import os
import speech_recognition as sr
from deep_translator import GoogleTranslator
import ffmpeg
import random
import string

# Constants
AUDIO_FILE_PATH = "audio/test-ph-3.m4a"
SOURCE_LANG = "fil-PH"
TARGET_LANG = "en"


def convert_audio_to_wav(input_audio_path, output_wav_path):
    """
    Convert any audio format (like M4A) to WAV using ffmpeg and return the output WAV file path.
    """
    try:
        ffmpeg.input(input_audio_path).output(output_wav_path, acodec='pcm_s16le', ar=44100).run()
        print(f"Audio successfully converted to WAV: {output_wav_path}")
        return output_wav_path
    except ffmpeg.Error as e:
        print(f"Error converting {input_audio_path} to WAV: {e}")
        return None
    except Exception as e:
        print(f"Error converting {input_audio_path} to WAV: {e}")
        return None


def recognize_speech_from_wav(model, wav_file_path, source_lang):
    """
    Recognize speech from a WAV file using the Whisper recognition model.
    """
    recognizer = sr.Recognizer()
    with sr.AudioFile(wav_file_path) as source:
        try:
            #print('Transcribing audio to text...')
            recognizer.adjust_for_ambient_noise(source)
            audio_data = recognizer.record(source)
            if model.lower() == "whisper":
                text = recognizer.recognize_whisper(audio_data, language=source_lang)
            elif model.lower() == "google":
                text = recognizer.recognize_google(audio_data, language=source_lang)
            else:
                print(f"Invalid model name: {model}")
                return None
            return text
        except sr.UnknownValueError:
            print("Could not understand the audio.")
            return None
        except sr.RequestError as e:
            print(f"Could not request results from the service; {e}")
            return None
        except Exception as e:
            print(f"Could not request results from the service; {e}")
            return None


def translate_text(text, target_lang):
    """
    Translate the recognized text into the target language using Google Translator.
    """
    try:
        return GoogleTranslator(source='auto', target=target_lang).translate(text)
    except Exception as e:
        print(f"Error translating text: {e}")
        return None


def process_audio_recognition(model="whisper", audio_path=None, source_lang="en", target_lang="en", translate=False):
    """
    Main function to handle audio recognition and optional translation.
    Converts the audio to WAV, recognizes speech, and optionally translates it.
    """
    wav_file =  audio_path
    if wav_file and not wav_file.endswith(".wav"):
        wav_file = convert_audio_to_wav(audio_path, ''.join(random.choices(string.ascii_uppercase + string.ascii_lowercase, k=5)) + "converted_audio.wav")

    if not wav_file:
        print(f"Failed to process the audio file: {audio_path}")
        return None

    text = recognize_speech_from_wav(model, wav_file, source_lang)
    if text:
        # print(f"############# RECOGNIZED TEXT ({source_lang}) ##################")
        # print(text)
        # print("################################################")

        if translate:
            translated_text = translate_text(text, target_lang)
            if translated_text:
                # print(f"############# TRANSLATED TEXT ({target_lang}) ##################")
                # print(translated_text)
                # print("################################################")
                text = translated_text

    # Cleanup temporary WAV file
    # try:
    #     #os.remove(wav_file)
    #     print(f"Temporary WAV file {wav_file} removed.")
    # except OSError as e:
    #     print(f"Error removing temporary WAV file {wav_file}: {e}")

    return text


if __name__ == '__main__':
    process_audio_recognition(AUDIO_FILE_PATH, SOURCE_LANG, TARGET_LANG, translate=True)