utilities_with_panel.py

from classes import numerize
import streamlit as st
import pandas as pd
import json
from classes import Channel, Scenario
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from classes import class_to_dict
from collections import OrderedDict
import io
import plotly
from pathlib import Path
import pickle
import yaml
from yaml import SafeLoader
from streamlit.components.v1 import html
import smtplib
from scipy.optimize import curve_fit
from sklearn.metrics import r2_score
from classes import class_from_dict
import os
import base64


color_palette = [
    "#F3F3F0",
    "#5E7D7E",
    "#2FA1FF",
    "#00EDED",
    "#00EAE4",
    "#304550",
    "#EDEBEB",
    "#7FBEFD",
    "#003059",
    "#A2F3F3",
    "#E1D6E2",
    "#B6B6B6",
]


CURRENCY_INDICATOR = "$"

try:
    st.session_state["bin_dict"]["Panel Level 1"] = st.session_state["bin_dict"].get(
        "Panel Level 1", []
    )
except Exception as e:
    st.error(
        "No tuned model available. Please build and tune a model to generate response curves."
    )
    st.stop()


def load_authenticator():
    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
    return authenticator


def nav_page(page_name, timeout_secs=3):
    nav_script = """
        <script type="text/javascript">
            function attempt_nav_page(page_name, start_time, timeout_secs) {
                var links = window.parent.document.getElementsByTagName("a");
                for (var i = 0; i < links.length; i++) {
                    if (links[i].href.toLowerCase().endsWith("/" + page_name.toLowerCase())) {
                        links[i].click();
                        return;
                    }
                }
                var elasped = new Date() - start_time;
                if (elasped < timeout_secs * 1000) {
                    setTimeout(attempt_nav_page, 100, page_name, start_time, timeout_secs);
                } else {
                    alert("Unable to navigate to page '" + page_name + "' after " + timeout_secs + " second(s).");
                }
            }
            window.addEventListener("load", function() {
                attempt_nav_page("%s", new Date(), %d);
            });
        </script>
    """ % (
        page_name,
        timeout_secs,
    )
    html(nav_script)


# def load_local_css(file_name):
#     with open(file_name) as f:
#         st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)


# def set_header():
#     return st.markdown(f"""<div class='main-header'>
#                     <h1>MMM LiME</h1>
#                     <img src="https://assets-global.website-files.com/64c8fffb0e95cbc525815b79/64df84637f83a891c1473c51_Vector%20(Stroke).svg   ">
#             </div>""", unsafe_allow_html=True)

path = os.path.dirname(__file__)

file_ = open(f"{path}/mastercard_logo.png", "rb")

contents = file_.read()

data_url = base64.b64encode(contents).decode("utf-8")

file_.close()


DATA_PATH = "./data"

IMAGES_PATH = "./data/images_224_224"

# New - Sprint 2
if "bin_dict" not in st.session_state:

    with open("data_import.pkl", "rb") as f:
        data = pickle.load(f)

        st.session_state["bin_dict"] = data["bin_dict"]

# is_panel = True if len(panel_col) > 0 else False

# manoj

is_panel = False

if 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


date_col = "Date"
# is_panel = False # flag if set to true - do panel level response curves


def load_local_css(file_name):

    with open(file_name) as f:

        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)


# def set_header():

#     return st.markdown(f"""<div class='main-header'>

#                     <h1>H & M Recommendations</h1>

#                     <img src="data:image;base64,{data_url}", alt="Logo">

#             </div>""", unsafe_allow_html=True)
path1 = os.path.dirname(__file__)

# file_1 = open(f"{path}/willbank.png", "rb")

# contents1 = file_1.read()

# data_url1 = base64.b64encode(contents1).decode("utf-8")

# file_1.close()


DATA_PATH1 = "./data"

IMAGES_PATH1 = "./data/images_224_224"


def set_header():
    return st.markdown(
        f"""<div class='main-header'>
                    <!-- <h1></h1> -->
                       <div >
                       <img class='blend-logo' src="data:image;base64,{data_url1}", alt="Logo">
                       </div>
                    <img class='blend-logo' src="data:image;base64,{data_url}", alt="Logo">
            </div>""",
        unsafe_allow_html=True,
    )


# def set_header():
#     logo_path = "./path/to/your/local/LIME_logo.png"  # Replace with the actual file path
#     text = "LiME"
#     return st.markdown(f"""<div class='main-header'>
#                     <img src="data:image/png;base64,{data_url}" alt="Logo" style="float: left; margin-right: 10px; width: 100px; height: auto;">
#                     <h1>{text}</h1>
#             </div>""", unsafe_allow_html=True)


def s_curve(x, K, b, a, x0):
    return K / (1 + b * np.exp(-a * (x - x0)))


def overview_test_data_prep_panel(X, df, spends_X, date_col, panel_col, target_col):
    """
    function to create the data which is used in initialize data fn
    X : X test with contributions
    df : originally uploaded data (media data) which has raw vars
    spends_X : spends of dates in X test
    """

    # define channels
    channels = {
        "paid_search": ["paid_search_impressions", "paid_search_clicks"],
        "fb_level_achieved_tier_1": [
            "fb_level_achieved_tier_1_impressions",
            "fb:_level_achieved_-_tier_1_clicks",
        ],
        "fb_level_achieved_tier_2": [
            "fb:_level_achieved_tier_2_impressions",
            "fb_level_achieved_tier_2_clicks",
        ],
        # "paid_social_others": [
        #     "paid_social_others_impressions",
        #     "paid_social_others_clicks",
        # ],
        "ga_app": ["ga_app_impressions", "ga_app_clicks"],
        "digital_tactic_others": [
            "digital_tactic_others_impressions",
            "digital_tactic_others_clicks",
        ],
        "kwai": ["kwai_impressions", "kwai_clicks"],
        "programmatic": ["programmatic_impressions", "programmatic_clicks"],
        # 'affiliates':['affiliates_clicks'],
        #
        # "indicacao":['indicacao_clicks'],
        #
        # "infleux":['infleux_clicks'],
        #
        # "influencer":['influencer_clicks']
    }
    channel_list = list(channels.keys())

    # map transformed variable to raw variable name & channel name
    # mapping eg : paid_search_clicks_lag_2 (transformed var) --> paid_search_clicks (raw var) --> paid_search (channel)
    variables = {}
    channel_and_variables = {}
    new_variables = {}
    new_channels_and_variables = {}

    for transformed_var in [
        col
        for col in X.drop(
            columns=[date_col, panel_col, target_col, "pred", "panel_effect"]
        ).columns
        if "_contr" not in col
    ]:
        if len([col for col in df.columns if col in transformed_var]) == 1:
            raw_var = [col for col in df.columns if col in transformed_var][0]

            # st.write(raw_var)

            variables[transformed_var] = raw_var
            # st.write(channels.keys())

            # Check if the list comprehension result is not empty before accessing the first element
            channels_list = [
                channel for channel, raw_vars in channels.items() if raw_var in raw_vars
            ]
            if channels_list:
                channel_and_variables[raw_var] = channels_list[0]
            else:
                # Handle the case where channels_list is empty
                # You might want to set a default value or handle it according to your use case
                channel_and_variables[raw_var] = None
        else:
            new_variables[transformed_var] = transformed_var
            new_channels_and_variables[transformed_var] = "base"

    # Raw DF
    raw_X = pd.merge(
        X[[date_col, panel_col]],
        df[[date_col, panel_col] + list(variables.values())],
        how="left",
        on=[date_col, panel_col],
    )
    assert len(raw_X) == len(X)

    raw_X_cols = []
    for i in raw_X.columns:
        if i in channel_and_variables.keys():
            raw_X_cols.append(channel_and_variables[i])
        else:
            raw_X_cols.append(i)
    raw_X.columns = raw_X_cols

    # Contribution DF
    contr_X = X[
        [date_col, panel_col, "panel_effect"]
        + [col for col in X.columns if "_contr" in col and "sum_" not in col]
    ].copy()
    new_variables = [
        col
        for col in contr_X.columns
        if "_flag" in col.lower() or "trend" in col.lower() or "sine" in col.lower()
    ]
    if len(new_variables) > 0:
        contr_X["const"] = contr_X[["panel_effect"] + new_variables].sum(axis=1)
        contr_X.drop(columns=["panel_effect"], inplace=True)
        contr_X.drop(columns=new_variables, inplace=True)
    else:
        contr_X.rename(columns={"panel_effect": "const"}, inplace=True)

    new_contr_X_cols = []
    for col in contr_X.columns:
        col_clean = col.replace("_contr", "")
        new_contr_X_cols.append(col_clean)
    contr_X.columns = new_contr_X_cols

    contr_X_cols = []
    for i in contr_X.columns:
        if i in variables.keys():
            contr_X_cols.append(channel_and_variables[variables[i]])
        else:
            contr_X_cols.append(i)
    contr_X.columns = contr_X_cols

    # Spends DF
    spends_X.columns = [col.replace("_cost", "") for col in spends_X.columns]

    raw_X.rename(columns={"date": "Date"}, inplace=True)
    contr_X.rename(columns={"date": "Date"}, inplace=True)
    spends_X.rename(columns={"date": "Week"}, inplace=True)

    # st.write(raw_X.shape, contr_X.shape, spends_X.shape)
    # Create excel
    file_name = "data_test_overview_panel@#" + target_col + ".xlsx"

    folder_path = os.path.join(st.session_state["project_path"], "metrics_level_data")

    if not os.path.exists(folder_path):
        os.mkdir(folder_path)

    file_path = os.path.join(folder_path, file_name)

    # os.mkdir(file_path)
    # folder_path = r"C:\Users\manojp1732@gmail.com\projet1\metrics_level_data"
    # file_name = "data_test_overview_panel@#total_approved_accounts_appsflyer.xlsx"
    # st.write(file_path)
    # raw_X.to_excel(file_path)
    with pd.ExcelWriter(file_path) as writer:
        raw_X.to_excel(writer, sheet_name="RAW DATA MMM", index=False)
        contr_X.to_excel(writer, sheet_name="CONTRIBUTION MMM", index=False)
        spends_X.to_excel(writer, sheet_name="SPEND INPUT", index=False)

    # Define the paths to the original files
    original_json_file_path = os.path.join(
        st.session_state["project_path"], "rcs_data_original.json"
    )
    original_pickle_file_path = os.path.join(
        st.session_state["project_path"], "scenario_data_original.pkl"
    )

    # Remove the original data file if it exists
    if os.path.exists(original_json_file_path):
        os.remove(original_json_file_path)
    if os.path.exists(original_pickle_file_path):
        os.remove(original_pickle_file_path)


def overview_test_data_prep_nonpanel(X, df, spends_X, date_col, target_col):
    """
    function to create the data which is used in initialize data fn
    """

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

    channel_list = list(channels.keys())

    # map transformed variable to raw variable name & channel name
    # mapping eg : paid_search_clicks_lag_2 (transformed var) --> paid_search_clicks (raw var) --> paid_search (channel)
    variables = {}
    channel_and_variables = {}
    new_variables = {}
    new_channels_and_variables = {}

    cols_to_del = list(
        set([date_col, target_col, "pred"]).intersection((set(X.columns)))
    )

    # remove exog cols from RAW data (exog cols are part of base, raw data needs media vars only)
    all_exog_vars = st.session_state['bin_dict']['Exogenous']
    all_exog_vars = [
        var.lower().replace(".", "_").replace("@", "_").replace(" ", "_").replace("-", "").replace(":", "").replace(
            "__", "_") for var in all_exog_vars]
    exog_cols = []
    if len(all_exog_vars) > 0:
        for col in X.columns:
            if len([exog_var for exog_var in all_exog_vars if exog_var in col]) > 0:
                exog_cols.append(col)
    cols_to_del=cols_to_del+exog_cols
    for transformed_var in [
        col for col in X.drop(columns=cols_to_del).columns if "_contr" not in col
    ]:  # also has 'const'
        print(transformed_var)
        if (
            len([col for col in df.columns if col in transformed_var]) == 1
        ):  # col is raw var
            raw_var = [col for col in df.columns if col in transformed_var][0]
            variables[transformed_var] = raw_var
            channel_and_variables[raw_var] = [
                channel for channel, raw_vars in channels.items() if raw_var in raw_vars
            ][0]
        else:  # when no corresponding raw var then base
            new_variables[transformed_var] = transformed_var
            new_channels_and_variables[transformed_var] = "base"

    # Raw DF
    raw_X = pd.merge(
        X[[date_col]],
        df[[date_col] + list(variables.values())],
        how="left",
        on=[date_col],
    )
    assert len(raw_X) == len(X)

    raw_X_cols = []
    for i in raw_X.columns:
        if i in channel_and_variables.keys():
            raw_X_cols.append(channel_and_variables[i])
        else:
            raw_X_cols.append(i)
    raw_X.columns = raw_X_cols

    # Contribution DF
    contr_X = X[
        [date_col] + [col for col in X.columns if "_contr" in col and "sum_" not in col]
    ].copy()
    # st.write(contr_X.columns)
    new_variables = [
        col
        for col in contr_X.columns
        if "_flag" in col.lower() or "trend" in col.lower() or "sine" in col.lower()
    ]
    if (
        len(new_variables) > 0
    ):  # if new vars are available, their contributions should be added to base (called const)
        contr_X["const_contr"] = contr_X[["const_contr"] + new_variables].sum(axis=1)
        contr_X.drop(columns=new_variables, inplace=True)

    new_contr_X_cols = []
    for col in contr_X.columns:
        col_clean = col.replace("_contr", "")
        new_contr_X_cols.append(col_clean)
    contr_X.columns = new_contr_X_cols

    contr_X_cols = []
    for i in contr_X.columns:
        if i in variables.keys():
            contr_X_cols.append(channel_and_variables[variables[i]])
        else:
            contr_X_cols.append(i)
    contr_X.columns = contr_X_cols

    # Spends DF
    # spends_X.columns = [
    #     col.replace("_cost", "").replace("_spends", "").replace("_spend", "")
    #     for col in spends_X.columns
    # ]
    spends_X_col_map = {col:bucket for col in spends_X.columns for bucket in channels.keys() if col in channels[bucket]}
    spends_X.rename(columns=spends_X_col_map, inplace=True)

    raw_X.rename(columns={"date": "Date"}, inplace=True)
    contr_X.rename(columns={"date": "Date"}, inplace=True)
    spends_X.rename(columns={"date": "Week"}, inplace=True)

    # Create excel
    file_name = "data_test_overview_panel@#" + target_col + ".xlsx"
    folder_path = os.path.join(st.session_state["project_path"], "metrics_level_data")

    if not os.path.exists(folder_path):
        os.mkdir(folder_path)

    file_path = os.path.join(folder_path, file_name)
    with pd.ExcelWriter(file_path) as writer:
        raw_X.to_excel(writer, sheet_name="RAW DATA MMM", index=False)
        contr_X.to_excel(writer, sheet_name="CONTRIBUTION MMM", index=False)
        spends_X.to_excel(writer, sheet_name="SPEND INPUT", index=False)

    # Define the paths to the original files
    original_json_file_path = os.path.join(
        st.session_state["project_path"], "rcs_data_original.json"
    )
    original_pickle_file_path = os.path.join(
        st.session_state["project_path"], "scenario_data_original.pkl"
    )

    # Remove the original data file if it exists
    if os.path.exists(original_json_file_path):
        os.remove(original_json_file_path)
    if os.path.exists(original_pickle_file_path):
        os.remove(original_pickle_file_path)


def initialize_data(target_col, is_panel, panel_col):
    # uopx_conv_rates = {'streaming_impressions' : 0.007,'digital_impressions' : 0.007,'search_clicks' : 0.00719,'tv_impressions' : 0.000173,
    #                    "digital_clicks":0.005,"streaming_clicks":0.004,'streaming_spends':1,"tv_spends":1,"search_spends":1,
    #                    "digital_spends":1}
    # print('State initialized')
    # excel = pd.read_excel("data_test_overview_panel.xlsx",sheet_name=None)
    # if is_panel:
    file_name = Path("metrics_level_data") / Path(
        "data_test_overview_panel@#" + target_col + ".xlsx"
    )
    # else:
    #     file_name = Path("metrics_level_data") / Path("data_test_overview_nonpanel@#" + target_col + ".xlsx")

    file_path = os.path.join(st.session_state["project_path"], file_name)
    if os.path.exists(file_path):
        excel = pd.read_excel(file_path, sheet_name=None)
    else:
        excel = pd.read_excel("data_test_overview_panel@#" + target_col + ".xlsx")

    raw_df = excel["RAW DATA MMM"]
    spend_df = excel["SPEND INPUT"]
    contri_df = excel["CONTRIBUTION MMM"]
    # Revenue_df = excel['Revenue']

    ## remove sesonalities, indices etc ...
    exclude_columns = [
        "Date",
        "Week",
        "Region",
        "Controls_Grammarly_Index_SeasonalAVG",
        "Controls_Quillbot_Index",
        "Daily_Positive_Outliers",
        "External_RemoteClass_Index",
        "Intervals ON 20190520-20190805 | 20200518-20200803 | 20210517-20210802",
        "Intervals ON 20190826-20191209 | 20200824-20201207 | 20210823-20211206",
        "Intervals ON 20201005-20201019",
        "Promotion_PercentOff",
        "Promotion_TimeBased",
        "Seasonality_Indicator_Chirstmas",
        "Seasonality_Indicator_NewYears_Days",
        "Seasonality_Indicator_Thanksgiving",
        "Trend 20200302 / 20200803",
        date_col,
    ]
    if is_panel:
        exclude_columns = exclude_columns + [panel_col]

    # Aggregate all 3 dfs to date level (from date-panel level)
    raw_df[date_col] = pd.to_datetime(raw_df[date_col])
    raw_df_aggregations = {c: "sum" for c in raw_df.columns if c not in exclude_columns}
    raw_df = raw_df.groupby(date_col).agg(raw_df_aggregations).reset_index()

    contri_df[date_col] = pd.to_datetime(contri_df[date_col])
    contri_df_aggregations = {
        c: "sum" for c in contri_df.columns if c not in exclude_columns
    }
    contri_df = contri_df.groupby(date_col).agg(contri_df_aggregations).reset_index()

    input_df = raw_df.sort_values(by=[date_col])

    output_df = contri_df.sort_values(by=[date_col])

    spend_df["Week"] = pd.to_datetime(
        spend_df["Week"], format="%Y-%m-%d", errors="coerce"
    )
    spend_df_aggregations = {
        c: "sum" for c in spend_df.columns if c not in exclude_columns
    }
    spend_df = spend_df.groupby("Week").agg(spend_df_aggregations).reset_index()
    # spend_df['Week'] = pd.to_datetime(spend_df['Week'], errors='coerce')
    # spend_df = spend_df.sort_values(by='Week')

    channel_list = [col for col in input_df.columns if col not in exclude_columns]

    response_curves = {}
    mapes = {}
    rmses = {}
    upper_limits = {}
    powers = {}
    r2 = {}
    conv_rates = {}
    output_cols = []
    channels = {}
    sales = None
    dates = input_df.Date.values
    actual_output_dic = {}
    actual_input_dic = {}

    # ONLY FOR TESTING
    # channel_list=['programmatic']
    infeasible_channels = [
        c
        for c in contri_df.select_dtypes(include=["float", "int"]).columns
        if contri_df[c].sum() <= 0
    ]

    # st.write(infeasible_channels)
    channel_list = list(set(channel_list) - set(infeasible_channels))
    # st.write(channel_list)
    channel_list = [
        col for col in channel_list if col != "ga_app" and col != "Unnamed: 4"
    ]
    # st.write(spend_df.columns)
    for inp_col in channel_list:

        # st.write(inp_col)

        # # New - Sprint 2
        # if is_panel:
        #     input_df1 = input_df.groupby([date_col]).agg({inp_col:'sum'}).reset_index() # aggregate spends on date
        #     spends = input_df1[inp_col].values
        # else :
        #     spends = input_df[inp_col].values
        # st.write(inp_col)
        spends = input_df[inp_col].values

        x = spends.copy()
        # upper limit for penalty
        upper_limits[inp_col] = 2 * x.max()

        # contribution
        # New - Sprint 2
        # out_col = [_col for _col in output_df.columns if _col.startswith(inp_col)][0]
        out_col=inp_col
        if is_panel:
            output_df1 = (
                output_df.groupby([date_col]).agg({out_col: "sum"}).reset_index()
            )
            y = output_df1[out_col].values.copy()
        else:
            y = output_df[out_col].values.copy()

        actual_output_dic[inp_col] = y.copy()
        actual_input_dic[inp_col] = x.copy()
        ##output cols aggregation
        output_cols.append(out_col)

        ## scale the input
        power = np.ceil(np.log(x.max()) / np.log(10)) - 3
        if power >= 0:
            x = x / 10**power

        x = x.astype("float64")
        y = y.astype("float64")
        # print('#printing yyyyyyyyy')
        # print(inp_col)
        # print(x.max())
        # print(y.max())
        # st.write(y.max(),x.max())
        print(y.max(), x.max())
        if y.max() <= 0.01:
            if x.max() <= 0.0:
                # st.write("here-here")
                bounds = ((0, 0, 0, 0), (3 * 0.01, 1000, 1, 0.01))

            else:
                # st.write("here")
                bounds = ((0, 0, 0, 0), (3 * 0.01, 1000, 1, 0.01))
        else:
            bounds = ((0, 0, 0, 0), (3 * y.max(), 1000, 1, x.max()))
        # bounds = ((y.max(), 3*y.max()),(0,1000),(0,1),(0,x.max()))
        params, _ = curve_fit(
            s_curve,
            x,
            y,
            p0=(2 * y.max(), 0.01, 1e-5, x.max()),
            bounds=bounds,
            maxfev=int(1e5),
        )
        mape = (100 * abs(1 - s_curve(x, *params) / y.clip(min=1))).mean()
        rmse = np.sqrt(((y - s_curve(x, *params)) ** 2).mean())
        r2_ = r2_score(y, s_curve(x, *params))

        response_curves[inp_col] = {
            "K": params[0],
            "b": params[1],
            "a": params[2],
            "x0": params[3],
        }
        mapes[inp_col] = mape
        rmses[inp_col] = rmse
        r2[inp_col] = r2_
        powers[inp_col] = power

        ## conversion rates
        # spend_col = [
        #     _col
        #     for _col in spend_df.columns
        #     if _col.startswith(inp_col.rsplit("_", 1)[0])
        # ][0]

        # print('#printing spendssss')
        # print(spend_col)
        conv = (
            spend_df.set_index("Week")[inp_col]
            / input_df.set_index("Date")[inp_col].clip(lower=1)
        ).reset_index()
        conv.rename(columns={"index": "Week"}, inplace=True)
        conv["year"] = conv.Week.dt.year
        conv_rates[inp_col] = list(conv.drop("Week", axis=1).mean().to_dict().values())[
            0
        ]
        ##print('Before',conv_rates[inp_col])
        # conv_rates[inp_col] = uopx_conv_rates[inp_col]
        ##print('After',(conv_rates[inp_col]))

        channel = Channel(
            name=inp_col,
            dates=dates,
            spends=spends,
            # conversion_rate = np.mean(list(conv_rates[inp_col].values())),
            conversion_rate=conv_rates[inp_col],
            response_curve_type="s-curve",
            response_curve_params={
                "K": params[0],
                "b": params[1],
                "a": params[2],
                "x0": params[3],
            },
            bounds=np.array([-10, 10]),
        )
        channels[inp_col] = channel
        if sales is None:
            sales = channel.actual_sales
        else:
            sales += channel.actual_sales
        # st.write(inp_col, channel.actual_sales)
    # st.write(output_cols)
    other_contributions = (
        output_df.drop([*output_cols], axis=1).sum(axis=1, numeric_only=True).values
    )
    correction = output_df.drop(["Date"], axis=1).sum(axis=1).values - (
        sales + other_contributions
    )

    scenario_test_df = pd.DataFrame(
        columns=["other_contributions", "correction", "sales"]
    )
    scenario_test_df["other_contributions"] = other_contributions
    scenario_test_df["correction"] = correction
    scenario_test_df["sales"] = sales
    # scenario_test_df.to_csv("test\scenario_test_df.csv", index=False)
    # output_df.to_csv("test\output_df.csv", index=False)

    scenario = Scenario(
        name="default",
        channels=channels,
        constant=other_contributions,
        correction=correction,
    )
    ## setting session variables
    st.session_state["initialized"] = True
    st.session_state["actual_df"] = input_df
    st.session_state["raw_df"] = raw_df
    st.session_state["contri_df"] = output_df
    default_scenario_dict = class_to_dict(scenario)
    st.session_state["default_scenario_dict"] = default_scenario_dict
    st.session_state["scenario"] = scenario
    st.session_state["channels_list"] = channel_list
    st.session_state["optimization_channels"] = {
        channel_name: False for channel_name in channel_list
    }
    st.session_state["rcs"] = response_curves

    # orig_rcs_path = os.path.join(
    #     st.session_state["project_path"], f"orig_rcs_{target_col}_{panel_col}.json"
    # )
    # if Path(orig_rcs_path).exists():
    #     with open(orig_rcs_path, "r") as f:
    #         st.session_state["orig_rcs"] = json.load(f)
    # else:
    #     st.session_state["orig_rcs"] = response_curves.copy()
    #     with open(orig_rcs_path, "w") as f:
    #         json.dump(st.session_state["orig_rcs"], f)

    st.session_state["powers"] = powers
    st.session_state["actual_contribution_df"] = pd.DataFrame(actual_output_dic)
    st.session_state["actual_input_df"] = pd.DataFrame(actual_input_dic)

    for channel in channels.values():
        st.session_state[channel.name] = numerize(
            channel.actual_total_spends * channel.conversion_rate, 1
        )

    st.session_state["xlsx_buffer"] = io.BytesIO()

    if Path("../saved_scenarios.pkl").exists():
        with open("../saved_scenarios.pkl", "rb") as f:
            st.session_state["saved_scenarios"] = pickle.load(f)
    else:
        st.session_state["saved_scenarios"] = OrderedDict()

    st.session_state["total_spends_change"] = 0
    st.session_state["optimization_channels"] = {
        channel_name: False for channel_name in channel_list
    }
    st.session_state["disable_download_button"] = True


# def initialize_data():
#     # fetch data from excel
#     output = pd.read_excel('data.xlsx',sheet_name=None)
#     raw_df = output['RAW DATA MMM']
#     contribution_df = output['CONTRIBUTION MMM']
#     Revenue_df = output['Revenue']

#     ## channels to be shows
#     channel_list = []
#     for col in raw_df.columns:
#         if 'click' in col.lower() or 'spend' in col.lower() or 'imp' in col.lower():
#             ##print(col)
#             channel_list.append(col)
#         else:
#             pass

#     ## NOTE : Considered only Desktop spends for all calculations
#     acutal_df = raw_df[raw_df.Region == 'Desktop'].copy()
#     ## NOTE : Considered one year of data
#     acutal_df = acutal_df[acutal_df.Date>'2020-12-31']
#     actual_df = acutal_df.drop('Region',axis=1).sort_values(by='Date')[[*channel_list,'Date']]

#     ##load response curves
#     with open('./grammarly_response_curves.json','r') as f:
#         response_curves = json.load(f)

#     ## create channel dict for scenario creation
#     dates = actual_df.Date.values
#     channels = {}
#     rcs = {}
#     constant = 0.
#     for i,info_dict in enumerate(response_curves):
#         name = info_dict.get('name')
#         response_curve_type = info_dict.get('response_curve')
#         response_curve_params = info_dict.get('params')
#         rcs[name] = response_curve_params
#         if name != 'constant':
#             spends = actual_df[name].values
#             channel = Channel(name=name,dates=dates,
#                             spends=spends,
#                             response_curve_type=response_curve_type,
#                             response_curve_params=response_curve_params,
#                             bounds=np.array([-30,30]))

#             channels[name] = channel
#         else:
#             constant = info_dict.get('value',0.) * len(dates)

#     ## create scenario
#     scenario = Scenario(name='default', channels=channels, constant=constant)
#     default_scenario_dict = class_to_dict(scenario)


#     ## setting session variables
#     st.session_state['initialized'] = True
#     st.session_state['actual_df'] = actual_df
#     st.session_state['raw_df'] = raw_df
#     st.session_state['default_scenario_dict'] = default_scenario_dict
#     st.session_state['scenario'] = scenario
#     st.session_state['channels_list'] = channel_list
#     st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
#     st.session_state['rcs'] = rcs
#     for channel in channels.values():
#         if channel.name not in st.session_state:
#             st.session_state[channel.name] = float(channel.actual_total_spends)

#     if 'xlsx_buffer' not in st.session_state:
#         st.session_state['xlsx_buffer'] = io.BytesIO()

#     ## for saving scenarios
#     if 'saved_scenarios' not in st.session_state:
#         if Path('../saved_scenarios.pkl').exists():
#             with open('../saved_scenarios.pkl','rb') as f:
#                 st.session_state['saved_scenarios'] = pickle.load(f)

#         else:
#             st.session_state['saved_scenarios'] = OrderedDict()

#     if 'total_spends_change' not in st.session_state:
#         st.session_state['total_spends_change'] = 0

#     if 'optimization_channels' not in st.session_state:
#         st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}


#     if 'disable_download_button' not in st.session_state:
#         st.session_state['disable_download_button'] = True
def create_channel_summary(scenario):
    summary_columns = []

    actual_spends_rows = []

    actual_sales_rows = []

    actual_roi_rows = []

    for channel in scenario.channels.values():

        name_mod = channel.name.replace("_", " ")

        if name_mod.lower().endswith(" imp"):
            name_mod = name_mod.replace("Imp", " Impressions")

        print(
            name_mod,
            channel.actual_total_spends,
            channel.conversion_rate,
            channel.actual_total_spends * channel.conversion_rate,
        )

        summary_columns.append(name_mod)

        actual_spends_rows.append(
            format_numbers(float(channel.actual_total_spends * channel.conversion_rate))
        )

        actual_sales_rows.append(format_numbers((float(channel.actual_total_sales))))

        actual_roi_rows.append(
            decimal_formater(
                format_numbers(
                    (channel.actual_total_sales)
                    / (channel.actual_total_spends * channel.conversion_rate),
                    include_indicator=False,
                    n_decimals=4,
                ),
                n_decimals=4,
            )
        )

    actual_summary_df = pd.DataFrame(
        [
            summary_columns,
            actual_spends_rows,
            actual_sales_rows,
            actual_roi_rows,
        ]
    ).T

    actual_summary_df.columns = ["Channel", "Spends", "Prospects", "ROI"]

    actual_summary_df["Prospects"] = actual_summary_df["Prospects"].map(
        lambda x: str(x)[1:]
    )

    return actual_summary_df


# def create_channel_summary(scenario):
#
#     # Provided data
#     data = {
#         'Channel': ['Paid Search', 'Ga will cid baixo risco', 'Digital tactic others', 'Fb la tier 1', 'Fb la tier 2', 'Paid social others', 'Programmatic', 'Kwai', 'Indicacao', 'Infleux', 'Influencer'],
#         'Spends': ['$ 11.3K', '$ 155.2K', '$ 50.7K', '$ 125.4K', '$ 125.2K', '$ 105K', '$ 3.3M', '$ 47.5K', '$ 55.9K', '$ 632.3K', '$ 48.3K'],
#         'Revenue': ['558.0K', '3.5M', '5.2M', '3.1M', '3.1M', '2.1M', '20.8M', '1.6M', '728.4K', '22.9M', '4.8M']
#     }
#
#     # Create DataFrame
#     df = pd.DataFrame(data)
#
#     # Convert currency strings to numeric values
#     df['Spends'] = df['Spends'].replace({'\$': '', 'K': '*1e3', 'M': '*1e6'}, regex=True).map(pd.eval).astype(int)
#     df['Revenue'] = df['Revenue'].replace({'\$': '', 'K': '*1e3', 'M': '*1e6'}, regex=True).map(pd.eval).astype(int)
#
#     # Calculate ROI
#     df['ROI'] = ((df['Revenue'] - df['Spends']) / df['Spends'])
#
#     # Format columns
#     format_currency = lambda x: f"${x:,.1f}"
#     format_roi = lambda x: f"{x:.1f}"
#
#     df['Spends'] = ['$ 11.3K', '$ 155.2K', '$ 50.7K', '$ 125.4K', '$ 125.2K', '$ 105K', '$ 3.3M', '$ 47.5K', '$ 55.9K', '$ 632.3K', '$ 48.3K']
#     df['Revenue'] =  ['$ 536.3K', '$ 3.4M', '$ 5M', '$ 3M', '$ 3M', '$ 2M', '$ 20M', '$ 1.5M', '$ 7.1M', '$ 22M', '$ 4.6M']
#     df['ROI'] = df['ROI'].apply(format_roi)
#
#     return df


# @st.cache_resource()
# def create_contribution_pie(_scenario):
#     colors_map = {
#         col: color
#         for col, color in zip(
#             st.session_state["channels_list"],
#             plotly.colors.n_colors(
#                 plotly.colors.hex_to_rgb("#BE6468"),
#                 plotly.colors.hex_to_rgb("#E7B8B7"),
#                 20,
#             ),
#         )
#     }
#     total_contribution_fig = make_subplots(
#         rows=1,
#         cols=2,
#         subplot_titles=["Spends", "Revenue"],
#         specs=[[{"type": "pie"}, {"type": "pie"}]],
#     )
#     total_contribution_fig.add_trace(
#         go.Pie(
#             labels=[
#                 channel_name_formating(channel_name)
#                 for channel_name in st.session_state["channels_list"]
#             ]
#             + ["Non Media"],
#             values=[
#                 round(
#                     _scenario.channels[channel_name].actual_total_spends
#                     * _scenario.channels[channel_name].conversion_rate,
#                     1,
#                 )
#                 for channel_name in st.session_state["channels_list"]
#             ]
#             + [0],
#             marker=dict(
#                 colors=[
#                     plotly.colors.label_rgb(colors_map[channel_name])
#                     for channel_name in st.session_state["channels_list"]
#                 ]
#                 + ["#F0F0F0"]
#             ),
#             hole=0.3,
#         ),
#         row=1,
#         col=1,
#     )

#     total_contribution_fig.add_trace(
#         go.Pie(
#             labels=[
#                 channel_name_formating(channel_name)
#                 for channel_name in st.session_state["channels_list"]
#             ]
#             + ["Non Media"],
#             values=[
#                 _scenario.channels[channel_name].actual_total_sales
#                 for channel_name in st.session_state["channels_list"]
#             ]
#             + [_scenario.correction.sum() + _scenario.constant.sum()],
#             hole=0.3,
#         ),
#         row=1,
#         col=2,
#     )

#     total_contribution_fig.update_traces(
#         textposition="inside", texttemplate="%{percent:.1%}"
#     )
#     total_contribution_fig.update_layout(
#         uniformtext_minsize=12,
#         title="Channel contribution",
#         uniformtext_mode="hide",
#     )
#     return total_contribution_fig


# @st.cache_resource()
# def create_contribution_pie(_scenario):
#     colors = plotly.colors.qualitative.Plotly  # A diverse color palette
#     colors_map = {
#         col: colors[i % len(colors)]
#         for i, col in enumerate(st.session_state["channels_list"])
#     }

#     total_contribution_fig = make_subplots(
#         rows=1,
#         cols=2,
#         subplot_titles=["Spends", "Revenue"],
#         specs=[[{"type": "pie"}, {"type": "pie"}]],
#     )

#     total_contribution_fig.add_trace(
#         go.Pie(
#             labels=[
#                 channel_name_formating(channel_name)
#                 for channel_name in st.session_state["channels_list"]
#             ]
#             + ["Non Media"],
#             values=[
#                 round(
#                     _scenario.channels[channel_name].actual_total_spends
#                     * _scenario.channels[channel_name].conversion_rate,
#                     1,
#                 )
#                 for channel_name in st.session_state["channels_list"]
#             ]
#             + [0],
#             marker=dict(
#                 colors=[
#                     colors_map[channel_name]
#                     for channel_name in st.session_state["channels_list"]
#                 ]
#                 + ["#F0F0F0"]
#             ),
#             hole=0.3,
#         ),
#         row=1,
#         col=1,
#     )

#     total_contribution_fig.add_trace(
#         go.Pie(
#             labels=[
#                 channel_name_formating(channel_name)
#                 for channel_name in st.session_state["channels_list"]
#             ]
#             + ["Non Media"],
#             values=[
#                 _scenario.channels[channel_name].actual_total_sales
#                 for channel_name in st.session_state["channels_list"]
#             ]
#             + [_scenario.correction.sum() + _scenario.constant.sum()],
#             marker=dict(
#                 colors=[
#                     colors_map[channel_name]
#                     for channel_name in st.session_state["channels_list"]
#                 ]
#                 + ["#F0F0F0"]
#             ),
#             hole=0.3,
#         ),
#         row=1,
#         col=2,
#     )

#     total_contribution_fig.update_traces(
#         textposition="inside", texttemplate="%{percent:.1%}"
#     )
#     total_contribution_fig.update_layout(
#         uniformtext_minsize=12,
#         title="Channel contribution",
#         uniformtext_mode="hide",
#     )
#     return total_contribution_fig


# @st.cache_resource()
def create_contribution_pie(_scenario, target_col):
    colors = plotly.colors.qualitative.Plotly  # A diverse color palette
    colors_map = {
        col: colors[i % len(colors)]
        for i, col in enumerate(st.session_state["channels_list"])
    }

    spends_values = [
        round(
            _scenario.channels[channel_name].actual_total_spends
            * _scenario.channels[channel_name].conversion_rate,
            1,
        )
        for channel_name in st.session_state["channels_list"]
    ]
    spends_values.append(0)  # Adding Non Media value

    revenue_values = [
        _scenario.channels[channel_name].actual_total_sales
        for channel_name in st.session_state["channels_list"]
    ]
    revenue_values.append(
        _scenario.correction.sum() + _scenario.constant.sum()
    )  # Adding Non Media value

    total_contribution_fig = make_subplots(
        rows=1,
        cols=2,
        subplot_titles=["Spends", target_col],
        specs=[[{"type": "pie"}, {"type": "pie"}]],
    )

    total_contribution_fig.add_trace(
        go.Pie(
            labels=[
                channel_name_formating(channel_name)
                for channel_name in st.session_state["channels_list"]
            ]
            + ["Non Media"],
            values=spends_values,
            marker=dict(
                colors=[
                    colors_map[channel_name]
                    for channel_name in st.session_state["channels_list"]
                ]
                + ["#F0F0F0"]
            ),
            hole=0.3,
        ),
        row=1,
        col=1,
    )

    total_contribution_fig.add_trace(
        go.Pie(
            labels=[
                channel_name_formating(channel_name)
                for channel_name in st.session_state["channels_list"]
            ]
            + ["Non Media"],
            values=revenue_values,
            marker=dict(
                colors=[
                    colors_map[channel_name]
                    for channel_name in st.session_state["channels_list"]
                ]
                + ["#F0F0F0"]
            ),
            hole=0.3,
        ),
        row=1,
        col=2,
    )

    total_contribution_fig.update_traces(
        textposition="inside", texttemplate="%{percent:.1%}"
    )
    total_contribution_fig.update_layout(
        uniformtext_minsize=12,
        title="Channel contribution",
        uniformtext_mode="hide",
    )
    return total_contribution_fig


# def create_contribuion_stacked_plot(scenario):
#     weekly_contribution_fig = make_subplots(rows=1, cols=2,subplot_titles=['Spends','Revenue'],specs=[[{"type": "bar"}, {"type": "bar"}]])
#     raw_df = st.session_state['raw_df']
#     df = raw_df.sort_values(by='Date')
#     x = df.Date
#     weekly_spends_data = []
#     weekly_sales_data = []
#     for channel_name in st.session_state['channels_list']:
#         weekly_spends_data.append((go.Bar(x=x,
#                                           y=scenario.channels[channel_name].actual_spends * scenario.channels[channel_name].conversion_rate,
#                                           name=channel_name_formating(channel_name),
#                                           hovertemplate="Date:%{x}<br>Spend:%{y:$.2s}",
#                                           legendgroup=channel_name)))
#         weekly_sales_data.append((go.Bar(x=x,
#                                          y=scenario.channels[channel_name].actual_sales,
#                                          name=channel_name_formating(channel_name),
#                                          hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
#                                          legendgroup=channel_name, showlegend=False)))
#     for _d in weekly_spends_data:
#         weekly_contribution_fig.add_trace(_d, row=1, col=1)
#     for _d in weekly_sales_data:
#         weekly_contribution_fig.add_trace(_d, row=1, col=2)
#     weekly_contribution_fig.add_trace(go.Bar(x=x,
#                                          y=scenario.constant + scenario.correction,
#                                          name='Non Media',
#                                          hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}"), row=1, col=2)
#     weekly_contribution_fig.update_layout(barmode='stack', title='Channel contribuion by week', xaxis_title='Date')
#     weekly_contribution_fig.update_xaxes(showgrid=False)
#     weekly_contribution_fig.update_yaxes(showgrid=False)
#     return weekly_contribution_fig

# @st.cache_resource()
# def create_channel_spends_sales_plot(channel):
#     if channel is not None:
#         x = channel.dates
#         _spends = channel.actual_spends * channel.conversion_rate
#         _sales = channel.actual_sales
#         channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
#         channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}"), secondary_y = False)
#         channel_sales_spends_fig.add_trace(go.Scatter(x=x, y=_spends,line=dict(color='#005b96'),name='Spends',hovertemplate="Date:%{x}<br>Spend:%{y:$.2s}"), secondary_y = True)
#         channel_sales_spends_fig.update_layout(xaxis_title='Date',yaxis_title='Revenue',yaxis2_title='Spends ($)',title='Channel spends and Revenue week wise')
#         channel_sales_spends_fig.update_xaxes(showgrid=False)
#         channel_sales_spends_fig.update_yaxes(showgrid=False)
#     else:
#         raw_df = st.session_state['raw_df']
#         df = raw_df.sort_values(by='Date')
#         x = df.Date
#         scenario = class_from_dict(st.session_state['default_scenario_dict'])
#         _sales = scenario.constant + scenario.correction
#         channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
#         channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}"), secondary_y = False)
#         # channel_sales_spends_fig.add_trace(go.Scatter(x=x, y=_spends,line=dict(color='#15C39A'),name='Spends',hovertemplate="Date:%{x}<br>Spend:%{y:$.2s}"), secondary_y = True)
#         channel_sales_spends_fig.update_layout(xaxis_title='Date',yaxis_title='Revenue',yaxis2_title='Spends ($)',title='Channel spends and Revenue week wise')
#         channel_sales_spends_fig.update_xaxes(showgrid=False)
#         channel_sales_spends_fig.update_yaxes(showgrid=False)
#     return channel_sales_spends_fig


# Define a shared color palette


# def create_contribution_pie():
#     color_palette = ['#F3F3F0', '#5E7D7E', '#2FA1FF', '#00EDED', '#00EAE4', '#304550', '#EDEBEB', '#7FBEFD', '#003059', '#A2F3F3', '#E1D6E2', '#B6B6B6']
#     total_contribution_fig = make_subplots(rows=1, cols=2, subplot_titles=['Spends', 'Revenue'], specs=[[{"type": "pie"}, {"type": "pie"}]])
#
#     channels_list = ['Paid Search', 'Ga will cid baixo risco', 'Digital tactic others', 'Fb la tier 1', 'Fb la tier 2', 'Paid social others', 'Programmatic', 'Kwai', 'Indicacao', 'Infleux', 'Influencer', 'Non Media']
#
#     # Assign colors from the limited palette to channels
#     colors_map = {col: color_palette[i % len(color_palette)] for i, col in enumerate(channels_list)}
#     colors_map['Non Media'] = color_palette[5]  # Assign fixed green color for 'Non Media'
#
#     # Hardcoded values for Spends and Revenue
#     spends_values = [0.5, 3.36, 1.1, 2.7, 2.7, 2.27, 70.6, 1, 1, 13.7, 1, 0]
#     revenue_values = [1, 4, 5, 3, 3, 2, 50.8, 1.5, 0.7, 13, 0, 16]
#
#     # Add trace for Spends pie chart
#     total_contribution_fig.add_trace(
#         go.Pie(
#             labels=[channel_name for channel_name in channels_list],
#             values=spends_values,
#             marker=dict(colors=[colors_map[channel_name] for channel_name in channels_list]),
#             hole=0.3
#         ),
#         row=1, col=1
#     )
#
#     # Add trace for Revenue pie chart
#     total_contribution_fig.add_trace(
#         go.Pie(
#             labels=[channel_name for channel_name in channels_list],
#             values=revenue_values,
#             marker=dict(colors=[colors_map[channel_name] for channel_name in channels_list]),
#             hole=0.3
#         ),
#         row=1, col=2
#     )
#
#     total_contribution_fig.update_traces(textposition='inside', texttemplate='%{percent:.1%}')
#     total_contribution_fig.update_layout(uniformtext_minsize=12, title='Channel contribution', uniformtext_mode='hide')
#     return total_contribution_fig

# @st.cache_resource()
# def create_contribuion_stacked_plot(_scenario):
#     weekly_contribution_fig = make_subplots(
#         rows=1,
#         cols=2,
#         subplot_titles=["Spends", "Revenue"],
#         specs=[[{"type": "bar"}, {"type": "bar"}]],
#     )
#     raw_df = st.session_state["raw_df"]
#     df = raw_df.sort_values(by="Date")
#     x = df.Date
#     weekly_spends_data = []
#     weekly_sales_data = []

#     for i, channel_name in enumerate(st.session_state["channels_list"]):
#         color = color_palette[i % len(color_palette)]

#         weekly_spends_data.append(
#             go.Bar(
#                 x=x,
#                 y=_scenario.channels[channel_name].actual_spends
#                 * _scenario.channels[channel_name].conversion_rate,
#                 name=channel_name_formating(channel_name),
#                 hovertemplate="Date:%{x}<br>Spend:%{y:$.2s}",
#                 legendgroup=channel_name,
#                 marker_color=color,
#             )
#         )

#         weekly_sales_data.append(
#             go.Bar(
#                 x=x,
#                 y=_scenario.channels[channel_name].actual_sales,
#                 name=channel_name_formating(channel_name),
#                 hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
#                 legendgroup=channel_name,
#                 showlegend=False,
#                 marker_color=color,
#             )
#         )

#     for _d in weekly_spends_data:
#         weekly_contribution_fig.add_trace(_d, row=1, col=1)
#     for _d in weekly_sales_data:
#         weekly_contribution_fig.add_trace(_d, row=1, col=2)

#     weekly_contribution_fig.add_trace(
#         go.Bar(
#             x=x,
#             y=_scenario.constant + _scenario.correction,
#             name="Non Media",
#             hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
#             marker_color=color_palette[-1],
#         ),
#         row=1,
#         col=2,
#     )

#     weekly_contribution_fig.update_layout(
#         barmode="stack",
#         title="Channel contribution by week",
#         xaxis_title="Date",
#     )
#     weekly_contribution_fig.update_xaxes(showgrid=False)
#     weekly_contribution_fig.update_yaxes(showgrid=False)
#     return weekly_contribution_fig


# @st.cache_resource()
def create_contribuion_stacked_plot(_scenario, target_col):
    color_palette = plotly.colors.qualitative.Plotly  # A diverse color palette

    weekly_contribution_fig = make_subplots(
        rows=1,
        cols=2,
        subplot_titles=["Spends", target_col],
        specs=[[{"type": "bar"}, {"type": "bar"}]],
    )

    raw_df = st.session_state["raw_df"]
    df = raw_df.sort_values(by="Date")
    x = df.Date
    weekly_spends_data = []
    weekly_sales_data = []

    for i, channel_name in enumerate(st.session_state["channels_list"]):
        color = color_palette[i % len(color_palette)]

        weekly_spends_data.append(
            go.Bar(
                x=x,
                y=_scenario.channels[channel_name].actual_spends
                * _scenario.channels[channel_name].conversion_rate,
                name=channel_name_formating(channel_name),
                hovertemplate="Date:%{x}<br>Spend:%{y:$.2s}",
                legendgroup=channel_name,
                marker_color=color,
            )
        )

        weekly_sales_data.append(
            go.Bar(
                x=x,
                y=_scenario.channels[channel_name].actual_sales,
                name=channel_name_formating(channel_name),
                hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
                legendgroup=channel_name,
                showlegend=False,
                marker_color=color,
            )
        )

    for _d in weekly_spends_data:
        weekly_contribution_fig.add_trace(_d, row=1, col=1)
    for _d in weekly_sales_data:
        weekly_contribution_fig.add_trace(_d, row=1, col=2)

    weekly_contribution_fig.add_trace(
        go.Bar(
            x=x,
            y=_scenario.constant + _scenario.correction,
            name="Non Media",
            hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
            marker_color=color_palette[-1],
        ),
        row=1,
        col=2,
    )

    weekly_contribution_fig.update_layout(
        barmode="stack",
        title="Channel contribution by week",
        xaxis_title="Date",
    )
    weekly_contribution_fig.update_xaxes(showgrid=False)
    weekly_contribution_fig.update_yaxes(showgrid=False)
    return weekly_contribution_fig


def create_channel_spends_sales_plot(channel, target_col):
    if channel is not None:
        x = channel.dates
        _spends = channel.actual_spends * channel.conversion_rate
        _sales = channel.actual_sales
        channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
        channel_sales_spends_fig.add_trace(
            go.Bar(
                x=x,
                y=_sales,
                marker_color=color_palette[
                    3
                ],  # You can choose a color from the palette
                name=target_col,
                hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
            ),
            secondary_y=False,
        )

        channel_sales_spends_fig.add_trace(
            go.Scatter(
                x=x,
                y=_spends,
                line=dict(
                    color=color_palette[2]
                ),  # You can choose another color from the palette
                name="Spends",
                hovertemplate="Date:%{x}<br>Spend:%{y:$.2s}",
            ),
            secondary_y=True,
        )

        channel_sales_spends_fig.update_layout(
            xaxis_title="Date",
            yaxis_title=target_col,
            yaxis2_title="Spends ($)",
            title="Weekly Channel Spends and " + target_col,
        )
        channel_sales_spends_fig.update_xaxes(showgrid=False)
        channel_sales_spends_fig.update_yaxes(showgrid=False)
    else:
        raw_df = st.session_state["raw_df"]
        df = raw_df.sort_values(by="Date")
        x = df.Date
        scenario = class_from_dict(st.session_state["default_scenario_dict"])
        _sales = scenario.constant + scenario.correction
        channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
        channel_sales_spends_fig.add_trace(
            go.Bar(
                x=x,
                y=_sales,
                marker_color=color_palette[
                    0
                ],  # You can choose a color from the palette
                name="Revenue",
                hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
            ),
            secondary_y=False,
        )

        channel_sales_spends_fig.update_layout(
            xaxis_title="Date",
            yaxis_title="Revenue",
            yaxis2_title="Spends ($)",
            title="Channel spends and Revenue week-wise",
        )
        channel_sales_spends_fig.update_xaxes(showgrid=False)
        channel_sales_spends_fig.update_yaxes(showgrid=False)

    return channel_sales_spends_fig


def format_numbers(value, n_decimals=1, include_indicator=True):
    if include_indicator:
        return f"{CURRENCY_INDICATOR} {numerize(value,n_decimals)}"
    else:
        return f"{numerize(value,n_decimals)}"


def decimal_formater(num_string, n_decimals=1):
    parts = num_string.split(".")
    if len(parts) == 1:
        return num_string + "." + "0" * n_decimals
    else:
        to_be_padded = n_decimals - len(parts[-1])
        if to_be_padded > 0:
            return num_string + "0" * to_be_padded
        else:
            return num_string


def channel_name_formating(channel_name):
    name_mod = channel_name.replace("_", " ")
    if name_mod.lower().endswith(" imp"):
        name_mod = name_mod.replace("Imp", "Spend")
    elif name_mod.lower().endswith(" clicks"):
        name_mod = name_mod.replace("Clicks", "Spend")
    return name_mod


def send_email(email, message):
    s = smtplib.SMTP("smtp.gmail.com", 587)
    s.starttls()
    s.login("geethu4444@gmail.com", "jgydhpfusuremcol")
    s.sendmail("geethu4444@gmail.com", email, message)
    s.quit()


# if __name__ == "__main__":
#     initialize_data()