import re
import functools
import requests
import pandas as pd
import plotly.express as px
import torch
import gradio as gr
from transformers import pipeline, Wav2Vec2ProcessorWithLM
from pyannote.audio import Pipeline
from librosa import load, resample
import whisperx

import re
alphabets= "([A-Za-z])"
prefixes = "(Mr|St|Mrs|Ms|Dr)[.]"
suffixes = "(Inc|Ltd|Jr|Sr|Co)"
starters = "(Mr|Mrs|Ms|Dr|He\s|She\s|It\s|They\s|Their\s|Our\s|We\s|But\s|However\s|That\s|This\s|Wherever)"
acronyms = "([A-Z][.][A-Z][.](?:[A-Z][.])?)"
websites = "[.](com|net|org|io|gov)"

def split(text):
    text = " " + text + "  "
    text = text.replace("\n"," ")
    text = re.sub(prefixes,"\\1<prd>",text)
    text = re.sub(websites,"<prd>\\1",text)
    if "Ph.D" in text: text = text.replace("Ph.D.","Ph<prd>D<prd>")
    text = re.sub("\s" + alphabets + "[.] "," \\1<prd> ",text)
    text = re.sub(acronyms+" "+starters,"\\1<stop> \\2",text)
    text = re.sub(alphabets + "[.]" + alphabets + "[.]" + alphabets + "[.]","\\1<prd>\\2<prd>\\3<prd>",text)
    text = re.sub(alphabets + "[.]" + alphabets + "[.]","\\1<prd>\\2<prd>",text)
    text = re.sub(" "+suffixes+"[.] "+starters," \\1<stop> \\2",text)
    text = re.sub(" "+suffixes+"[.]"," \\1<prd>",text)
    text = re.sub(" " + alphabets + "[.]"," \\1<prd>",text)
    if "”" in text: text = text.replace(".”","”.")
    if "\"" in text: text = text.replace(".\"","\".")
    if "!" in text: text = text.replace("!\"","\"!")
    if "?" in text: text = text.replace("?\"","\"?")
    text = text.replace(".",".<stop>")
    text = text.replace("?","?<stop>")
    text = text.replace("!","!<stop>")
    text = text.replace("<prd>",".")
    sentences = text.split("<stop>")
    sentences = sentences[:-1]
    sentences = [s.strip() for s in sentences]
    return sentences


def speech_to_text(speech_file, speaker_segmentation, whisper, alignment_model, metadata, whisper_device):
    speaker_output = speaker_segmentation(speech_file)
    result = whisper.transcribe(speech_file)

    chunks = whisperx.align(result["segments"], alignment_model, metadata, speech_file, whisper_device)["word_segments"]

    diarized_output = []
    i = 0
    speaker_counter = 0

    # New iteration every time the speaker changes
    for turn, _, _ in speaker_output.itertracks(yield_label=True):

        speaker = "Customer" if speaker_counter % 2 == 0 else "Support"
        diarized = ""
        while i < len(chunks) and chunks[i]["end"] <= turn.end:
            diarized += chunks[i]["text"] + " "
            i += 1

        if diarized != "":
            # diarized = rpunct.punctuate(re.sub(eng_pattern, "", diarized), lang="en")

            diarized_output.extend(
                [
                    (diarized, speaker),
                    ("from {:.2f}-{:.2f}".format(turn.start, turn.end), None),
                ]
            )

            speaker_counter += 1

    return diarized_output