import whisper
import os
import nltk
import boto3
import random

nltk.download('words')
from nltk.corpus import words
from helper.constants import *

def speech_to_text(file_path):
    if os.path.exists(file_path):
        model = whisper.load_model("base")
        result = model.transcribe(file_path, fp16=False, language='English', verbose=False)
        return result["text"]
    else:
        print("File not found:", file_path)
        raise Exception("File " + file_path + " not found.")

def text_to_speech(text: str, file_name: str):
    # Initialize the Amazon Polly client
    client = boto3.client(
        'polly',
        region_name='eu-west-1',
        aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
        aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY")
    )
    voice = random.choice(ALL_NEURAL_VOICES)['Id']
    # Initialize an empty list to store audio segments
    audio_segments = []
    for part in divide_text(text):
        tts_response = client.synthesize_speech(
            Engine="neural",
            Text=part,
            OutputFormat="mp3",
            VoiceId=voice
        )
        audio_segments.append(tts_response['AudioStream'].read())
    # Combine the audio segments into a single audio file
    combined_audio = b"".join(audio_segments)
    # Save the combined audio to a single file
    with open(file_name, "wb") as f:
        f.write(combined_audio)

    print("Speech segments saved to " + file_name)

def conversation_text_to_speech(conversation: list, file_name: str):
    # Initialize the Amazon Polly client
    client = boto3.client(
        'polly',
        region_name='eu-west-1',
        aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
        aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY")
    )
    # Initialize an empty list to store audio segments
    audio_segments = []
    # Iterate through the text segments, convert to audio segments, and store them
    for segment in conversation:
        response = client.synthesize_speech(
            Engine="neural", 
            Text=segment["text"],
            OutputFormat="mp3",
            VoiceId=segment["voice"]
        )
        audio_segments.append(response['AudioStream'].read())
    # Combine the audio segments into a single audio file
    combined_audio = b"".join(audio_segments)
    # Save the combined audio to a single file
    with open(file_name, "wb") as f:
        f.write(combined_audio)

    print("Speech segments saved to " + file_name)

def has_words(text: str):
    english_words = set(words.words())
    words_in_input = text.split()
    return any(word.lower() in english_words for word in words_in_input)

def has_10_words(text: str):
    english_words = set(words.words())
    words_in_input = text.split()
    english_word_count = sum(1 for word in words_in_input if word.lower() in english_words)
    return english_word_count >= 10

def divide_text(text, max_length=3000):
    if len(text) <= max_length:
        return [text]

    divisions = []
    current_position = 0

    while current_position < len(text):
        next_position = min(current_position + max_length, len(text))
        next_period_position = text.rfind('.', current_position, next_position)

        if next_period_position != -1 and next_period_position > current_position:
            divisions.append(text[current_position:next_period_position + 1])
            current_position = next_period_position + 1
        else:
            # If no '.' found in the next chunk, split at max_length
            divisions.append(text[current_position:next_position])
            current_position = next_position

    return divisions