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, 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 sentiment(diarized, emotion_pipeline):
    """
    diarized: a list of tuples. Each tuple has a string to be displayed and a label for highlighting.
        The start/end times are not highlighted [(speaker text, speaker id), (start time/end time, None)]

    This function gets the customer's sentiment and returns a list for highlighted text as well
    as a plot of sentiment over time.
    """

    customer_sentiments = []

    to_plot = []
    plot_sentences = []

    # used to set the x range of ticks on the plot
    x_min = 100
    x_max = 0

    for i in range(0, len(diarized), 2):
        speaker_speech, speaker_id = diarized[i]
        times, _ = diarized[i + 1]

        sentences = split_into_sentences(speaker_speech)
        start_time, end_time = times[5:].split("-")
        start_time, end_time = float(start_time), float(end_time)
        interval_size = (end_time - start_time) / len(sentences)

        if "Customer" in speaker_id:

            outputs = emotion_pipeline(sentences)

            for idx, (o, t) in enumerate(zip(outputs, sentences)):
                sent = "neutral"
                if o["score"] > thresholds[o["label"]]:
                    customer_sentiments.append(
                        (t + f"({round(idx*interval_size+start_time,1)} s)", o["label"])
                    )
                    if o["label"] in {"joy", "love", "surprise"}:
                        sent = "positive"
                    elif o["label"] in {"sadness", "anger", "fear"}:
                        sent = "negative"
                if sent != "neutral":
                    to_plot.append((start_time + idx * interval_size, sent))
                    plot_sentences.append(t)

            if start_time < x_min:
                x_min = start_time
            if end_time > x_max:
                x_max = end_time

    x_min -= 5
    x_max += 5

    fig = create_fig(x_min, x_max, plot_sentences)

    return customer_sentiments, 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