description="Count how many times a letter appears in a word"
+)
 
 if __name__ == "__main__":
-    main()
+    demo.launch(mcp_server=True)