import pandas as pd
import streamlit as st

best_results_df = pd.read_csv("assets/best_tasks_with_hits.csv")


worst_results_df = pd.read_csv("assets/worst_tasks_with_hits.csv")

show_worst_best_statistics = st.sidebar.checkbox(
    label="show worst/best statistics grouped by area"
)

show_area_aggregated_results = st.sidebar.checkbox(
    label="show results aggregated by area"
)
if show_worst_best_statistics:
    st.markdown(
        """
    ## Worst/best queries
    The following are top 10 worst/best queries per area by number of hits.
    There are at least 10 documents per query in the test set, so number of hits/10 is the accuracy.
    """
    )
    sort_key = st.selectbox("sort by", list(best_results_df.columns))
    st.markdown("## Queries with best results")
    st.table(best_results_df.sort_values(sort_key, ascending=False))
    st.markdown("## Queries with worst results")
    st.table(worst_results_df.sort_values(sort_key, ascending=False))

if show_area_aggregated_results:
    st.markdown("## Area aggregated results")
    best_results_agg = best_results_df.groupby("area").agg("mean").reset_index()
    worst_results_agg = worst_results_df.groupby("area").agg("mean").reset_index()
    sort_key = st.selectbox("sort by", list(best_results_agg.columns))
    st.markdown("Best results")
    st.table(best_results_agg.sort_values(sort_key, ascending=False))
    st.markdown("Worst results")
    st.table(worst_results_agg.sort_values(sort_key, ascending=False))