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, ReplyOnPause, AudioStreamHandler
import json
import asyncio
import uvicorn
from queue import Queue
import logging

# 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 feed_audio(self, audio_data):
        """Feed audio data directly to the recorder for live transcription"""
        if not self.is_running or not self.recorder:
            return
            
        try:
            # Normalize if needed
            if isinstance(audio_data, np.ndarray):
                if audio_data.dtype != np.float32:
                    audio_data = audio_data.astype(np.float32)
                
                # Convert to int16 for the recorder
                audio_int16 = (audio_data * 32767).astype(np.int16)
                audio_bytes = audio_int16.tobytes()
                
                # Feed to recorder
                self.recorder.feed_audio(audio_bytes)
                
                # Also process for speaker detection
                self.process_audio_chunk(audio_data)
                
            elif isinstance(audio_data, bytes):
                # Feed raw bytes directly
                self.recorder.feed_audio(audio_data)
                
                # Convert to float for speaker detection
                audio_int16 = np.frombuffer(audio_data, dtype=np.int16)
                audio_float = audio_int16.astype(np.float32) / 32768.0
                self.process_audio_chunk(audio_float)
                
            logger.debug("Audio fed to recorder")
        except Exception as e:
            logger.error(f"Error feeding audio: {e}")
    
    def live_text_detected(self, text):
        """Callback for real-time transcription updates"""
        with self.transcription_lock:
            self.last_transcription = text.strip()
            
        # Update the display immediately on new transcription
        self.update_conversation_display()
    
    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}")


# Create diarization handler for FastRTC
class DiarizationAudioHandler(AudioStreamHandler):
    def __init__(self, diarization_system):
        super().__init__()
        self.diarization_system = diarization_system
        
    def receive(self, frame):
        """Process incoming audio frame"""
        if not self.diarization_system.is_running:
            return
            
        try:
            # Extract audio data
            sample_rate, audio_array = frame
            
            # Send audio to diarization system for processing
            self.diarization_system.feed_audio(audio_array)
        except Exception as e:
            logger.error(f"Error processing FastRTC audio: {e}")
    
    def copy(self):
        """Return a fresh handler instance"""
        return DiarizationAudioHandler(self.diarization_system)
    
    def shutdown(self):
        """Clean up resources"""
        pass
    
    def start_up(self):
        """Initialize resources"""
        logger.info("DiarizationAudioHandler started")


# Global diarization system instance
diarization_system = RealtimeSpeakerDiarization()

def initialize_system():
    """Initialize the diarization system"""
    try:
        success = diarization_system.initialize_models()
        if success:
            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()
        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 handler wrapper function for FastRTC
def diarization_handler(audio_data):
    """Handler function for FastRTC stream"""
    try:
        # Process the audio data
        diarization_system.process_audio_chunk(audio_data[1], audio_data[0])
        
        # Just yield the original audio back (echo)
        # This can be changed to just return None since we don't need echo
        # This can be changed to just return None since we don't need echo
        yield audio_data
        
    except Exception as e:
        logger.error(f"Error in diarization handler: {e}")

# Create FastRTC stream with ReplyOnPause pattern
stream = Stream(
    handler=ReplyOnPause(diarization_handler),
    modality="audio",
    mode="send-receive",
    ui_args={
        "title": "Real-time Speaker Diarization",
        "description": "Live transcription with automatic speaker identification"
    }
)

# Main execution
if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description="Real-time Speaker Diarization System")
    parser.add_argument("--mode", choices=["ui", "api", "both"], default="ui", 
                       help="Run mode: FastRTC UI, 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()
    
    # Initialize the system before running anything
    initialize_system()
    start_recording()
    
    if args.mode == "ui":
        # Launch the FastRTC built-in UI
        stream.ui.launch(
            server_name=args.host,
            server_port=args.port,
            share=True,
            show_error=True
        )
    
    elif args.mode == "api":
        # Run FastAPI only
        app = FastAPI()
        stream.mount(app)
        uvicorn.run(
            app, 
            host=args.host, 
            port=args.port,
            log_level="info"
        )
    
    elif args.mode == "both":
        # Run both FastRTC UI and API
        import threading
        
        def run_fastapi():
            app = FastAPI()
            stream.mount(app)
            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 FastRTC UI in main thread
        stream.ui.launch(
            server_name=args.host,
            server_port=args.port,
            share=True,
            show_error=True
        )