pages/4_Model_Build 2.py

"""
MMO Build Sprint 3
additions : adding more variables to session state for saved model : random effect, predicted train & test

MMO Build Sprint 4
additions : ability to run models for different response metrics
"""

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from Eda_functions import format_numbers
import numpy as np
import pickle
from st_aggrid import AgGrid
from st_aggrid import GridOptionsBuilder, GridUpdateMode
from utilities import set_header, load_local_css
from st_aggrid import GridOptionsBuilder
import time
import itertools
import statsmodels.api as sm
import numpy as npc
import re
import itertools
from sklearn.metrics import (
    mean_absolute_error,
    r2_score,
    mean_absolute_percentage_error,
)
from sklearn.preprocessing import MinMaxScaler
import os
import matplotlib.pyplot as plt
from statsmodels.stats.outliers_influence import variance_inflation_factor
import yaml
from yaml import SafeLoader
import streamlit_authenticator as stauth

st.set_option("deprecation.showPyplotGlobalUse", False)
import statsmodels.api as sm
import statsmodels.formula.api as smf

from datetime import datetime
import seaborn as sns
from Data_prep_functions import *
import sqlite3
from utilities import update_db
from datetime import datetime, timedelta
 
@st.cache_resource(show_spinner=False)
# def save_to_pickle(file_path, final_df):
#     # Open the file in write-binary mode and dump the objects
#     with open(file_path, "wb") as f:
#         pickle.dump({file_path: final_df}, f)
@st.cache_resource(show_spinner=True)
def prepare_data_df(data):
    data = data[data["pos_count"] == data["pos_count"].max()].reset_index(
        drop=True
    )  # Sprint4 -- Srishti -- only show models with the lowest num of neg coeffs
    data.sort_values(by=["ADJR2"], ascending=False, inplace=True)
    data.drop_duplicates(subset="Model_iteration", inplace=True)

    # Applying the function to each row in the DataFrame
    data["coefficients"] = data["coefficients"].apply(process_dict)

    # Convert dictionary items into separate DataFrame columns
    coefficients_df = data["coefficients"].apply(pd.Series)

    # Rename the columns to remove any trailing underscores and capitalize the words
    coefficients_df.columns = [
        col.strip("_").replace("_", " ").title() for col in coefficients_df.columns
    ]

    # Normalize each row so that the sum equals 100%
    coefficients_df = coefficients_df.apply(
        lambda x: round((x / x.sum()) * 100, 2), axis=1
    )

    # Join the new columns back to the original DataFrame
    data = data.join(coefficients_df)

    data_df = data[
        [
            "Model_iteration",
            "MAPE",
            "ADJR2",
            "R2",
            "Total Positive Contributions",
            "Significance",
        ]
        + list(coefficients_df.columns)
    ]
    data_df.rename(columns={"Model_iteration": "Model Iteration"}, inplace=True)
    data_df.insert(0, "Rank", range(1, len(data_df) + 1))

    return coefficients_df, data_df
def format_display(inp):
        return inp.title().replace("_", " ").strip()

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 mdf_predict(X_df, mdf, random_eff_df):
    X = X_df.copy()
    X["fixed_effect"] = mdf.predict(X)
    X = pd.merge(X, random_eff_df, on=panel_col, how="left")
    X["pred"] = X["fixed_effect"] + X["random_effect"]
    # X.to_csv('Test/megred_df.csv',index=False)
    X.drop(columns=["fixed_effect", "random_effect"], inplace=True)
    return X["pred"]


st.set_page_config(
    page_title="Model Build",
    page_icon=":shark:",
    layout="wide",
    initial_sidebar_state="collapsed",
)

load_local_css("styles.css")
set_header()

# Check for authentication status
for k, v in st.session_state.items():
    if k not in [
        "logout",
        "login",
        "config",
        "model_build_button",
    ] 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)

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

    if not is_state_initiaized:

        if "session_name" not in st.session_state:
            st.session_state["session_name"] = None

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

    st.title("1. Build Your Model")

    if not os.path.exists(
        os.path.join(st.session_state["project_path"], "data_import.pkl")
    ):
        st.error("Please move to Data Import Page and save.")
        st.stop()
    with open(
        os.path.join(st.session_state["project_path"], "data_import.pkl"), "rb"
    ) as f:
        data = pickle.load(f)
        st.session_state["bin_dict"] = data["bin_dict"]

    if not os.path.exists(
        os.path.join(
            st.session_state["project_path"], "final_df_transformed.pkl"
        )
    ):
        st.error(
            "Please move to Transformation Page and save transformations."
        )
        st.stop()
    with open(
        os.path.join(
            st.session_state["project_path"], "final_df_transformed.pkl"
        ),
        "rb",
    ) as f:
        data = pickle.load(f)
        media_data = data["final_df_transformed"]


    # Sprint4 - available response metrics is a list of all reponse metrics in the data
    ## these will be put in a drop down

    st.session_state["media_data"] = media_data

    if "available_response_metrics" not in st.session_state:
        # st.session_state['available_response_metrics'] = ['Total Approved Accounts - Revenue',
        #                                                   'Total Approved Accounts - Appsflyer',
        #                                                   'Account Requests - Appsflyer',
        #                                                   'App Installs - Appsflyer']

        st.session_state["available_response_metrics"] = st.session_state[
            "bin_dict"
        ]["Response Metrics"]
    # Sprint4
    if "is_tuned_model" not in st.session_state:
        st.session_state["is_tuned_model"] = {}
    for resp_metric in st.session_state["available_response_metrics"]:
        resp_metric = (
            resp_metric.lower()
            .replace(" ", "_")
            .replace("-", "")
            .replace(":", "")
            .replace("__", "_")
        )
        st.session_state["is_tuned_model"][resp_metric] = False

    # Sprint4 - used_response_metrics is a list of resp metrics for which user has created & saved a model
    if "used_response_metrics" not in st.session_state:
        st.session_state["used_response_metrics"] = []

    # Sprint4 - saved_model_names
    if "saved_model_names" not in st.session_state:
        st.session_state["saved_model_names"] = []

    if "Model" not in st.session_state:
        if (
            "session_state_saved"
            in st.session_state["project_dct"]["model_build"].keys()
            and st.session_state["project_dct"]["model_build"][
                "session_state_saved"
            ]
            is not None
            and "Model"
            in st.session_state["project_dct"]["model_build"][
                "session_state_saved"
            ].keys()
        ):
            st.session_state["Model"] = st.session_state["project_dct"][
                "model_build"
            ]["session_state_saved"]["Model"]
        else:
            st.session_state["Model"] = {}

    date_col = "date"
    date = media_data[date_col]

    # Sprint4 - select a response metric
    default_target_idx = (
        st.session_state["project_dct"]["model_build"].get(
            "sel_target_col", None
        )
        if st.session_state["project_dct"]["model_build"].get(
            "sel_target_col", None
        )
        is not None
        else st.session_state["available_response_metrics"][0]
    )

    start_cols = st.columns(2)
    min_date = min(date)
    max_date = max(date)

    with start_cols[0]:
        sel_target_col = st.selectbox(
            "Select the response metric",
            st.session_state["available_response_metrics"],
            index=st.session_state["available_response_metrics"].index(
                default_target_idx
            ),
            format_func=format_display
        )
        # , on_change=reset_save())
        st.session_state["project_dct"]["model_build"][
            "sel_target_col"
        ] = sel_target_col


    default_test_start = min_date + (3*(max_date-min_date)/4)

    with start_cols[1]:
        test_start = st.date_input(
            "Select test start date",
            default_test_start,
            min_value=min_date,
            max_value=max_date,
        )
    train_idx = media_data[media_data[date_col] <= pd.to_datetime(test_start)].index[-1]
    # st.write(train_idx, media_data.index[-1])

    target_col = (
        sel_target_col.lower()
        .replace(" ", "_")
        .replace("-", "")
        .replace(":", "")
        .replace("__", "_")
    )
    new_name_dct = {
        col: col.lower()
        .replace(".", "_")
        .lower()
        .replace("@", "_")
        .replace(" ", "_")
        .replace("-", "")
        .replace(":", "")
        .replace("__", "_")
        for col in media_data.columns
    }
    media_data.columns = [
        col.lower()
        .replace(".", "_")
        .replace("@", "_")
        .replace(" ", "_")
        .replace("-", "")
        .replace(":", "")
        .replace("__", "_")
        for col in media_data.columns
    ]
    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

    if "is_panel" not in st.session_state:
        st.session_state["is_panel"] = is_panel

    if is_panel:
        media_data.sort_values([date_col, panel_col], inplace=True)
    else:
        media_data.sort_values(date_col, inplace=True)

    media_data.reset_index(drop=True, inplace=True)

    st.session_state["date"] = date
    y = media_data[target_col]

    if is_panel:
        spends_data = media_data[
            [
                c
                for c in media_data.columns
                if "_cost" in c.lower() or "_spend" in c.lower()
            ]
            + [date_col, panel_col]
        ]
        # Sprint3 - spends for resp curves
    else:
        spends_data = media_data[
            [
                c
                for c in media_data.columns
                if "_cost" in c.lower() or "_spend" in c.lower()
            ]
            + [date_col]
        ]

    y = media_data[target_col]
    media_data.drop([date_col], axis=1, inplace=True)
    media_data.reset_index(drop=True, inplace=True)

    columns = st.columns(2)

    old_shape = media_data.shape

    if "old_shape" not in st.session_state:
        st.session_state["old_shape"] = old_shape

    if "media_data" not in st.session_state:
        st.session_state["media_data"] = pd.DataFrame()

    # Sprint3
    if "orig_media_data" not in st.session_state:
        st.session_state["orig_media_data"] = pd.DataFrame()

    # Sprint3 additions
    if "random_effects" not in st.session_state:
        st.session_state["random_effects"] = pd.DataFrame()
    if "pred_train" not in st.session_state:
        st.session_state["pred_train"] = []
    if "pred_test" not in st.session_state:
        st.session_state["pred_test"] = []
    # end of Sprint3 additions

    # Section 3 - Create combinations

    # bucket=['paid_search', 'kwai','indicacao','infleux', 'influencer','FB: Level Achieved - Tier 1 Impressions',
    #       ' FB: Level Achieved - Tier 2 Impressions','paid_social_others',
    #         ' GA App: Will And Cid Pequena Baixo Risco Clicks',
    #       'digital_tactic_others',"programmatic"
    #       ]

    # srishti - bucket names changed
    bucket = [
        "paid_search",
        "kwai",
        "indicacao",
        "infleux",
        "influencer",
        "fb_level_achieved_tier_2",
        "fb_level_achieved_tier_1",
        "paid_social_others",
        "ga_app",
        "digital_tactic_others",
        "programmatic",
    ]

    # with columns[0]:
    #     if st.button('Create Combinations of Variables'):

    top_3_correlated_features = []
    # # for col in st.session_state['media_data'].columns[:19]:
    # original_cols = [c for c in st.session_state['media_data'].columns if
    #                  "_clicks" in c.lower() or "_impressions" in c.lower()]
    # original_cols = [c for c in original_cols if "_lag" not in c.lower() and "_adstock" not in c.lower()]

    original_cols = (
        st.session_state["bin_dict"]["Media"]
        + st.session_state["bin_dict"]["Internal"]
    )

    original_cols = [
        col.lower()
        .replace(".", "_")
        .replace("@", "_")
        .replace(" ", "_")
        .replace("-", "")
        .replace(":", "")
        .replace("__", "_")
        for col in original_cols
    ]
    original_cols = [col for col in original_cols if "_cost" not in col]
    # for col in st.session_state['media_data'].columns[:19]:
    for col in original_cols:  # srishti - new
        corr_df = (
            pd.concat(
                [st.session_state["media_data"].filter(regex=col), y], axis=1
            )
            .corr()[target_col]
            .iloc[:-1]
        )
        top_3_correlated_features.append(
            list(corr_df.sort_values(ascending=False).head(2).index)
        )
    flattened_list = [
        item for sublist in top_3_correlated_features for item in sublist
    ]
    # all_features_set={var:[col for col in flattened_list if var in col] for var in bucket}
    all_features_set = {
        var: [col for col in flattened_list if var in col]
        for var in bucket
        if len([col for col in flattened_list if var in col]) > 0
    }  # srishti
    channels_all = [values for values in all_features_set.values()]
    st.session_state["combinations"] = list(itertools.product(*channels_all))
    # if 'combinations' not in st.session_state:
    #   st.session_state['combinations']=combinations_all

    st.session_state["final_selection"] = st.session_state["combinations"]
    # st.success('Created combinations')

    # revenue.reset_index(drop=True,inplace=True)
    y.reset_index(drop=True, inplace=True)
    if "Model_results" not in st.session_state:
        st.session_state["Model_results"] = {
            "Model_object": [],
            "Model_iteration": [],
            "Feature_set": [],
            "MAPE": [],
            "R2": [],
            "ADJR2": [],
            "pos_count": [],
        }

    def reset_model_result_dct():
        st.session_state["Model_results"] = {
            "Model_object": [],
            "Model_iteration": [],
            "Feature_set": [],
            "MAPE": [],
            "R2": [],
            "ADJR2": [],
            "pos_count": [],
        }

        # if st.button('Build Model'):

    if "iterations" not in st.session_state:
        st.session_state["iterations"] = 0

    if "final_selection" not in st.session_state:
        st.session_state["final_selection"] = False

    save_path = r"Model/"
    if st.session_state["final_selection"]:
        st.write(
            f'Total combinations created {format_numbers(len(st.session_state["final_selection"]))}'
        )

    # st.session_state["project_dct"]["model_build"]["all_iters_check"] = False

    checkbox_default = (
        st.session_state["project_dct"]["model_build"]["all_iters_check"]
        if st.session_state["project_dct"]["model_build"]["all_iters_check"]
        is not None
        else False
    )
    end_date = test_start - timedelta(days=1)
    disp_str = "Data Split -- Training Period: " + min_date.strftime("%B %d, %Y") + " - " + end_date.strftime("%B %d, %Y") +", Testing Period: " + test_start.strftime("%B %d, %Y") + " - " + max_date.strftime("%B %d, %Y")
    st.markdown(disp_str)
    if st.checkbox("Build all iterations", value=checkbox_default):
        # st.session_state["project_dct"]["model_build"]["all_iters_check"]
        iterations = len(st.session_state["final_selection"])
        st.session_state["project_dct"]["model_build"][
            "all_iters_check"
        ] = True

    else:
        iterations = st.number_input(
            "Select the number of iterations to perform",
            min_value=0,
            step=100,
            value=st.session_state["iterations"],
            on_change=reset_model_result_dct,
        )
        st.session_state["project_dct"]["model_build"][
            "all_iters_check"
        ] = False
        st.session_state["project_dct"]["model_build"][
            "iterations"
        ] = iterations

        # st.stop()

    # build_button = st.session_state["project_dct"]["model_build"]["build_button"] if \
    #     "build_button" in st.session_state["project_dct"]["model_build"].keys() else False
    # model_button =st.button('Build Model', on_click=reset_model_result_dct, key='model_build_button')
    # if
    # if model_button:
    if st.button(
        "Build Model",
        on_click=reset_model_result_dct,
        key="model_build_button",
    ):
        if iterations < 1:
            st.error("Please select number of iterations")
            st.stop()
        st.session_state["project_dct"]["model_build"]["build_button"] = True
        st.session_state["iterations"] = iterations

        # Section 4 - Model
        # st.session_state['media_data'] = st.session_state['media_data'].fillna(method='ffill')
        st.session_state["media_data"] = st.session_state["media_data"].ffill()
        progress_bar = st.progress(0)  # Initialize the progress bar
        # time_remaining_text = st.empty()  # Create an empty space for time remaining text
        start_time = time.time()  # Record the start time
        progress_text = st.empty()

        # time_elapsed_text = st.empty()
        # for i, selected_features in enumerate(st.session_state["final_selection"][40000:40000 + int(iterations)]):
        # for i, selected_features in enumerate(st.session_state["final_selection"]):

        if is_panel == True:
            for i, selected_features in enumerate(
                st.session_state["final_selection"][0 : int(iterations)]
            ):  # srishti
                df = st.session_state["media_data"]

                fet = [var for var in selected_features if len(var) > 0]
                inp_vars_str = " + ".join(fet)  # new

                X = df[fet]
                y = df[target_col]
                ss = MinMaxScaler()
                X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)

                X[target_col] = y  # Sprint2
                X[panel_col] = df[panel_col]  # Sprint2

                X_train = X.iloc[:train_idx]
                X_test = X.iloc[train_idx:]
                y_train = y.iloc[:train_idx]
                y_test = y.iloc[train_idx:]

                print(X_train.shape)
                # model = sm.OLS(y_train, X_train).fit()
                md_str = target_col + " ~ " + inp_vars_str
                # md = smf.mixedlm("total_approved_accounts_revenue ~ {}".format(inp_vars_str),
                #                 data=X_train[[target_col] + fet],
                #                 groups=X_train[panel_col])
                md = smf.mixedlm(
                    md_str,
                    data=X_train[[target_col] + fet],
                    groups=X_train[panel_col],
                )
                mdf = md.fit()
                predicted_values = mdf.fittedvalues

                coefficients = mdf.fe_params.to_dict()
                model_positive = [
                    col for col in coefficients.keys() if coefficients[col] > 0
                ]

                pvalues = [var for var in list(mdf.pvalues) if var <= 0.06]

                if (len(model_positive) / len(selected_features)) > 0 and (
                    len(pvalues) / len(selected_features)
                ) >= 0:  # srishti - changed just for testing, revert later
                    # predicted_values = model.predict(X_train)
                    mape = mean_absolute_percentage_error(
                        y_train, predicted_values
                    )
                    r2 = r2_score(y_train, predicted_values)
                    adjr2 = 1 - (1 - r2) * (len(y_train) - 1) / (
                        len(y_train) - len(selected_features) - 1
                    )

                    filename = os.path.join(save_path, f"model_{i}.pkl")
                    with open(filename, "wb") as f:
                        pickle.dump(mdf, f)
                    # with open(r"C:\Users\ManojP\Documents\MMM\simopt\Model\model.pkl", 'rb') as file:
                    #   model = pickle.load(file)

                    st.session_state["Model_results"]["Model_object"].append(
                        filename
                    )
                    st.session_state["Model_results"][
                        "Model_iteration"
                    ].append(i)
                    st.session_state["Model_results"]["Feature_set"].append(
                        fet
                    )
                    st.session_state["Model_results"]["MAPE"].append(mape)
                    st.session_state["Model_results"]["R2"].append(r2)
                    st.session_state["Model_results"]["pos_count"].append(
                        len(model_positive)
                    )
                    st.session_state["Model_results"]["ADJR2"].append(adjr2)

                current_time = time.time()
                time_taken = current_time - start_time
                time_elapsed_minutes = time_taken / 60
                completed_iterations_text = f"{i + 1}/{iterations}"
                progress_bar.progress((i + 1) / int(iterations))
                progress_text.text(
                    f"Completed iterations: {completed_iterations_text},Time Elapsed (min): {time_elapsed_minutes:.2f}"
                )
            st.write(
                f'Out of {st.session_state["iterations"]} iterations : {len(st.session_state["Model_results"]["Model_object"])} valid models'
            )

        else:

            for i, selected_features in enumerate(
                st.session_state["final_selection"][0 : int(iterations)]
            ):  # srishti
                df = st.session_state["media_data"]

                fet = [var for var in selected_features if len(var) > 0]
                inp_vars_str = " + ".join(fet)

                X = df[fet]
                y = df[target_col]
                ss = MinMaxScaler()
                X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)
                X = sm.add_constant(X)
                X_train = X.iloc[:130]
                X_test = X.iloc[130:]
                y_train = y.iloc[:130]
                y_test = y.iloc[130:]

                model = sm.OLS(y_train, X_train).fit()

                coefficients = model.params.to_list()
                model_positive = [coef for coef in coefficients if coef > 0]
                predicted_values = model.predict(X_train)
                pvalues = [var for var in list(model.pvalues) if var <= 0.06]

                # if (len(model_possitive) / len(selected_features)) > 0.9 and (len(pvalues) / len(selected_features)) >= 0.8:
                if (len(model_positive) / len(selected_features)) > 0 and (
                    len(pvalues) / len(selected_features)
                ) >= 0.5:  # srishti - changed just for testing, revert later VALID MODEL CRITERIA
                    # predicted_values = model.predict(X_train)
                    mape = mean_absolute_percentage_error(
                        y_train, predicted_values
                    )
                    adjr2 = model.rsquared_adj
                    r2 = model.rsquared

                    filename = os.path.join(save_path, f"model_{i}.pkl")
                    with open(filename, "wb") as f:
                        pickle.dump(model, f)
                    # with open(r"C:\Users\ManojP\Documents\MMM\simopt\Model\model.pkl", 'rb') as file:
                    #   model = pickle.load(file)

                    st.session_state["Model_results"]["Model_object"].append(
                        filename
                    )
                    st.session_state["Model_results"][
                        "Model_iteration"
                    ].append(i)
                    st.session_state["Model_results"]["Feature_set"].append(
                        fet
                    )
                    st.session_state["Model_results"]["MAPE"].append(mape)
                    st.session_state["Model_results"]["R2"].append(r2)
                    st.session_state["Model_results"]["ADJR2"].append(adjr2)
                    st.session_state["Model_results"]["pos_count"].append(
                        len(model_positive)
                    )

                current_time = time.time()
                time_taken = current_time - start_time
                time_elapsed_minutes = time_taken / 60
                completed_iterations_text = f"{i + 1}/{iterations}"
                progress_bar.progress((i + 1) / int(iterations))
                progress_text.text(
                    f"Completed iterations: {completed_iterations_text},Time Elapsed (min): {time_elapsed_minutes:.2f}"
                )
            st.write(
                f'Out of {st.session_state["iterations"]} iterations : {len(st.session_state["Model_results"]["Model_object"])} valid models'
            )

        pd.DataFrame(st.session_state["Model_results"]).to_csv(
            "model_output.csv"
        )

        def to_percentage(value):
            return f"{value * 100:.1f}%"

    ## Section 5 - Select Model
    st.title("2. Select Models")
    show_results_defualt = (
        st.session_state["project_dct"]["model_build"]["show_results_check"]
        if st.session_state["project_dct"]["model_build"]["show_results_check"]
        is not None
        else False
    )
    if "tick" not in st.session_state:
        st.session_state["tick"] = False
    if st.checkbox(
        "Show results of top 10 models (based on MAPE and Adj. R2)",
        value=True,
    ):
        st.session_state["project_dct"]["model_build"][
            "show_results_check"
        ] = True
        st.session_state["tick"] = True
        st.write(
            "Select one model iteration to generate performance metrics for it:"
        )
        data = pd.DataFrame(st.session_state["Model_results"])
        data = data[data["pos_count"] == data["pos_count"].max()].reset_index(
            drop=True
        )  # Sprint4 -- Srishti -- only show models with the lowest num of neg coeffs
        data.sort_values(by=["ADJR2"], ascending=False, inplace=True)
        data.drop_duplicates(subset="Model_iteration", inplace=True)
        top_10 = data.head(10)
        top_10["Rank"] = np.arange(1, len(top_10) + 1, 1)
        top_10[["MAPE", "R2", "ADJR2"]] = np.round(
            top_10[["MAPE", "R2", "ADJR2"]], 4
        ).applymap(to_percentage)
        top_10_table = top_10[
            ["Rank", "Model_iteration", "MAPE", "ADJR2", "R2"]
        ]
        # top_10_table.columns=[['Rank','Model Iteration Index','MAPE','Adjusted R2','R2']]
        gd = GridOptionsBuilder.from_dataframe(top_10_table)
        gd.configure_pagination(enabled=True)

        gd.configure_selection(
            use_checkbox=True,
            selection_mode="single",
            pre_select_all_rows=False,
            pre_selected_rows=[1],
        )

        gridoptions = gd.build()

        table = AgGrid(
            top_10,
            gridOptions=gridoptions,
            update_mode=GridUpdateMode.SELECTION_CHANGED,
        )

        selected_rows = table.selected_rows
        # if st.session_state["selected_rows"] != selected_rows:
        #   st.session_state["build_rc_cb"] = False
        st.session_state["selected_rows"] = selected_rows
    #     st.write(
    #     """
    # ### Filter Results

    # Use the filters below to refine the displayed model results. This helps in isolating models that do not meet the required business criteria, ensuring only the most relevant models are considered for further analysis. If multiple models meet the criteria, select the first model, as it is considered the best-ranked based on evaluation criteria.
    # """
    # )

    # data = pd.DataFrame(st.session_state["Model_results"])
    # coefficients_df, data_df = prepare_data_df(data)

    # # Define the structure of the empty DataFrame
    # filter_df_data = {
    #     "Channel Name": pd.Series([], dtype="str"),
    #     "Filter Condition": pd.Series([], dtype="str"),
    #     "Percent Contribution": pd.Series([], dtype="str"),
    # }
    # filter_df = pd.DataFrame(filter_df_data)

    # filter_df_editable = st.data_editor(
    #     filter_df,
    #     column_config={
    #         "Channel Name": st.column_config.SelectboxColumn(
    #             options=list(coefficients_df.columns),
    #             required=True,
    #             default="Base Sales",
    #         ),
    #         "Filter Condition": st.column_config.SelectboxColumn(
    #             options=[
    #                 "<",
    #                 ">",
    #                 "=",
    #                 "<=",
    #                 ">=",
    #             ],
    #             required=True,
    #             default=">",
    #         ),
    #         "Percent Contribution": st.column_config.NumberColumn(
    #             required=True, default=0
    #         ),
    #     },
    #     hide_index=True,
    #     use_container_width=True,
    #     num_rows="dynamic",
    # )

    # # Apply filters from filter_df_editable to data_df
    # if "filtered_df" not in st.session_state:
    #     st.session_state["filtered_df"] = data_df.copy()

    # if st.button("Filter", args=(data_df)):
    #     st.session_state["filtered_df"] = data_df.copy()
    #     for index, row in filter_df_editable.iterrows():
    #         channel_name = row["Channel Name"]
    #         condition = row["Filter Condition"]
    #         value = row["Percent Contribution"]

    #         if channel_name in st.session_state["filtered_df"].columns:
    #             # Construct the query string based on the condition
    #             query_string = f"`{channel_name}` {condition} {value}"
    #             st.session_state["filtered_df"] = st.session_state["filtered_df"].query(
    #                 query_string
    #             )

    # # After filtering, check if the DataFrame is empty
    # if st.session_state["filtered_df"].empty:
    #     # Display a warning message if no rows meet the filter criteria
    #     st.warning("No model meets the specified filter conditions", icon="⚠️")
    #     st.stop()  # Optionally stop further execution

    # # Output the filtered data
    # st.write("Select one model iteration to generate performance metrics for it:")
    # st.dataframe(st.session_state["filtered_df"], hide_index=True)

    #############################################################################################

    # top_10 = data.head(10)
    # top_10["Rank"] = np.arange(1, len(top_10) + 1, 1)
    # top_10[["MAPE", "R2", "ADJR2"]] = np.round(
    #     top_10[["MAPE", "R2", "ADJR2"]], 4
    # ).applymap(to_percentage)

    # top_10_table = top_10[
    #     ["Rank", "Model_iteration", "MAPE", "ADJR2", "R2"]
    #     + list(coefficients_df.columns)
    # ]
    # top_10_table.columns=[['Rank','Model Iteration Index','MAPE','Adjusted R2','R2']]

    # gd = GridOptionsBuilder.from_dataframe(top_10_table)
    # gd.configure_pagination(enabled=True)

    # gd.configure_selection(
    #     use_checkbox=True,
    #     selection_mode="single",
    #     pre_select_all_rows=False,
    #     pre_selected_rows=[1],
    # )

    # gridoptions = gd.build()

    # table = AgGrid(
    #     top_10, gridOptions=gridoptions, update_mode=GridUpdateMode.SELECTION_CHANGED
    # )

    # selected_rows = table.selected_rows

    # gd = GridOptionsBuilder.from_dataframe(st.session_state["filtered_df"])
    # gd.configure_pagination(enabled=True)

    # gd.configure_selection(
    #     use_checkbox=True,
    #     selection_mode="single",
    #     pre_select_all_rows=False,
    #     pre_selected_rows=[1],
    # )

    # gridoptions = gd.build()

    # table = AgGrid(
    #     st.session_state["filtered_df"],
    #     gridOptions=gridoptions,
    #     update_mode=GridUpdateMode.SELECTION_CHANGED,
    # )

    # selected_rows_table = table.selected_rows

    # Dataframe
    # display_df = st.session_state.filtered_df.rename(columns={"Rank": "Model Number"})
    # st.dataframe(display_df, hide_index=True)

    # min_rank = min(st.session_state["filtered_df"]["Rank"])
    # max_rank = max(st.session_state["filtered_df"]["Rank"])
    # available_ranks = st.session_state["filtered_df"]["Rank"].unique()

    # # Get row number input from the user
    # rank_number = st.number_input(
    #     "Select model by Model Number:",
    #     min_value=min_rank,
    #     max_value=max_rank,
    #     value=min_rank,
    #     step=1,
    # )

    # # Get row
    # if rank_number not in available_ranks:
    #     st.warning("No model is available with selected Rank", icon="⚠️")
    #     st.stop()

    # Find the row that matches the selected rank
    # selected_rows = st.session_state["filtered_df"][
    #     st.session_state["filtered_df"]["Rank"] == rank_number
    # ]

    # selected_rows = [
    #     (selected_rows.to_dict(orient="records")[0] if not selected_rows.empty else {})
    # ]

    # if st.session_state["selected_rows"] != selected_rows:
    #   st.session_state["build_rc_cb"] = False
    st.session_state["selected_rows"] = selected_rows
    if "Model" not in st.session_state:
        st.session_state["Model"] = {}

    # Section 6 - Display Results


        # Section 6 - Display Results

    if len(selected_rows) > 0:
        st.header("2.1 Results Summary")

        model_object = data[
            data["Model_iteration"] == selected_rows[0]["Model_iteration"]
        ]["Model_object"]
        features_set = data[
            data["Model_iteration"] == selected_rows[0]["Model_iteration"]
        ]["Feature_set"]

        with open(str(model_object.values[0]), "rb") as file:
            # print(file)
            model = pickle.load(file)
        st.write(model.summary())
        st.header("2.2 Actual vs. Predicted Plot")

        if is_panel:
            df = st.session_state["media_data"]
            X = df[features_set.values[0]]
            y = df[target_col]

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

            # Sprint2 changes
            X[target_col] = y  # new
            X[panel_col] = df[panel_col]
            X[date_col] = date

            X_train = X.iloc[:train_idx]
            X_test = X.iloc[train_idx:].reset_index(drop=True)
            y_train = y.iloc[:train_idx]
            y_test = y.iloc[train_idx:].reset_index(drop=True)

            test_spends = spends_data[
                train_idx:
            ]  # Sprint3 - test spends for resp curves
            random_eff_df = get_random_effects(
                media_data, panel_col, model
            )
            train_pred = model.fittedvalues
            test_pred = mdf_predict(X_test, model, random_eff_df)
            print("__" * 20, test_pred.isna().sum())

        else:
            df = st.session_state["media_data"]
            X = df[features_set.values[0]]
            y = df[target_col]

            ss = MinMaxScaler()
            X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)
            X = sm.add_constant(X)

            X[date_col] = date

            X_train = X.iloc[:130]
            X_test = X.iloc[130:].reset_index(drop=True)
            y_train = y.iloc[:130]
            y_test = y.iloc[130:].reset_index(drop=True)

            test_spends = spends_data[
                130:
            ]  # Sprint3 - test spends for resp curves
            train_pred = model.predict(
                X_train[features_set.values[0] + ["const"]]
            )
            test_pred = model.predict(
                X_test[features_set.values[0] + ["const"]]
            )

        # save x test to test - srishti
        # x_test_to_save = X_test.copy()
        # x_test_to_save['Actuals'] = y_test
        # x_test_to_save['Predictions'] = test_pred
        #
        # x_train_to_save = X_train.copy()
        # x_train_to_save['Actuals'] = y_train
        # x_train_to_save['Predictions'] = train_pred
        #
        # x_train_to_save.to_csv('Test/x_train_to_save.csv', index=False)
        # x_test_to_save.to_csv('Test/x_test_to_save.csv', index=False)

        st.session_state["X"] = X_train
        st.session_state["features_set"] = features_set.values[0]
        print(
            "**" * 20, "selected model features : ", features_set.values[0]
        )
        metrics_table, line, actual_vs_predicted_plot = (
            plot_actual_vs_predicted(
                X_train[date_col],
                y_train,
                train_pred,
                model,
                target_column=sel_target_col,
                is_panel=is_panel,
            )
        )  # Sprint2

        st.plotly_chart(actual_vs_predicted_plot, use_container_width=True)

        st.markdown("## 2.3 Residual Analysis")
        columns = st.columns(2)
        with columns[0]:
            fig = plot_residual_predicted(
                y_train, train_pred, X_train
            )  # Sprint2
            st.plotly_chart(fig)

        with columns[1]:
            st.empty()
            fig = qqplot(y_train, train_pred)  # Sprint2
            st.plotly_chart(fig)

        with columns[0]:
            fig = residual_distribution(y_train, train_pred)  # Sprint2
            st.pyplot(fig)

        vif_data = pd.DataFrame()
        # X=X.drop('const',axis=1)
        X_train_orig = (
            X_train.copy()
        )  # Sprint2 -- creating a copy of xtrain. Later deleting panel, target & date from xtrain
        del_col_list = list(
            set([target_col, panel_col, date_col]).intersection(
                set(X_train.columns)
            )
        )
        X_train.drop(columns=del_col_list, inplace=True)  # Sprint2

        vif_data["Variable"] = X_train.columns
        vif_data["VIF"] = [
            variance_inflation_factor(X_train.values, i)
            for i in range(X_train.shape[1])
        ]
        vif_data.sort_values(by=["VIF"], ascending=False, inplace=True)
        vif_data = np.round(vif_data)
        vif_data["VIF"] = vif_data["VIF"].astype(float)
        st.header("2.4 Variance Inflation Factor (VIF)")
        # st.dataframe(vif_data)
        color_mapping = {
            "darkgreen": (vif_data["VIF"] < 3),
            "orange": (vif_data["VIF"] >= 3) & (vif_data["VIF"] <= 10),
            "darkred": (vif_data["VIF"] > 10),
        }

        # Create a horizontal bar plot
        fig, ax = plt.subplots()
        fig.set_figwidth(10)  # Adjust the width of the figure as needed

        # Sort the bars by descending VIF values
        vif_data = vif_data.sort_values(by="VIF", ascending=False)

        # Iterate through the color mapping and plot bars with corresponding colors
        for color, condition in color_mapping.items():
            subset = vif_data[condition]
            bars = ax.barh(
                subset["Variable"], subset["VIF"], color=color, label=color
            )

            # Add text annotations on top of the bars
            for bar in bars:
                width = bar.get_width()
                ax.annotate(
                    f"{width:}",
                    xy=(width, bar.get_y() + bar.get_height() / 2),
                    xytext=(5, 0),
                    textcoords="offset points",
                    va="center",
                )

        # Customize the plot
        ax.set_xlabel("VIF Values")
        # ax.set_title('2.4 Variance Inflation Factor (VIF)')
        # ax.legend(loc='upper right')

        # Display the plot in Streamlit
        st.pyplot(fig)

        with st.expander("Results Summary Test data"):
            # ss = MinMaxScaler()
            # X_test = pd.DataFrame(ss.fit_transform(X_test), columns=X_test.columns)
            st.header("2.2 Actual vs. Predicted Plot")

            metrics_table, line, actual_vs_predicted_plot = (
                plot_actual_vs_predicted(
                    X_test[date_col],
                    y_test,
                    test_pred,
                    model,
                    target_column=sel_target_col,
                    is_panel=is_panel,
                )
            )  # Sprint2

            st.plotly_chart(
                actual_vs_predicted_plot, use_container_width=True
            )

            st.markdown("## 2.3 Residual Analysis")
            columns = st.columns(2)
            with columns[0]:
                fig = plot_residual_predicted(
                    y, test_pred, X_test
                )  # Sprint2
                st.plotly_chart(fig)

            with columns[1]:
                st.empty()
                fig = qqplot(y, test_pred)  # Sprint2
                st.plotly_chart(fig)

            with columns[0]:
                fig = residual_distribution(y, test_pred)  # Sprint2
                st.pyplot(fig)

        value = False
        save_button_model = st.checkbox(
            "Save this model to tune", key="build_rc_cb"
        )  # , on_click=set_save())

        if save_button_model:
            mod_name = st.text_input("Enter model name")
            if len(mod_name) > 0:
                mod_name = (
                    mod_name + "__" + target_col
                )  # Sprint4 - adding target col to model name
                if is_panel:
                    pred_train = model.fittedvalues
                    pred_test = mdf_predict(X_test, model, random_eff_df)
                else:
                    st.session_state["features_set"] = st.session_state[
                        "features_set"
                    ] + ["const"]
                    pred_train = model.predict(
                        X_train_orig[st.session_state["features_set"]]
                    )
                    pred_test = model.predict(
                        X_test[st.session_state["features_set"]]
                    )

                st.session_state["Model"][mod_name] = {
                    "Model_object": model,
                    "feature_set": st.session_state["features_set"],
                    "X_train": X_train_orig,
                    "X_test": X_test,
                    "y_train": y_train,
                    "y_test": y_test,
                    "pred_train": pred_train,
                    "pred_test": pred_test,
                }
                st.session_state["X_train"] = X_train_orig
                st.session_state["X_test_spends"] = test_spends
                st.session_state["saved_model_names"].append(mod_name)
                # Sprint3 additions
                if is_panel:
                    random_eff_df = get_random_effects(
                        media_data, panel_col, model
                    )
                    st.session_state["random_effects"] = random_eff_df

                with open(
                    os.path.join(
                        st.session_state["project_path"], "best_models.pkl"
                    ),
                    "wb",
                ) as f:
                    pickle.dump(st.session_state["Model"], f)
                    st.success(
                        mod_name
                        + " model saved! Proceed to the next page to tune the model"
                    )

                    urm = st.session_state["used_response_metrics"]
                    urm.append(sel_target_col)
                    st.session_state["used_response_metrics"] = list(
                        set(urm)
                    )
                    mod_name = ""
                    # Sprint4 - add the formatted name of the target col to used resp metrics
                value = False

                st.session_state["project_dct"]["model_build"][
                    "session_state_saved"
                ] = {}
                for key in [
                    "Model",
                    "bin_dict",
                    "used_response_metrics",
                    "date",
                    "saved_model_names",
                    "media_data",
                    "X_test_spends",
                ]:
                    st.session_state["project_dct"]["model_build"][
                        "session_state_saved"
                    ][key] = st.session_state[key]

                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("4_Model_Build.py")

                st.toast("💾 Saved Successfully!")
else:
    st.session_state["project_dct"]["model_build"][
        "show_results_check"
    ] = False