import gradio as gr
from gradio.inputs import Textbox

import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize, word_tokenize

import re
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import torch
import random
import string
import soundfile as sf
import boto3
from io import BytesIO
import os

AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
S3_BUCKET_NAME = os.getenv("BUCKET_NAME")

device = "cuda" if torch.cuda.is_available() else "cpu"
# load the processor
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
# load the model
model = SpeechT5ForTextToSpeech.from_pretrained(
    "microsoft/speecht5_tts").to(device)
# load the vocoder, that is the voice encoder
vocoder = SpeechT5HifiGan.from_pretrained(
    "microsoft/speecht5_hifigan").to(device)
# we load this dataset to get the speaker embeddings
embeddings_dataset = load_dataset(
    "Matthijs/cmu-arctic-xvectors", split="validation")

# speaker ids from the embeddings dataset
speakers = {
    'awb': 0,     # Scottish male
    'bdl': 1138,  # US male
    'clb': 2271,  # US female
    'jmk': 3403,  # Canadian male
    'ksp': 4535,  # Indian male
    'rms': 5667,  # US male
    'slt': 6799   # US female
}

def generateAudio(text_to_audio, s3_save_as):
    
    s3_save_as = '-'.join(s3_save_as.split()) + ".wav"
    
    def cut_text(text, max_tokens=500):
        # Remove non-alphanumeric characters, except periods and commas
        text = re.sub(r"[^\w\s.,]", "", text)
    
        # Replace multiple spaces with a single space
        text = re.sub(r"\s{2,}", " ", text)
    
        # Remove line breaks
        text = re.sub(r"\n", " ", text)
    
        sentences = sent_tokenize(text)
        tokens = []
        for sentence in sentences:
            tokens.extend(word_tokenize(sentence))
    
        if len(tokens) <= max_tokens:
            return text
    
        cut_tokens = tokens[:max_tokens]
        cut = ' '.join(cut_tokens)
        return cut

    def save_audio_to_s3(audio):
        # Create an instance of the S3 client
        s3 = boto3.client('s3',
                          aws_access_key_id=AWS_ACCESS_KEY_ID,
                          aws_secret_access_key=AWS_SECRET_ACCESS_KEY)

        # Full path of the file in the bucket
        s3_key = "public/" + s3_save_as

        # Upload the audio file to the S3 bucket
        s3.upload_fileobj(audio, S3_BUCKET_NAME, s3_key)

    def save_text_to_speech(text, speaker=None):
        # Preprocess text and recortar
        text = cut_text(text, max_tokens=500)
        # preprocess text
        inputs = processor(text=text, return_tensors="pt").to(device)
        if speaker is not None:
            # load xvector containing speaker's voice characteristics from a dataset
            speaker_embeddings = torch.tensor(
                embeddings_dataset[speaker]["xvector"]).unsqueeze(0).to(device)
        else:
            # random vector, meaning a random voice
            speaker_embeddings = torch.randn((1, 512)).to(device)
        # generate speech with the models
        speech = model.generate_speech(
            inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
        
        # create BytesIO object to store the audio
        audio_buffer = BytesIO()
        # save the generated speech to the BytesIO buffer
        sf.write(audio_buffer, speech.cpu().numpy(), samplerate=16000, format='WAV')
        audio_buffer.seek(0)

        # Save the audio to S3
        save_audio_to_s3(audio_buffer)

    save_text_to_speech(text_to_audio, 2271)
    return s3_save_as
    

iface = gr.Interface(fn=generateAudio, inputs=[Textbox(label="text_to_audio"), Textbox(label="S3url")], outputs="text")
iface.launch()