"""
MMO Build Sprint 3
additions : contributions calculated using tuned Mixed LM model
pending : contributions calculations using - 1. not tuned Mixed LM model, 2. tuned OLS model, 3. not tuned OLS model

MMO Build Sprint 4
additions : response metrics selection
pending : contributions calculations using - 1. not tuned Mixed LM model, 2. tuned OLS model, 3. not tuned OLS model
"""

import streamlit as st
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import pickle
import os

from utilities_with_panel import load_local_css, set_header
import yaml
from yaml import SafeLoader
import streamlit_authenticator as stauth
import sqlite3
from utilities import update_db

st.set_page_config(layout="wide")
load_local_css("styles.css")
set_header()
for k, v in st.session_state.items():
    # print(k, v)
    if k not in [
        "logout",
        "login",
        "config",
        "build_tuned_model",
    ] and not k.startswith("FormSubmitter"):
        st.session_state[k] = v
with open("config.yaml") as file:
    config = yaml.load(file, Loader=SafeLoader)
    st.session_state["config"] = config
authenticator = stauth.Authenticate(
    config["credentials"],
    config["cookie"]["name"],
    config["cookie"]["key"],
    config["cookie"]["expiry_days"],
    config["preauthorized"],
)
st.session_state["authenticator"] = authenticator
name, authentication_status, username = authenticator.login("Login", "main")
auth_status = st.session_state.get("authentication_status")

if auth_status == True:
    authenticator.logout("Logout", "main")
    is_state_initiaized = st.session_state.get("initialized", False)

    if "project_dct" not in st.session_state:
        st.error("Please load a project from Home page")
        st.stop()

    conn = sqlite3.connect(
        r"DB/User.db", check_same_thread=False
    )  # connection with sql db
    c = conn.cursor()

    if not os.path.exists(
        os.path.join(st.session_state["project_path"], "tuned_model.pkl")
    ):
        st.error("Please save a tuned model")
        st.stop()

    if (
        "session_state_saved"
        in st.session_state["project_dct"]["model_tuning"].keys()
        and st.session_state["project_dct"]["model_tuning"][
            "session_state_saved"
        ]
        != []
    ):
        for key in [
            "used_response_metrics",
            "is_tuned_model",
            "media_data",
            "X_test_spends",
        ]:
            st.session_state[key] = st.session_state["project_dct"][
                "model_tuning"
            ]["session_state_saved"][key]
    elif (
        "session_state_saved"
        in st.session_state["project_dct"]["model_build"].keys()
        and st.session_state["project_dct"]["model_build"][
            "session_state_saved"
        ]
        != []
    ):
        for key in [
            "used_response_metrics",
            "date",
            "saved_model_names",
            "media_data",
            "X_test_spends",
        ]:
            st.session_state[key] = st.session_state["project_dct"][
                "model_build"
            ]["session_state_saved"][key]
    else:
        st.error("Please tune a model first")
    st.session_state["bin_dict"] = st.session_state["project_dct"][
        "model_build"
    ]["session_state_saved"]["bin_dict"]
    st.session_state["media_data"].columns = [
        c.lower() for c in st.session_state["media_data"].columns
    ]

    from utilities_with_panel import (
        overview_test_data_prep_panel,
        overview_test_data_prep_nonpanel,
        initialize_data,
        create_channel_summary,
        create_contribution_pie,
        create_contribuion_stacked_plot,
        create_channel_spends_sales_plot,
        format_numbers,
        channel_name_formating,
    )

    import plotly.graph_objects as go
    import streamlit_authenticator as stauth
    import yaml
    from yaml import SafeLoader
    import time

    def get_random_effects(media_data, panel_col, mdf):
        random_eff_df = pd.DataFrame(columns=[panel_col, "random_effect"])
        for i, market in enumerate(media_data[panel_col].unique()):
            print(i, end="\r")
            intercept = mdf.random_effects[market].values[0]
            random_eff_df.loc[i, "random_effect"] = intercept
            random_eff_df.loc[i, panel_col] = market

        return random_eff_df

    def process_train_and_test(train, test, features, panel_col, target_col):
        X1 = train[features]

        ss = MinMaxScaler()
        X1 = pd.DataFrame(ss.fit_transform(X1), columns=X1.columns)

        X1[panel_col] = train[panel_col]
        X1[target_col] = train[target_col]

        if test is not None:
            X2 = test[features]
            X2 = pd.DataFrame(ss.transform(X2), columns=X2.columns)
            X2[panel_col] = test[panel_col]
            X2[target_col] = test[target_col]
            return X1, X2
        return X1

    def mdf_predict(X_df, mdf, random_eff_df):
        X = X_df.copy()
        X = pd.merge(
            X,
            random_eff_df[[panel_col, "random_effect"]],
            on=panel_col,
            how="left",
        )
        X["pred_fixed_effect"] = mdf.predict(X)

        X["pred"] = X["pred_fixed_effect"] + X["random_effect"]
        X.to_csv("Test/merged_df_contri.csv", index=False)
        X.drop(columns=["pred_fixed_effect", "random_effect"], inplace=True)

        return X

    # target='Revenue'

    # is_panel=False
    # is_panel = st.session_state['is_panel']
    panel_col = [
        col.lower()
        .replace(".", "_")
        .replace("@", "_")
        .replace(" ", "_")
        .replace("-", "")
        .replace(":", "")
        .replace("__", "_")
        for col in st.session_state["bin_dict"]["Panel Level 1"]
    ][
        0
    ]  # set the panel column
    is_panel = True if len(panel_col) > 0 else False
    date_col = "date"

    # Sprint4 - if used_response_metrics is not blank, then select one of the used_response_metrics, else target is revenue by default
    if (
        "used_response_metrics" in st.session_state
        and st.session_state["used_response_metrics"] != []
    ):
        sel_target_col = st.selectbox(
            "Select the response metric",
            st.session_state["used_response_metrics"],
        )
        target_col = (
            sel_target_col.lower()
            .replace(" ", "_")
            .replace("-", "")
            .replace(":", "")
            .replace("__", "_")
        )
    else:
        sel_target_col = "Total Approved Accounts - Revenue"
        target_col = "total_approved_accounts_revenue"

    target = sel_target_col

    # Sprint4 - Look through all saved tuned models, only show saved models of the sel resp metric (target_col)
    # saved_models = st.session_state['saved_model_names']
    # Sprint4 - get the model obj of the selected model
    # st.write(sel_model_dict)

    # Sprint3 - Contribution
    if is_panel:
        # read tuned mixedLM model
        # if st.session_state["tuned_model"] is not None :
        if st.session_state["is_tuned_model"][target_col] == True:  # Sprint4
            with open(
                os.path.join(
                    st.session_state["project_path"], "tuned_model.pkl"
                ),
                "rb",
            ) as file:
                model_dict = pickle.load(file)
            saved_models = list(model_dict.keys())
            # st.write(saved_models)
            required_saved_models = [
                m.split("__")[0]
                for m in saved_models
                if m.split("__")[1] == target_col
            ]
            sel_model = st.selectbox(
                "Select the model to review", required_saved_models
            )
            sel_model_dict = model_dict[sel_model + "__" + target_col]

            model = sel_model_dict["Model_object"]
            X_train = sel_model_dict["X_train_tuned"]
            X_test = sel_model_dict["X_test_tuned"]
            best_feature_set = sel_model_dict["feature_set"]

        else:  # if non tuned model to be used # Pending
            with open(
                os.path.join(
                    st.session_state["project_path"], "best_models.pkl"
                ),
                "rb",
            ) as file:
                model_dict = pickle.load(file)
            # st.write(model_dict)
            saved_models = list(model_dict.keys())
            required_saved_models = [
                m.split("__")[0]
                for m in saved_models
                if m.split("__")[1] == target_col
            ]
            sel_model = st.selectbox(
                "Select the model to review", required_saved_models
            )
            sel_model_dict = model_dict[sel_model + "__" + target_col]
            # st.write(sel_model, sel_model_dict)
            model = sel_model_dict["Model_object"]
            X_train = sel_model_dict["X_train"]
            X_test = sel_model_dict["X_test"]
            best_feature_set = sel_model_dict["feature_set"]

        # Calculate contributions

        with open(
            os.path.join(st.session_state["project_path"], "data_import.pkl"),
            "rb",
        ) as f:
            data = pickle.load(f)

        # Accessing the loaded objects
        st.session_state["orig_media_data"] = data["final_df"]

        st.session_state["orig_media_data"].columns = [
            col.lower()
            .replace(".", "_")
            .replace("@", "_")
            .replace(" ", "_")
            .replace("-", "")
            .replace(":", "")
            .replace("__", "_")
            for col in st.session_state["orig_media_data"].columns
        ]

        media_data = st.session_state["media_data"]

        # st.session_state['orig_media_data']=st.session_state["media_data"]

        # st.write(media_data)

        contri_df = pd.DataFrame()

        y = []
        y_pred = []

        random_eff_df = get_random_effects(media_data, panel_col, model)
        random_eff_df["fixed_effect"] = model.fe_params["Intercept"]
        random_eff_df["panel_effect"] = (
            random_eff_df["random_effect"] + random_eff_df["fixed_effect"]
        )
        # random_eff_df.to_csv("Test/random_eff_df_contri.csv", index=False)

        coef_df = pd.DataFrame(model.fe_params)
        coef_df.reset_index(inplace=True)
        coef_df.columns = ["feature", "coef"]

        # coef_df.reset_index().to_csv("Test/coef_df_contri1.csv",index=False)
        # print(model.fe_params)

        x_train_contribution = X_train.copy()
        x_test_contribution = X_test.copy()

        # preprocessing not needed since X_train is already preprocessed
        # X1, X2 = process_train_and_test(x_train_contribution, x_test_contribution, best_feature_set, panel_col, target_col)
        # x_train_contribution[best_feature_set] = X1[best_feature_set]
        # x_test_contribution[best_feature_set] = X2[best_feature_set]

        x_train_contribution = mdf_predict(
            x_train_contribution, model, random_eff_df
        )
        x_test_contribution = mdf_predict(
            x_test_contribution, model, random_eff_df
        )

        x_train_contribution = pd.merge(
            x_train_contribution,
            random_eff_df[[panel_col, "panel_effect"]],
            on=panel_col,
            how="left",
        )
        x_test_contribution = pd.merge(
            x_test_contribution,
            random_eff_df[[panel_col, "panel_effect"]],
            on=panel_col,
            how="left",
        )

        for i in range(len(coef_df))[1:]:
            coef = coef_df.loc[i, "coef"]
            col = coef_df.loc[i, "feature"]
            x_train_contribution[str(col) + "_contr"] = (
                coef * x_train_contribution[col]
            )
            x_test_contribution[str(col) + "_contr"] = (
                coef * x_train_contribution[col]
            )

        x_train_contribution["sum_contributions"] = (
            x_train_contribution.filter(regex="contr").sum(axis=1)
        )
        x_train_contribution["sum_contributions"] = (
            x_train_contribution["sum_contributions"]
            + x_train_contribution["panel_effect"]
        )

        x_test_contribution["sum_contributions"] = x_test_contribution.filter(
            regex="contr"
        ).sum(axis=1)
        x_test_contribution["sum_contributions"] = (
            x_test_contribution["sum_contributions"]
            + x_test_contribution["panel_effect"]
        )

        # # test
        x_train_contribution.to_csv(
            "Test/x_train_contribution.csv", index=False
        )
        x_test_contribution.to_csv("Test/x_test_contribution.csv", index=False)
        #
        # st.session_state['orig_media_data'].to_csv("Test/transformed_data.csv",index=False)
        # st.session_state['X_test_spends'].to_csv("Test/test_spends.csv",index=False)
        # # st.write(st.session_state['orig_media_data'].columns)

        # st.write(date_col,panel_col)
        # st.write(x_test_contribution)

        overview_test_data_prep_panel(
            x_test_contribution,
            st.session_state["orig_media_data"],
            st.session_state["X_test_spends"],
            date_col,
            panel_col,
            target_col,
        )

    else:  # NON PANEL
        if st.session_state["is_tuned_model"][target_col] == True:  # Sprint4
            with open(
                os.path.join(
                    st.session_state["project_path"], "tuned_model.pkl"
                ),
                "rb",
            ) as file:
                model_dict = pickle.load(file)
            saved_models = list(model_dict.keys())
            required_saved_models = [
                m.split("__")[0]
                for m in saved_models
                if m.split("__")[1] == target_col
            ]
            sel_model = st.selectbox(
                "Select the model to review", required_saved_models
            )
            sel_model_dict = model_dict[sel_model + "__" + target_col]

            model = sel_model_dict["Model_object"]
            X_train = sel_model_dict["X_train_tuned"]
            X_test = sel_model_dict["X_test_tuned"]
            best_feature_set = sel_model_dict["feature_set"]

        else:  # Sprint4
            with open(
                os.path.join(
                    st.session_state["project_path"], "best_models.pkl"
                ),
                "rb",
            ) as file:
                model_dict = pickle.load(file)
            saved_models = list(model_dict.keys())
            required_saved_models = [
                m.split("__")[0]
                for m in saved_models
                if m.split("__")[1] == target_col
            ]
            sel_model = st.selectbox(
                "Select the model to review", required_saved_models
            )
            sel_model_dict = model_dict[sel_model + "__" + target_col]

            model = sel_model_dict["Model_object"]
            X_train = sel_model_dict["X_train"]
            X_test = sel_model_dict["X_test"]
            best_feature_set = sel_model_dict["feature_set"]

        x_train_contribution = X_train.copy()
        x_test_contribution = X_test.copy()

        x_train_contribution["pred"] = model.predict(
            x_train_contribution[best_feature_set]
        )
        x_test_contribution["pred"] = model.predict(
            x_test_contribution[best_feature_set]
        )

        for num, i in enumerate(model.params.values):
            col = best_feature_set[num]
            x_train_contribution[col + "_contr"] = X_train[col] * i
            x_test_contribution[col + "_contr"] = X_test[col] * i

        x_test_contribution.to_csv(
            "Test/x_test_contribution_non_panel.csv", index=False
        )
        overview_test_data_prep_nonpanel(
            x_test_contribution,
            st.session_state["orig_media_data"].copy(),
            st.session_state["X_test_spends"].copy(),
            date_col,
            target_col,
        )
    # for k, v in st.session_sta
    # te.items():

    #     if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
    #         st.session_state[k] = v

    # authenticator = st.session_state.get('authenticator')

    # if authenticator is None:
    #     authenticator = load_authenticator()

    # name, authentication_status, username = authenticator.login('Login', 'main')
    # auth_status = st.session_state['authentication_status']

    # if auth_status:
    #     authenticator.logout('Logout', 'main')

    #     is_state_initiaized = st.session_state.get('initialized',False)
    #     if not is_state_initiaized:

    initialize_data(target_col)
    scenario = st.session_state["scenario"]
    raw_df = st.session_state["raw_df"]
    st.header("Overview of previous spends")

    # st.write(scenario.actual_total_spends)
    # st.write(scenario.actual_total_sales)
    columns = st.columns((1, 1, 3))

    with columns[0]:
        st.metric(
            label="Spends",
            value=format_numbers(float(scenario.actual_total_spends)),
        )
    ###print(f"##################### {scenario.actual_total_sales} ##################")
    with columns[1]:
        st.metric(
            label=target,
            value=format_numbers(
                float(scenario.actual_total_sales), include_indicator=False
            ),
        )

    actual_summary_df = create_channel_summary(scenario)
    actual_summary_df["Channel"] = actual_summary_df["Channel"].apply(
        channel_name_formating
    )

    columns = st.columns((2, 1))
    with columns[0]:
        with st.expander("Channel wise overview"):
            st.markdown(
                actual_summary_df.style.set_table_styles(
                    [
                        {
                            "selector": "th",
                            "props": [("background-color", "#11B6BD")],
                        },
                        {
                            "selector": "tr:nth-child(even)",
                            "props": [("background-color", "#11B6BD")],
                        },
                    ]
                ).to_html(),
                unsafe_allow_html=True,
            )

    st.markdown("<hr>", unsafe_allow_html=True)
    ##############################

    st.plotly_chart(
        create_contribution_pie(scenario), use_container_width=True
    )
    st.markdown("<hr>", unsafe_allow_html=True)

    ################################3
    st.plotly_chart(
        create_contribuion_stacked_plot(scenario), use_container_width=True
    )
    st.markdown("<hr>", unsafe_allow_html=True)
    #######################################

    selected_channel_name = st.selectbox(
        "Channel",
        st.session_state["channels_list"] + ["non media"],
        format_func=channel_name_formating,
    )
    selected_channel = scenario.channels.get(selected_channel_name, None)

    st.plotly_chart(
        create_channel_spends_sales_plot(selected_channel),
        use_container_width=True,
    )

    st.markdown("<hr>", unsafe_allow_html=True)

    if st.checkbox("Save this session", key="save"):
        project_dct_path = os.path.join(
            st.session_state["project_path"], "project_dct.pkl"
        )
        with open(project_dct_path, "wb") as f:
            pickle.dump(st.session_state["project_dct"], f)
        update_db("7_Current_Media_Performance.py")