import gradio as gr

import git
import os
os.system('git clone https://github.com/Edresson/Coqui-TTS -b multilingual-torchaudio-SE TTS')
os.system('pip install -q -e TTS/')
os.system('pip install -q torchaudio==0.9.0')

os.system('pip install voicefixer --upgrade')
from voicefixer import VoiceFixer
voicefixer = VoiceFixer()

import sys
TTS_PATH = "TTS/"

# add libraries into environment
sys.path.append(TTS_PATH) # set this if TTS is not installed globally

import os
import string
import time
import argparse
import json

import numpy as np
import IPython
from IPython.display import Audio

import torch
import torchaudio
from speechbrain.pretrained import SpectralMaskEnhancement

import whisper
model1 = whisper.load_model("tiny")

import openai

enhance_model = SpectralMaskEnhancement.from_hparams(
source="speechbrain/metricgan-plus-voicebank",
savedir="pretrained_models/metricgan-plus-voicebank",
run_opts={"device":"cuda"},
)

mes = [
    {"role": "system", "content": "You are my personal assistant. Try to be helpful."}
]

res = []

from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
try:
  from TTS.utils.audio import AudioProcessor
except:
  from TTS.utils.audio import AudioProcessor


from TTS.tts.models import setup_model
from TTS.config import load_config
from TTS.tts.models.vits import *  

OUT_PATH = 'out/'

# create output path
os.makedirs(OUT_PATH, exist_ok=True)

# model vars 
MODEL_PATH = '/home/user/app/best_model_latest.pth.tar'
CONFIG_PATH = '/home/user/app/config.json'
TTS_LANGUAGES = "/home/user/app/language_ids.json"
TTS_SPEAKERS = "/home/user/app/speakers.json"
USE_CUDA = torch.cuda.is_available()  

# load the config
C = load_config(CONFIG_PATH)


# load the audio processor
ap = AudioProcessor(**C.audio)

speaker_embedding = None

C.model_args['d_vector_file'] = TTS_SPEAKERS
C.model_args['use_speaker_encoder_as_loss'] = False

model = setup_model(C)
model.language_manager.set_language_ids_from_file(TTS_LANGUAGES)
# print(model.language_manager.num_languages, model.embedded_language_dim)
# print(model.emb_l)
cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))
# remove speaker encoder
model_weights = cp['model'].copy()
for key in list(model_weights.keys()):
  if "speaker_encoder" in key:
    del model_weights[key]

model.load_state_dict(model_weights)


model.eval()

if USE_CUDA:
    model = model.cuda()

# synthesize voice
use_griffin_lim = False

os.system('pip install -q pydub ffmpeg-normalize')

CONFIG_SE_PATH = "config_se.json"
CHECKPOINT_SE_PATH = "SE_checkpoint.pth.tar"

from TTS.tts.utils.speakers import SpeakerManager
from pydub import AudioSegment
import librosa

SE_speaker_manager = SpeakerManager(encoder_model_path=CHECKPOINT_SE_PATH, encoder_config_path=CONFIG_SE_PATH, use_cuda=USE_CUDA)

def compute_spec(ref_file):
  y, sr = librosa.load(ref_file, sr=ap.sample_rate)
  spec = ap.spectrogram(y)
  spec = torch.FloatTensor(spec).unsqueeze(0)
  return spec
  

def greet(Text2, audio, Voicetoclone,VoiceMicrophone):
    
    openai.api_key = Text2

    # load audio and pad/trim it to fit 30 seconds
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)

    # make log-Mel spectrogram and move to the same device as the model
    mel = whisper.log_mel_spectrogram(audio).to(model1.device)

    # detect the spoken language
    _, probs = model1.detect_language(mel)
    print(f"Detected language: {max(probs, key=probs.get)}")

    # decode the audio
    options = whisper.DecodingOptions()
    result = whisper.decode(model1, mel, options)
    res.append(result.text)

    messages = mes

    # chatgpt
    n = len(res)
    content = res[n-1]
    messages.append({"role": "user", "content": content})

    completion = openai.ChatCompletion.create(
      model = "gpt-3.5-turbo",
      messages = messages
    )

    chat_response = completion.choices[0].message.content

    messages.append({"role": "assistant", "content": chat_response})   
    
    text= "%s" % (chat_response + "")
    if Voicetoclone is not None:
      reference_files= "%s" % (Voicetoclone)
      print("path url")
      print(Voicetoclone)
      sample= str(Voicetoclone)
    else:
      reference_files= "%s" % (VoiceMicrophone)
      print("path url")
      print(VoiceMicrophone)
      sample= str(VoiceMicrophone)
    size= len(reference_files)*sys.getsizeof(reference_files)
    size2= size / 1000000
    if (size2 > 0.012) or len(text)>2000:
      message="File is greater than 30mb or Text inserted is longer than 2000 characters. Please re-try with smaller sizes."
      print(message)
      raise SystemExit("File is greater than 30mb. Please re-try or Text inserted is longer than 2000 characters. Please re-try with smaller sizes.")
    else:
      os.system('ffmpeg-normalize $sample -nt rms -t=-27 -o $sample -ar 16000 -f')
      reference_emb = SE_speaker_manager.compute_d_vector_from_clip(reference_files)
      model.length_scale = 1  # scaler for the duration predictor. The larger it is, the slower the speech.
      model.inference_noise_scale = 0.3 # defines the noise variance applied to the random z vector at inference.
      model.inference_noise_scale_dp = 0.3 # defines the noise variance applied to the duration predictor z vector at inference.
      text = text
      model.language_manager.language_id_mapping
      language_id = 0
    
      print(" > text: {}".format(text))
      wav, alignment, _, _ = synthesis(
                        model,
                        text,
                        C,
                        "cuda" in str(next(model.parameters()).device),
                        ap,
                        speaker_id=None,
                        d_vector=reference_emb,
                        style_wav=None,
                        language_id=language_id,
                        enable_eos_bos_chars=C.enable_eos_bos_chars,
                        use_griffin_lim=True,
                        do_trim_silence=False,
                    ).values()
      print("Generated Audio")
      IPython.display.display(Audio(wav, rate=ap.sample_rate))
      #file_name = text.replace(" ", "_")
      #file_name = file_name.translate(str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
      file_name="Audio.wav"
      out_path = os.path.join(OUT_PATH, file_name)
      print(" > Saving output to {}".format(out_path))
      ap.save_wav(wav, out_path)
    
      voicefixer.restore(input=out_path, # input wav file path
                      output="audio1.wav", # output wav file path
                      cuda=True, # whether to use gpu acceleration
                      mode = 0) # You can try out mode 0, 1, or 2 to find out the best result
    
      noisy = enhance_model.load_audio(
      "audio1.wav"
      ).unsqueeze(0)

      enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))
      torchaudio.save("enhanced.wav", enhanced.cpu(), 16000)
      return [result.text, chat_response, "enhanced.wav"]

demo = gr.Interface(
    fn=greet, 
    inputs=[
        gr.Textbox(lines=1, label = "请填写您的OpenAI-API-key"),
        gr.Audio(source="microphone", label='在这里进行对话', type="filepath"),
        gr.Audio(source="upload", type="filepath", label='请上传您喜欢的声音(wav文件)').
        gr.Audio(source="microphone", type="filepath", label = "请用麦克风上传您喜欢的声音，与wav文件二选一即可"),
    ],
#    outputs=[gr.Textbox(label="Speech to Text"), gr.Textbox(label="ChatGPT Output"), "audio"],
    outputs="audio",
    title="🥳💬💕 - TalktoAI，随时随地，谈天说地！"
    #theme="huggingface",
    #description = "🤖 - 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！TalktoAI - Enable the future！",
    )
demo.launch(show_error = True)