import json
from pathlib import Path

import pandas as pd
import streamlit as st

from category_classification.models import models as class_models
from languages import *
from results import process_results

page_title = {en: "Papers classification", ru: "Классификация статей"}
model_label = {en: "Select model", ru: "Выберете модель"}
title_label = {en: "Title", ru: "Название статьи"}
authors_label = {en: "Author(s)", ru: "Автор(ы)"}
abstract_label = {en: "Abstract", ru: "Аннотация"}
metrics_label = {en: "Test metrics", ru: "Метрики на тренировочном датасете"}

with open(
    Path(__file__).parent / "category_classification" / "test_results.json", "r"
) as metric_f:
    metrics = json.load(metric_f)


def text_area_height(line_height: int):
    return 34 * line_height


@st.cache_data
def load_class_model(name):
    model = class_models.get_model(name)
    return model


lang = st.pills(label=langs_str, options=langs)
if lang is None:
    lang = en
st.title(page_title[lang])
model_name = st.selectbox(
    model_label[lang], options=class_models.get_model_names_by_lang(lang)
)
title = st.text_area(title_label[lang], height=text_area_height(2))
authors = st.text_area(authors_label[lang], height=text_area_height(2))
abstract = st.text_area(abstract_label[lang], height=text_area_height(5))

if title:
    input = {"title": title, "abstract": abstract, "authors": authors}
    model = load_class_model(model_name)
    results = model(input)
    results = process_results(results, lang)
    st.dataframe(results, hide_index=True)

lang_metrics = pd.DataFrame(metrics[lang])
if not lang_metrics.empty:
    with st.expander(metrics_label[lang]):
        st.dataframe(lang_metrics)