from faster_whisper import WhisperModel
from transformers import pipeline
import os
from settings import MODEL_PATH_AGE_GENDER, MODEL_PATH_METEO, MODEL_PATH_V2_FAST, LEFT_CHANNEL_TEMP_PATH, RIGHT_CHANNEL_TEMP_PATH
from audio_utils import debug_print, get_settings, split_input_stereo_channels, process_waveforms, post_process_transcripts, post_merge_consecutive_segments_from_text, cleanup_temp_files
from shout_detector import shout  
from silence_detector import silence  
from meteo_detector import classify_meteo_event  
from age_gender_detector import age_gender, WavLMWrapper

hf_token = os.getenv("HF_AUTH_TOKEN") 

ASR_MODEL = None
AGE_GENDER_MODEL = None
METEO_MODEL = None

def get_asr_model(DEVICE, COMPUTE_TYPE):
    global ASR_MODEL
    if ASR_MODEL is None:
        debug_print("[MODEL LOADING]Loading ASR model...")
        ASR_MODEL = WhisperModel(
            MODEL_PATH_V2_FAST,
            device=DEVICE,
            compute_type=COMPUTE_TYPE
        )
        debug_print("[MODEL LOADING]ASR model loaded")
    return ASR_MODEL

def get_age_gender_model(DEVICE):
    global AGE_GENDER_MODEL
    if AGE_GENDER_MODEL is None:
        debug_print("[MODEL LOADING]Loading Age/Gender model...")
        AGE_GENDER_MODEL = WavLMWrapper.from_pretrained(MODEL_PATH_AGE_GENDER).to(DEVICE)
        AGE_GENDER_MODEL.eval()
        debug_print("[MODEL LOADING]Age/Gender model loaded")
    return AGE_GENDER_MODEL

def get_meteo_model(DEVICE):
    global METEO_MODEL
    if METEO_MODEL is None:
        debug_print("[MODEL LOADING]Loading Meteo model...")
        METEO_MODEL = pipeline(
            task="text-classification",
            model=MODEL_PATH_METEO,
            tokenizer=MODEL_PATH_METEO,
            top_k=None,
            device=0 if DEVICE == "cuda" else -1,
            token=hf_token
        )
        debug_print("[MODEL LOADING]Meteo model loaded")
    return METEO_MODEL

def transcribe_faster_asr(left_waveform, right_waveform, model):
    left_result, _ = model.transcribe(left_waveform, beam_size=5, task="transcribe")
    right_result, _ = model.transcribe(right_waveform, beam_size=5, task="transcribe")
    return list(left_result), list(right_result)

def generate_fase_2(audio_path, model_version, civil_channel):
    
    DEVICE, COMPUTE_TYPE = get_settings()

    asr_model = get_asr_model(DEVICE, COMPUTE_TYPE)
    age_gender_model = get_age_gender_model(DEVICE)
    meteo_model = get_meteo_model(DEVICE)

    actual_compute_type = asr_model.model.compute_type
    debug_print(f"[SETTINGS] Device: {DEVICE}, Compute type: {actual_compute_type}")

    split_input_stereo_channels(audio_path)
    left_waveform, right_waveform = process_waveforms(DEVICE, actual_compute_type)

    debug_print(f"[SETTINGS] Civil channel: {civil_channel}")
    left_result, right_result = transcribe_faster_asr(left_waveform, right_waveform, asr_model)

    silence_event = silence(audio_path)
    civil_waveform = left_waveform if civil_channel == "Left" else right_waveform
    civil_path = LEFT_CHANNEL_TEMP_PATH if civil_channel == "Left" else RIGHT_CHANNEL_TEMP_PATH
    shout_event = shout(civil_path)
    age, sex, age_group = age_gender(civil_waveform, age_gender_model, DEVICE)
    age = f"{age_group} (aprox. {age} años)"

    clean_output_asr, clean_output_meteo = post_process_transcripts(left_result, right_result, civil_channel)
    text = '\n' + clean_output_asr

    meteo_event = classify_meteo_event(clean_output_meteo, meteo_model, threshold=0.0)

    cleanup_temp_files(LEFT_CHANNEL_TEMP_PATH, RIGHT_CHANNEL_TEMP_PATH)

    return text, sex, age, silence_event, shout_event, meteo_event