Start.py

import streamlit as st
from common.static import start_caption
from version import __version__ as version


st.set_page_config(
    page_title='TS Clustering',
    layout='centered'
)

custom_footer = f"""
    <style>
    /* This is to hide Streamlit footer */
    footer {{visibility: hidden;}}
    footer:after {{
        content:'v.{version} (88milesprower. 2022)'; 
        visibility: visible;
        display: block;
        position: relative;
        padding: 5px;
        top: 2px;
    }}
"""

st.markdown(custom_footer, unsafe_allow_html=True)

st.header("Time series clustering")
st.caption(start_caption)


# def app():
#     st.header("Time series clustering")
#     st.caption(caption)

    # set_session_state()
    #
    # data_opts: DataOptions = get_options()
    #
    # btn_start_clustering = st.button(
    #     "Make clustering",
    #     on_click=set_btn_clicked, kwargs={"btn_name": "btn_start_clustering"}
    # )
    #
    # if not (btn_start_clustering or st.session_state["btn_start_clustering"]):
    #     return
    #
    # df_bm = load_bms(start_date=data_opts.date, bm_list=data_opts.bm_list)
    # df_corr = get_correlations(df_bm)
    # df_dist = 1 - df_corr
    # df_clusters, labels = get_clusters(df_dist, eps=data_opts.eps)
    # df_mds = get_mds(df_dist)
    # df_mds["cluster"] = labels
    #
    # col_plot, col_cluster_chart = st.columns(2)
    #
    # if ("actions" not in st.session_state) or (not st.session_state["actions"]):
    #     set_actions(labels)
    #
    # col_table, col_margin, col_transformers = st.columns((4, 0.2, 1))
    # cluster_table = render_cluster_table(df_clusters, col_table)
    # selected_cluster_rows = cluster_table["selected_rows"]
    #
    # if selected_cluster_rows:
    #     st.session_state["selected_cluster_row"] = selected_cluster_rows[0]
    #
    # render_clusters_plot(df_mds, col_plot)
    # render_cluster_chart(df_bm, col_cluster_chart)
    # render_transformer_settings(col_transformers)
    #
    # btn_do_transformations = st.button("Aggregate and save")
    #
    # if btn_do_transformations:
    #     df_transformed = transform_bm(df_bm, cluster_table["data"])
    #     persist(df_transformed)
    #     render_transformed(df_transformed)