import sys
import time
import requests
import threading
import json
from unittest.mock import MagicMock

# Comprehensive workaround for missing _lzma in some Python builds
try:
    import lzma
except ImportError:
    mock_lzma = MagicMock()
    mock_lzma.FORMAT_XZ = 1
    mock_lzma.FORMAT_ALONE = 2
    mock_lzma.FORMAT_RAW = 3
    mock_lzma.CHECK_NONE = 0
    mock_lzma.CHECK_CRC32 = 1
    mock_lzma.CHECK_CRC64 = 4
    mock_lzma.CHECK_SHA256 = 10
    sys.modules["_lzma"] = MagicMock()
    sys.modules["lzma"] = mock_lzma

import mlx_whisper
import numpy as np
import sounddevice as sd
import queue
import torch
from silero_vad import load_silero_vad, get_speech_timestamps
from transformers import MarianMTModel, MarianTokenizer

# Parameters
WHISPER_MODEL = "mlx-community/whisper-small-mlx" 
INGEST_URL = "https://emiapi.reynafamily.com/live-captions/ingest"

# Translation models (English -> Target)
# Map to the specific keys requested by the backend
TARGET_LANGS = {
    "es": "Helsinki-NLP/opus-mt-en-es",
    "fr": "Helsinki-NLP/opus-mt-en-fr",
    "ar": "Helsinki-NLP/opus-mt-en-ar" # Added Arabic as discussed before
}

CHANNELS = 1
SAMPLERATE = 16000
BLOCK_SIZE = 512
VAD_THRESHOLD = 0.5 
BUFFER_LIMIT = SAMPLERATE * 30 
MIN_SILENCE_DURATION_MS = 500 

audio_queue = queue.Queue()
ingest_queue = queue.Queue()

def callback(indata, frames, time, status):
    if status:
        print(status, file=sys.stderr)
    audio_queue.put(indata.copy())

def ingest_worker():
    """Background thread to handle server ingestion with retries."""
    while True:
        payload = ingest_queue.get()
        if payload is None: break
        
        delay = 1
        max_delay = 15
        success = False
        
        while not success:
            try:
                response = requests.post(INGEST_URL, json=payload, timeout=5)
                if response.status_code == 200:
                    success = True
                else:
                    print(f"\n[Ingest Error] Server returned {response.status_code}. Retrying in {delay}s...")
            except Exception as e:
                print(f"\n[Ingest Error] {e}. Retrying in {delay}s...")
            
            if not success:
                time.sleep(delay)
                delay = min(delay * 2, max_delay)
        
        ingest_queue.task_done()

def main():
    device = "mps" if torch.backends.mps.is_available() else "cpu"
    print(f"Using device: {device}")

    # Start ingest thread
    threading.Thread(target=ingest_worker, daemon=True).start()

    # 1. Load models
    print(f"Loading Multilingual Whisper model '{WHISPER_MODEL}'...")
    
    translation_engines = {}
    for lang_key, model_id in TARGET_LANGS.items():
        print(f"Loading {lang_key} translation model ({model_id})...")
        tokenizer = MarianTokenizer.from_pretrained(model_id)
        model = MarianMTModel.from_pretrained(model_id).to(device)
        translation_engines[lang_key] = (model, tokenizer)

    print("Loading Silero VAD model...")
    vad_model = load_silero_vad()
    print("Models loaded.")

    # 2. Select Audio Device
    print("\nAvailable Audio Devices:")
    print(sd.query_devices())
    
    try:
        device_input = input("\nSelect input device index (or press Enter for default): ")
        device_index = int(device_input) if device_input.strip() else None
    except ValueError:
        print("Invalid input, using default device.")
        device_index = None

    print(f"\nStarting live transcription & server ingest... (Press Ctrl+C to stop)")
    
    audio_buffer = []
    speech_started = False

    try:
        with sd.InputStream(samplerate=SAMPLERATE, channels=CHANNELS, callback=callback, blocksize=BLOCK_SIZE, device=device_index):
            while True:
                while not audio_queue.empty():
                    data = audio_queue.get()
                    audio_buffer.append(data.flatten())

                if len(audio_buffer) > 0:
                    current_audio = np.concatenate(audio_buffer)
                    audio_tensor = torch.from_numpy(current_audio)
                    
                    speech_timestamps = get_speech_timestamps(
                        audio_tensor, 
                        vad_model, 
                        sampling_rate=SAMPLERATE,
                        threshold=VAD_THRESHOLD,
                        min_silence_duration_ms=MIN_SILENCE_DURATION_MS
                    )

                    if len(speech_timestamps) > 0:
                        speech_started = True
                        last_end = speech_timestamps[-1]['end']
                        buffer_len_samples = len(current_audio)
                        
                        if (buffer_len_samples - last_end) > (SAMPLERATE * MIN_SILENCE_DURATION_MS / 1000) or buffer_len_samples > BUFFER_LIMIT:
                            
                            # 1. Transcribe & Detect Language
                            transcription_result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=WHISPER_MODEL)
                            original_text = transcription_result['text'].strip()
                            detected_lang = transcription_result.get('language', 'en')
                            
                            if original_text:
                                print(f"\n[{detected_lang.upper()}]: {original_text}")
                                
                                # Prepare payload
                                payload = {"original": original_text}
                                # Rule 3: include source language key
                                if detected_lang in TARGET_LANGS or detected_lang == "en":
                                    payload[detected_lang] = original_text

                                # 2. Bridge to English if not already English
                                if detected_lang != "en":
                                    bridge_result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=WHISPER_MODEL, task="translate")
                                    english_text = bridge_result['text'].strip()
                                    payload["en"] = english_text
                                else:
                                    english_text = original_text

                                # 3. Translate from English to other languages
                                if english_text:
                                    # Limit input length
                                    clean_en = english_text[:247] + "..." if len(english_text) > 250 else english_text
                                    
                                    for lang_key, (model, tokenizer) in translation_engines.items():
                                        # Skip if we already filled this (e.g. detected lang was 'es')
                                        if lang_key in payload: continue
                                        
                                        inputs = tokenizer(clean_en, return_tensors="pt", padding=True).to(device)
                                        with torch.no_grad():
                                            translated_tokens = model.generate(**inputs, max_new_tokens=150)
                                        translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
                                        payload[lang_key] = translated_text
                                
                                # Queue for background ingestion
                                ingest_queue.put(payload)
                                print(f"Sent to ingest: {list(payload.keys())}")
                            
                            audio_buffer = []
                            speech_started = False
                    
                    elif not speech_started and len(current_audio) > SAMPLERATE * 2:
                        audio_buffer = []

    except KeyboardInterrupt:
        print("\nStopped by user.")
    except Exception as e:
        print(f"\nError: {e}")

if __name__ == "__main__":
    import multiprocessing
    multiprocessing.freeze_support()
    main()