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

from process_kpi.process_lcg_capacity import load_and_process_lcg_data
from utils.convert_to_excel import convert_dfs


class LcgCapacity:
    final_results = None


# Streamlit UI
st.title(" 📊 LCG Analysis")
doc_col, image_col = st.columns(2)

with doc_col:
    st.write(
        """This app allows you to analyze the LCG of a network. 
        It provides insights into the utilization of LCG resources, 
        helping you identify potential capacity issues and plan for upgrades.
        
        The report should be run with a minimum of 3 days of data.
        - Daily Aggregated
        - LCG level
        - Exported in CSV format.
        """
    )

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

uploaded_file = st.file_uploader("Upload LCG report in CSV format", type="csv")

param_col1, param_col2, param_col3 = st.columns(3)
param_col4, param_col5, param_col6 = st.columns(3)


# num_last_days
# num_threshold_days
# lcg_utilization_threshold
# difference_between_lcgs

if uploaded_file is not None:
    LcgCapacity.final_results = None
    with param_col1:
        num_last_days = st.number_input(
            "Number of days for analysis",
            min_value=3,
            max_value=30,
            value=7,
        )
    with param_col2:
        num_threshold_days = st.number_input(
            "Number of days for threshold",
            min_value=1,
            max_value=30,
            value=2,
        )
    with param_col3:
        lcg_utilization_threshold = st.number_input(
            "LCG Utilization Threshold (%)",
            min_value=0,
            max_value=100,
            value=80,
        )
    with param_col4:
        difference_between_lcgs = st.number_input(
            "Difference between LCgs (%)",
            min_value=0,
            max_value=100,
            value=20,
        )
    if st.button("Analyze Data", type="primary"):
        # Input validation
        try:
            if num_threshold_days > num_last_days:
                st.warning(
                    "Number of threshold days cannot be greater than number of analysis days"
                )
                st.stop()

            if num_last_days < 3:
                st.warning(
                    "Analysis period should be at least 3 days for meaningful results"
                )
                st.stop()

            if lcg_utilization_threshold <= 0 or lcg_utilization_threshold > 100:
                st.warning("LCG utilization threshold must be between 1 and 100")
                st.stop()

            with st.spinner("Processing data..."):
                results = load_and_process_lcg_data(
                    uploaded_file,
                    num_last_days,
                    num_threshold_days,
                    lcg_utilization_threshold,
                    difference_between_lcgs,
                )
        except Exception as e:
            st.error(f"An error occurred during input validation: {str(e)}")
            st.stop()
        if results is not None:
            lcg_analysis_df = results[0]
            kpi_df = results[1]
            LcgCapacity.final_results = convert_dfs(
                [lcg_analysis_df, kpi_df], ["lcg_analysis", "kpi"]
            )
            st.download_button(
                on_click="ignore",
                type="primary",
                label="Download the Analysis Report",
                data=LcgCapacity.final_results,
                file_name="LCG_Capacity_Report.xlsx",
                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
            )
        st.write(lcg_analysis_df)
        # Add dataframe and Pie chart with "final_comments" distribution
        st.markdown("***")
        st.markdown(":blue[**Final comment distribution**]")
        final_comments_df = (
            lcg_analysis_df.groupby("final_comments")
            .size()
            .reset_index(name="count")
            .sort_values(by="count", ascending=False)
        )
        final_comments_df["percent"] = (
            final_comments_df["count"] / final_comments_df["count"].sum()
        ) * 100
        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", "percent"],
                title="Final Comments Distribution",
            )
            fig.update_layout(height=600)
            fig.update_traces(
                texttemplate="<b>%{label}</b><br> %{value}  <b>(%{customdata[1]:.1f}%)</b>",
                textfont_size=15,
                textposition="outside",
            )
            st.plotly_chart(fig)

        # Add dataframe and Bar chart with "final_comments" distribution per Region
        st.markdown("***")
        st.markdown(":blue[**Final comment distribution per Region**]")
        final_comments_df = (
            lcg_analysis_df.groupby(["Region", "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.bar(
                final_comments_df,
                x="Region",
                y="count",
                color="final_comments",
                title="Final Comments Distribution per Region",
                text="count",
            )
            fig.update_traces(textposition="outside")
            fig.update_layout(height=600)
            st.plotly_chart(fig)

        # Add map plot with scatter_map with code ,Longitude,Latitude,final_comments
        st.markdown("***")
        st.markdown(":blue[**Final comments distribution**]")
        final_comments_map_df = lcg_analysis_df[
            ["code", "Longitude", "Latitude", "final_comments"]
        ].dropna(subset=["code", "Longitude", "Latitude", "final_comments"])

        # replace empty strings with "Cell OK"
        # final_comments_map_df["final_comments"] = final_comments_map_df[
        #     "final_comments"
        # ].replace("", "Cell OK")
        # add size column equalt to 20
        final_comments_map_df["size"] = 20

        fig = px.scatter_map(
            final_comments_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)