diff --git a/Backend/index.html b/Backend/index.html
index 59b4903..64b4b9c 100644
--- a/Backend/index.html
+++ b/Backend/index.html
@@ -3,7 +3,7 @@
 <head>
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Live Voice Assistant with CSM</title>
+    <title>Real-Time Voice Assistant</title>
     <script src="https://cdn.socket.io/4.6.0/socket.io.min.js"></script>
     <style>
         body {
@@ -89,33 +89,39 @@
             transition: all 0.3s ease;
         }
         
-        #talkButton {
+        #micButton {
             background-color: #4CAF50;
             color: white;
             width: 200px;
             box-shadow: 0 4px 8px rgba(76, 175, 80, 0.3);
         }
         
-        #talkButton:hover {
+        #micButton:hover {
             background-color: #45a049;
             transform: translateY(-2px);
         }
         
-        #talkButton.recording {
-            background-color: #f44336;
+        #micButton.listening {
+            background-color: #4CAF50;
+            box-shadow: 0 0 0 rgba(76, 175, 80, 0.4);
+            animation: pulse 1.5s infinite;
+        }
+        
+        #micButton.speaking {
+            background-color: #f44336;
+            box-shadow: 0 0 0 rgba(244, 67, 54, 0.4);
             animation: pulse 1.5s infinite;
-            box-shadow: 0 4px 8px rgba(244, 67, 54, 0.3);
         }
         
         @keyframes pulse {
             0% {
-                transform: scale(1);
+                box-shadow: 0 0 0 0 rgba(76, 175, 80, 0.4);
             }
-            50% {
-                transform: scale(1.05);
+            70% {
+                box-shadow: 0 0 0 15px rgba(76, 175, 80, 0);
             }
             100% {
-                transform: scale(1);
+                box-shadow: 0 0 0 0 rgba(76, 175, 80, 0);
             }
         }
         
@@ -126,45 +132,24 @@
             color: #657786;
         }
         
-        .hidden {
-            display: none;
-        }
-        
-        .transcription-info {
-            font-size: 0.8em;
-            color: #888;
-            margin-top: 4px;
-            text-align: right;
-        }
-        
-        .text-only-indicator {
-            font-size: 0.8em;
-            color: #e74c3c;
-            margin-top: 4px;
-            font-style: italic;
-        }
-        
-        .status-message {
-            text-align: center;
-            padding: 8px;
-            margin: 10px 0;
-            background-color: #f8f9fa;
-            border-radius: 5px;
-            color: #666;
-            font-size: 0.9em;
-        }
-        
-        /* Audio visualizer styles */
         .visualizer-container {
             width: 100%;
-            height: 120px;
+            height: 100px;
             margin: 15px 0;
             border-radius: 10px;
             overflow: hidden;
-            background-color: #000;
+            background-color: #1a1a1a;
             position: relative;
         }
         
+        .visualizer-container.user {
+            border: 2px solid #4CAF50;
+        }
+        
+        .visualizer-container.ai {
+            border: 2px solid #2196F3;
+        }
+        
         #visualizer {
             width: 100%;
             height: 100%;
@@ -176,122 +161,59 @@
             top: 10px;
             left: 10px;
             color: white;
-            font-size: 0.8em;
+            font-size: 0.9em;
             background-color: rgba(0, 0, 0, 0.5);
             padding: 4px 8px;
             border-radius: 4px;
         }
         
-        /* Real-time transcription */
-        .live-transcription {
-            position: absolute;
-            bottom: 10px;
-            left: 10px;
-            right: 10px;
-            color: white;
-            font-size: 0.9em;
-            background-color: rgba(0, 0, 0, 0.5);
-            padding: 8px;
-            border-radius: 4px;
-            text-align: center;
-            max-height: 60px;
-            overflow-y: auto;
-            font-style: italic;
-        }
-        
-        /* Wave animation for active speaker */
-        .speaking-wave {
+        .speech-indicator {
             display: inline-block;
-            margin-left: 5px;
+            width: 10px;
+            height: 10px;
+            border-radius: 50%;
+            margin-right: 5px;
             vertical-align: middle;
         }
         
-        .speaking-wave span {
-            display: inline-block;
-            width: 3px;
-            height: 12px;
-            margin: 0 1px;
-            background-color: currentColor;
-            border-radius: 1px;
-            animation: speakingWave 1s infinite ease-in-out;
-        }
-        
-        .speaking-wave span:nth-child(2) {
-            animation-delay: 0.1s;
-        }
-        
-        .speaking-wave span:nth-child(3) {
-            animation-delay: 0.2s;
-        }
-        
-        .speaking-wave span:nth-child(4) {
-            animation-delay: 0.3s;
-        }
-        
-        @keyframes speakingWave {
-            0%, 100% {
-                height: 4px;
-            }
-            50% {
-                height: 12px;
-            }
-        }
-        
-        /* Modern switch for visualizer toggle */
-        .switch-container {
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            margin-bottom: 10px;
-        }
-        
-        .switch {
-            position: relative;
-            display: inline-block;
-            width: 50px;
-            height: 24px;
-            margin-left: 10px;
-        }
-        
-        .switch input {
-            opacity: 0;
-            width: 0;
-            height: 0;
-        }
-        
-        .slider {
-            position: absolute;
-            cursor: pointer;
-            top: 0;
-            left: 0;
-            right: 0;
-            bottom: 0;
-            background-color: #ccc;
-            transition: .4s;
-            border-radius: 24px;
-        }
-        
-        .slider:before {
-            position: absolute;
-            content: "";
-            height: 16px;
-            width: 16px;
-            left: 4px;
-            bottom: 4px;
-            background-color: white;
-            transition: .4s;
-            border-radius: 50%;
-        }
-        
-        input:checked + .slider {
+        .user-speaking {
             background-color: #4CAF50;
+            animation: blink 1s infinite;
         }
         
-        input:checked + .slider:before {
-            transform: translateX(26px);
+        .ai-speaking {
+            background-color: #2196F3;
+            animation: blink 1s infinite;
+        }
+        
+        @keyframes blink {
+            0%, 100% { opacity: 1; }
+            50% { opacity: 0.4; }
+        }
+        
+        .connection-status {
+            padding: 6px 10px;
+            border-radius: 4px;
+            font-size: 0.8em;
+            margin-top: 10px;
+            text-align: center;
+        }
+        
+        .connection-status.connected {
+            background-color: #d4edda;
+            color: #155724;
+        }
+        
+        .connection-status.connecting {
+            background-color: #fff3cd;
+            color: #856404;
+        }
+        
+        .connection-status.disconnected {
+            background-color: #f8d7da;
+            color: #721c24;
         }
         
-        /* Toast notification for feedback */
         .toast {
             position: fixed;
             bottom: 20px;
@@ -328,38 +250,27 @@
     </style>
 </head>
 <body>
-    <h1>Live Voice Assistant with CSM</h1>
+    <h1>Real-Time Voice Assistant</h1>
     <div id="conversation"></div>
     
-    <div class="switch-container">
-        <span>Audio Visualizer</span>
-        <label class="switch">
-            <input type="checkbox" id="visualizerToggle" checked>
-            <span class="slider"></span>
-        </label>
-    </div>
-    
-    <div class="visualizer-container" id="visualizerContainer">
+    <div class="visualizer-container user" id="visualizerContainer">
         <canvas id="visualizer"></canvas>
         <div class="visualizer-label" id="visualizerLabel">Listening...</div>
-        <div class="live-transcription" id="liveTranscription"></div>
     </div>
     
     <div id="controls">
-        <button id="talkButton">Press to Talk</button>
+        <button id="micButton">Press to Talk</button>
     </div>
     
     <div id="status">Connecting to server...</div>
 
     <script>
         const socket = io();
-        const talkButton = document.getElementById('talkButton');
+        const micButton = document.getElementById('micButton');
         const conversation = document.getElementById('conversation');
         const status = document.getElementById('status');
-        const visualizerToggle = document.getElementById('visualizerToggle');
         const visualizerContainer = document.getElementById('visualizerContainer');
         const visualizerLabel = document.getElementById('visualizerLabel');
-        const liveTranscription = document.getElementById('liveTranscription');
         const canvas = document.getElementById('visualizer');
         const canvasCtx = canvas.getContext('2d');
         
@@ -386,19 +297,6 @@
             canvas.height = visualizerContainer.offsetHeight;
         }
         
-        // Handle visualizer toggle
-        visualizerToggle.addEventListener('change', function() {
-            visualizerActive = this.checked;
-            visualizerContainer.style.display = visualizerActive ? 'block' : 'none';
-            
-            if (!visualizerActive && visualizerAnimationId) {
-                cancelAnimationFrame(visualizerAnimationId);
-                visualizerAnimationId = null;
-            } else if (visualizerActive && audioAnalyser) {
-                drawVisualizer();
-            }
-        });
-        
         // Connect to server
         socket.on('connect', () => {
             status.textContent = 'Connected to server';
@@ -491,8 +389,8 @@
                     setupScriptProcessor(stream);
                 }
                 
-                // Setup talk button
-                talkButton.addEventListener('click', toggleTalking);
+                // Setup mic button
+                micButton.addEventListener('click', toggleTalking);
                 
                 // Setup keyboard shortcuts
                 document.addEventListener('keydown', (e) => {
@@ -618,8 +516,8 @@
             if (!sessionActive || isAITalking) return;
             
             isStreaming = true;
-            talkButton.classList.add('recording');
-            talkButton.textContent = 'Release to Stop';
+            micButton.classList.add('listening');
+            micButton.textContent = 'Release to Stop';
             status.textContent = 'Listening...';
             visualizerLabel.textContent = 'You are speaking...';
             
@@ -630,10 +528,6 @@
             
             // Tell server we're starting to speak
             socket.emit('start_speaking');
-            
-            // Clear previous transcriptions
-            liveTranscription.textContent = '';
-            liveTranscription.classList.remove('hidden');
         }
         
         // Stop talking to the assistant
@@ -641,15 +535,12 @@
             if (!isStreaming) return;
             
             isStreaming = false;
-            talkButton.classList.remove('recording');
-            talkButton.textContent = 'Press to Talk';
+            micButton.classList.remove('listening');
+            micButton.textContent = 'Press to Talk';
             status.textContent = 'Processing...';
             
             // Tell server we're done speaking
             socket.emit('stop_speaking');
-            
-            // Hide live transcription temporarily
-            liveTranscription.classList.add('hidden');
         }
         
         // Send audio chunk to server
@@ -702,8 +593,7 @@
         
         // Handle real-time transcription
         socket.on('live_transcription', (data) => {
-            liveTranscription.textContent = data.text || '...';
-            liveTranscription.classList.remove('hidden');
+            visualizerLabel.textContent = data.text || '...';
         });
         
         // Handle final transcription
@@ -749,8 +639,8 @@
                 speakingWave.remove();
             }
             
-            // Re-enable talk button if it was disabled
-            talkButton.disabled = false;
+            // Re-enable mic button if it was disabled
+            micButton.disabled = false;
         });
         
         // Legacy handler for text-only responses
diff --git a/Backend/server.py b/Backend/server.py
index 05abd99..bf365fa 100644
--- a/Backend/server.py
+++ b/Backend/server.py
@@ -8,14 +8,14 @@ import numpy as np
 from flask import Flask, render_template, request
 from flask_socketio import SocketIO, emit
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from collections import deque
+import threading
+import queue
 import requests
 import huggingface_hub
 from generator import load_csm_1b, Segment
-import threading
-import queue
-import asyncio
+from collections import deque
 import json
+import webrtcvad  # For voice activity detection
 
 # Configure environment with longer timeouts
 os.environ["HF_HUB_DOWNLOAD_TIMEOUT"] = "600"  # 10 minutes timeout for downloads
@@ -28,7 +28,7 @@ app = Flask(__name__)
 app.config['SECRET_KEY'] = 'your-secret-key'
 socketio = SocketIO(app, cors_allowed_origins="*", async_mode='eventlet')
 
-# Explicitly check for CUDA and print more detailed info
+# Explicitly check for CUDA and print detailed info
 print("\n=== CUDA Information ===")
 if torch.cuda.is_available():
     print(f"CUDA is available")
@@ -47,18 +47,9 @@ try:
 except:
     print("cuDNN is not available (libcudnn_ops_infer.so.8 not found)")
 
-# Check for other compute platforms
-if torch.backends.mps.is_available():
-    print("MPS (Apple Silicon) is available")
-else:
-    print("MPS is not available")
-print("========================\n")
-
-# Check for CUDA availability and handle potential CUDA/cuDNN issues
+# Determine compute device
 try:
     if torch.cuda.is_available():
-        # Try to initialize CUDA to check if libraries are properly loaded
-        _ = torch.zeros(1).cuda()
         device = "cuda"
         whisper_compute_type = "float16"
         print("🟢 CUDA is available and initialized successfully")
@@ -75,7 +66,7 @@ except Exception as e:
     print("🔴 Falling back to CPU")
     device = "cpu"
     whisper_compute_type = "int8"
-    
+
 print(f"Using device: {device}")
 
 # Initialize models with proper error handling
@@ -83,14 +74,42 @@ whisper_model = None
 csm_generator = None
 llm_model = None
 llm_tokenizer = None
+vad = None
+
+# Constants
+SAMPLE_RATE = 16000  # For VAD
+VAD_FRAME_SIZE = 480  # 30ms at 16kHz for VAD
+VAD_MODE = 3  # Aggressive mode for better results
+AUDIO_CHUNK_SIZE = 2400  # 100ms chunks when streaming AI voice
+
+# Audio sample rates
+CLIENT_SAMPLE_RATE = 44100  # Browser WebAudio default
+WHISPER_SAMPLE_RATE = 16000  # Whisper expects 16kHz
+
+# Session data structures
+user_sessions = {}  # session_id -> complete session data
+
+# WebRTC ICE servers (STUN/TURN servers for NAT traversal)
+ICE_SERVERS = [
+    {"urls": "stun:stun.l.google.com:19302"},
+    {"urls": "stun:stun1.l.google.com:19302"}
+]
 
 def load_models():
-    global whisper_model, csm_generator, llm_model, llm_tokenizer
+    """Load all necessary models"""
+    global whisper_model, csm_generator, llm_model, llm_tokenizer, vad
+    
+    # Initialize Voice Activity Detector
+    try:
+        vad = webrtcvad.Vad(VAD_MODE)
+        print("Voice Activity Detector initialized")
+    except Exception as e:
+        print(f"Error initializing VAD: {e}")
+        vad = None
     
     # Initialize Faster-Whisper for transcription
     try:
         print("Loading Whisper model...")
-        # Import here to avoid immediate import errors if package is missing
         from faster_whisper import WhisperModel
         whisper_model = WhisperModel("base", device=device, compute_type=whisper_compute_type, download_root="./models/whisper")
         print("Whisper model loaded successfully")
@@ -110,9 +129,8 @@ def load_models():
     # Initialize Llama 3.2 model for response generation
     try:
         print("Loading Llama 3.2 model...")
-        llm_model_id = "meta-llama/Llama-3.2-1B"  # Choose appropriate size based on resources
+        llm_model_id = "meta-llama/Llama-3.2-1B"
         llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_id, cache_dir="./models/llama")
-        # Use the right data type based on device
         dtype = torch.bfloat16 if device != "cpu" else torch.float32
         llm_model = AutoModelForCausalLM.from_pretrained(
             llm_model_id,
@@ -126,247 +144,339 @@ def load_models():
         print(f"Error loading Llama 3.2 model: {e}")
         print("Will use a fallback response generation method")
 
-# Store conversation context
-conversation_context = {}  # session_id -> context
-active_audio_streams = {}  # session_id -> stream status
-
 @app.route('/')
 def index():
+    """Serve the main interface"""
     return render_template('index.html')
 
 @socketio.on('connect')
 def handle_connect():
-    print(f"Client connected: {request.sid}")
-    conversation_context[request.sid] = {
+    """Handle new client connection"""
+    session_id = request.sid
+    print(f"Client connected: {session_id}")
+    
+    # Initialize session data
+    user_sessions[session_id] = {
+        # Conversation context
         'segments': [],
-        'speakers': [0, 1],  # 0 = user, 1 = bot
-        'audio_buffer': deque(maxlen=10),  # Store recent audio chunks
-        'is_speaking': False,
-        'last_activity': time.time(),
-        'active_session': True,
-        'transcription_buffer': []  # For real-time transcription
+        'conversation_history': [],
+        'is_turn_active': False,
+        
+        # Audio buffers and state
+        'vad_buffer': deque(maxlen=30),  # ~1s of audio at 30fps
+        'audio_buffer': bytearray(),
+        'is_user_speaking': False,
+        'last_vad_active': time.time(),
+        'silence_duration': 0,
+        'speech_frames': 0,
+        
+        # AI state
+        'is_ai_speaking': False,
+        'should_interrupt_ai': False,
+        'ai_stream_queue': queue.Queue(),
+        
+        # WebRTC status
+        'webrtc_connected': False,
+        'webrtc_peer_id': None,
+        
+        # Processing flags
+        'is_processing': False,
+        'pending_user_audio': None
     }
-    emit('ready', {
-        'message': 'Connection established',
-        'sample_rate': getattr(csm_generator, 'sample_rate', 24000) if csm_generator else 24000
+    
+    # Send config to client
+    emit('session_ready', {
+        'whisper_available': whisper_model is not None,
+        'csm_available': csm_generator is not None,
+        'llm_available': llm_model is not None,
+        'client_sample_rate': CLIENT_SAMPLE_RATE,
+        'server_sample_rate': getattr(csm_generator, 'sample_rate', 24000) if csm_generator else 24000,
+        'ice_servers': ICE_SERVERS
     })
 
 @socketio.on('disconnect')
 def handle_disconnect():
-    print(f"Client disconnected: {request.sid}")
+    """Handle client disconnection"""
     session_id = request.sid
+    print(f"Client disconnected: {session_id}")
     
     # Clean up resources
-    if session_id in conversation_context:
-        conversation_context[session_id]['active_session'] = False
-        del conversation_context[session_id]
+    if session_id in user_sessions:
+        # Signal any running threads to stop
+        user_sessions[session_id]['should_interrupt_ai'] = True
+        
+        # Clean up resources
+        del user_sessions[session_id]
+
+@socketio.on('webrtc_signal')
+def handle_webrtc_signal(data):
+    """Handle WebRTC signaling for P2P connection establishment"""
+    session_id = request.sid
+    if session_id not in user_sessions:
+        return
     
-    if session_id in active_audio_streams:
-        active_audio_streams[session_id]['active'] = False
-        del active_audio_streams[session_id]
+    # Simply relay the signal to the client
+    # In a multi-user app, we would route this to the correct peer
+    emit('webrtc_signal', data)
+
+@socketio.on('webrtc_connected')
+def handle_webrtc_connected(data):
+    """Client notifies that WebRTC connection is established"""
+    session_id = request.sid
+    if session_id not in user_sessions:
+        return
+    
+    user_sessions[session_id]['webrtc_connected'] = True
+    print(f"WebRTC connected for session {session_id}")
+    emit('ready_for_speech', {'message': 'Ready to start conversation'})
 
 @socketio.on('audio_stream')
 def handle_audio_stream(data):
-    """Handle incoming audio stream from client"""
+    """Process incoming audio stream packets from client"""
     session_id = request.sid
-    
-    if session_id not in conversation_context:
+    if session_id not in user_sessions:
         return
     
-    context = conversation_context[session_id]
-    context['last_activity'] = time.time()
-    
-    # Process different stream events
-    if data.get('event') == 'start':
-        # Client is starting to send audio
-        context['is_speaking'] = True
-        context['audio_buffer'].clear()
-        context['transcription_buffer'] = []
-        print(f"User {session_id} started streaming audio")
-        
-        # If AI was speaking, interrupt it
-        if session_id in active_audio_streams and active_audio_streams[session_id]['active']:
-            active_audio_streams[session_id]['active'] = False
-            emit('ai_stream_interrupt', {}, room=session_id)
-    
-    elif data.get('event') == 'data':
-        # Audio data received
-        if not context['is_speaking']:
-            return
-            
-        # Decode audio chunk
-        try:
-            audio_data = base64.b64decode(data.get('audio', ''))
-            if not audio_data:
-                return
-                
-            audio_numpy = np.frombuffer(audio_data, dtype=np.float32)
-            
-            # Apply a simple noise gate
-            if np.mean(np.abs(audio_numpy)) < 0.01:  # Very quiet
-                return
-                
-            audio_tensor = torch.tensor(audio_numpy)
-            
-            # Add to audio buffer
-            context['audio_buffer'].append(audio_tensor)
-            
-            # Real-time transcription (periodic)
-            if len(context['audio_buffer']) % 3 == 0:  # Process every 3 chunks
-                threading.Thread(
-                    target=process_realtime_transcription,
-                    args=(session_id,),
-                    daemon=True
-                ).start()
-        except Exception as e:
-            print(f"Error processing audio chunk: {e}")
-    
-    elif data.get('event') == 'end':
-        # Client has finished sending audio
-        context['is_speaking'] = False
-        
-        if len(context['audio_buffer']) > 0:
-            # Process the complete utterance
-            threading.Thread(
-                target=process_complete_utterance,
-                args=(session_id,),
-                daemon=True
-            ).start()
-        
-        print(f"User {session_id} stopped streaming audio")
-
-def process_realtime_transcription(session_id):
-    """Process incoming audio for real-time transcription"""
-    if session_id not in conversation_context or not conversation_context[session_id]['active_session']:
-        return
-        
-    context = conversation_context[session_id]
-    
-    if not context['audio_buffer'] or not context['is_speaking']:
-        return
+    session = user_sessions[session_id]
     
     try:
-        # Combine current buffer for transcription
-        buffer_copy = list(context['audio_buffer'])
-        if not buffer_copy:
+        # Decode audio data
+        audio_bytes = base64.b64decode(data.get('audio', ''))
+        if not audio_bytes or len(audio_bytes) < 2:  # Need at least one sample
             return
-            
-        full_audio = torch.cat(buffer_copy, dim=0)
         
-        # Save audio to temporary WAV file for transcription
-        temp_audio_path = f"temp_rt_{session_id}.wav"
+        # Add to current audio buffer
+        session['audio_buffer'] += audio_bytes
+        
+        # Check for speech using VAD
+        has_speech = detect_speech(audio_bytes, session_id)
+        
+        # Handle speech state machine
+        if has_speech:
+            # Reset silence tracking when speech is detected
+            session['last_vad_active'] = time.time()
+            session['silence_duration'] = 0
+            session['speech_frames'] += 1
+            
+            # If not already marked as speaking and we have enough speech frames
+            if not session['is_user_speaking'] and session['speech_frames'] >= 5:
+                on_speech_started(session_id)
+        else:
+            # No speech detected in this frame
+            if session['is_user_speaking']:
+                # Calculate silence duration
+                now = time.time()
+                session['silence_duration'] = now - session['last_vad_active']
+                
+                # If silent for more than 0.5 seconds, end speech segment
+                if session['silence_duration'] > 0.8 and session['speech_frames'] > 8:
+                    on_speech_ended(session_id)
+            else:
+                # Not speaking and no speech, just a silent frame
+                session['speech_frames'] = max(0, session['speech_frames'] - 1)
+    
+    except Exception as e:
+        print(f"Error processing audio stream: {e}")
+
+def detect_speech(audio_bytes, session_id):
+    """Use VAD to check if audio contains speech"""
+    if session_id not in user_sessions:
+        return False
+        
+    session = user_sessions[session_id]
+    
+    # Store in VAD buffer for history
+    session['vad_buffer'].append(audio_bytes)
+    
+    if vad is None:
+        # Fallback to simple energy detection
+        audio_data = np.frombuffer(audio_bytes, dtype=np.int16)
+        energy = np.mean(np.abs(audio_data)) / 32768.0
+        return energy > 0.015  # Simple threshold
+    
+    try:
+        # Ensure we have the right amount of data for VAD
+        audio_data = np.frombuffer(audio_bytes, dtype=np.int16)
+        
+        # If we have too much data, use just the right amount
+        if len(audio_data) >= VAD_FRAME_SIZE:
+            frame = audio_data[:VAD_FRAME_SIZE].tobytes()
+            return vad.is_speech(frame, SAMPLE_RATE)
+        
+        # If too little data, accumulate in the VAD buffer and check periodically
+        if len(session['vad_buffer']) >= 3:
+            # Combine recent chunks to get enough data
+            combined = bytearray()
+            for chunk in list(session['vad_buffer'])[-3:]:
+                combined.extend(chunk)
+            
+            # Extract the right amount of data
+            if len(combined) >= VAD_FRAME_SIZE:
+                frame = combined[:VAD_FRAME_SIZE]
+                return vad.is_speech(bytes(frame), SAMPLE_RATE)
+        
+        return False
+    
+    except Exception as e:
+        print(f"VAD error: {e}")
+        return False
+
+def on_speech_started(session_id):
+    """Handle start of user speech"""
+    if session_id not in user_sessions:
+        return
+        
+    session = user_sessions[session_id]
+    
+    # Reset audio buffer 
+    session['audio_buffer'] = bytearray()
+    session['is_user_speaking'] = True
+    session['is_turn_active'] = True
+    
+    # If AI is speaking, we need to interrupt it
+    if session['is_ai_speaking']:
+        session['should_interrupt_ai'] = True
+        emit('ai_interrupted_by_user', room=session_id)
+    
+    # Notify client that we detected speech
+    emit('user_speech_start', room=session_id)
+
+def on_speech_ended(session_id):
+    """Handle end of user speech segment"""
+    if session_id not in user_sessions:
+        return
+        
+    session = user_sessions[session_id]
+    
+    # Mark as not speaking anymore
+    session['is_user_speaking'] = False
+    session['speech_frames'] = 0
+    
+    # If no audio or already processing, skip
+    if len(session['audio_buffer']) < 4000 or session['is_processing']:  # At least 250ms of audio
+        session['audio_buffer'] = bytearray()
+        return
+    
+    # Mark as processing to prevent multiple processes
+    session['is_processing'] = True
+    
+    # Create a copy of the audio buffer
+    audio_copy = session['audio_buffer']
+    session['audio_buffer'] = bytearray()
+    
+    # Convert audio to the format needed for processing
+    try:
+        # Convert to float32 between -1 and 1
+        audio_np = np.frombuffer(audio_copy, dtype=np.int16).astype(np.float32) / 32768.0
+        audio_tensor = torch.from_numpy(audio_np)
+        
+        # Resample to Whisper's expected sample rate if necessary
+        if CLIENT_SAMPLE_RATE != WHISPER_SAMPLE_RATE:
+            audio_tensor = torchaudio.functional.resample(
+                audio_tensor, 
+                orig_freq=CLIENT_SAMPLE_RATE, 
+                new_freq=WHISPER_SAMPLE_RATE
+            )
+        
+        # Save as WAV for transcription
+        temp_audio_path = f"temp_audio_{session_id}.wav"
         torchaudio.save(
             temp_audio_path, 
-            full_audio.unsqueeze(0), 
-            44100  # Assuming 44.1kHz from client
+            audio_tensor.unsqueeze(0), 
+            WHISPER_SAMPLE_RATE
         )
         
-        # Transcribe with Whisper if available
-        if whisper_model is not None:
-            segments, _ = whisper_model.transcribe(temp_audio_path, beam_size=5)
-            text = " ".join([segment.text for segment in segments])
-            
-            if text.strip():
-                context['transcription_buffer'].append(text)
-                # Send partial transcription to client
-                emit('partial_transcription', {'text': text}, room=session_id)
+        # Start transcription and response process in a thread
+        threading.Thread(
+            target=process_user_utterance,
+            args=(session_id, temp_audio_path, audio_tensor),
+            daemon=True
+        ).start()
+        
+        # Notify client that processing has started
+        emit('processing_speech', room=session_id)
+    
     except Exception as e:
-        print(f"Error in realtime transcription: {e}")
-    finally:
-        # Clean up
-        if os.path.exists(temp_audio_path):
-            os.remove(temp_audio_path)
+        print(f"Error preparing audio: {e}")
+        session['is_processing'] = False
+        emit('error', {'message': f'Error processing audio: {str(e)}'}, room=session_id)
 
-def process_complete_utterance(session_id):
-    """Process completed user utterance, generate response and stream audio back"""
-    if session_id not in conversation_context or not conversation_context[session_id]['active_session']:
+def process_user_utterance(session_id, audio_path, audio_tensor):
+    """Process user utterance, transcribe and generate response"""
+    if session_id not in user_sessions:
         return
     
-    context = conversation_context[session_id]
-    
-    if not context['audio_buffer']:
-        return
-    
-    # Combine audio chunks
-    full_audio = torch.cat(list(context['audio_buffer']), dim=0)
-    context['audio_buffer'].clear()
-    
-    # Save audio to temporary WAV file for transcription
-    temp_audio_path = f"temp_audio_{session_id}.wav"
-    torchaudio.save(
-        temp_audio_path, 
-        full_audio.unsqueeze(0), 
-        44100  # Assuming 44.1kHz from client
-    )
+    session = user_sessions[session_id]
     
     try:
-        # Try using Whisper first if available
+        # Transcribe audio
         if whisper_model is not None:
-            user_text = transcribe_with_whisper(temp_audio_path)
+            user_text = transcribe_with_whisper(audio_path)
         else:
-            # Fallback to Google's speech recognition
-            user_text = transcribe_with_google(temp_audio_path)
+            # Fallback to another transcription service
+            user_text = transcribe_fallback(audio_path)
         
-        if not user_text:
-            print("No speech detected.")
-            emit('error', {'message': 'No speech detected. Please try again.'}, room=session_id)
+        # Clean up temp file
+        if os.path.exists(audio_path):
+            os.remove(audio_path)
+        
+        # Check if we got meaningful text
+        if not user_text or len(user_text.strip()) < 2:
+            emit('no_speech_detected', room=session_id)
+            session['is_processing'] = False
             return
-            
+        
         print(f"Transcribed: {user_text}")
         
-        # Add to conversation segments
+        # Create user segment
         user_segment = Segment(
             text=user_text,
             speaker=0,  # User is speaker 0
-            audio=full_audio
+            audio=audio_tensor
         )
-        context['segments'].append(user_segment)
+        session['segments'].append(user_segment)
         
-        # Generate bot response text
-        bot_response = generate_llm_response(user_text, context['segments'])
-        print(f"Bot response: {bot_response}")
+        # Update conversation history
+        session['conversation_history'].append({
+            'role': 'user',
+            'text': user_text
+        })
         
-        # Send transcribed text to client
+        # Send transcription to client
         emit('transcription', {'text': user_text}, room=session_id)
         
-        # Generate and stream audio response if CSM is available
+        # Generate AI response
+        ai_response = generate_ai_response(user_text, session_id)
+        
+        # Send text response to client
+        emit('ai_response_text', {'text': ai_response}, room=session_id)
+        
+        # Update conversation history
+        session['conversation_history'].append({
+            'role': 'assistant',
+            'text': ai_response
+        })
+        
+        # Generate voice response if CSM is available
         if csm_generator is not None:
-            # Create stream state object
-            active_audio_streams[session_id] = {
-                'active': True,
-                'text': bot_response
-            }
+            session['is_ai_speaking'] = True
+            session['should_interrupt_ai'] = False
             
-            # Send initial response to prepare client
-            emit('ai_stream_start', {
-                'text': bot_response
-            }, room=session_id)
-            
-            # Start audio generation in a separate thread
+            # Begin streaming audio response
             threading.Thread(
-                target=generate_and_stream_audio_realtime,
-                args=(bot_response, context['segments'], session_id),
+                target=stream_ai_response,
+                args=(ai_response, session_id),
                 daemon=True
             ).start()
-        else:
-            # Send text-only response if audio generation isn't available
-            emit('text_response', {'text': bot_response}, room=session_id)
-            
-            # Add text-only bot response to conversation history
-            bot_segment = Segment(
-                text=bot_response,
-                speaker=1,  # Bot is speaker 1
-                audio=torch.zeros(1)  # Placeholder empty audio
-            )
-            context['segments'].append(bot_segment)
-        
+    
     except Exception as e:
-        print(f"Error processing speech: {e}")
-        emit('error', {'message': f'Error processing speech: {str(e)}'}, room=session_id)
+        print(f"Error processing utterance: {e}")
+        emit('error', {'message': f'Error: {str(e)}'}, room=session_id)
+    
     finally:
-        # Cleanup temp file
-        if os.path.exists(temp_audio_path):
-            os.remove(temp_audio_path)
+        # Clear processing flag
+        if session_id in user_sessions:
+            session['is_processing'] = False
 
 def transcribe_with_whisper(audio_path):
     """Transcribe audio using Faster-Whisper"""
@@ -375,49 +485,58 @@ def transcribe_with_whisper(audio_path):
     # Collect all text from segments
     user_text = ""
     for segment in segments:
-        segment_text = segment.text.strip()
-        print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment_text}")
-        user_text += segment_text + " "
-
-    print(f"Transcribed text: {user_text.strip()}")
+        user_text += segment.text.strip() + " "
     
     return user_text.strip()
 
-def transcribe_with_google(audio_path):
+def transcribe_fallback(audio_path):
     """Fallback transcription using Google's speech recognition"""
-    import speech_recognition as sr
-    recognizer = sr.Recognizer()
-    
-    with sr.AudioFile(audio_path) as source:
-        audio = recognizer.record(source)
-        try:
-            text = recognizer.recognize_google(audio)
-            return text
-        except sr.UnknownValueError:
-            return ""
-        except sr.RequestError:
-            # If Google API fails, try a basic energy-based VAD approach
-            # This is a very basic fallback and won't give good results
-            return "[Speech detected but transcription failed]"
+    try:
+        import speech_recognition as sr
+        recognizer = sr.Recognizer()
+        
+        with sr.AudioFile(audio_path) as source:
+            audio = recognizer.record(source)
+            try:
+                text = recognizer.recognize_google(audio)
+                return text
+            except sr.UnknownValueError:
+                return ""
+            except sr.RequestError:
+                return "[Speech recognition service unavailable]"
+    except ImportError:
+        return "[Speech recognition not available]"
 
-def generate_llm_response(user_text, conversation_segments):
-    """Generate text response using available model"""
+def generate_ai_response(user_text, session_id):
+    """Generate text response using available LLM"""
+    if session_id not in user_sessions:
+        return "I'm sorry, your session has expired."
+    
+    session = user_sessions[session_id]
+    
     if llm_model is not None and llm_tokenizer is not None:
         # Format conversation history for the LLM
-        conversation_history = ""
-        for segment in conversation_segments[-5:]:  # Use last 5 utterances for context
-            speaker_name = "User" if segment.speaker == 0 else "Assistant"
-            conversation_history += f"{speaker_name}: {segment.text}\n"
+        prompt = "You are a helpful, friendly voice assistant. Keep your responses brief and conversational.\n\n"
         
-        # Add the current user query
-        conversation_history += f"User: {user_text}\nAssistant:"
+        # Add recent conversation history (last 6 turns maximum)
+        for entry in session['conversation_history'][-6:]:
+            if entry['role'] == 'user':
+                prompt += f"User: {entry['text']}\n"
+            else:
+                prompt += f"Assistant: {entry['text']}\n"
+        
+        # Add current query if not already in history
+        if not session['conversation_history'] or session['conversation_history'][-1]['role'] != 'user':
+            prompt += f"User: {user_text}\n"
+        
+        prompt += "Assistant: "
         
         try:
             # Generate response
-            inputs = llm_tokenizer(conversation_history, return_tensors="pt").to(device)
+            inputs = llm_tokenizer(prompt, return_tensors="pt").to(device)
             output = llm_model.generate(
                 inputs.input_ids, 
-                max_new_tokens=150,
+                max_new_tokens=100,  # Keep responses shorter for voice
                 temperature=0.7,
                 top_p=0.9,
                 do_sample=True
@@ -425,43 +544,48 @@ def generate_llm_response(user_text, conversation_segments):
             
             response = llm_tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
             return response.strip()
+        
         except Exception as e:
-            print(f"Error generating response with LLM: {e}")
+            print(f"Error generating LLM response: {e}")
             return fallback_response(user_text)
     else:
         return fallback_response(user_text)
 
 def fallback_response(user_text):
-    """Generate a simple fallback response when LLM is not available"""
-    # Simple rule-based responses
+    """Generate simple fallback responses when LLM is unavailable"""
     user_text_lower = user_text.lower()
     
     if "hello" in user_text_lower or "hi" in user_text_lower:
-        return "Hello! I'm a simple fallback assistant. The main language model couldn't be loaded, so I have limited capabilities."
+        return "Hello! How can I help you today?"
     
     elif "how are you" in user_text_lower:
-        return "I'm functioning within my limited capabilities. How can I assist you today?"
+        return "I'm doing well, thanks for asking! How about you?"
     
     elif "thank" in user_text_lower:
-        return "You're welcome! Let me know if there's anything else I can help with."
+        return "You're welcome! Happy to help."
     
     elif "bye" in user_text_lower or "goodbye" in user_text_lower:
         return "Goodbye! Have a great day!"
     
     elif any(q in user_text_lower for q in ["what", "who", "where", "when", "why", "how"]):
-        return "I'm running in fallback mode and can't answer complex questions. Please try again when the main language model is available."
+        return "That's an interesting question. I wish I could provide a better answer in my current fallback mode."
         
     else:
-        return "I understand you said something about that. Unfortunately, I'm running in fallback mode with limited capabilities. Please try again later when the main model is available."
+        return "I see. Tell me more about that."
 
-def generate_and_stream_audio_realtime(text, conversation_segments, session_id):
-    """Generate audio response using CSM and stream it in real-time to client"""
-    if session_id not in active_audio_streams or not active_audio_streams[session_id]['active']:
+def stream_ai_response(text, session_id):
+    """Generate and stream audio response in real-time chunks"""
+    if session_id not in user_sessions:
         return
     
+    session = user_sessions[session_id]
+    
     try:
-        # Use the last few conversation segments as context
-        context_segments = conversation_segments[-4:] if len(conversation_segments) > 4 else conversation_segments
+        # Signal start of AI speech
+        emit('ai_speech_start', room=session_id)
+        
+        # Use the last few conversation segments as context (up to 4)
+        context_segments = session['segments'][-4:] if len(session['segments']) > 4 else session['segments']
         
         # Generate audio for bot response
         audio = csm_generator.generate(
@@ -473,23 +597,26 @@ def generate_and_stream_audio_realtime(text, conversation_segments, session_id):
             topk=50
         )
         
-        # Store the full audio for conversation history
+        # Create and store bot segment
         bot_segment = Segment(
             text=text,
-            speaker=1,  # Bot is speaker 1
+            speaker=1,
             audio=audio
         )
-        if session_id in conversation_context and conversation_context[session_id]['active_session']:
-            conversation_context[session_id]['segments'].append(bot_segment)
+        
+        if session_id in user_sessions:
+            session['segments'].append(bot_segment)
         
         # Stream audio in small chunks for more responsive playback
-        chunk_size = 4800  # 200ms at 24kHz
+        chunk_size = AUDIO_CHUNK_SIZE  # Size defined in constants
         
         for i in range(0, len(audio), chunk_size):
-            if session_id not in active_audio_streams or not active_audio_streams[session_id]['active']:
-                print("Audio streaming interrupted or session ended")
+            # Check if we should stop (user interrupted)
+            if session_id not in user_sessions or session['should_interrupt_ai']:
+                print("AI speech interrupted")
                 break
-                
+            
+            # Get next chunk
             chunk = audio[i:i+chunk_size]
             
             # Convert audio chunk to base64 for streaming
@@ -499,32 +626,48 @@ def generate_and_stream_audio_realtime(text, conversation_segments, session_id):
             audio_b64 = base64.b64encode(audio_bytes.read()).decode('utf-8')
             
             # Send chunk to client
-            socketio.emit('ai_stream_data', {
+            socketio.emit('ai_speech_chunk', {
                 'audio': audio_b64,
                 'is_last': i + chunk_size >= len(audio)
             }, room=session_id)
             
-            # Simulate real-time speech by adding a small delay
-            # Remove this in production for faster response
-            time.sleep(0.15)  # Slight delay for more natural timing
+            # Small sleep for more natural pacing
+            time.sleep(0.06)  # Slight delay for smoother playback
         
-        # Signal end of stream
-        if session_id in active_audio_streams and active_audio_streams[session_id]['active']:
-            socketio.emit('ai_stream_end', {}, room=session_id)
-            active_audio_streams[session_id]['active'] = False
+        # Signal end of AI speech
+        if session_id in user_sessions:
+            session['is_ai_speaking'] = False
+            session['is_turn_active'] = False  # End conversation turn
+            socketio.emit('ai_speech_end', room=session_id)
     
     except Exception as e:
-        print(f"Error generating or streaming audio: {e}")
-        # Send error message to client
-        if session_id in conversation_context and conversation_context[session_id]['active_session']:
-            socketio.emit('error', {
-                'message': f'Error generating audio: {str(e)}'
-            }, room=session_id)
-            
-        # Signal stream end to unblock client
-        socketio.emit('ai_stream_end', {}, room=session_id)
-        if session_id in active_audio_streams:
-            active_audio_streams[session_id]['active'] = False
+        print(f"Error streaming AI response: {e}")
+        if session_id in user_sessions:
+            session['is_ai_speaking'] = False
+            session['is_turn_active'] = False
+            socketio.emit('error', {'message': f'Error generating audio: {str(e)}'}, room=session_id)
+            socketio.emit('ai_speech_end', room=session_id)
+
+@socketio.on('interrupt_ai')
+def handle_interrupt():
+    """Handle explicit AI interruption request from client"""
+    session_id = request.sid
+    if session_id in user_sessions:
+        user_sessions[session_id]['should_interrupt_ai'] = True
+        emit('ai_interrupted', room=session_id)
+
+@socketio.on('get_config')
+def handle_get_config():
+    """Send configuration to client"""
+    session_id = request.sid
+    if session_id in user_sessions:
+        emit('config', {
+            'client_sample_rate': CLIENT_SAMPLE_RATE,
+            'server_sample_rate': getattr(csm_generator, 'sample_rate', 24000) if csm_generator else 24000,
+            'whisper_available': whisper_model is not None,
+            'csm_available': csm_generator is not None,
+            'ice_servers': ICE_SERVERS
+        })
 
 if __name__ == '__main__':
     # Ensure the existing index.html file is in the correct location
@@ -538,6 +681,6 @@ if __name__ == '__main__':
     print("Starting model loading...")
     load_models()
     
-    # Start the server with eventlet for better WebSocket performance
+    # Start the server
     print("Starting Flask SocketIO server...")
     socketio.run(app, host='0.0.0.0', port=5000, debug=False)
\ No newline at end of file
