import pandas as pd
import plotly.express as px
import streamlit as st

from process_kpi.process_lte_capacity import process_lte_bh_report
from utils.convert_to_excel import convert_lte_analysis_dfs
from utils.kpi_analysis_utils import LteCapacity

st.title("📊 LTE Capacity Analysis")
doc_col, image_col = st.columns(2)

with doc_col:
    st.write(
        """
        The report analyzes LTE capacity based on:
        - Dump file required
        - BH Cell level KPI report in CSV format
        - Availability and PRB usage thresholds
        """
    )

with image_col:
    st.image("./assets/lte_capacity.png", width=250)

file1, file2 = st.columns(2)

with file1:
    uploaded_dump = st.file_uploader("Upload Dump file in xlsb format", type="xlsb")
with file2:
    uploaded_bh_report = st.file_uploader(
        "Upload LTE Busy Hour Report in CSV format", type="csv"
    )

# Parameters
param_col1, param_col2 = st.columns(2)
param_col3, param_col4 = st.columns(2)
param_col5, param_col6 = st.columns(2)

with param_col1:
    num_last_days = st.number_input(
        "Number of last days for analysis", value=7, min_value=1
    )

with param_col2:
    num_threshold_days = st.number_input(
        "Number of days for threshold", value=3, min_value=1
    )

with param_col3:
    availability_threshold = st.number_input(
        "Availability threshold (%)", value=95.0, min_value=0.0, max_value=100.0
    )

with param_col4:
    prb_usage_threshold = st.number_input(
        "PRB usage threshold (%)", value=80.0, min_value=0.0, max_value=100.0
    )

with param_col5:
    prb_diff_between_cells = st.number_input(
        "Maximum PRB usage difference between cells (%)",
        value=20.0,
        min_value=0.0,
        max_value=100.0,
    )

with param_col6:
    #     DL PRB Util p TTI Lev_10
    # E-UTRAN Avg PRB usage per TTI DL
    main_prb_to_use = st.selectbox(
        "Main PRB to use",
        ["DL PRB Util p TTI Lev_10", "E-UTRAN Avg PRB usage per TTI DL"],
        index=1,
    )


if uploaded_dump is not None and uploaded_bh_report is not None:
    if st.button("Analyze Data", type="primary"):
        with st.spinner("Processing data..."):
            results = process_lte_bh_report(
                dump_path=uploaded_dump,
                bh_report_path=uploaded_bh_report,
                num_last_days=num_last_days,
                num_threshold_days=num_threshold_days,
                availability_threshold=availability_threshold,
                prb_usage_threshold=prb_usage_threshold,
                prb_diff_between_cells_threshold=prb_diff_between_cells,
                main_prb_to_use=main_prb_to_use,
            )
        if results is not None:
            bh_report: pd.DataFrame = results[0]
            lte_analysis_df: pd.DataFrame = results[1]
            LteCapacity.final_results = convert_lte_analysis_dfs(
                [lte_analysis_df, bh_report], ["LTE_Analysis", "LTE_BH_Report"]
            )
            st.download_button(
                on_click="ignore",
                type="primary",
                label="Download the Analysis Report",
                data=LteCapacity.final_results,
                file_name="LTE_Analysis_Report.xlsx",
                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
            )
            st.write(lte_analysis_df)
            # Add dataframe and Pie chart with "final_comments" distribution
            st.markdown("***")
            st.markdown(":blue[**Final comment distribution**]")
            final_comments_df = (
                lte_analysis_df.groupby("final_comments")
                .size()
                .reset_index(name="count")
                .sort_values(by="count", ascending=False)
            )
            final_comments_col1, final_comments_col2 = st.columns((1, 3))
            with final_comments_col1:
                st.write(final_comments_df)
            with final_comments_col2:
                fig = px.pie(
                    final_comments_df,
                    names="final_comments",
                    values="count",
                    hover_name="final_comments",
                    hover_data=["count"],
                    title="Final comment distribution",
                )
                fig.update_layout(height=600)
                fig.update_traces(
                    texttemplate="%{label}: %{value}",
                    textfont_size=15,
                    textposition="outside",
                )
                st.plotly_chart(fig, use_container_width=True)
            # Add dataframe and Pie chart with "final_comments" distribution where num_congested_cells > 0
            st.markdown("***")
            st.markdown(":blue[**Congested cells distribution**]")
            congested_cells_df = (
                lte_analysis_df[lte_analysis_df["num_congested_cells"] > 0]
                .groupby("final_comments")
                .size()
                .reset_index(name="count")
                .sort_values(by="count", ascending=False)
            )
            congested_cells_col1, congested_cells_col2 = st.columns((1, 3))
            with congested_cells_col1:
                st.write(congested_cells_df)
            with congested_cells_col2:
                fig = px.pie(
                    congested_cells_df,
                    names="final_comments",
                    values="count",
                    hover_name="final_comments",
                    hover_data=["count"],
                    title="Congested cells distribution",
                )
                fig.update_layout(height=600)
                fig.update_traces(
                    texttemplate="%{label}: %{value}",
                    textfont_size=15,
                    textposition="outside",
                )
                st.plotly_chart(fig, use_container_width=True)

            # Add dataframe and Bar chart with "final_comments" distribution where num_congested_cells > 0 per Region
            st.markdown("***")
            st.markdown(":blue[**Congested cells distribution per Region**]")
            congested_cells_region_df = (
                lte_analysis_df[lte_analysis_df["num_congested_cells"] > 0]
                .groupby(["Region", "final_comments"])
                .size()
                .reset_index(name="count")
                .sort_values(by="count", ascending=False)
            )
            congested_cells_region_col1, congested_cells_region_col2 = st.columns(
                (1, 3)
            )
            with congested_cells_region_col1:
                st.write(congested_cells_region_df)
            with congested_cells_region_col2:
                fig = px.bar(
                    congested_cells_region_df,
                    x="Region",
                    y="count",
                    color="final_comments",
                    title="Congested cells distribution per Region",
                )
                fig.update_layout(height=600)
                fig.update_traces(
                    texttemplate="%{value}", textfont_size=15, textposition="outside"
                )
                st.plotly_chart(fig, use_container_width=True)
            # Add dataframe and Bar chart with "final_comments" distribution where num_congested_cells > 0 per Region groupby region
            st.markdown("***")
            st.markdown(
                ":blue[**Congested cells distribution per Region groupby Region**]"
            )
            congested_cells_region_groupby_region_df = (
                lte_analysis_df[lte_analysis_df["num_congested_cells"] > 0]
                .groupby(["Region"])
                .size()
                .reset_index(name="count")
                .sort_values(by="count", ascending=False)
            )
            (
                congested_cells_region_groupby_region_col1,
                congested_cells_region_groupby_region_col2,
            ) = st.columns((1, 3))
            with congested_cells_region_groupby_region_col1:
                st.write(congested_cells_region_groupby_region_df)
            with congested_cells_region_groupby_region_col2:
                fig = px.bar(
                    congested_cells_region_groupby_region_df,
                    x="Region",
                    y="count",
                    title="Congested cells distribution per Region groupby Region",
                )
                fig.update_layout(height=600)
                fig.update_traces(
                    texttemplate="%{value}", textfont_size=15, textposition="outside"
                )
                st.plotly_chart(fig, use_container_width=True)
            # create a map plot with scatter_map with code ,Longitude,Latitude,final_comments
            st.markdown("***")
            st.markdown(":blue[**Final comments distribution**]")
            map_df = lte_analysis_df[
                ["code", "Longitude", "Latitude", "final_comments"]
            ].dropna(subset=["code", "Longitude", "Latitude", "final_comments"])
            # add size column equalt to 20
            map_df["size"] = 20

            fig = px.scatter_map(
                map_df,
                lat="Latitude",
                lon="Longitude",
                color="final_comments",
                size="size",
                zoom=10,
                height=600,
                title="Final comments distribution",
                hover_data={
                    "code": True,
                    "final_comments": True,
                },
                hover_name="code",
            )
            fig.update_layout(mapbox_style="open-street-map")
            st.plotly_chart(fig, use_container_width=True)