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
import json
import io
import wave
import asyncio
import uvicorn
import logging

# Configure logging to reduce noise
logging.getLogger("uvicorn").setLevel(logging.WARNING)
logging.getLogger("gradio").setLevel(logging.WARNING)

# 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 load_model(self):
        """Load the ECAPA-TDNN model with error handling"""
        try:
            # Try to import speechbrain
            try:
                from speechbrain.pretrained import EncoderClassifier
            except ImportError:
                print("SpeechBrain not available. Using fallback embedding model.")
                return self._load_fallback_model()
            
            self.model = EncoderClassifier.from_hparams(
                source="speechbrain/spkrec-ecapa-voxceleb",
                savedir=self.cache_dir,
                run_opts={"device": self.device}
            )
            
            self.model_loaded = True
            print("ECAPA-TDNN model loaded successfully!")
            return True
        except Exception as e:
            print(f"Error loading ECAPA-TDNN model: {e}")
            return self._load_fallback_model()
    
    def _load_fallback_model(self):
        """Fallback to a simple embedding model if SpeechBrain is not available"""
        print("Using fallback embedding model (simple spectral features)")
        self.model_loaded = True
        return True
    
    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 self.model is not None:
                # Use SpeechBrain model
                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()
            else:
                # Use fallback method - simple spectral features
                return self._extract_simple_features(audio)
        except Exception as e:
            print(f"Error extracting embedding: {e}")
            return self._extract_simple_features(audio)
    
    def _extract_simple_features(self, audio):
        """Simple fallback feature extraction"""
        try:
            # Ensure audio is numpy array
            if isinstance(audio, torch.Tensor):
                audio = audio.numpy()
            
            # Basic spectral features as a fallback
            fft = np.fft.fft(audio)
            magnitude = np.abs(fft)
            
            # Take first 192 features to match expected embedding dimension
            features = magnitude[:self.embedding_dim] if len(magnitude) >= self.embedding_dim else np.pad(magnitude, (0, self.embedding_dim - len(magnitude)))
            
            # Normalize
            features = features / (np.linalg.norm(features) + 1e-8)
            
            return features.astype(np.float32)
        except Exception as e:
            print(f"Error in fallback feature extraction: {e}")
            return np.random.randn(self.embedding_dim).astype(np.float32)


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("Speaker diarization model loaded successfully!")
                return True
            else:
                print("Failed to load speaker diarization 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:
                if self.recorder:
                    self.recorder.stop()
            elif prob_sentence_end:
                if self.recorder:
                    self.recorder.post_speech_silence_duration = SILENCE_THRESHS[0]
            else:
                if self.recorder:
                    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:
                if self.recorder and hasattr(self.recorder, 'last_transcription_bytes'):
                    bytes_data = self.recorder.last_transcription_bytes
                    self.sentence_queue.put((text, bytes_data))
                else:
                    # Use audio buffer as fallback
                    if self.audio_buffer:
                        audio_data = np.concatenate(self.audio_buffer)
                        bytes_data = audio_data.tobytes()
                        self.sentence_queue.put((text, bytes_data))
                        self.audio_buffer = []  # Clear buffer after use
                
                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:
            # Check if RealtimeSTT is available
            try:
                from RealtimeSTT import AudioToTextRecorder
                recorder_available = True
            except ImportError:
                print("RealtimeSTT not available. Using simulated audio processing.")
                recorder_available = False
            
            if recorder_available:
                # Setup recorder configuration
                recorder_config = {
                    'spinner': False,
                    'use_microphone': True,
                    '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()
            
            if recorder_available:
                # Start transcription thread
                self.transcription_thread = threading.Thread(target=self.run_transcription, daemon=True)
                self.transcription_thread.start()
                return "Recording started successfully! Please speak into your microphone."
            else:
                return "Simulation mode active. Speaker diarization ready for audio input."
            
        except Exception as e:
            return f"Error starting recording: {e}"
    
    def run_transcription(self):
        """Run the transcription loop"""
        try:
            while self.is_running and self.recorder:
                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:
            try:
                self.recorder.stop()
            except:
                pass
        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!"
        self.audio_buffer = []
        
        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 process_audio(self, audio_data):
        """Process audio data from external sources"""
        if not self.is_running:
            return
        
        try:
            # Handle different audio data formats
            if isinstance(audio_data, tuple) and len(audio_data) == 2:
                sample_rate, audio_array = audio_data
            else:
                audio_array = audio_data
                sample_rate = SAMPLE_RATE
            
            # Convert to int16 format
            if audio_array.dtype != np.int16:
                if audio_array.dtype == np.float32 or audio_array.dtype == np.float64:
                    audio_array = (audio_array * 32767).astype(np.int16)
                else:
                    audio_array = audio_array.astype(np.int16)
            
            # Store in buffer for later processing
            self.audio_buffer.append(audio_array)
            
            # Process if we have enough audio data
            if len(self.audio_buffer) > 10:  # Process every ~0.5 seconds of audio
                combined_audio = np.concatenate(self.audio_buffer)
                
                # Simulate transcription for demonstration
                if len(combined_audio) > SAMPLE_RATE:  # At least 1 second of audio
                    # In a real implementation, this would be transcribed text
                    demo_text = f"Sample speech segment {len(self.full_sentences) + 1}"
                    self.process_final_text(demo_text)
                
                self.audio_buffer = []  # Clear buffer
                
        except Exception as e:
            print(f"Error processing audio: {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):
                # Audio input component
                audio_input = gr.Audio(
                    label="🎙️ Audio Input",
                    sources=["microphone"],
                    type="numpy",
                    streaming=True
                )
                
                # 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 microphone input above to record audio
                4. Watch real-time transcription with speaker labels
                5. 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]))
        
        # Audio processing function
        def process_audio_stream(audio_data):
            if audio_data is not None and diarization_system.is_running:
                diarization_system.process_audio(audio_data)
                return diarization_system.get_formatted_conversation()
            return None
        
        # 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]
        )
        
        # Connect audio input to processing function
        audio_input.stream(
            process_audio_stream,
            inputs=[audio_input],
            outputs=[conversation_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


# 1) Create FastAPI app
app = FastAPI()

# 2) Create Gradio interface
gradio_interface = create_interface()

# 3) Mount Gradio onto FastAPI at root
app = gr.mount_gradio_app(app, gradio_interface, path="/")

# 4) Local dev via uvicorn; HF Spaces will auto-detect 'app' and ignore this
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)