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

from dash.dependencies import Input, Output, State
import dash
from dash.exceptions import PreventUpdate
import dash_table
import json
import pandas as pd

from ...app import app
from .models import get_tribes, get_exams, get_unstack_scores, get_students_from_exam


@app.callback(
    [
        Output("exam_select", "options"),
        Output("exam_select", "value"),
    ],
    [Input("tribe", "value")],
)
def update_exams_choices(tribe):
    if not tribe:
        raise PreventUpdate
    exams = get_exams(tribe)
    exams.reset_index(inplace=True)
    if not exams.empty:
        return [
            {"label": e["name"], "value": e.to_json()} for i, e in exams.iterrows()
        ], exams.loc[0].to_json()
    return [], None


@app.callback(
    [
        Output("scores_table", "columns"),
        Output("scores_table", "data"),
        Output("scores_table", "style_data_conditional"),
        Output("scores_table", "fixed_columns"),
    ],
    [
        Input("exam_select", "value"),
    ],
)
def update_scores_store(exam):
    ctx = dash.callback_context
    if not exam:
        return [[], [], [], {}]
    exam = pd.DataFrame.from_dict([json.loads(exam)])
    scores = get_unstack_scores(exam)
    fixed_columns = [
        "exercise",
        "question",
        "competence",
        "theme",
        "comment",
        "score_rate",
        "is_leveled",
    ]
    columns = fixed_columns + list(get_students_from_exam(exam))

    return [
        [{"id": c, "name": c} for c in columns],
        scores.to_dict("records"),
        [],
        {"headers": True, "data": len(fixed_columns)},
    ]