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, ReplyOnPause, get_cloudflare_turn_credentials_async, get_cloudflare_turn_credentials
import json
import io
import wave
import asyncio
import uvicorn

# 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 = 0.7
EMBEDDING_HISTORY_SIZE = 5
MIN_SEGMENT_DURATION = 1.0
DEFAULT_MAX_SPEAKERS = 4
ABSOLUTE_MAX_SPEAKERS = 10

# Global variables
FAST_SENTENCE_END = True
SAMPLE_RATE = 16000
BUFFER_SIZE = 512
CHANNELS = 1

# Speaker colors
SPEAKER_COLORS = [
    "#FFFF00",  # Yellow
    "#FF0000",  # Red
    "#00FF00",  # Green
    "#00FFFF",  # Cyan
    "#FF00FF",  # Magenta
    "#0000FF",  # Blue
    "#FF8000",  # Orange
    "#00FF80",  # Spring Green
    "#8000FF",  # Purple
    "#FFFFFF",  # White
]

SPEAKER_COLOR_NAMES = [
    "Yellow", "Red", "Green", "Cyan", "Magenta", 
    "Blue", "Orange", "Spring Green", "Purple", "White"
]


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 _download_model(self):
        """Download pre-trained SpeechBrain ECAPA-TDNN model if not present"""
        model_url = "https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb/resolve/main/embedding_model.ckpt"
        model_path = os.path.join(self.cache_dir, "embedding_model.ckpt")
        
        if not os.path.exists(model_path):
            print(f"Downloading ECAPA-TDNN model to {model_path}...")
            urllib.request.urlretrieve(model_url, model_path)
        
        return model_path
    
    def load_model(self):
        """Load the ECAPA-TDNN model"""
        try:
            from speechbrain.pretrained import EncoderClassifier
            
            model_path = self._download_model()
            
            self.model = EncoderClassifier.from_hparams(
                source="speechbrain/spkrec-ecapa-voxceleb",
                savedir=self.cache_dir,
                run_opts={"device": self.device}
            )
            
            self.model_loaded = True
            return True
        except Exception as e:
            print(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):
                waveform = torch.tensor(audio, dtype=torch.float32).unsqueeze(0)
            else:
                waveform = audio.unsqueeze(0)
            
            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:
            print(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
    
    def extract_embedding(self, audio_int16):
        try:
            float_audio = audio_int16.astype(np.float32) / 32768.0
            
            if np.abs(float_audio).max() > 1.0:
                float_audio = float_audio / np.abs(float_audio).max()
            
            embedding = self.encoder.embed_utterance(float_audio)
            
            return embedding
        except Exception as e:
            print(f"Embedding extraction error: {e}")
            return np.zeros(self.encoder.embedding_dim)


class SpeakerChangeDetector:
    """Speaker change detector that supports a configurable number of speakers"""
    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.previous_embeddings = []
        self.last_change_time = time.time()
        self.mean_embeddings = [None] * self.max_speakers
        self.speaker_embeddings = [[] for _ in range(self.max_speakers)]
        self.last_similarity = 0.0
        self.active_speakers = set([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:
            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
        
        if new_max > self.max_speakers:
            self.mean_embeddings.extend([None] * (new_max - self.max_speakers))
            self.speaker_embeddings.extend([[] for _ in range(new_max - self.max_speakers)])
        else:
            self.mean_embeddings = self.mean_embeddings[:new_max]
            self.speaker_embeddings = self.speaker_embeddings[: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, 0.99))
        
    def add_embedding(self, embedding, timestamp=None):
        """Add a new embedding and check if there's a speaker change"""
        current_time = timestamp or time.time()
        
        if not self.previous_embeddings:
            self.previous_embeddings.append(embedding)
            self.speaker_embeddings[self.current_speaker].append(embedding)
            if self.mean_embeddings[self.current_speaker] is None:
                self.mean_embeddings[self.current_speaker] = embedding.copy()
            return self.current_speaker, 1.0
        
        current_mean = self.mean_embeddings[self.current_speaker]
        if current_mean is not None:
            similarity = 1.0 - cosine(embedding, current_mean)
        else:
            similarity = 1.0 - cosine(embedding, self.previous_embeddings[-1])
        
        self.last_similarity = similarity
        
        time_since_last_change = current_time - self.last_change_time
        is_speaker_change = False
        
        if time_since_last_change >= MIN_SEGMENT_DURATION:
            if similarity < self.change_threshold:
                best_speaker = self.current_speaker
                best_similarity = similarity
                
                for speaker_id in range(self.max_speakers):
                    if speaker_id == self.current_speaker:
                        continue
                        
                    speaker_mean = self.mean_embeddings[speaker_id]
                    
                    if speaker_mean is not None:
                        speaker_similarity = 1.0 - cosine(embedding, speaker_mean)
                        
                        if speaker_similarity > best_similarity:
                            best_similarity = speaker_similarity
                            best_speaker = speaker_id
                
                if best_speaker != self.current_speaker:
                    is_speaker_change = True
                    self.current_speaker = best_speaker
                elif len(self.active_speakers) < self.max_speakers:
                    for new_id in range(self.max_speakers):
                        if new_id not in self.active_speakers:
                            is_speaker_change = True
                            self.current_speaker = new_id
                            self.active_speakers.add(new_id)
                            break
        
        if is_speaker_change:
            self.last_change_time = current_time
        
        self.previous_embeddings.append(embedding)
        if len(self.previous_embeddings) > EMBEDDING_HISTORY_SIZE:
            self.previous_embeddings.pop(0)
        
        self.speaker_embeddings[self.current_speaker].append(embedding)
        self.active_speakers.add(self.current_speaker)
        
        if len(self.speaker_embeddings[self.current_speaker]) > 30:
            self.speaker_embeddings[self.current_speaker] = self.speaker_embeddings[self.current_speaker][-30:]
            
        if self.speaker_embeddings[self.current_speaker]:
            self.mean_embeddings[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 about the speaker change detector"""
        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
        }


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.displayed_text = ""
        self.last_realtime_text = ""
        self.is_running = False
        self.change_threshold = DEFAULT_CHANGE_THRESHOLD
        self.max_speakers = DEFAULT_MAX_SPEAKERS
        self.current_conversation = ""
        self.audio_buffer = []
        
    def initialize_models(self):
        """Initialize the speaker encoder model"""
        try:
            device_str = "cuda" if torch.cuda.is_available() else "cpu"
            print(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
                )
                print("ECAPA-TDNN model loaded successfully!")
                return True
            else:
                print("Failed to load ECAPA-TDNN model")
                return False
        except Exception as e:
            print(f"Model initialization error: {e}")
            return False
    
    def live_text_detected(self, text):
        """Callback for real-time transcription updates"""
        text = text.strip()
        if text:
            sentence_delimiters = '.?!。'
            prob_sentence_end = (
                len(self.last_realtime_text) > 0
                and text[-1] in sentence_delimiters
                and self.last_realtime_text[-1] in sentence_delimiters
            )

            self.last_realtime_text = text

            if prob_sentence_end and FAST_SENTENCE_END:
                self.recorder.stop()
            elif prob_sentence_end:
                self.recorder.post_speech_silence_duration = SILENCE_THRESHS[0]
            else:
                self.recorder.post_speech_silence_duration = SILENCE_THRESHS[1]
    
    def process_final_text(self, text):
        """Process final transcribed text with speaker embedding"""
        text = text.strip()
        if text:
            try:
                bytes_data = self.recorder.last_transcription_bytes
                self.sentence_queue.put((text, bytes_data))
                self.pending_sentences.append(text)
            except Exception as e:
                print(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, bytes_data = self.sentence_queue.get(timeout=1)
                
                # Convert audio data to int16
                audio_int16 = np.frombuffer(bytes_data, dtype=np.int16)
                
                # Extract speaker embedding
                speaker_embedding = self.audio_processor.extract_embedding(audio_int16)
                
                # Store sentence and embedding
                self.full_sentences.append((text, speaker_embedding))
                
                # Fill in missing speaker assignments
                while len(self.sentence_speakers) < len(self.full_sentences) - 1:
                    self.sentence_speakers.append(0)
                
                # Detect speaker changes
                speaker_id, similarity = self.speaker_detector.add_embedding(speaker_embedding)
                self.sentence_speakers.append(speaker_id)
                
                # Remove from pending
                if text in self.pending_sentences:
                    self.pending_sentences.remove(text)
                
                # Update conversation display
                self.current_conversation = self.get_formatted_conversation()
                    
            except queue.Empty:
                continue
            except Exception as e:
                print(f"Error processing sentence: {e}")
    
    def start_recording(self):
        """Start the recording and transcription process"""
        if self.encoder is None:
            return "Please initialize models first!"
        
        try:
            # Setup recorder configuration for manual audio input
            recorder_config = {
                'spinner': False,
                'use_microphone': False,  # We'll feed audio manually
                '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,
                '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,
                'buffer_size': BUFFER_SIZE,
                'sample_rate': SAMPLE_RATE,
            }

            self.recorder = AudioToTextRecorder(**recorder_config)
            
            # Start sentence processing thread
            self.is_running = True
            self.sentence_thread = threading.Thread(target=self.process_sentence_queue, daemon=True)
            self.sentence_thread.start()
            
            # Start transcription thread
            self.transcription_thread = threading.Thread(target=self.run_transcription, daemon=True)
            self.transcription_thread.start()
            
            return "Recording started successfully! FastRTC audio input ready."
            
        except Exception as 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:
            print(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"""
        self.full_sentences = []
        self.sentence_speakers = []
        self.pending_sentences = []
        self.displayed_text = ""
        self.last_realtime_text = ""
        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 with speaker colors"""
        try:
            sentences_with_style = []
            
            # Process completed sentences
            for i, sentence in enumerate(self.full_sentences):
                sentence_text, _ = sentence
                if i >= len(self.sentence_speakers):
                    color = "#FFFFFF"
                    speaker_name = "Unknown"
                else:
                    speaker_id = self.sentence_speakers[i]
                    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};"><b>{speaker_name}:</b> {sentence_text}</span>')
            
            # Add pending sentences
            for pending_sentence in self.pending_sentences:
                sentences_with_style.append(
                    f'<span style="color:#60FFFF;"><b>Processing:</b> {pending_sentence}</span>')
            
            if sentences_with_style:
                return "<br><br>".join(sentences_with_style)
            else:
                return "Waiting for speech input..."
                
        except Exception as e:
            return f"Error formatting conversation: {e}"
    
    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)}",
                "",
                "**Speaker Segment Counts:**"
            ]
            
            for i in range(status['max_speakers']):
                color_name = SPEAKER_COLOR_NAMES[i] if i < len(SPEAKER_COLOR_NAMES) else f"Speaker {i+1}"
                status_lines.append(f"Speaker {i+1} ({color_name}): {status['speaker_counts'][i]}")
            
            return "\n".join(status_lines)
            
        except Exception as e:
            return f"Error getting status: {e}"

    def feed_audio_data(self, audio_data):
        """Feed audio data to the recorder"""
        if not self.is_running or not self.recorder:
            return
        
        try:
            # Ensure audio is in the correct format (16-bit PCM)
            if isinstance(audio_data, np.ndarray):
                if audio_data.dtype != np.int16:
                    # Convert float to int16
                    if audio_data.dtype == np.float32 or audio_data.dtype == np.float64:
                        audio_data = (audio_data * 32767).astype(np.int16)
                    else:
                        audio_data = audio_data.astype(np.int16)
                
                # Convert to bytes
                audio_bytes = audio_data.tobytes()
            else:
                audio_bytes = audio_data
            
            # Feed to recorder
            self.recorder.feed_audio(audio_bytes)
            
        except Exception as e:
            print(f"Error feeding audio data: {e}")


# FastRTC Audio Handler
class DiarizationHandler(AsyncStreamHandler):
    def __init__(self, diarization_system):
        super().__init__(reply_on_pause=ReplyOnPause.NEVER)
        self.diarization_system = diarization_system
        
    def copy(self):
        # Return a fresh handler for each new stream connection
        return DiarizationHandler(self.diarization_system)
    
    async def emit(self):
        """Not used in this implementation"""
        return None
    
    async def receive(self, frame):
        """Receive audio data from FastRTC and process it"""
        try:
            if self.diarization_system.is_running:
                # Frame should be a numpy array of audio data
                if hasattr(frame, 'data'):
                    audio_data = frame.data
                else:
                    audio_data = frame
                
                # Feed audio data to the diarization system
                self.diarization_system.feed_audio_data(audio_data)
                
        except Exception as e:
            print(f"Error in FastRTC handler: {e}")


# Global instance
diarization_system = RealtimeSpeakerDiarization()


def initialize_system():
    """Initialize the diarization system"""
    success = diarization_system.initialize_models()
    if success:
        return "✅ System initialized successfully! Models loaded."
    else:
        return "❌ Failed to initialize system. Please check the logs."


def start_recording():
    """Start recording and transcription"""
    return diarization_system.start_recording()


def stop_recording():
    """Stop recording and transcription"""
    return diarization_system.stop_recording()


def clear_conversation():
    """Clear the conversation"""
    return diarization_system.clear_conversation()


def update_settings(threshold, max_speakers):
    """Update system settings"""
    return diarization_system.update_settings(threshold, max_speakers)


def get_conversation():
    """Get the current conversation"""
    return diarization_system.get_formatted_conversation()


def get_status():
    """Get system status"""
    return diarization_system.get_status_info()


# Create Gradio interface
def create_interface():
    with gr.Blocks(title="Real-time Speaker Diarization", theme=gr.themes.Monochrome()) as interface:
        gr.Markdown("# 🎤 Real-time Speech Recognition with Speaker Diarization")
        gr.Markdown("This app performs real-time speech recognition with automatic speaker identification and color-coding.")
        
        with gr.Row():
            with gr.Column(scale=2):
                # Main conversation display
                conversation_output = gr.HTML(
                    value="<i>Click 'Initialize System' to start...</i>",
                    label="Live Conversation"
                )
                
                # Control buttons
                with gr.Row():
                    init_btn = gr.Button("🔧 Initialize System", variant="secondary")
                    start_btn = gr.Button("🎙️ Start Recording", variant="primary", interactive=False)
                    stop_btn = gr.Button("⏹️ Stop Recording", variant="stop", interactive=False)
                    clear_btn = gr.Button("🗑️ Clear Conversation", interactive=False)
                
                # Status display
                status_output = gr.Textbox(
                    label="System Status",
                    value="System not initialized",
                    lines=8,
                    interactive=False
                )
            
            with gr.Column(scale=1):
                # Settings panel
                gr.Markdown("## ⚙️ Settings")
                
                threshold_slider = gr.Slider(
                    minimum=0.1,
                    maximum=0.95,
                    step=0.05,
                    value=DEFAULT_CHANGE_THRESHOLD,
                    label="Speaker Change Sensitivity",
                    info="Lower values = more sensitive to speaker changes"
                )
                
                max_speakers_slider = gr.Slider(
                    minimum=2,
                    maximum=ABSOLUTE_MAX_SPEAKERS,
                    step=1,
                    value=DEFAULT_MAX_SPEAKERS,
                    label="Maximum Number of Speakers"
                )
                
                update_settings_btn = gr.Button("Update Settings")
                
                # Instructions
                gr.Markdown("## 📝 Instructions")
                gr.Markdown("""
                1. Click **Initialize System** to load models
                2. Click **Start Recording** to begin processing
                3. Use the FastRTC interface below to connect your microphone
                4. Allow microphone access when prompted
                5. Speak into your microphone
                6. Watch real-time transcription with speaker labels
                7. Adjust settings as needed
                """)
                
                # Speaker color legend
                gr.Markdown("## 🎨 Speaker Colors")
                color_info = []
                for i, (color, name) in enumerate(zip(SPEAKER_COLORS, SPEAKER_COLOR_NAMES)):
                    color_info.append(f'<span style="color:{color};">■</span> Speaker {i+1} ({name})')
                
                gr.HTML("<br>".join(color_info[:DEFAULT_MAX_SPEAKERS]))
                
                # FastRTC Integration Notice
                gr.Markdown("""
                ## ℹ️ About FastRTC
                This app uses FastRTC for low-latency audio streaming. 
                For optimal performance, use a modern browser and allow microphone access when prompted.
                """)
        
        # Auto-refresh conversation and status
        def refresh_display():
            return diarization_system.get_formatted_conversation(), diarization_system.get_status_info()
        
        # Event handlers
        def on_initialize():
            result = initialize_system()
            if "successfully" in result:
                return (
                    result,
                    gr.update(interactive=True),   # start_btn
                    gr.update(interactive=True),   # clear_btn
                    get_conversation(),
                    get_status()
                )
            else:
                return (
                    result,
                    gr.update(interactive=False),  # start_btn
                    gr.update(interactive=False),  # clear_btn
                    get_conversation(),
                    get_status()
                )
        
        def on_start():
            result = start_recording()
            return (
                result,
                gr.update(interactive=False),  # start_btn
                gr.update(interactive=True),   # stop_btn
            )
        
        def on_stop():
            result = stop_recording()
            return (
                result,
                gr.update(interactive=True),   # start_btn
                gr.update(interactive=False),  # stop_btn
            )
        
        # Connect event handlers
        init_btn.click(
            on_initialize,
            outputs=[status_output, start_btn, clear_btn, conversation_output, status_output]
        )
        
        start_btn.click(
            on_start,
            outputs=[status_output, start_btn, stop_btn]
        )
        
        stop_btn.click(
            on_stop,
            outputs=[status_output, start_btn, stop_btn]
        )
        
        clear_btn.click(
            clear_conversation,
            outputs=[status_output]
        )
        
        update_settings_btn.click(
            update_settings,
            inputs=[threshold_slider, max_speakers_slider],
            outputs=[status_output]
        )
        
        # Auto-refresh every 2 seconds when recording
        refresh_timer = gr.Timer(2.0)
        refresh_timer.tick(
            refresh_display,
            outputs=[conversation_output, status_output]
        )
    
    return interface


# Create API router for endpoints
router = APIRouter()

# Health check endpoint
@router.get("/health")
async def health_check():
    """Health check endpoint"""
    return {
        "status": "healthy",
        "timestamp": time.time(),
        "system_initialized": diarization_system.encoder is not None,
        "recording_active": diarization_system.is_running
    }


# API endpoint to get conversation
@router.get("/api/conversation")
async def get_conversation_api():
    """API endpoint to get current conversation"""
    return {
        "conversation": diarization_system.get_formatted_conversation(),
        "status": diarization_system.get_status_info(),
        "is_recording": diarization_system.is_running
    }


# API endpoint to control recording
@router.post("/api/control/{action}")
async def control_recording(action: str):
    """API endpoint to control recording (start/stop/clear/initialize)"""
    if action == "start":
        result = diarization_system.start_recording()
    elif action == "stop":
        result = diarization_system.stop_recording()
    elif action == "clear":
        result = diarization_system.clear_conversation()
    elif action == "initialize":
        result = initialize_system()
    else:
        return {"error": "Invalid action. Use: start, stop, clear, or initialize"}
    
    return {"result": result, "is_recording": diarization_system.is_running}


# Main application setup
def create_app():
    """Create and configure the FastAPI app with Gradio and FastRTC"""
    # Create FastAPI app
    app = FastAPI(
        title="Real-time Speaker Diarization",
        description="Real-time speech recognition with speaker diarization using FastRTC",
        version="1.0.0"
    )
    
    # Include API routes
    app.include_router(router)
    
    # Create Gradio interface
    gradio_interface = create_interface()
    
    # Mount Gradio interface
    app = gr.mount_gradio_app(app, gradio_interface, path="/")
    
    # Setup FastRTC stream
    try:
        # Create the handler
        handler = DiarizationHandler(diarization_system)
        
        # Get TURN credentials
        hf_token = os.environ.get("HF_TOKEN")
        if not hf_token:
            print("Warning: HF_TOKEN not set. Audio streaming may not work properly.")
            # Use basic STUN server as fallback
            rtc_config = {
                "iceServers": [{"urls": "stun:stun.l.google.com:19302"}]
            }
        else:
            # Get Cloudflare TURN credentials
            turn_credentials = get_cloudflare_turn_credentials(hf_token)
            rtc_config = {
                "iceServers": [
                    {"urls": "stun:stun.l.google.com:19302"},
                    {
                        "urls": f"turn:{turn_credentials['urls'][0]}",
                        "username": turn_credentials["username"],
                        "credential": turn_credentials["credential"]
                    }
                ]
            }
        
        # Create FastRTC stream
        stream = Stream(
            handler=handler,
            rtc_config=rtc_config,
            audio_sample_rate=SAMPLE_RATE,
            audio_channels=CHANNELS
        )
        
        # Add FastRTC endpoints
        app.mount("/stream", stream.app)
        
        print("FastRTC stream configured successfully!")
        
    except Exception as e:
        print(f"Warning: Failed to setup FastRTC stream: {e}")
        print("Audio streaming will not be available.")
    
    return app


# Main entry point
if __name__ == "__main__":
    # Create the app
    app = create_app()
    
    # Configuration
    host = os.environ.get("HOST", "0.0.0.0")
    port = int(os.environ.get("PORT", 7860))
    
    print(f"""
    🎤 Real-time Speaker Diarization Server
    =====================================
    
    Starting server on: http://{host}:{port}
    
    Features:
    - Real-time speech recognition
    - Speaker diarization with color coding
    - FastRTC low-latency audio streaming
    - Web interface for easy interaction
    
    Make sure to:
    1. Set HF_TOKEN environment variable for TURN server access
    2. Allow microphone access in your browser
    3. Use a modern browser for best performance
    
    API Endpoints:
    - GET  /health - Health check
    - GET  /api/conversation - Get current conversation
    - POST /api/control/{{action}} - Control recording (start/stop/clear/initialize)
    - WS   /stream - FastRTC audio stream endpoint
    
    """)
    
    # Run the server
    uvicorn.run(
        app,
        host=host,
        port=port,
        log_level="info",
        access_log=True
    )