app.py

import gradio as gr
import numpy as np
import queue
import torch
import time
import threading
import os
import urllib.request
import torchaudio
from scipy.spatial.distance import cosine
from RealtimeSTT import AudioToTextRecorder
from fastapi import FastAPI, APIRouter
from fastrtc import Stream, AsyncStreamHandler
import json
import asyncio
import uvicorn
from queue import Queue
import logging
from gradio_webrtc import WebRTC

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Simplified configuration parameters
SILENCE_THRESHS = [0, 0.4]
FINAL_TRANSCRIPTION_MODEL = "distil-large-v3"
FINAL_BEAM_SIZE = 5
REALTIME_TRANSCRIPTION_MODEL = "distil-small.en"
REALTIME_BEAM_SIZE = 5
TRANSCRIPTION_LANGUAGE = "en"
SILERO_SENSITIVITY = 0.4
WEBRTC_SENSITIVITY = 3
MIN_LENGTH_OF_RECORDING = 0.7
PRE_RECORDING_BUFFER_DURATION = 0.35

# Speaker change detection parameters
DEFAULT_CHANGE_THRESHOLD = 0.65
EMBEDDING_HISTORY_SIZE = 5
MIN_SEGMENT_DURATION = 1.5
DEFAULT_MAX_SPEAKERS = 4
ABSOLUTE_MAX_SPEAKERS = 8

# Global variables
SAMPLE_RATE = 16000
BUFFER_SIZE = 1024
CHANNELS = 1

# Speaker colors - more distinguishable colors
SPEAKER_COLORS = [
    "#FF6B6B",  # Red
    "#4ECDC4",  # Teal
    "#45B7D1",  # Blue
    "#96CEB4",  # Green
    "#FFEAA7",  # Yellow
    "#DDA0DD",  # Plum
    "#98D8C8",  # Mint
    "#F7DC6F",  # Gold
]

SPEAKER_COLOR_NAMES = [
    "Red", "Teal", "Blue", "Green", "Yellow", "Plum", "Mint", "Gold"
]


class SpeechBrainEncoder:
    """ECAPA-TDNN encoder from SpeechBrain for speaker embeddings"""
    def __init__(self, device="cpu"):
        self.device = device
        self.model = None
        self.embedding_dim = 192
        self.model_loaded = False
        self.cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "speechbrain")
        os.makedirs(self.cache_dir, exist_ok=True)
    
    def load_model(self):
        """Load the ECAPA-TDNN model"""
        try:
            from speechbrain.pretrained import EncoderClassifier
            
            self.model = EncoderClassifier.from_hparams(
                source="speechbrain/spkrec-ecapa-voxceleb",
                savedir=self.cache_dir,
                run_opts={"device": self.device}
            )
            
            self.model_loaded = True
            logger.info("ECAPA-TDNN model loaded successfully!")
            return True
        except Exception as e:
            logger.error(f"Error loading ECAPA-TDNN model: {e}")
            return False
    
    def embed_utterance(self, audio, sr=16000):
        """Extract speaker embedding from audio"""
        if not self.model_loaded:
            raise ValueError("Model not loaded. Call load_model() first.")
        
        try:
            if isinstance(audio, np.ndarray):
                # Ensure audio is float32 and properly normalized
                audio = audio.astype(np.float32)
                if np.max(np.abs(audio)) > 1.0:
                    audio = audio / np.max(np.abs(audio))
                waveform = torch.tensor(audio).unsqueeze(0)
            else:
                waveform = audio.unsqueeze(0)
            
            # Resample if necessary
            if sr != 16000:
                waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000)
            
            with torch.no_grad():
                embedding = self.model.encode_batch(waveform)
                
            return embedding.squeeze().cpu().numpy()
        except Exception as e:
            logger.error(f"Error extracting embedding: {e}")
            return np.zeros(self.embedding_dim)


class AudioProcessor:
    """Processes audio data to extract speaker embeddings"""
    def __init__(self, encoder):
        self.encoder = encoder
        self.audio_buffer = []
        self.min_audio_length = int(SAMPLE_RATE * 1.0)  # Minimum 1 second of audio
    
    def add_audio_chunk(self, audio_chunk):
        """Add audio chunk to buffer"""
        self.audio_buffer.extend(audio_chunk)
        
        # Keep buffer from getting too large
        max_buffer_size = int(SAMPLE_RATE * 10)  # 10 seconds max
        if len(self.audio_buffer) > max_buffer_size:
            self.audio_buffer = self.audio_buffer[-max_buffer_size:]
    
    def extract_embedding_from_buffer(self):
        """Extract embedding from current audio buffer"""
        if len(self.audio_buffer) < self.min_audio_length:
            return None
            
        try:
            # Use the last portion of the buffer for embedding
            audio_segment = np.array(self.audio_buffer[-self.min_audio_length:], dtype=np.float32)
            
            # Normalize audio
            if np.max(np.abs(audio_segment)) > 0:
                audio_segment = audio_segment / np.max(np.abs(audio_segment))
            else:
                return None
            
            embedding = self.encoder.embed_utterance(audio_segment)
            return embedding
        except Exception as e:
            logger.error(f"Embedding extraction error: {e}")
            return None


class SpeakerChangeDetector:
    """Improved speaker change detector"""
    def __init__(self, embedding_dim=192, change_threshold=DEFAULT_CHANGE_THRESHOLD, max_speakers=DEFAULT_MAX_SPEAKERS):
        self.embedding_dim = embedding_dim
        self.change_threshold = change_threshold
        self.max_speakers = min(max_speakers, ABSOLUTE_MAX_SPEAKERS)
        self.current_speaker = 0
        self.speaker_embeddings = [[] for _ in range(self.max_speakers)]
        self.speaker_centroids = [None] * self.max_speakers
        self.last_change_time = time.time()
        self.last_similarity = 1.0
        self.active_speakers = set([0])
        self.segment_counter = 0
        
    def set_max_speakers(self, max_speakers):
        """Update the maximum number of speakers"""
        new_max = min(max_speakers, ABSOLUTE_MAX_SPEAKERS)
        
        if new_max < self.max_speakers:
            # Remove speakers beyond the new limit
            for speaker_id in list(self.active_speakers):
                if speaker_id >= new_max:
                    self.active_speakers.discard(speaker_id)
            
            if self.current_speaker >= new_max:
                self.current_speaker = 0
        
        # Resize arrays
        if new_max > self.max_speakers:
            self.speaker_embeddings.extend([[] for _ in range(new_max - self.max_speakers)])
            self.speaker_centroids.extend([None] * (new_max - self.max_speakers))
        else:
            self.speaker_embeddings = self.speaker_embeddings[:new_max]
            self.speaker_centroids = self.speaker_centroids[:new_max]
        
        self.max_speakers = new_max
        
    def set_change_threshold(self, threshold):
        """Update the threshold for detecting speaker changes"""
        self.change_threshold = max(0.1, min(threshold, 0.95))
        
    def add_embedding(self, embedding, timestamp=None):
        """Add a new embedding and detect speaker changes"""
        current_time = timestamp or time.time()
        self.segment_counter += 1
        
        # Initialize first speaker
        if not self.speaker_embeddings[0]:
            self.speaker_embeddings[0].append(embedding)
            self.speaker_centroids[0] = embedding.copy()
            self.active_speakers.add(0)
            return 0, 1.0
        
        # Calculate similarity with current speaker
        current_centroid = self.speaker_centroids[self.current_speaker]
        if current_centroid is not None:
            similarity = 1.0 - cosine(embedding, current_centroid)
        else:
            similarity = 0.5
        
        self.last_similarity = similarity
        
        # Check for speaker change
        time_since_last_change = current_time - self.last_change_time
        speaker_changed = False
        
        if time_since_last_change >= MIN_SEGMENT_DURATION and similarity < self.change_threshold:
            # Find best matching speaker
            best_speaker = self.current_speaker
            best_similarity = similarity
            
            for speaker_id in self.active_speakers:
                if speaker_id == self.current_speaker:
                    continue
                    
                centroid = self.speaker_centroids[speaker_id]
                if centroid is not None:
                    speaker_similarity = 1.0 - cosine(embedding, centroid)
                    if speaker_similarity > best_similarity and speaker_similarity > self.change_threshold:
                        best_similarity = speaker_similarity
                        best_speaker = speaker_id
            
            # If no good match found and we can add a new speaker
            if best_speaker == self.current_speaker and len(self.active_speakers) < self.max_speakers:
                for new_id in range(self.max_speakers):
                    if new_id not in self.active_speakers:
                        best_speaker = new_id
                        self.active_speakers.add(new_id)
                        break
            
            if best_speaker != self.current_speaker:
                self.current_speaker = best_speaker
                self.last_change_time = current_time
                speaker_changed = True
        
        # Update speaker embeddings and centroids
        self.speaker_embeddings[self.current_speaker].append(embedding)
        
        # Keep only recent embeddings (sliding window)
        max_embeddings = 20
        if len(self.speaker_embeddings[self.current_speaker]) > max_embeddings:
            self.speaker_embeddings[self.current_speaker] = self.speaker_embeddings[self.current_speaker][-max_embeddings:]
        
        # Update centroid
        if self.speaker_embeddings[self.current_speaker]:
            self.speaker_centroids[self.current_speaker] = np.mean(
                self.speaker_embeddings[self.current_speaker], axis=0
            )
        
        return self.current_speaker, similarity
    
    def get_color_for_speaker(self, speaker_id):
        """Return color for speaker ID"""
        if 0 <= speaker_id < len(SPEAKER_COLORS):
            return SPEAKER_COLORS[speaker_id]
        return "#FFFFFF"
    
    def get_status_info(self):
        """Return status information"""
        speaker_counts = [len(self.speaker_embeddings[i]) for i in range(self.max_speakers)]
        
        return {
            "current_speaker": self.current_speaker,
            "speaker_counts": speaker_counts,
            "active_speakers": len(self.active_speakers),
            "max_speakers": self.max_speakers,
            "last_similarity": self.last_similarity,
            "threshold": self.change_threshold,
            "segment_counter": self.segment_counter
        }


class RealtimeSpeakerDiarization:
    def __init__(self):
        self.encoder = None
        self.audio_processor = None
        self.speaker_detector = None
        self.recorder = None
        self.sentence_queue = queue.Queue()
        self.full_sentences = []
        self.sentence_speakers = []
        self.pending_sentences = []
        self.current_conversation = ""
        self.is_running = False
        self.change_threshold = DEFAULT_CHANGE_THRESHOLD
        self.max_speakers = DEFAULT_MAX_SPEAKERS
        self.last_transcription = ""
        self.transcription_lock = threading.Lock()
        
    def initialize_models(self):
        """Initialize the speaker encoder model"""
        try:
            device_str = "cuda" if torch.cuda.is_available() else "cpu"
            logger.info(f"Using device: {device_str}")
            
            self.encoder = SpeechBrainEncoder(device=device_str)
            success = self.encoder.load_model()
            
            if success:
                self.audio_processor = AudioProcessor(self.encoder)
                self.speaker_detector = SpeakerChangeDetector(
                    embedding_dim=self.encoder.embedding_dim,
                    change_threshold=self.change_threshold,
                    max_speakers=self.max_speakers
                )
                logger.info("Models initialized successfully!")
                return True
            else:
                logger.error("Failed to load models")
                return False
        except Exception as e:
            logger.error(f"Model initialization error: {e}")
            return False
    
    def live_text_detected(self, text):
        """Callback for real-time transcription updates"""
        with self.transcription_lock:
            self.last_transcription = text.strip()
    
    def process_final_text(self, text):
        """Process final transcribed text with speaker embedding"""
        text = text.strip()
        if text:
            try:
                # Get audio data for this transcription
                audio_bytes = getattr(self.recorder, 'last_transcription_bytes', None)
                if audio_bytes:
                    self.sentence_queue.put((text, audio_bytes))
                else:
                    # If no audio bytes, use current speaker
                    self.sentence_queue.put((text, None))
                    
            except Exception as e:
                logger.error(f"Error processing final text: {e}")
    
    def process_sentence_queue(self):
        """Process sentences in the queue for speaker detection"""
        while self.is_running:
            try:
                text, audio_bytes = self.sentence_queue.get(timeout=1)
                
                current_speaker = self.speaker_detector.current_speaker
                
                if audio_bytes:
                    # Convert audio data and extract embedding
                    audio_int16 = np.frombuffer(audio_bytes, dtype=np.int16)
                    audio_float = audio_int16.astype(np.float32) / 32768.0
                    
                    # Extract embedding
                    embedding = self.audio_processor.encoder.embed_utterance(audio_float)
                    if embedding is not None:
                        current_speaker, similarity = self.speaker_detector.add_embedding(embedding)
                
                # Store sentence with speaker
                with self.transcription_lock:
                    self.full_sentences.append((text, current_speaker))
                    self.update_conversation_display()
                    
            except queue.Empty:
                continue
            except Exception as e:
                logger.error(f"Error processing sentence: {e}")
    
    def update_conversation_display(self):
        """Update the conversation display"""
        try:
            sentences_with_style = []
            
            for sentence_text, speaker_id in self.full_sentences:
                color = self.speaker_detector.get_color_for_speaker(speaker_id)
                speaker_name = f"Speaker {speaker_id + 1}"
                sentences_with_style.append(
                    f'<span style="color:{color}; font-weight: bold;">{speaker_name}:</span> '
                    f'<span style="color:#333333;">{sentence_text}</span>'
                )
            
            # Add current transcription if available
            if self.last_transcription:
                current_color = self.speaker_detector.get_color_for_speaker(self.speaker_detector.current_speaker)
                current_speaker = f"Speaker {self.speaker_detector.current_speaker + 1}"
                sentences_with_style.append(
                    f'<span style="color:{current_color}; font-weight: bold; opacity: 0.7;">{current_speaker}:</span> '
                    f'<span style="color:#666666; font-style: italic;">{self.last_transcription}...</span>'
                )
            
            if sentences_with_style:
                self.current_conversation = "<br><br>".join(sentences_with_style)
            else:
                self.current_conversation = "<i>Waiting for speech input...</i>"
                
        except Exception as e:
            logger.error(f"Error updating conversation display: {e}")
            self.current_conversation = f"<i>Error: {str(e)}</i>"
    
    def start_recording(self):
        """Start the recording and transcription process"""
        if self.encoder is None:
            return "Please initialize models first!"
        
        try:
            # Setup recorder configuration
            recorder_config = {
                'spinner': False,
                'use_microphone': False,  # Change to False for Hugging Face Spaces
                'model': FINAL_TRANSCRIPTION_MODEL,
                'language': TRANSCRIPTION_LANGUAGE,
                'silero_sensitivity': SILERO_SENSITIVITY,
                'webrtc_sensitivity': WEBRTC_SENSITIVITY,
                'post_speech_silence_duration': SILENCE_THRESHS[1],
                'min_length_of_recording': MIN_LENGTH_OF_RECORDING,
                'pre_recording_buffer_duration': PRE_RECORDING_BUFFER_DURATION,
                'min_gap_between_recordings': 0,
                'enable_realtime_transcription': True,
                'realtime_processing_pause': 0.1,
                'realtime_model_type': REALTIME_TRANSCRIPTION_MODEL,
                'on_realtime_transcription_update': self.live_text_detected,
                'beam_size': FINAL_BEAM_SIZE,
                'beam_size_realtime': REALTIME_BEAM_SIZE,
                'sample_rate': SAMPLE_RATE,
            }

            self.recorder = AudioToTextRecorder(**recorder_config)
            
            # Start processing threads
            self.is_running = True
            self.sentence_thread = threading.Thread(target=self.process_sentence_queue, daemon=True)
            self.sentence_thread.start()
            
            self.transcription_thread = threading.Thread(target=self.run_transcription, daemon=True)
            self.transcription_thread.start()
            
            return "Recording started successfully!"
            
        except Exception as e:
            logger.error(f"Error starting recording: {e}")
            return f"Error starting recording: {e}"
    
    def run_transcription(self):
        """Run the transcription loop"""
        try:
            while self.is_running:
                self.recorder.text(self.process_final_text)
        except Exception as e:
            logger.error(f"Transcription error: {e}")
    
    def stop_recording(self):
        """Stop the recording process"""
        self.is_running = False
        if self.recorder:
            self.recorder.stop()
        return "Recording stopped!"
    
    def clear_conversation(self):
        """Clear all conversation data"""
        with self.transcription_lock:
            self.full_sentences = []
            self.last_transcription = ""
            self.current_conversation = "Conversation cleared!"
        
        if self.speaker_detector:
            self.speaker_detector = SpeakerChangeDetector(
                embedding_dim=self.encoder.embedding_dim,
                change_threshold=self.change_threshold,
                max_speakers=self.max_speakers
            )
        
        return "Conversation cleared!"
    
    def update_settings(self, threshold, max_speakers):
        """Update speaker detection settings"""
        self.change_threshold = threshold
        self.max_speakers = max_speakers
        
        if self.speaker_detector:
            self.speaker_detector.set_change_threshold(threshold)
            self.speaker_detector.set_max_speakers(max_speakers)
        
        return f"Settings updated: Threshold={threshold:.2f}, Max Speakers={max_speakers}"
    
    def get_formatted_conversation(self):
        """Get the formatted conversation"""
        return self.current_conversation
    
    def get_status_info(self):
        """Get current status information"""
        if not self.speaker_detector:
            return "Speaker detector not initialized"
        
        try:
            status = self.speaker_detector.get_status_info()
            
            status_lines = [
                f"**Current Speaker:** {status['current_speaker'] + 1}",
                f"**Active Speakers:** {status['active_speakers']} of {status['max_speakers']}",
                f"**Last Similarity:** {status['last_similarity']:.3f}",
                f"**Change Threshold:** {status['threshold']:.2f}",
                f"**Total Sentences:** {len(self.full_sentences)}",
                f"**Segments Processed:** {status['segment_counter']}",
                "",
                "**Speaker Activity:**"
            ]
            
            for i in range(status['max_speakers']):
                color_name = SPEAKER_COLOR_NAMES[i] if i < len(SPEAKER_COLOR_NAMES) else f"Speaker {i+1}"
                count = status['speaker_counts'][i]
                active = "🟢" if count > 0 else "⚫"
                status_lines.append(f"{active} Speaker {i+1} ({color_name}): {count} segments")
            
            return "\n".join(status_lines)
            
        except Exception as e:
            return f"Error getting status: {e}"

    def process_audio_chunk(self, audio_data, sample_rate=16000):
        """Process audio chunk from FastRTC input"""
        if not self.is_running or self.audio_processor is None:
            return
            
        try:
            # Ensure audio is float32
            if isinstance(audio_data, np.ndarray):
                if audio_data.dtype != np.float32:
                    audio_data = audio_data.astype(np.float32)
            else:
                audio_data = np.array(audio_data, dtype=np.float32)
            
            # Ensure mono
            if len(audio_data.shape) > 1:
                audio_data = np.mean(audio_data, axis=1) if audio_data.shape[1] > 1 else audio_data.flatten()
            
            # Normalize if needed
            if np.max(np.abs(audio_data)) > 1.0:
                audio_data = audio_data / np.max(np.abs(audio_data))
            
            # Add to audio processor buffer for speaker detection
            self.audio_processor.add_audio_chunk(audio_data)
            
            # Periodically extract embeddings for speaker detection
            if len(self.audio_processor.audio_buffer) % (SAMPLE_RATE // 2) == 0:  # Every 0.5 seconds
                embedding = self.audio_processor.extract_embedding_from_buffer()
                if embedding is not None:
                    self.speaker_detector.add_embedding(embedding)
                    
        except Exception as e:
            logger.error(f"Error processing audio chunk: {e}")


# FastRTC Audio Handler
class DiarizationHandler(AsyncStreamHandler):
    def __init__(self, diarization_system):
        super().__init__()
        self.diarization_system = diarization_system
        self.audio_buffer = []
        self.buffer_size = BUFFER_SIZE
        
    def copy(self):
        """Return a fresh handler for each new stream connection"""
        return DiarizationHandler(self.diarization_system)
    
    async def emit(self):
        """Not used - we only receive audio"""
        return None
    
    async def receive(self, frame):
        """Receive audio data from FastRTC"""
        try:
            if not self.diarization_system.is_running:
                return
                
            # Extract audio data
            audio_data = getattr(frame, 'data', frame)
            
            # Convert to numpy array
            if isinstance(audio_data, bytes):
                audio_array = np.frombuffer(audio_data, dtype=np.int16).astype(np.float32) / 32768.0
            elif isinstance(audio_data, (list, tuple)):
                audio_array = np.array(audio_data, dtype=np.float32)
            else:
                audio_array = np.array(audio_data, dtype=np.float32)
            
            # Ensure 1D
            if len(audio_array.shape) > 1:
                audio_array = audio_array.flatten()
            
            # Buffer audio chunks
            self.audio_buffer.extend(audio_array)
            
            # Process in chunks
            while len(self.audio_buffer) >= self.buffer_size:
                chunk = np.array(self.audio_buffer[:self.buffer_size])
                self.audio_buffer = self.audio_buffer[self.buffer_size:]
                
                # Process asynchronously
                await self.process_audio_async(chunk)
                
        except Exception as e:
            logger.error(f"Error in FastRTC receive: {e}")
    
    async def process_audio_async(self, audio_data):
        """Process audio data asynchronously"""
        try:
            loop = asyncio.get_event_loop()
            await loop.run_in_executor(
                None, 
                self.diarization_system.process_audio_chunk, 
                audio_data, 
                SAMPLE_RATE
            )
        except Exception as e:
            logger.error(f"Error in async audio processing: {e}")


# Global instances
diarization_system = RealtimeSpeakerDiarization()
audio_handler = None

def initialize_system():
    """Initialize the diarization system"""
    global audio_handler
    try:
        success = diarization_system.initialize_models()
        if success:
            audio_handler = DiarizationHandler(diarization_system)
            return "✅ System initialized successfully!"
        else:
            return "❌ Failed to initialize system. Check logs for details."
    except Exception as e:
        logger.error(f"Initialization error: {e}")
        return f"❌ Initialization error: {str(e)}"

def start_recording():
    """Start recording and transcription"""
    try:
        result = diarization_system.start_recording()
        # Connect WebRTC to server stream
        audio_webrtc.stream_url = "/stream"  # This is your FastRTC endpoint
        return result
    except Exception as e:
        return f"❌ Failed to start recording: {str(e)}"

def stop_recording():
    """Stop recording and transcription"""
    try:
        result = diarization_system.stop_recording()
        return f"⏹️ {result}"
    except Exception as e:
        return f"❌ Failed to stop recording: {str(e)}"

def clear_conversation():
    """Clear the conversation"""
    try:
        result = diarization_system.clear_conversation()
        return f"🗑️ {result}"
    except Exception as e:
        return f"❌ Failed to clear conversation: {str(e)}"

def update_settings(threshold, max_speakers):
    """Update system settings"""
    try:
        result = diarization_system.update_settings(threshold, max_speakers)
        return f"⚙️ {result}"
    except Exception as e:
        return f"❌ Failed to update settings: {str(e)}"

def get_conversation():
    """Get the current conversation"""
    try:
        return diarization_system.get_formatted_conversation()
    except Exception as e:
        return f"<i>Error getting conversation: {str(e)}</i>"

def get_status():
    """Get system status"""
    try:
        return diarization_system.get_status_info()
    except Exception as e:
        return f"Error getting status: {str(e)}"

# Create Gradio interface
def create_interface():
    with gr.Blocks(title="Real-time Speaker Diarization", theme=gr.themes.Soft()) as interface:
        gr.Markdown("# 🎤 Real-time Speech Recognition with Speaker Diarization")
        gr.Markdown("Live transcription with automatic speaker identification using FastRTC audio streaming.")
        
        with gr.Row():
            with gr.Column(scale=2):
                # Add WebRTC component for audio streaming
                audio_webrtc = WebRTC(
                    label="Audio Input",
                    sources=["microphone"],
                    streaming=True,
                    rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}
                )
                
                # Conversation display
                conversation_output = gr.HTML(
                    value="<div style='padding: 20px; background: #f8f9fa; border-radius: 10px; min-height: 300px;'><i>Click 'Initialize System' to start...</i></div>",
                    label="Live Conversation"
                )
                
                # Control buttons
                with gr.Row():
                    init_btn = gr.Button("🔧 Initialize System", variant="secondary", size="lg")
                    start_btn = gr.Button("🎙️ Start", variant="primary", size="lg", interactive=False)
                    stop_btn = gr.Button("⏹️ Stop", variant="stop", size="lg", interactive=False)
                    clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="lg", interactive=False)
                
                # Status display
                status_output = gr.Textbox(
                    label="System Status",
                    value="Ready to initialize...",
                    lines=8,
                    interactive=False
                )
            
            with gr.Column(scale=1):
                # Settings
                gr.Markdown("## ⚙️ Settings")
                
                threshold_slider = gr.Slider(
                    minimum=0.3,
                    maximum=0.9,
                    step=0.05,
                    value=DEFAULT_CHANGE_THRESHOLD,
                    label="Speaker Change Sensitivity",
                    info="Lower = more sensitive"
                )
                
                max_speakers_slider = gr.Slider(
                    minimum=2,
                    maximum=ABSOLUTE_MAX_SPEAKERS,
                    step=1,
                    value=DEFAULT_MAX_SPEAKERS,
                    label="Maximum Speakers"
                )
                
                update_btn = gr.Button("Update Settings", variant="secondary")
                
                # Instructions
                gr.Markdown("""
                ## 📋 Instructions
                1. **Initialize** the system (loads AI models)
                2. **Start** recording 
                3. **Speak** - system will transcribe and identify speakers
                4. **Monitor** real-time results below
                
                ## 🎨 Speaker Colors
                - 🔴 Speaker 1 (Red)
                - 🟢 Speaker 2 (Teal) 
                - 🔵 Speaker 3 (Blue)
                - 🟡 Speaker 4 (Green)
                - 🟣 Speaker 5 (Yellow)
                - 🟤 Speaker 6 (Plum)
                - 🟫 Speaker 7 (Mint)
                - 🟨 Speaker 8 (Gold)
                """)
        
        # Event handlers
        def on_initialize():
            result = initialize_system()
            if "✅" in result:
                return result, gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
            else:
                return result, gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
        
        def on_start():
            result = start_recording()
            # Connect WebRTC to server stream
            audio_webrtc.stream_url = "/stream"  # This is your FastRTC endpoint
            return result, gr.update(interactive=False), gr.update(interactive=True)
        
        def on_stop():
            result = stop_recording()
            return result, gr.update(interactive=True), gr.update(interactive=False)
        
        def on_clear():
            result = clear_conversation()
            return result
        
        def on_update_settings(threshold, max_speakers):
            result = update_settings(threshold, int(max_speakers))
            return result
        
        def refresh_conversation():
            return get_conversation()
        
        def refresh_status():
            return get_status()
        
        # Button click handlers
        init_btn.click(
            fn=on_initialize,
            outputs=[status_output, start_btn, stop_btn, clear_btn]
        )
        
        start_btn.click(
            fn=on_start,
            outputs=[status_output, start_btn, stop_btn]
        )
        
        stop_btn.click(
            fn=on_stop,
            outputs=[status_output, start_btn, stop_btn]
        )
        
        clear_btn.click(
            fn=on_clear,
            outputs=[status_output]
        )
        
        update_btn.click(
            fn=on_update_settings,
            inputs=[threshold_slider, max_speakers_slider],
            outputs=[status_output]
        )
        
        # Auto-refresh conversation display every 1 second
        conversation_timer = gr.Timer(1)
        conversation_timer.tick(refresh_conversation, outputs=[conversation_output])
        
        # Auto-refresh status every 2 seconds  
        status_timer = gr.Timer(2)
        status_timer.tick(refresh_status, outputs=[status_output])
    
    return interface


# FastAPI setup for FastRTC integration
app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Real-time Speaker Diarization API"}

@app.get("/health")
async def health_check():
    return {"status": "healthy", "system_running": diarization_system.is_running}

@app.post("/initialize")
async def api_initialize():
    result = initialize_system()
    return {"result": result, "success": "✅" in result}

@app.post("/start")
async def api_start():
    result = start_recording()
    return {"result": result, "success": "🎙️" in result}

@app.post("/stop")
async def api_stop():
    result = stop_recording()
    return {"result": result, "success": "⏹️" in result}

@app.post("/clear")
async def api_clear():
    result = clear_conversation()
    return {"result": result}

@app.get("/conversation")
async def api_get_conversation():
    return {"conversation": get_conversation()}

@app.get("/status")
async def api_get_status():
    return {"status": get_status()}

@app.post("/settings")
async def api_update_settings(threshold: float, max_speakers: int):
    result = update_settings(threshold, max_speakers)
    return {"result": result}

# FastRTC Stream setup
if audio_handler:
    stream = Stream(handler=audio_handler)
    app.include_router(stream.router, prefix="/stream")


# Main execution
if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description="Real-time Speaker Diarization System")
    parser.add_argument("--mode", choices=["gradio", "api", "both"], default="gradio", 
                       help="Run mode: gradio interface, API only, or both")
    parser.add_argument("--host", default="0.0.0.0", help="Host to bind to")
    parser.add_argument("--port", type=int, default=7860, help="Port to bind to")
    parser.add_argument("--api-port", type=int, default=8000, help="API port (when running both)")
    
    args = parser.parse_args()
    
    if args.mode == "gradio":
        # Run Gradio interface only
        interface = create_interface()
        interface.launch(
            server_name=args.host,
            server_port=args.port,
            share=True,
            show_error=True
        )
    
    elif args.mode == "api":
        # Run FastAPI only
        uvicorn.run(
            app, 
            host=args.host, 
            port=args.port,
            log_level="info"
        )
    
    elif args.mode == "both":
        # Run both Gradio and FastAPI
        import multiprocessing
        import threading
        
        def run_gradio():
            interface = create_interface()
            interface.launch(
                server_name=args.host,
                server_port=args.port,
                share=True,
                show_error=True
            )
        
        def run_fastapi():
            uvicorn.run(
                app,
                host=args.host,
                port=args.api_port,
                log_level="info"
            )
        
        # Start FastAPI in a separate thread
        api_thread = threading.Thread(target=run_fastapi, daemon=True)
        api_thread.start()
        
        # Start Gradio in main thread
        run_gradio()