import time
import webbrowser
from collections.abc import Collection, Iterable
from threading import Thread
from typing import Any, Final, cast

import pandas as pd

# import dash_cytoscape as cyto
import plotly.express as px
from dash import (
    Dash,
    Input,
    Output,
    State,
    callback,
    dash_table,
    dcc,
    html,
)
from pandas import DataFrame
from plotly.graph_objects import Figure

import lang_main.io
from lang_main.analysis import graphs, tokens
from lang_main.analysis.timeline import (
    calc_delta_to_next_failure,
    filter_timeline_cands,
)
from lang_main.constants import (
    NAME_DELTA_FEAT_TO_NEXT_FAILURE,
    NAME_DELTA_FEAT_TO_REPAIR,
    SAVE_PATH_FOLDER,
    SPCY_MODEL,
)
from lang_main.errors import EmptyEdgesError, EmptyGraphError
from lang_main.pipelines.predefined import (
    build_tk_graph_render_pipe,
    build_tk_graph_rescaling_pipe,
)
from lang_main.types import (
    DataFrameTLFiltered,
    EntryPoints,
    HTMLColumns,
    HTMLTable,
    ObjectID,
    TimelineCandidates,
)

# ** data
# p_df = Path(r'../results/test_20240619/TIMELINE.pkl').resolve()
p_df = lang_main.io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE_POST)
(data,) = cast(tuple[DataFrame], lang_main.io.load_pickle(p_df))
# data = cleanup_descriptions(data, properties=['ErledigungsBeschreibung'])
# p_tl = Path(r'../results/test_20240619/TIMELINE_POSTPROCESSING.pkl').resolve()
p_tl = lang_main.io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE_CANDS)
cands, texts = cast(
    tuple[TimelineCandidates, dict[ObjectID, str]], lang_main.io.load_pickle(p_tl)
)
# ** necessary pipelines
rescaling_pipe = build_tk_graph_rescaling_pipe(
    exit_point=EntryPoints.TIMELINE_TK_GRAPH_RESCALED,
    save_result=False,
)
BASE_NETWORK_NAME: Final[str] = 'test_timeline'
# RENDER_FOLDER: Final[Path] = Path.cwd() / 'assets/'
graph_render_pipe = build_tk_graph_render_pipe(
    with_subgraphs=False,
    base_network_name=BASE_NETWORK_NAME,
)
# PTH_RENDERED_GRAPH = f'assets/{BASE_NETWORK_NAME}.svg'
PTH_RENDERED_GRAPH = lang_main.io.get_entry_point(
    SAVE_PATH_FOLDER,
    BASE_NETWORK_NAME,
    file_ext='.svg',
)

# NAME_DELTA_FEAT_TO_NEXT_FAILURE: Final[str] = 'Zeitspanne bis zum nächsten Ereignis [Tage]'
TABLE_FEATS_OVERVIEW: Final[list[str]] = [
    'ErstellungsDatum',
    'ErledigungsDatum',
    NAME_DELTA_FEAT_TO_REPAIR,
    'VorgangsTypName',
    'VorgangsBeschreibung',
    'ErledigungsBeschreibung',
]
TABLE_FEATS_DATES: Final[list[str]] = [
    'ErstellungsDatum',
    'ErledigungsDatum',
]
TABLE_FEATS_BEST_ACTIONS: Final[list[str]] = [
    'ErstellungsDatum',
    'ErledigungsDatum',
    'VorgangsTypName',
    'VorgangsBeschreibung',
    'ErledigungsBeschreibung',
    NAME_DELTA_FEAT_TO_NEXT_FAILURE,
]

# ** figure config
MARKERS_OCCURRENCES: Final[dict[str, Any]] = {
    'size': 12,
    'color': 'yellow',
    'line': {
        'width': 2,
        'color': 'red',
    },
}
MARKERS_DELTA: Final[dict[str, Any]] = {
    'size': 8,
    'color': 'red',
    'symbol': 'cross',
}
HOVER_DATA: Final[dict[str, Any]] = {
    'ErstellungsDatum': '|%d.%m.%Y',
    'ErledigungsDatum': '|%d.%m.%Y',
    'VorgangsBeschreibung': True,
    'ErledigungsBeschreibung': True,
}
HOVER_DATA_DELTA: Final[dict[str, Any]] = {
    'ErstellungsDatum': '|%d.%m.%Y',
    'ErledigungsDatum': '|%d.%m.%Y',
    'VorgangsDatum': '|%d.%m.%Y',
    NAME_DELTA_FEAT_TO_REPAIR: True,
    'VorgangsBeschreibung': True,
    'ErledigungsBeschreibung': True,
}

# ** graph
p_tk_graph = lang_main.io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TK_GRAPH_POST)
ret = lang_main.io.load_pickle(p_tk_graph)
tk_graph = cast(graphs.TokenGraph, ret[0])
tk_graph_filtered = graphs.filter_graph_by_edge_weight(tk_graph, 150, None)
tk_graph_filtered = graphs.filter_graph_by_node_degree(tk_graph_filtered, 1, None)


graph_layout = html.Div(
    [
        dcc.Store(id='graph-store', storage_type='memory'),
        # dcc.Store(id='graph-store-cyto-curr_cands', storage_type='memory'),
        html.Div(id='output'),
        html.Div(
            [
                html.H2('Token Graph', style={'margin': 0}),
            ],
            style={
                'display': 'flex',
                'marginBottom': '1em',
            },
        ),
        html.Div(
            [
                html.H3('Graph'),
                html.Button(
                    'Download Bild',
                    id='bt-reset',
                    style={
                        'marginLeft': 'auto',
                        'width': '300px',
                    },
                ),
                dcc.Download(id='static-graph-download'),
                dcc.Loading(
                    id='loading-graph-render',
                    children=html.Div(
                        [
                            html.Img(
                                id='static-graph-img',
                                alt='static rendered graph',
                                style={
                                    'width': 'auto',
                                    'height': 'auto',
                                },
                            ),
                            html.P(id='info-graph-errors', children=[]),
                        ],
                        style={
                            'border': '3px solid black',
                            'borderRadius': '25px',
                            'marginTop': '1em',
                            'marginBottom': '2em',
                            'padding': '7px',
                        },
                    ),
                ),
            ],
            style={'marginTop': '1em'},
        ),
    ],
)


# ** app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = Dash(__name__, external_stylesheets=external_stylesheets)


app.layout = html.Div(
    [
        html.H1(children='Demo Zeitreihenanalyse', style={'textAlign': 'center'}),
        html.Div(
            children=[
                html.H2('Wählen Sie ein Objekt aus (ObjektID):'),
                dcc.Dropdown(
                    list(cands.keys()),
                    id='selector-obj_id',
                    placeholder='ObjektID auswählen...',
                ),
            ]
        ),
        html.Div(
            children=[
                html.H3(id='object-text'),
                dcc.Dropdown(id='selector-candidates'),
                dcc.Graph(id='figure-occurrences'),
                dcc.Graph(id='figure-delta'),
            ]
        ),
        html.Div(
            children=[
                html.Div(
                    [
                        html.H5('Überblick ähnlicher Vorgänge'),
                        dash_table.DataTable(id='table-candidates'),
                    ],
                    style={'paddingBottom': '1em'},
                ),
                html.Div(
                    [
                        html.H5(
                            (
                                'Maßnahmen sortiert nach längstem Zeitraum '
                                'bis zum nächsten Ereignis'
                            )
                        ),
                        dash_table.DataTable(id='table-best-actions'),
                    ]
                ),
            ],
            style={'marginBottom': '2em', 'padding': '2em'},
        ),
        graph_layout,
    ],
    style={'margin': '2em'},
)


# ** selectors of candidates
@callback(
    Output('object-text', 'children'),
    Input('selector-obj_id', 'value'),
    prevent_initial_call=True,
)
def update_obj_text(obj_id):
    obj_id = int(obj_id)
    obj_text = texts[obj_id]
    headline = f'HObjektText: {obj_text}'
    return headline


@callback(
    [
        Output('selector-candidates', 'options'),
        Output('selector-candidates', 'value'),
    ],
    Input('selector-obj_id', 'value'),
    prevent_initial_call=True,
)
def update_choice_candidates(obj_id):
    obj_id = int(obj_id)
    choices = list(range(1, len(cands[obj_id]) + 1))
    return choices, choices[0]


# ** helpers to filter DataFrame
def filter_candidates(
    data: DataFrame,
    idx: int,
    obj_id: ObjectID,
) -> DataFrameTLFiltered:
    # assert correct data type because of Dash
    idx = int(idx)
    obj_id = int(obj_id)

    data = filter_timeline_cands(
        data=data,
        cands=cands,
        obj_id=obj_id,
        entry_idx=(idx - 1),  # idx in Dashboard starts with 1
    )

    return data


# ** figure generation
# TODO check possible storage of pre-filtered result
# TODO change input of ``update_table_candidates`` and ``display_candidates_as_graph``
# TODO to storage component
@callback(
    [
        Output('figure-occurrences', 'figure'),
        Output('figure-delta', 'figure'),
    ],
    Input('selector-candidates', 'value'),
    State('selector-obj_id', 'value'),
    prevent_initial_call=True,
)
def update_timeline(index, obj_id):
    obj_id = int(obj_id)
    obj_text = texts[obj_id]
    title_occurrences = f'HObjektText: {obj_text}'
    title_delta = f'HObjektText: {obj_text}, Differenz Erstellung und Erledigung'
    df = filter_candidates(data, idx=index, obj_id=obj_id)
    # figure
    fig_occurrences = fig_timeline_occurrences(df, title_occurrences)
    fig_delta = fig_timeline_delta(df, title_delta, delta_feature=NAME_DELTA_FEAT_TO_REPAIR)

    return fig_occurrences, fig_delta


def fig_timeline_occurrences(
    df: DataFrame,
    title: str,
) -> Figure:
    fig = px.line(
        data_frame=df,
        x='ErstellungsDatum',
        y='ObjektID',
        title=title,
        hover_data=HOVER_DATA,
    )
    fig.update_traces(
        mode='markers+lines', marker=MARKERS_OCCURRENCES, marker_symbol='diamond'
    )
    fig.update_xaxes(
        tickformat='%B\n%Y',
        rangeslider_visible=True,
    )
    fig.update_yaxes(type='category')
    fig.update_layout(hovermode='x unified')

    return fig


def fig_timeline_delta(
    df: DataFrame,
    title: str,
    delta_feature: str,
) -> Figure:
    fig = px.scatter(
        data_frame=df,
        x='ErstellungsDatum',
        y=delta_feature,
        title=title,
        hover_data=HOVER_DATA_DELTA,
    )
    fig.update_traces(marker=MARKERS_DELTA)
    fig.update_xaxes(tickformat='%B\n%Y')
    fig.update_yaxes(dtick=1)
    fig.update_layout(hovermode='x unified')

    return fig


def transform_to_HTML_table(
    data: DataFrame,
    target_features: Collection[str],
    date_cols: Iterable[str] | None = None,
    sorting_feature: str | None = None,
    sorting_ascending: bool = True,
) -> tuple[HTMLColumns, HTMLTable]:
    target_features = list(target_features)
    data = data.copy()
    data = data.filter(items=target_features, axis=1)

    if sorting_feature is not None:
        data = data.sort_values(by='ErstellungsDatum', ascending=sorting_ascending)

    if date_cols is not None:
        for col in date_cols:
            data[col] = data[col].dt.strftime(r'%Y-%m-%d')

    columns = [{'name': col, 'id': col} for col in data.columns]
    table_data = data.to_dict('records')

    return columns, table_data


# 'table-best-actions'
# ** HTML table
@callback(
    [
        Output('table-candidates', 'columns'),
        Output('table-candidates', 'data'),
        Output('table-best-actions', 'columns'),
        Output('table-best-actions', 'data'),
    ],
    Input('selector-candidates', 'value'),
    State('selector-obj_id', 'value'),
    prevent_initial_call=True,
)
def update_tables_candidates(
    index,
    obj_id,
) -> tuple[HTMLColumns, HTMLTable, HTMLColumns, HTMLTable]:
    cands = filter_candidates(data, idx=index, obj_id=obj_id)
    overview_cols, overview_table = transform_to_HTML_table(
        data=cands,
        target_features=TABLE_FEATS_OVERVIEW,
        date_cols=TABLE_FEATS_DATES,
        sorting_feature='ErstellungsDatum',
        sorting_ascending=True,
    )
    # df = df.filter(items=TABLE_FEATS_OVERVIEW, axis=1).sort_values(
    #     by='ErstellungsDatum', ascending=True
    # )
    # cols = [{'name': i, 'id': i} for i in df.columns]
    # # convert dates to strings
    # for col in TABLE_FEATS_DATES:
    #     df[col] = df[col].dt.strftime(r'%Y-%m-%d')

    # table_data = df.to_dict('records')

    cands_best_actions = calc_delta_to_next_failure(
        data=cands,
        date_feature='ErstellungsDatum',
        name_delta_feature=NAME_DELTA_FEAT_TO_NEXT_FAILURE,
    )
    best_actions_cols, best_actions_table = transform_to_HTML_table(
        data=cands_best_actions,
        target_features=TABLE_FEATS_BEST_ACTIONS,
        date_cols=TABLE_FEATS_DATES,
    )

    return overview_cols, overview_table, best_actions_cols, best_actions_table


# ** graph callbacks
@app.callback(
    [
        Output('graph-store', 'data'),
        Output('static-graph-img', 'src'),
        Output('info-graph-errors', 'children'),
    ],
    # Input('graph-build-btn', 'n_clicks'),
    Input('selector-candidates', 'value'),
    State('selector-obj_id', 'value'),
    prevent_initial_call=True,
)
def display_candidates_as_graph(index, obj_id):
    error_msg = ''
    t1 = time.perf_counter()
    df = filter_candidates(data, idx=index, obj_id=obj_id)
    t2 = time.perf_counter()
    print(f'Time for filtering: {t2 - t1} s')

    t1 = time.perf_counter()
    tk_graph_cands, _ = tokens.build_token_graph(
        data=df,
        model=SPCY_MODEL,
        target_feature='VorgangsBeschreibung',
        build_map=False,
        logging_graph=False,
    )
    t2 = time.perf_counter()
    print(f'Time for graph building: {t2 - t1} s')

    # ** now start rendering pipeline in Cytoscape
    # rescale graph
    try:
        t1 = time.perf_counter()
        _, tk_graph_rescaled_undirected = cast(
            tuple[graphs.TokenGraph, graphs.Graph],
            rescaling_pipe.run(starting_values=(tk_graph_cands,)),
        )
        # render graph in Cytoscape and export image
        _ = graph_render_pipe.run(starting_values=(tk_graph_rescaled_undirected,))
        # load image as b64 encoded string
        b64_img = lang_main.io.encode_file_to_base64_str(PTH_RENDERED_GRAPH)
        static_img = f'data:image/svg+xml;base64,{b64_img}'
        graph_to_store = lang_main.io.encode_to_base64_str(tk_graph_cands)

        # place image in browser
        t2 = time.perf_counter()
        print(f'Time for graph rescaling and rendering: {t2 - t1} s')
    except (EmptyGraphError, EmptyEdgesError):
        graph_to_store = ''
        static_img = ''
        error_msg = 'Graph ist leer und konnte nicht generiert werden!'
    finally:
        return graph_to_store, static_img, error_msg


@callback(
    Output('static-graph-download', 'data'),
    Input('bt-reset', 'n_clicks'),
    prevent_initial_call=True,
)
def func(n_clicks):
    return dcc.send_file(path=PTH_RENDERED_GRAPH)


def _start_webbrowser():
    host = '127.0.0.1'
    port = '8050'
    adress = f'http://{host}:{port}/'
    time.sleep(2)
    webbrowser.open_new(adress)


def main():
    webbrowser_thread = Thread(target=_start_webbrowser, daemon=True)
    webbrowser_thread.start()
    app.run(debug=True)


if __name__ == '__main__':
    main()