#!/usr/bin/env python
# coding: utf-8

import gradio as gr
import numpy as np
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    TextClassificationPipeline,
    pipeline,
)
from sklearn import preprocessing
from langdetect import detect
from matplotlib import pyplot as plt
import imageio
import logging
import warnings

logging.getLogger().setLevel(logging.INFO)

DESCRIPTION = """Diese Anwendung teilt Vorstösse an das federführende Departement zu und
    macht einen Vorschlag für das zuständige Amt. Der Vorschlag der Anwendung ist nicht
    100% richtig. Der Zuteilungsvorschlag muss von einer Fachperson geprüft und die
    effektive Zuteilung muss nach eigenem Ermessen erfolgen. \n\n
    Cette application attribue les interventions au département chef de file et fait une
    proposition à l'office compétent. La proposition de l'application n'est pas correcte
    à 100%. La proposition d'attribution doit être vérifiée par un spécialiste et l'attribution
    effective doit être faite à la discrétion de l'utilisateur."""
TITLE_DE = (
    "Automatische Zuteilung von Vorstössen an das federführende Departement bzw. Amt"
)
TITLE_FR = "Où aller ? Classification des départements & bureaux"
UNKNOWN_LANG_TEXT = (
    "The language is not recognized, it must be either in German or in French."
)
PLACEHOLDER_TEXT = "Geben Sie bitte den Titel und den 'Submitted Text' des Vorstoss ein.\nVeuillez entrer le titre et le 'Submitted Text' de la requête."

UNSURE_DE_TEXT = "Das ML-Modell ist nicht sicher. Die Zuteilung könnte sein: \n\n"
UNSURE_FR_TEXT = "Le modèle ML n'est pas sûr. L'allocation pourrait être: \n\n"

ML_MODEL_SURE = 0.6

BARS_DEP_FR = (
    "DDPS",
    "DFI",
    "AS-MPC",
    "DFJP",
    "DEFR",
    "DETEC",
    "DFAE",
    "Parl",
    "ChF",
    "DFF",
    "AF",
    "TF",
)
BARS_DEP_DE = (
    "VBS",
    "EDI",
    "AB-BA",
    "EJPD",
    "WBF",
    "UVEK",
    "EDA",
    "Parl",
    "BK",
    "EFD",
    "BV",
    "BGer",
)


def load_model(modelFolder):
    """Loads model from model_folder & creates a text classification pipeline."""
    model = AutoModelForSequenceClassification.from_pretrained(modelFolder)
    tokenizer = AutoTokenizer.from_pretrained(modelFolder)
    pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
    return pipe


def translate_to_de(SubmittedText):
    """Translates french user input to German for the model to reach better classification."""
    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-fr-de")
    translatedText = translator(SubmittedText[0:1000])
    text = translatedText[0]["translation_text"]
    return text


def create_bar_plot(rates, barnames):
    y_pos = np.arange(len(barnames))
    plt.barh(y_pos, rates)
    plt.yticks(y_pos, barnames)

    # Save the bar chart as png and load it (enables better display)
    plt.savefig("rates.png")
    im = imageio.v2.imread("rates.png")

    return im, barnames


def show_chosen_category(barnames, rates, language):
    """Creates the output text
    - adds disclaimer if ML model is not sure
    - when unsure, adds all categories with prob. > 10% to output"""
    maxRate = np.max(rates)
    maxIndex = np.argmax(rates)

    distance = "\t\t\t\t\t"

    # ML model not sure if highest probability < 60%
    if maxRate < ML_MODEL_SURE:
        name = UNSURE_FR_TEXT if language == "fr" else UNSURE_DE_TEXT

        # Show each department that has a probability > 10%
        i = 0
        while i == 0:
            if rates[maxIndex] >= 0.1:
                chosenScore = str(rates[maxIndex])[2:4]
                chosenCat = barnames[maxIndex]
                name = name + "\t" + chosenScore + "%" + distance + chosenCat + "\n"
                rates[maxIndex] = 0
                maxIndex = np.argmax(rates)
            else:
                i = 1

    # ML model pretty sure, show only one department
    else:
        name = str(maxRate)[2:4] + "%" + distance + barnames[maxIndex]

    return name


pipeDep = load_model("saved_model_dep")
pipeOffice = load_model("saved_model_office")

labelencoderOffice = preprocessing.LabelEncoder()
labelencoderOffice.classes_ = np.load("classes_office.npy")


def textclassification(SubmittedText):
    language = detect(SubmittedText)
    logging.info(
        f"SubmittedText received. Detected language: {language}. SubmittedText: {SubmittedText}"
    )

    # Translate the input to german if necessary
    if language == "fr":
        SubmittedText = translate_to_de(SubmittedText)
    elif language != "de":
        return UNKNOWN_LANG_TEXT, None, None, None

    # Make the prediction with the 1000 first characters
    images = []
    chosenCategoryTexts = []

    labelsDep = BARS_DEP_FR if language == "fr" else BARS_DEP_DE
    labelsOffice = labelencoderOffice.classes_

    for pipe, barnames in zip((pipeDep, pipeOffice), (labelsDep, labelsOffice)):
        plt.clf()

        # catch deprecation warning, as new functionality following the deprecated way
        # sorts results the wrong way and cannot be easily fixed
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore")
            prediction = pipe(SubmittedText[0:1000], return_all_scores=True)
        rates = [row["score"] for row in prediction[0]]

        # Create barplot & output text
        im, barnames = create_bar_plot(rates, barnames)
        images.append(im)

        chosenCategoryText = show_chosen_category(barnames, rates, language)
        chosenCategoryTexts.append(chosenCategoryText)

    # return chosenCategoryText & image for both predictions
    logging.info(
        f"Prediction Department: {chosenCategoryTexts[0]}\n\nPrediction Amt: {chosenCategoryTexts[1]}"
    )
    return chosenCategoryTexts[0], images[0], chosenCategoryTexts[1], images[1]


# Launch UI
with gr.Blocks(
    # Set theme matching BK CH
    gr.themes.Monochrome(
        primary_hue="red",
        secondary_hue="red",
        font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"],
    )
) as demo:
    gr.Markdown(f"# {TITLE_DE}\n # {TITLE_FR}\n\n {DESCRIPTION}")

    # Organize layout in three columns for input, prediction I and prediction II
    with gr.Row():
        with gr.Column(scale=2):
            name = gr.Textbox(
                label="Vorstosstext:", lines=28, placeholder=PLACEHOLDER_TEXT
            )
            predict_btn = gr.Button("Submit | Soumettre")
        with gr.Column(scale=2):
            output_text_dep = gr.Textbox(label="Vorschlag Departement:")
            output_image_dep = gr.Image(label="Departement")
        with gr.Column(scale=2):
            output_text_office = gr.Textbox(label="Vorschlag Amt:")
            output_image_office = gr.Image(label="Amt")

    predict_btn.click(
        fn=textclassification,
        inputs=name,
        outputs=[
            output_text_dep,
            output_image_dep,
            output_text_office,
            output_image_office,
        ],
        api_name="predict",
    )

demo.launch()