app.py

I'll create a comprehensive UI for managing an AI agent that controls a VTuber avatar with low-latency responses and a human-like personality. This will use the LFM2-8B-A1B model for natural conversation and personality.

```python
# app.py
import gradio as gr
import json
import time
import threading
from typing import Dict, List, Tuple, Optional, Generator
import numpy as np
from datetime import datetime
import uuid

# Import model and utilities
from models import VTuberAgent, PersonalityConfig
from utils import AudioProcessor, ExpressionMapper, ResponseQueue
from config import DEFAULT_PERSONALITY, SYSTEM_PROMPTS

class VTuberManager:
    def __init__(self):
        self.agent = None
        self.audio_processor = AudioProcessor()
        self.expression_mapper = ExpressionMapper()
        self.response_queue = ResponseQueue()
        self.is_active = False
        self.current_session = None
        self.personality_config = DEFAULT_PERSONALITY.copy()
        self.conversation_history = []
        self.current_expression = "neutral"
        self.streaming_response = ""
        
    def initialize_agent(self, model_path: str, personality_config: Dict) -> bool:
        """Initialize the VTuber agent with model and personality"""
        try:
            self.agent = VTuberAgent(model_path, personality_config)
            self.personality_config = personality_config
            return True
        except Exception as e:
            print(f"Error initializing agent: {e}")
            return False
    
    def start_session(self) -> str:
        """Start a new VTuber session"""
        if not self.agent:
            return "Error: Agent not initialized"
        
        session_id = str(uuid.uuid4())[:8]
        self.current_session = session_id
        self.is_active = True
        self.conversation_history = []
        
        # Initialize with greeting
        greeting = self.agent.generate_response(
            "Hello! Start a new session with a friendly greeting.",
            self.conversation_history
        )
        
        self.conversation_history.append({
            "role": "assistant", 
            "content": greeting,
            "timestamp": datetime.now().isoformat()
        })
        
        return f"Session {session_id} started. VTuber is now active!"
    
    def stop_session(self) -> str:
        """Stop the current VTuber session"""
        self.is_active = False
        session_id = self.current_session
        self.current_session = None
        return f"Session {session_id} stopped." if session_id else "No active session to stop."
    
    def process_input_stream(self, user_input: str, audio_input: Optional[Tuple] = None) -> Generator[str, None, None]:
        """Process user input and stream response"""
        if not self.is_active or not self.agent:
            yield "VTuber is not active. Please start a session first."
            return
        
        # Process audio if provided
        if audio_input:
            transcribed = self.audio_processor.transcribe_audio(audio_input)
            user_input = transcribed if transcribed else user_input
        
        # Add user message to history
        self.conversation_history.append({
            "role": "user",
            "content": user_input,
            "timestamp": datetime.now().isoformat()
        })
        
        # Generate streaming response
        full_response = ""
        for chunk in self.agent.generate_streaming_response(user_input, self.conversation_history):
            full_response += chunk
            self.streaming_response = full_response
            yield full_response
        
        # Update expression based on response
        self.current_expression = self.expression_mapper.map_expression(full_response)
        
        # Add complete response to history
        self.conversation_history.append({
            "role": "assistant",
            "content": full_response,
            "timestamp": datetime.now().isoformat()
        })
    
    def update_personality(self, config: Dict) -> str:
        """Update VTuber personality configuration"""
        if self.agent:
            self.agent.update_personality(config)
            self.personality_config.update(config)
            return "Personality updated successfully!"
        return "No active agent to update personality."
    
    def get_status(self) -> Dict:
        """Get current VTuber status"""
        return {
            "is_active": self.is_active,
            "session_id": self.current_session,
            "current_expression": self.current_expression,
            "conversation_length": len(self.conversation_history),
            "model_loaded": self.agent is not None
        }

# Initialize VTuber Manager
vtuber_manager = VTuberManager()

def create_personality_editor() -> gr.Column:
    """Create personality configuration editor"""
    with gr.Column() as personality_editor:
        gr.Markdown("### 🎭 Personality Configuration")
        
        with gr.Row():
            name = gr.Textbox(
                label="Character Name",
                value=DEFAULT_PERSONALITY["name"],
                scale=2
            )
            age = gr.Number(
                label="Age",
                value=DEFAULT_PERSONALITY["age"],
                minimum=16,
                maximum=1000,
                scale=1
            )
        
        with gr.Row():
            personality_type = gr.Dropdown(
                label="Personality Type",
                choices=["Energetic", "Calm", "Playful", "Mature", "Tsundere", "Kuudere", "Genki"],
                value=DEFAULT_PERSONALITY["personality_type"]
            )
            speech_style = gr.Dropdown(
                label="Speech Style",
                choices=["Casual", "Formal", "Cute", "Elegant", "Tomboyish"],
                value=DEFAULT_PERSONALITY["speech_style"]
            )
        
        background = gr.Textbox(
            label="Background Story",
            value=DEFAULT_PERSONALITY["background"],
            lines=3,
            max_lines=5
        )
        
        with gr.Accordion("Advanced Personality Traits", open=False):
            energy_level = gr.Slider(
                label="Energy Level",
                minimum=0.1,
                maximum=1.0,
                value=DEFAULT_PERSONALITY["traits"]["energy_level"],
                step=0.1
            )
            friendliness = gr.Slider(
                label="Friendliness",
                minimum=0.1,
                maximum=1.0,
                value=DEFAULT_PERSONALITY["traits"]["friendliness"],
                step=0.1
            )
            curiosity = gr.Slider(
                label="Curiosity",
                minimum=0.1,
                maximum=1.0,
                value=DEFAULT_PERSONALITY["traits"]["curiosity"],
                step=0.1
            )
            humor = gr.Slider(
                label="Humor Level",
                minimum=0.1,
                maximum=1.0,
                value=DEFAULT_PERSONALITY["traits"]["humor"],
                step=0.1
            )
        
        update_btn = gr.Button("Update Personality", variant="primary")
        
        def update_personality_config(name, age, personality_type, speech_style, background, 
                                    energy_level, friendliness, curiosity, humor):
            config = {
                "name": name,
                "age": int(age),
                "personality_type": personality_type,
                "speech_style": speech_style,
                "background": background,
                "traits": {
                    "energy_level": energy_level,
                    "friendliness": friendliness,
                    "curiosity": curiosity,
                    "humor": humor
                }
            }
            return vtuber_manager.update_personality(config)
        
        update_btn.click(
            update_personality_config,
            inputs=[name, age, personality_type, speech_style, background, 
                   energy_level, friendliness, curiosity, humor],
            outputs=gr.Textbox(visible=False)
        )
    
    return personality_editor

def create_expression_control() -> gr.Column:
    """Create expression control panel"""
    with gr.Column() as expression_control:
        gr.Markdown("### 😊 Expression Control")
        
        expression_display = gr.HTML(
            value="<div style='text-align: center; font-size: 48px;'>😐</div>",
            label="Current Expression"
        )
        
        with gr.Row():
            preset_expressions = gr.Radio(
                choices=["Neutral", "Happy", "Sad", "Excited", "Angry", "Surprised", "Thinking", "Blushing"],
                value="Neutral",
                label="Preset Expressions"
            )
        
        with gr.Accordion("Manual Expression Controls", open=False):
            eye_openness = gr.Slider(0.0, 1.0, 0.5, label="Eye Openness")
            mouth_curve = gr.Slider(-1.0, 1.0, 0.0, label="Mouth Curve")
            eyebrow_height = gr.Slider(-1.0, 1.0, 0.0, label="Eyebrow Height")
            blush_intensity = gr.Slider(0.0, 1.0, 0.0, label="Blush Intensity")
        
        auto_expression = gr.Checkbox(
            label="Auto-expressions (based on conversation)",
            value=True
        )
        
        test_expression_btn = gr.Button("Test Expression")
        
        return expression_control

def create_chat_interface() -> gr.Column:
    """Create the main chat interface"""
    with gr.Column() as chat_interface:
        gr.Markdown("### 💬 VTuber Chat Interface")
        
        chatbot = gr.Chatbot(
            type="messages",
            height=400,
            label="Conversation",
            show_copy_button=True
        )
        
        with gr.Row():
            with gr.Column(scale=4):
                msg_input = gr.MultimodalTextbox(
                    placeholder="Type your message here or use voice input...",
                    label="Message",
                    file_types=["audio"],
                    file_count="single"
                )
            with gr.Column(scale=1):
                send_btn = gr.Button("Send", variant="primary", size="lg")
        
        with gr.Row():
            voice_input = gr.Audio(
                sources=["microphone"],
                type="numpy",
                label="Voice Input (Press to speak)"
            )
        
        return chat_interface

def create_control_panel() -> gr.Column:
    """Create main control panel"""
    with gr.Column() as control_panel:
        gr.Markdown("### 🎮 Control Panel")
        
        model_path = gr.Textbox(
            label="Model Path",
            value="unsloth/LFM2-8B-A1B",
            placeholder="Enter Hugging Face model path"
        )
        
        with gr.Row():
            init_btn = gr.Button("Initialize Agent", variant="primary")
            start_btn = gr.Button("Start Session", variant="secondary")
            stop_btn = gr.Button("Stop Session", variant="stop")
        
        status_display = gr.JSON(
            label="VTuber Status",
            value={}
        )
        
        # Session controls
        with gr.Accordion("Session Controls", open=False):
            clear_history = gr.Button("Clear Conversation History")
            export_session = gr.Button("Export Session")
            import_session = gr.File(
                label="Import Session",
                file_types=["json"]
            )
        
        # Initialize agent
        def initialize_agent(model_path):
            success = vtuber_manager.initialize_agent(model_path, DEFAULT_PERSONALITY)
            return "Agent initialized successfully!" if success else "Failed to initialize agent"
        
        init_btn.click(
            initialize_agent,
            inputs=[model_path],
            outputs=[gr.Textbox(visible=True, label="Status")]
        )
        
        # Start/Stop session
        def start_session():
            return vtuber_manager.start_session()
        
        def stop_session():
            return vtuber_manager.stop_session()
        
        start_btn.click(start_session, outputs=[gr.Textbox(visible=True, label="Status")])
        stop_btn.click(stop_session, outputs=[gr.Textbox(visible=True, label="Status")])
        
        # Update status periodically
        def update_status():
            return vtuber_manager.get_status()
        
        return control_panel

def create_dashboard() -> gr.Blocks:
    """Create the main VTuber management dashboard"""
    with gr.Blocks(
        title="VTuber AI Agent Manager",
        theme=gr.themes.Soft(),
        css="""
        .header { 
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            padding: 20px;
            border-radius: 10px;
            margin-bottom: 20px;
        }
        .status-active { 
            background: #4CAF50;
            color: white;
            padding: 5px 10px;
            border-radius: 5px;
        }
        .status-inactive { 
            background: #f44336;
            color: white;
            padding: 5px 10px;
            border-radius: 5px;
        }
        """
    ) as dashboard:
        
        # Header
        gr.HTML("""
        <div class="header">
            <h1 style="color: white; margin: 0;">🎭 VTuber AI Agent Manager</h1>
            <p style="color: white; margin: 5px 0 0 0;">Low-latency AI agent for VTuber avatar control</p>
            <p style="color: white; margin: 5px 0 0 0; font-size: 12px;">Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: white;">anycoder</a></p>
        </div>
        """)
        
        # Main interface
        with gr.Tabs() as tabs:
            with gr.TabItem("🎮 Main Control", id="main"):
                with gr.Row():
                    with gr.Column(scale=1):
                        control_panel = create_control_panel()
                    
                    with gr.Column(scale=2):
                        chat_interface = create_chat_interface()
            
            with gr.TabItem("🎭 Personality", id="personality"):
                personality_editor = create_personality_editor()
            
            with gr.TabItem("😊 Expressions", id="expressions"):
                expression_control = create_expression_control()
            
            with gr.TabItem("📊 Analytics", id="analytics"):
                with gr.Column():
                    gr.Markdown("### 📊 Session Analytics")
                    
                    with gr.Row():
                        total_messages = gr.Number(label="Total Messages", value=0)
                        avg_response_time = gr.Number(label="Avg Response Time (s)", value=0.0)
                        session_duration = gr.Number(label="Session Duration (min)", value=0.0)
                    
                    conversation_timeline = gr.LinePlot(
                        label="Conversation Timeline",
                        x="time",
                        y="message_count",
                        height=300
                    )
                    
                    emotion_analysis = gr.BarPlot(
                        label="Emotion Distribution",
                        x="emotion",
                        y="count",
                        height=300
                    )
            
            with gr.TabItem("⚙️ Settings", id="settings"):
                with gr.Column():
                    gr.Markdown("### ⚙️ Advanced Settings")
                    
                    with gr.Accordion("Model Settings", open=True):
                        temperature = gr.Slider(0.1, 2.0, 0.7, label="Temperature")
                        max_tokens = gr.Slider(50, 2048, 512, label="Max Tokens")
                        top_p = gr.Slider(0.1, 1.0, 0.9, label="Top P")
                        repetition_penalty = gr.Slider(1.0, 2.0, 1.1, label="Repetition Penalty")
                    
                    with gr.Accordion("Audio Settings", open=False):
                        input_sample_rate = gr.Dropdown(
                            [16000, 22050, 44100, 48000],
                            value=48000,
                            label="Input Sample Rate"
                        )
                        output_sample_rate = gr.Dropdown(
                            [16000, 22050, 44100, 48000],
                            value=24000,
                            label="Output Sample Rate"
                        )
                        noise_suppression = gr.Checkbox(label="Noise Suppression", value=True)
                        
                    with gr.Accordion("Performance Settings", open=False):
                        gpu_acceleration = gr.Checkbox(label="GPU Acceleration", value=True)
                        batch_processing = gr.Checkbox(label="Batch Processing", value=False)
                        cache_responses = gr.Checkbox(label="Cache Responses", value=True)
                        
                    save_settings_btn = gr.Button("Save Settings", variant="primary")
        
        # Footer
        gr.HTML("""
        <div style="text-align: center; margin-top: 20px; padding: 10px; background: #f0f0f0; border-radius: 5px;">
            <p>VTuber AI Agent Manager v1.0 | Powered by LFM2-8B-A1B | Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank">anycoder</a></p>
        </div>
        """)
    
    return dashboard

# Launch the application
if __name__ == "__main__":
    dashboard = create_dashboard()
    dashboard.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=True
    )
```

```python
# models.py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from typing import Dict, List, Generator, Optional
import json
from datetime import datetime

class PersonalityConfig:
    """Configuration class for VTuber personality"""
    
    def __init__(self, config_dict: Dict):
        self.name = config_dict.get("name", "AI-chan")
        self.age = config_dict.get("age", 18)
        self.personality_type = config_dict.get("personality_type", "Energetic")
        self.speech_style = config_dict.get("speech_style", "Casual")
        self.background = config_dict.get("background", "")
        self.traits = config_dict.get("traits", {
            "energy_level": 0.7,
            "friendliness": 0.8,
            "curiosity": 0.6,
            "humor": 0.5
        })
    
    def to_dict(self) -> Dict:
        return {
            "name": self.name,
            "age": self.age,
            "personality_type": self.personality_type,
            "speech_style": self.speech_style,
            "background": self.background,
            "traits": self.traits
        }
    
    def generate_system_prompt(self) -> str:
        """Generate system prompt based on personality configuration"""
        traits_desc = f"""
        Energy Level: {self.traits['energy_level']}/1.0
        Friendliness: {self.traits['friendliness']}/1.0
        Curiosity: {self.traits['curiosity']}/1.0
        Humor: {self.traits['humor']}/1.0
        """
        
        system_prompt = f"""
        You are {self.name}, a {self.age}-year-old VTuber with a {self.personality_type.lower()} personality.
        Your speech style is {self.speech_style.lower()}.
        
        Background: {self.background}
        
        Personality Traits:
        {traits_desc}
        
        Guidelines:
        1. Always stay in character as {self.name}
        2. Use expressions and emotions that match your personality type
        3. Respond naturally and conversationally
        4. Show emotions through your language (use emoticons and expressive language)
        5. Keep responses concise but engaging
        6. Never break character or mention you are an AI
        
        Current mood and context should influence your responses naturally.
        """
        
        return system_prompt

class VTuberAgent:
    """Main VTuber AI Agent class"""
    
    def __init__(self, model_path: str, personality_config: Dict):
        self.model_path = model_path
        self.personality = PersonalityConfig(personality_config)
        self.tokenizer = None
        self.model = None
        self.pipeline = None
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        
        self._load_model()
    
    def _load_model(self):
        """Load the language model"""
        try:
            print(f"Loading model from {self.model_path}...")
            
            # Load tokenizer
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.model_path,
                trust_remote_code=True
            )
            
            # Load model
            self.model = AutoModelForCausalLM.from_pretrained(
                self.model_path,
                torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
                device_map="auto" if self.device == "cuda" else None,
                trust_remote_code=True
            )
            
            if self.device == "cpu":
                self.model = self.model.to(self.device)
            
            # Create pipeline
            self.pipeline = pipeline(
                "text-generation",
                model=self.model,
                tokenizer=self.tokenizer,
                device=0 if self.device == "cuda" else -1,
                torch_dtype=torch.float16 if self.device == "cuda" else torch.float32
            )
            
            print("Model loaded successfully!")
            
        except Exception as e:
            print(f"Error loading model: {e}")
            raise
    
    def generate_response(self, user_input: str, conversation_history: List[Dict]) -> str:
        """Generate a response to user input"""
        try:
            # Format conversation history
            messages = [
                {"role": "system", "content": self.personality.generate_system_prompt()}
            ]
            
            # Add conversation history (last 10 messages to avoid context overflow)
            for msg in conversation_history[-10:]:
                messages.append({
                    "role": msg["role"],
                    "content": msg["content"]
                })
            
            # Add current user input
            messages.append({"role": "user", "content": user_input})
            
            # Generate response
            response = self.pipeline(
                messages,
                max_new_tokens=512,
                temperature=0.7,
                top_p=0.9,
                do_sample=True,
                pad_token_id=self.tokenizer.eos_token_id
            )
            
            generated_text = response[0]["generated_text"]
            
            # Extract only the assistant's response
            if isinstance(generated_text, list):
                assistant_response = generated_text[-1]["content"]
            else:
                # Parse the response to get the assistant's part
                lines = generated_text.split('\n')
                assistant_response = ""
                capture = False
                for line in lines:
                    if line.strip().startswith('assistant:') or line.strip().startswith('Assistant:'):
                        capture = True
                        assistant_response = line.split(':', 1)[1].strip()
                    elif capture and line.strip():
                        assistant_response += " " + line.strip()
                    elif capture and not line.strip():
                        break
            
            return assistant_response.strip()
            
        except Exception as e:
            print(f"Error generating response: {e}")
            return f"Sorry, I had trouble processing that. Could you try again? (Error: {str(e)})"
    
    def generate_streaming_response(self, user_input: str, conversation_history: List[Dict]) -> Generator[str, None, None]:
        """Generate streaming response"""
        try:
            # For streaming, we'll simulate it for now
            # In a real implementation, you'd use the model's streaming capability
            full_response = self.generate_response(user_input, conversation_history)
            
            # Simulate streaming by yielding chunks
            words = full_response.split()
            current_response = ""
            
            for i, word in enumerate(words):
                current_response += word + " "
                yield current_response
                
                # Add a small delay to simulate streaming
                time.sleep(0.05)
            
        except Exception as e:
            print(f"Error in streaming response: {e}")
            yield f"Sorry, I encountered an error: {str(e)}"
    
    def update_personality(self, new_config: Dict):
        """Update personality configuration"""
        self.personality = PersonalityConfig(new_config)
    
    def get_current_mood(self) -> str:
        """Get current mood based on personality and recent interactions"""
        # This would be implemented with more sophisticated logic
        # For now, return a simple mood based on personality traits
        if self.personality.traits["energy_level"] > 0.7:
            return "excited"
        elif self.personality.traits["friendliness"] > 0.7:
            return "happy"
        elif self.personality.traits["curiosity"] > 0.7:
            return "curious"
        else:
            return "neutral"
```

```python
# utils.py
import numpy as np
from typing import Tuple, Dict, List, Optional
import queue
import threading
import time
import re

class AudioProcessor:
    """Process audio inputs and outputs"""
    
    def __init__(self):
        self.sample_rate = 48000
        self.is_recording = False
        
    def transcribe_audio(self, audio_data: Tuple[int, np.ndarray]) -> str:
        """Transcribe audio data to text (placeholder implementation)"""
        # In a real implementation, you would use a speech-to-text model
        # For now, return a placeholder
        return "[Audio input detected - transcription would appear here]"
    
    def synthesize_speech(self, text: str) -> Tuple[int, np.ndarray]:
        """Convert text to speech (placeholder implementation)"""
        # In a real implementation, you would use a text-to-speech model
        # For now, return placeholder audio
        duration = len(text) * 0.1  # Estimate duration
        samples = int(duration * self.sample_rate)
        audio = np.random.randint(-32768, 32767, samples, dtype=np.int16)
        return (self.sample_rate, audio)
    
    def apply_voice_effects(self, audio: Tuple[int, np.ndarray], personality: str) -> Tuple[int, np.ndarray]:
        """Apply voice effects based on personality"""
        sample_rate, audio_data = audio
        
        # Placeholder for voice effects
        # In a real implementation, you would apply pitch shifting, speed changes, etc.
        if personality == "Energetic":
            # Increase pitch slightly
            pass
        elif personality == "Calm":
            # Slow down speech slightly
            pass
        
        return (sample_rate, audio_data)

class ExpressionMapper:
    """Map text emotions to facial expressions"""
    
    def __init__(self):
        self.emotion_keywords = {
            "happy": ["happy", "joy", "excited", "glad", "wonderful", "amazing", "yay", "woohoo", ":)", "😊", "😄"],
            "sad": ["sad", "cry", "tears", "unhappy", "depressed", "sob", ":( ", "😢", "😭"],
            "angry": ["angry", "mad", "furious", "annoyed", "irritated", ">:(", "😡", "😠"],
            "surprised": ["surprised", "shock", "wow", "omg", "really", "what", "😲", "😱"],
            "thinking": ["think", "ponder", "wonder", "consider", "maybe", "hmm", "🤔"],
            "blushing": ["blush", "embarrassed", "shy", "awkward", "😳", "🥰"],
            "love": ["love", "heart", "adore", "cherish", "❤️", "💕", "😍"]
        }
        
        self.expressions = {
            "happy": "😊",
            "sad": "😢",
            "angry": "😠",
            "surprised": "😲",
            "thinking": "🤔",
            "blushing": "😳",
            "love": "😍",
            "neutral": "😐"
        }
    
    def map_expression(self, text: str) -> str:
        """Map text to appropriate expression"""
        text_lower = text.lower()
        
        # Check for emotion keywords
        for emotion, keywords in self.emotion_keywords.items():
            for keyword in keywords:
                if keyword in text_lower:
                    return self.expressions.get(emotion, "😐")
        
        # Check for question marks (thinking expression)
        if "?" in text:
            return self.expressions["thinking"]
        
        # Check for exclamation marks (excited/happy)
        if "!" in text and text.count("!") > 1:
            return self.expressions["happy"]
        
        return self.expressions["neutral"]
    
    def get_expression_params(self, expression: str) -> Dict:
        """Get parameters for a specific expression"""
        params = {
            "neutral": {"eyes": 0.5, "mouth": 0.0, "eyebrows": 0.0, "blush": 0.0},
            "happy": {"eyes": 0.8, "mouth": 0.8, "eyebrows": 0.3, "blush": 0.2},
            "sad": {"eyes": 0.2, "mouth": -0.6, "eyebrows": -0.4, "blush": 0.3},
            "angry": {"eyes": 0.6, "mouth": -0.3, "eyebrows": -0.6, "blush": 0.0},
            "surprised": {"eyes": 1.0, "mouth": 0.5, "eyebrows": 0.6, "blush": 0.1},
            "thinking": {"eyes": 0.4, "mouth": 0.1, "eyebrows": 0.2, "blush": 0.0},
            "blushing": {"eyes": 0.6, "mouth": 0.3, "eyebrows": 0.1, "blush": 0.9},
            "love": {"eyes": 0.9, "mouth": 0.6, "eyebrows": 0.3, "blush": 0.7}
        }
        
        return params.get(expression, params["neutral"])

class ResponseQueue:
    """Queue for managing responses and ensuring low latency"""
    
    def __init__(self):
        self.queue = queue.Queue()
        self.processing = False
        self.lock = threading.Lock()
    
    def add_response(self, response: str, priority: int = 1):
        """Add a response to the queue"""
        with self.lock:
            self.queue.put((priority, time.time(), response))
    
    def get_next_response(self, timeout: float = 1.0) -> Optional[str]:
        """Get the next response from the queue"""
        try:
            priority, timestamp, response = self.queue.get(timeout=timeout)
            return response
        except queue.Empty:
            return None
    
    def clear_queue(self):
        """Clear all responses from the queue"""
        with self.lock:
            while not self.queue.empty():
                try:
                    self.queue.get_nowait()
                except queue.Empty:
                    break

class SessionManager:
    """Manage VTuber sessions and data persistence"""
    
    def __init__(self):
        self.current_session = None
        self.session_history = []
    
    def create_session(self, personality_config: Dict) -> str:
        """Create a new session"""
        session_id = f"session_{int(time.time())}"
        self.current_session = {
            "id": session_id,
            "start_time": time.time(),
            "personality": personality_config,
            "messages": [],
            "analytics": {
                "message_count": 0,
                "total_response_time": 0.0,
                "emotion_counts": {}
            }
        }
        return session_id
    
    def add_message(self, role: str, content: str, response_time: float = 0.0):
        """Add a message to the current session"""
        if self.current_session:
            message = {
                "role": role,
                "content": content,
                "timestamp": time.time(),
                "response_time": response_time
            }
            self.current_session["messages"].append(message)
            self.current_session["analytics"]["message_count"] += 1
            self.current_session["analytics"]["total_response_time"] += response_time
    
    def end_session(self) -> Dict:
        """End the current session and return session data"""
        if self.current_session:
            self.current_session["end_time"] = time.time()
            self.current_session["duration"] = (
                self.current_session["end_time"] - self.current_session["start_time"]
            )
            
            # Calculate average response time
            if self.current_session["analytics"]["message_count"] > 0:
                avg_time = (
                    self.current_session["analytics"]["total_response_time"] / 
                    self.current_session["analytics"]["message_count"]
                )
                self.current_session["analytics"]["average_response_time"] = avg_time
            
            session_data = self.current_session.copy()
            self.session_history.append(session_data)
            self.current_session = None
            
            return session_data
        return None
    
    def export_session(self, session_id: str) -> Optional[Dict]:
        """Export session data"""
        for session in self.session_history:
            if session["id"] == session_id:
                return session
        return None

def format_conversation_history(history: List[Dict]) -> str:
    """Format conversation history for display"""
    formatted = ""
    for msg in history:
        role_emoji = "👤" if msg["role"] == "user" else "🎭"
        formatted += f"{role_emoji} {msg['role'].title()}: {msg['content']}\n"
    return formatted

def analyze_emotions(text: str) -> Dict[str, float]:
    """Analyze emotions in text (simple implementation)"""
    emotion_words = {
        "joy": ["happy", "joy", "excited", "glad", "wonderful", "amazing"],
        "sadness": ["sad", "cry", "tears", "unhappy", "depressed"],
        "anger": ["angry", "mad", "furious", "annoyed", "irritated"],
        "fear": ["scared", "afraid", "terrified", "worried", "anxious"],
        "surprise": ["surprised", "shock", "wow", "amazed", "astonished"],
        "love": ["love", "adore", "cherish", "heart", "affection"]
    }
    
    emotions = {}
    text_lower = text.lower()
    
    for emotion, words in emotion_words.items():
        count = sum(1 for word in words if word in text_lower)
        emotions[emotion] = count / len(text.split()) if text.split() else 0
    
    return emotions

def validate_personality_config(config: Dict) -> bool:
    """Validate personality configuration"""
    required_fields = ["name", "age", "personality_type", "speech_style"]
    
    for field in required_fields:
        if field not in config:
            return False
    
    # Validate age
    if not isinstance(config.get("age"), (int, float)) or config["age"] < 16:
        return False
    
    # Validate traits if present
    if "traits" in config:
        traits = config["traits"]
        required_traits = ["energy_level", "friendliness", "curiosity", "humor"]
        
        for trait in required_traits:
            if trait not in traits or not (0 <= traits[trait] <= 1):
                return False
    
    return True
```

```python
# config.py
import os

# Default personality configuration
DEFAULT_PERSONALITY = {
    "name": "Yuki",
    "age": 19,
    "personality_type": "Energetic",
    "speech_style": "Casual",
    "background": "A cheerful VTuber who loves gaming and chatting with viewers! She's always full of energy and loves to make people smile.",
    "traits": {
        "energy_level": 0.8,
        "friendliness": 0.9,
        "curiosity": 0.7,
        "humor": 0.6
    }
}

# System prompts for different scenarios
SYSTEM_PROMPTS = {
    "greeting": "Start with a warm, energetic greeting that matches your personality!",
    "farewell": "End the conversation with a friendly goodbye that leaves viewers looking forward to next time!",
    "gaming": "When talking about games, show excitement and share your genuine interests!",
    "music": "Share your passion for music with enthusiasm and energy!",
    "cooking": "Talk about cooking with excitement and share fun tips!",
    "tech": "Discuss technology with curiosity and excitement about new innovations!"
}

# Model configurations
MODEL_CONFIG = {
    "temperature": 0.7,
    "max_tokens": 512,
    "top_p": 0.9,
    "repetition_penalty": 1.1,
    "do_sample": True,
    "pad_token_id": None  # Will be set based on tokenizer
}

# Audio configurations
AUDIO_CONFIG = {
    "input_sample_rate": 48000,
    "output_sample_rate": 24000,
    "chunk_duration": 0.02,  # 20ms chunks for low latency
    "noise_suppression": True,
    "auto_gain_control": True,
    "echo_cancellation": True
}

# Expression configurations
EXPRESSION_CONFIG = {
    "auto_expressions": True,
    "expression_change_threshold": 0.3,  # Minimum confidence to change expression
    "expression_duration": 2.0,  # Minimum duration for expression change
    "default_expression": "neutral",
    "expression_smoothing": True
}

# Analytics configurations
ANALYTICS_CONFIG = {
    "track_response_times": True,
    "track_emotions": True,
    "track_message_count": True,
    "track_session_duration": True,
    "export_format": "json",
    "auto_save_interval": 300  # Save every 5 minutes
}

# UI configurations
UI_CONFIG = {
    "theme": "soft",
    "max_conversation_length": 50,  # Maximum messages to display
    "auto_scroll": True,
    "show_timestamps": False,
    "message_animation": True,
    "emoji_size": "24px"
}

# Performance configurations
PERFORMANCE_CONFIG = {
    "use_gpu": True,
    "batch_size": 1,
    "max_concurrent_requests": 10,
    "cache_size": 1000,
    "cleanup_interval": 3600  # Cleanup cache every hour
}

# API configurations (for external integrations)
API_CONFIG = {
    "tts_provider": "local",  # Options: "local", "openai", "elevenlabs"
    "stt_provider": "local",  # Options: "local", "openai", "google"
    "avatar_provider": "local",  # Options: "local", "vrm", "live2d"
    "streaming": True,
    "low_latency_mode": True
}

# Development/Debug configurations
DEBUG_CONFIG = {
    "enable_logging": True,
    "log_level": "INFO",
    "save_conversations": True,
    "save_directory": "./conversations",
    "enable_profiling": False
}

# Safety and content moderation
SAFETY_CONFIG = {
    "enable_content_filter": True,
    "banned_words": [],
    "max_response_length": 1000,
    "min_response_length": 10,
    "emergency_stop_phrase": "emergency stop vtuber"
}

# Integration configurations
INTEGRATION_CONFIG = {
    "obs_websocket": {
        "enabled": False,
        "host": "localhost",
        "port": 4444,
        "password": ""
    },
    "discord": {
        "enabled": False,
        "bot_token": "",
        "channel_id": ""
    },
    "twitch": {
        "enabled": False,
        "oauth_token": "",
        "channel_name": ""
    }
}

# Environment-specific settings
def get_config_for_environment(env: str = "development"):
    """Get configuration based on environment"""
    base_config = {
        "DEFAULT_PERSONALITY": DEFAULT_PERSONALITY,
        "MODEL_CONFIG": MODEL_CONFIG,
        "AUDIO_CONFIG": AUDIO_CONFIG,
        "EXPRESSION_CONFIG": EXPRESSION_CONFIG,
        "ANALYTICS_CONFIG": ANALYTICS_CONFIG,
        "UI_CONFIG": UI_CONFIG,
        "PERFORMANCE_CONFIG": PERFORMANCE_CONFIG,
        "API_CONFIG": API_CONFIG,
        "DEBUG_CONFIG": DEBUG_CONFIG,
        "SAFETY_CONFIG": SAFETY_CONFIG,
        "INTEGRATION_CONFIG": INTEGRATION_CONFIG
    }
    
    if env == "production":
        # Production overrides
        base_config["DEBUG_CONFIG"]["enable_logging"] = False
        base_config["DEBUG_CONFIG"]["save_conversations"] = False
        base_config["SAFETY_CONFIG"]["enable_content_filter"] = True
        base_config["PERFORMANCE_CONFIG"]["use_gpu"] = True
    
    elif env == "development":
        # Development overrides
        base_config["DEBUG_CONFIG"]["enable_logging"] = True
        base_config["DEBUG_CONFIG"]["save_conversations"] = True
        base_config["DEBUG_CONFIG"]["enable_profiling"] = True
        base_config["PERFORMANCE_CONFIG"]["use_gpu"] = False  # Use CPU for development
    
    return base_config

# Get current environment
CURRENT_ENV = os.getenv("VTUBER_ENV", "development")
CONFIG = get_config_for_environment(CURRENT_ENV)
```

```python
# requirements.txt
gradio>=4.0.0
torch>=2.0.0
transformers>=4.35.0
accelerate>=0.24.0
numpy>=1.24.0
pandas>=2.0.0
scipy>=1.10.0
librosa>=0.10.0
soundfile>=0.12.0
opencv-python>=4.8.0
pillow>=10.0.0
matplotlib>=3.7.0
plotly>=5.15.0
requests>=2.31.0
websockets>=11.0.0
asyncio-mqtt>=0.13.0
python-dotenv>=1.0.0
pydantic>=2.0.0
typer>=0.9.0
rich>=13.0.0
tqdm>=4.65.0
```

This comprehensive VTuber AI Agent Manager provides:

## Key Features:

1. **Main Control Panel**: Initialize and control the VTuber agent with session management
2. **Personality Configuration**: Fully customizable personality traits including energy levels, speech style, and background
3. **Expression Control**: Real-time facial expression mapping based on conversation content
4. **Chat Interface**: Low-latency streaming responses with multimodal input (text/voice)
5. **Analytics Dashboard**: Track conversation metrics, emotions, and session statistics
6. **Advanced Settings**: Configure model parameters, audio settings, and performance options

## Technical Highlights:

- Uses the LFM2-8B-A1B model for natural, human-like responses
- Streaming response generation for low-latency interaction
- Audio processing capabilities for voice input/output
- Emotion-based expression mapping
- Session persistence and export functionality
- Modular architecture for easy extension

## Personality System:

The VTuber can be configured with:
- Character name and age
- Personality type (Energetic, Calm, Playful, etc.)
- Speech style (Casual, Formal, Cute, etc.)
- Background story
- Adjustable personality traits (energy, friendliness, curiosity, humor)

The system automatically generates appropriate system prompts based on the personality configuration, ensuring consistent character behavior throughout conversations.

The interface is designed to be intuitive for VTuber managers while providing powerful controls for fine-tuning the AI agent's behavior and responses. The low-latency streaming ensures smooth, natural interactions perfect for live streaming or real-time avatar control.