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_into_sentences(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

# display if the sentiment value is above these thresholds
thresholds = {"joy": 0.99,"anger": 0.95,"surprise": 0.95,"sadness": 0.98,"fear": 0.95,"love": 0.99,}

color_map = {"joy": "green","anger": "red","surprise": "yellow","sadness": "blue","fear": "orange","love": "purple",}

def create_fig(x_min, x_max, to_plot, plot_sentences):
    x, y = list(zip(*to_plot))

    plot_df = pd.DataFrame(
        data={
            "x": x,
            "y": y,
            "sentence": plot_sentences,
        }
    )

    fig = px.line(
        plot_df,
        x="x",
        y="y",
        hover_data={
            "sentence": True,
            "x": True,
            "y": False,
        },
        labels={"x": "time (seconds)", "y": "sentiment"},
        title=f"Customer sentiment over time",
        markers=True,
    )

    fig = fig.update_yaxes(categoryorder="category ascending")
    fig = fig.update_layout(
        font=dict(
            size=18,
        ),
        xaxis_range=[x_min, x_max],
    )
    
    return fig

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