import os
import re
import functools
from functools import partial

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
import whisperx

from utils import split, create_fig, color_map, thresholds
from utils import speech_to_text as stt

os.environ["TOKENIZERS_PARALLELISM"] = "false"
device = 0 if torch.cuda.is_available() else -1


# Audio components
whisper_device = "cuda" if torch.cuda.is_available() else "cpu"
whisper = whisperx.load_model("tiny.en", whisper_device)
alignment_model, metadata = whisperx.load_align_model(language_code="en", device=whisper_device)
speaker_segmentation = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",
                                    use_auth_token=os.environ['ENO_TOKEN'])


# Text components
emotion_pipeline = pipeline(
    "text-classification",
    model="bhadresh-savani/distilbert-base-uncased-emotion",
    device=device,
)
summarization_pipeline = pipeline(
    "summarization",
    model="knkarthick/MEETING_SUMMARY",
    device=device
)

EXAMPLES = [["Customer_Support_Call.wav"]]


speech_to_text = partial(
    stt, 
    speaker_segmentation=speaker_segmentation, 
    whisper=whisper, 
    alignment_model=alignment_model, 
    metadata=metadata, 
    whisper_device=whisper_device
    )

def summarize(diarized, check, summarization_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)]
    check is a list of speaker ids whose speech will get summarized
    """

    if len(check) == 0:
        return ""

    text = ""
    for d in diarized:
        if len(check) == 2 and d[1] is not None:
            text += f"\n{d[1]}: {d[0]}"
        elif d[1] in check:
            text += f"\n{d[0]}"

    # inner function cached because outer function cannot be cached
    @functools.lru_cache(maxsize=128)
    def call_summarize_api(text):
        return summarization_pipeline(text)[0]["summary_text"]

    return call_summarize_api(text)

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.
    """

    customer_sentiments = []
    sentences = split(speaker_speech)

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

        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)

        sentences = split_into_sentences(speaker_speech)
        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"

    return customer_sentiments

with gr.Blocks() as demo:

    with gr.Row():
        with gr.Column():
            audio = gr.Audio(label="Audio file", type="filepath")
            btn = gr.Button("Transcribe and Diarize")

            gr.Markdown("**Call Transcript:**")
            diarized = gr.HighlightedText(label="Call Transcript")
            gr.Markdown("Choose speaker to summarize:")
            check = gr.CheckboxGroup(
                choices=["Customer", "Support"], show_label=False, type="value"
            )
            summary = gr.Textbox(lines=4)
            sentiment_btn = gr.Button("Get Customer Sentiment")
            analyzed = gr.HighlightedText(color_map=color_map)

        with gr.Column():
            gr.Markdown("## Example Files")
            gr.Examples(
                examples=EXAMPLES,
                inputs=[audio],
                outputs=[diarized],
                fn=speech_to_text,
                cache_examples=True
            )
    # when example button is clicked, convert audio file to text and diarize
    btn.click(
        fn=speech_to_text,
        inputs=audio,
        outputs=diarized,
    )
    # when summarize checkboxes are changed, create summary
    check.change(fn=partial(summarize, summarization_pipeline=summarization_pipeline), inputs=[diarized, check], outputs=summary)

    # when sentiment button clicked, display highlighted text and plot
    sentiment_btn.click(fn=partial(sentiment, emotion_pipeline=emotion_pipeline), inputs=diarized, outputs=[analyzed])

demo.launch(debug=1)