from logging import getLogger
from pathlib import Path

import pandas as pd
import plotly.express as px
import streamlit as st
from st_aggrid import AgGrid, ColumnsAutoSizeMode, GridOptionsBuilder
from streamlit_plotly_events import plotly_events

from utilities import initialization

initialization()


# @st.cache(show_spinner=False)
# def initialize_state():
#     with st.spinner("Loading app..."):
#         if 'model' not in st.session_state:
#             model = Top2Vec.load('models/model.pkl')
#             model._check_model_status()
#             model.hierarchical_topic_reduction(num_topics=20)
#
#             st.session_state.model = model
#             st.session_state.umap_model = joblib.load(proj_dir / 'models' / 'umap.sav')
#             logger.info("loading data...")
#
#         if 'data' not in st.session_state:
#             logger.info("loading data...")
#             data = pd.read_csv(proj_dir / 'data' / 'data.csv')
#             data['topic_id'] = data['topic_id'].apply(lambda x: f'{x:02d}')
#             st.session_state.data = data
#             st.session_state.selected_data = data
#             st.session_state.all_topics = list(data.topic_id.unique())
#
#         if 'topics' not in st.session_state:
#             logger.info("loading topics...")
#             topics = pd.read_csv(proj_dir / 'data' / 'topics.csv')
#             topics['topic_id'] = topics['topic_id'].apply(lambda x: f'{x:02d}')
#             st.session_state.topics = topics


def reset():
    logger.info("Resetting...")
    st.session_state.selected_data = st.session_state.data
    st.session_state.selected_points = []


def filter_df():
    if st.session_state.selected_points:
        points_df = pd.DataFrame(st.session_state.selected_points).loc[:, ['x', 'y']]
        st.session_state.selected_data = st.session_state.data.merge(points_df, on=['x', 'y'])
        logger.info(f"Updates selected_data: {len(st.session_state.selected_data)}")
    else:
        logger.info(f"Lame")


def reset():
    st.session_state.selected_data = st.session_state.data
    st.session_state.selected_points = []


def main():
    st.write(""" 
    # Topic Modeling
    This shows a 2d representation of documents embeded in a semantic space. Each dot is a document
    and the dots close represent documents that are close in meaning.
    
    Zoom in and explore a topic of your choice. You can see the documents you select with the `lasso` or `box`
    tool below in the corresponding tabs."""
             )

    st.button("Reset", help="Will Reset the selected points and the selected topics", on_click=reset)
    data_to_model = st.session_state.data.sort_values(by='topic_id',
                                                      ascending=True)  # to make legend sorted https://bioinformatics.stackexchange.com/a/18847
    data_to_model['topic_id'].replace(st.session_state.topic_str_to_word, inplace=True)
    fig = px.scatter(data_to_model, x='x', y='y', color='topic_id', template='plotly_dark',
                     hover_data=['id', 'topic_id', 'x', 'y'])
    st.session_state.selected_points = plotly_events(fig, select_event=True, click_event=False)
    filter_df()

    tab1, tab2 = st.tabs(["Docs", "Topics"])

    with tab1:
        if st.session_state.selected_points:
            filter_df()
            cols = ['id', 'topic_id', 'documents']
            data = st.session_state.selected_data[cols]
            data['topic_word'] = data.topic_id.replace(st.session_state.topic_str_to_word)
            ordered_cols = ['id', 'topic_id', 'topic_word', 'documents']
            builder = GridOptionsBuilder.from_dataframe(data[ordered_cols])
            builder.configure_pagination()
            go = builder.build()
            AgGrid(st.session_state.selected_data[cols], theme='streamlit', gridOptions=go,
                   columns_auto_size_mode=ColumnsAutoSizeMode.FIT_CONTENTS)
        else:
            st.markdown('Select points in the graph with the `lasso` or `box` select tools to populate this table.')

    def get_topics_counts() -> pd.DataFrame:
        topic_counts = st.session_state.selected_data["topic_id"].value_counts().to_frame()
        merged = topic_counts.merge(st.session_state.topics, left_index=True, right_on='topic_id')
        cleaned = merged.drop(['topic_id_y'], axis=1).rename({'topic_id_x': 'topic_count'}, axis=1)
        cols = ['topic_id'] + [col for col in cleaned.columns if col != 'topic_id']
        return cleaned[cols]

    with tab2:
        if st.session_state.selected_points:
            filter_df()
            cols = ['topic_id', 'topic_count', 'topic_0']
            topic_counts = get_topics_counts()
            # st.write(topic_counts.columns)
            builder = GridOptionsBuilder.from_dataframe(topic_counts[cols])
            builder.configure_pagination()
            builder.configure_column('topic_0', header_name='Topic Word', wrap_text=True)
            go = builder.build()
            AgGrid(topic_counts.loc[:, cols], theme='streamlit', gridOptions=go,
                   columns_auto_size_mode=ColumnsAutoSizeMode.FIT_ALL_COLUMNS_TO_VIEW)
        else:
            st.markdown('Select points in the graph with the `lasso` or `box` select tools to populate this table.')


if __name__ == "__main__":
    # Setting up Logger and proj_dir
    logger = getLogger(__name__)
    proj_dir = Path(__file__).parents[2]

    # For max width tables
    pd.set_option('display.max_colwidth', 0)

    # Streamlit settings
    # st.set_page_config(layout="wide")
    md_title = "# Document Explorer 📖"
    st.markdown(md_title)
    st.sidebar.markdown(md_title)

    # initialize_state()
    main()