utilities.py

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, class_convert_to_dict
import os
import base64
import sqlite3
import datetime
from classes import numerize

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


CURRENCY_INDICATOR = "$"

database_file = r"DB/User.db"

conn = sqlite3.connect(database_file, check_same_thread=False)  # connection with sql db
c = conn.cursor()


def update_db(page_name):

    modified_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")

    c.execute(
        "Update sessions SET last_edited_page= ?, updated_time =? WHERE project_name =? AND owner =?",
        (
            page_name,
            modified_time,
            st.session_state["project_name"],
            st.session_state["username"],
        ),
    )

    conn.commit()


# def load_authenticator():
#     with open("config.yaml") as file:
#         config = yaml.load(file, Loader=SafeLoader)
#         st.session_state["config"] = config
#     authenticator = stauth.Authenticate(
#         credentials=config["credentials"],
#         cookie_name=config["cookie"]["name"],
#         key=config["cookie"]["key"],
#         cookie_expiry_days=config["cookie"]["expiry_days"],
#         preauthorized=config["preauthorized"],
#     )
#     st.session_state["authenticator"] = authenticator
#     return authenticator


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

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

#         if not is_state_initiaized:

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

#     return name


# def authentication():
#     with open("config.yaml") as file:
#         config = yaml.load(file, Loader=SafeLoader)

#         authenticator = stauth.Authenticate(
#             config["credentials"],
#             config["cookie"]["name"],
#             config["cookie"]["key"],
#             config["cookie"]["expiry_days"],
#             config["preauthorized"],
#         )

#     name, authentication_status, username = authenticator.login(
#         "Login", "main"
#     )
#     return authenticator, name, authentication_status, username


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"
dir


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_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 panel_level(input_df, date_column="Date"):
    # Ensure 'Date' is set as the index
    if date_column not in input_df.index.names:
        input_df = input_df.set_index(date_column)

    # Select numeric columns only (excluding 'Date' since it's now the index)
    numeric_columns_df = input_df.select_dtypes(include="number")

    # Group by 'Date' (which is the index) and sum the numeric columns
    aggregated_df = numeric_columns_df.groupby(input_df.index).sum()

    # Reset index if you want 'Date' back as a column
    aggregated_df = aggregated_df.reset_index()

    return aggregated_df


def fetch_actual_data(
    panel=None,
    target_file="Overview_data_test.xlsx",
    updated_rcs=None,
    metrics=None,
):
    excel = pd.read_excel(Path(target_file), sheet_name=None)

    # Extract dataframes for raw data, spend input, and contribution MMM
    raw_df = excel["RAW DATA MMM"]
    spend_df = excel["SPEND INPUT"]
    contri_df = excel["CONTRIBUTION MMM"]

    # Check if the panel is not None
    if panel is not None and panel != "Aggregated":
        raw_df = raw_df[raw_df["Panel"] == panel].drop(columns=["Panel"])
        spend_df = spend_df[spend_df["Panel"] == panel].drop(columns=["Panel"])
        contri_df = contri_df[contri_df["Panel"] == panel].drop(columns=["Panel"])
    elif panel == "Aggregated":
        raw_df = panel_level(raw_df, date_column="Date")
        spend_df = panel_level(spend_df, date_column="Week")
        contri_df = panel_level(contri_df, date_column="Date")

    # Revenue_df = excel['Revenue']

    ## remove sesonalities, indices etc ...
    unnamed_cols = [col for col in raw_df.columns if col.lower().startswith("unnamed")]
    ## remove sesonalities, indices etc ...

    exclude_columns = [
        "Date",
        "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",
    ] + unnamed_cols

    raw_df["Date"] = pd.to_datetime(raw_df["Date"])
    contri_df["Date"] = pd.to_datetime(contri_df["Date"])
    input_df = raw_df.sort_values(by="Date")
    output_df = contri_df.sort_values(by="Date")
    spend_df["Week"] = pd.to_datetime(
        spend_df["Week"], format="%Y-%m-%d", errors="coerce"
    )
    spend_df.sort_values(by="Week", inplace=True)

    # 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]
    channel_list = list(set(channel_list) - set(["fb_level_achieved_tier_1", "ga_app"]))

    # SRISHTI
    infeasible_channels = [
        c
        for c in contri_df.select_dtypes(include=["float", "int"]).columns
        if contri_df[c].sum() <= 0
    ]
    # st.write(channel_list)
    channel_list = list(set(channel_list) - set(infeasible_channels))

    upper_limits = {}
    output_cols = []
    actual_output_dic = {}
    actual_input_dic = {}

    for inp_col in channel_list:
        # 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
        # out_col = [_col for _col in output_df.columns if _col.startswith(inp_col)][0]
        out_col = inp_col
        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)
    print(actual_input_dic)
    return pd.DataFrame(actual_input_dic), pd.DataFrame(actual_output_dic)


def initialize_data(
    panel=None,
    target_file="Overview_data_test.xlsx",
    updated_rcs=None,
    metrics=None,
):
    # 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')
    print(f"## [DEBUG] [UTILS]: {target_file}")
    excel = pd.read_excel(Path(target_file), sheet_name=None)

    # Extract dataframes for raw data, spend input, and contribution MMM
    raw_df = excel["RAW DATA MMM"]
    spend_df = excel["SPEND INPUT"]
    contri_df = excel["CONTRIBUTION MMM"]

    # Check if the panel is not None
    if panel is not None and panel != "Aggregated":
        raw_df = raw_df[raw_df["Panel"] == panel].drop(columns=["Panel"])
        spend_df = spend_df[spend_df["Panel"] == panel].drop(columns=["Panel"])
        contri_df = contri_df[contri_df["Panel"] == panel].drop(columns=["Panel"])
    elif panel == "Aggregated":
        raw_df = panel_level(raw_df, date_column="Date")
        spend_df = panel_level(spend_df, date_column="Week")
        contri_df = panel_level(contri_df, date_column="Date")

    # Revenue_df = excel['Revenue']
    unique_key = f"{metrics}-{panel}"
    ## remove sesonalities, indices etc ...
    unnamed_cols = [col for col in raw_df.columns if col.lower().startswith("unnamed")]
    ## remove sesonalities, indices etc ...
    exclude_columns = [
        "Date",
        "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",
    ] + unnamed_cols

    raw_df["Date"] = pd.to_datetime(raw_df["Date"])
    contri_df["Date"] = pd.to_datetime(contri_df["Date"])
    input_df = raw_df.sort_values(by="Date")
    output_df = contri_df.sort_values(by="Date")
    spend_df["Week"] = pd.to_datetime(
        spend_df["Week"], format="%Y-%m-%d", errors="coerce"
    )
    spend_df.sort_values(by="Week", inplace=True)

    # 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]
    # channel_list = list(
    #     set(channel_list) - set(["fb_level_achieved_tier_1", "ga_app"])
    # )
    infeasible_channels = [
        c
        for c in contri_df.select_dtypes(include=["float", "int"]).columns
        if contri_df[c].sum() <= 0
    ]
    channel_list = list(set(channel_list) - set(infeasible_channels))

    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 = {}

    for inp_col in channel_list:
        # 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
        out_col = [_col for _col in output_df.columns if _col.startswith(inp_col)][0]
        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())
        # SRISHTI

        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, x.max()))
        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()))

        # 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],
        }

        updated_rcs_key = f"{metrics}#@{panel}#@{inp_col}"
        if updated_rcs is not None and updated_rcs_key in list(updated_rcs.keys()):
            response_curves[inp_col] = updated_rcs[updated_rcs_key]

        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
    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 = 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.copy()

    # orig_rcs_path = os.path.join(
    #     st.session_state["project_path"], f"orig_rcs_{metrics}_{panel}.json"
    # )
    # print("##########################")
    # print(orig_rcs_path)
    # if Path(orig_rcs_path).exists():
    #     print("fetched"*100)
    #     with open(orig_rcs_path, "r") as f:
    #         st.session_state["orig_rcs"] = json.load(f)
    # else:
    #     print("created"*100)
    #     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

    rcs_data = {}
    for channel in st.session_state["rcs"]:
        # Convert to native Python lists and types
        x = list(st.session_state["actual_input_df"][channel].values.astype(float))
        y = list(
            st.session_state["actual_contribution_df"][channel].values.astype(float)
        )
        power = float(np.ceil(np.log(max(x)) / np.log(10)) - 3)
        x_plot = list(np.linspace(0, 5 * max(x), 100))

        rcs_data[channel] = {
            "K": float(st.session_state["rcs"][channel]["K"]),
            "b": float(st.session_state["rcs"][channel]["b"]),
            "a": float(st.session_state["rcs"][channel]["a"]),
            "x0": float(st.session_state["rcs"][channel]["x0"]),
            "power": power,
            "x": x,
            "y": y,
            "x_plot": x_plot,
        }

    return rcs_data, scenario


# 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):

    # 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(allow_output_mutation=True)
# def create_contribution_pie(scenario):
#     #c1f7dc
#     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'),23))}
#     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(allow_output_mutation=True)

# 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(allow_output_mutation=True)
# 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


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


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=color_palette[
                    3
                ],  # You can choose a color from the palette
                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=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="Revenue",
            yaxis2_title="Spends ($)",
            title="Channel spends and Revenue week-wise",
        )
        channel_sales_spends_fig.update_xaxes(showgrid=False)
        channel_sales_spends_fig.update_yaxes(showgrid=False)
    else:
        raw_df = st.session_state["raw_df"]
        df = raw_df.sort_values(by="Date")
        x = df.Date
        scenario = class_from_dict(st.session_state["default_scenario_dict"])
        _sales = scenario.constant + scenario.correction
        channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
        channel_sales_spends_fig.add_trace(
            go.Bar(
                x=x,
                y=_sales,
                marker_color=color_palette[
                    0
                ],  # You can choose a color from the palette
                name="Revenue",
                hovertemplate="Date:%{x}<br>Revenue:%{y:$.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 value is None:
        return None
    _value = value if value < 1 else numerize(value, n_decimals)
    if include_indicator:
        return f"{CURRENCY_INDICATOR} {_value}"
    else:
        return f"{_value}"


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()


def project_selection():

    users = {
    "ioannis": "Ioannis Papadopoulos",
    "sharon": "Sharon Sheng",
    "herman": "Herman Kwong",
    "ismail": "Ismail Mohammed",
    "geetha": "Geetha Krishna",
    "srishti": "Srishti Verma",
    "samkeet": "Samkeet Sangai",
    "manoj": "Manoj P",
    "loveesh": "Loveesh Bhatt",
    "bhavya": "Bhavya Jayantilal Kanzariya",
    "pritisha": "Pritisha Punukollu",
    "ashish": "Ashish Sharma",
    "swarupa": "Swarupa Parepalli",
    }
    first_name = st.text_input("Enter Name").lower()

    if st.button("Load saved projects"):

        if len(first_name) == 0 or first_name not in users.keys():
            st.warning("Enter a valid name")
            st.stop()

        st.session_state["username"] = users[first_name]

        c.execute(
            "SELECT email, user_id, user_type FROM users WHERE username = ?",
            (st.session_state["username"],),
        )

        user_data = c.fetchone()
        email, user_id, user_type = user_data

        c.execute(
            "SELECT Distinct project_name, last_edited_page, updated_time as last_updated FROM sessions WHERE owner=?",
            (st.session_state["username"],),
        )

        session_summary = c.fetchall()

        folder_path = r"Users"
        user_folder_path = os.path.join(folder_path, email)

        session_summary_df = pd.DataFrame(
            session_summary,
            columns=["Project Name", "Last Page Edited", "Modified Date"],
        )

        session_summary_df["Modified Date"] = session_summary_df["Modified Date"].map(
            lambda x: pd.to_datetime(x)
        )

        session_summary_df = session_summary_df.sort_values(
            by=["Modified Date"], ascending=False
        )

        st.session_state["summary_df"] = session_summary_df

        # st.write(st.session_state["project_name"][0])
        if len(session_summary_df) == 0:
            st.warning("No projects found please create a project in home page")
            st.stop()
        st.session_state["project_name"] = session_summary_df.iloc[0][0]
        st.session_state["project_path"] = os.path.join(
            user_folder_path, st.session_state["project_name"]
        )
        project_dct_path = os.path.join(
            st.session_state["project_path"], "project_dct.pkl"
        )

        with open(project_dct_path, "rb") as f:
            try:
                st.session_state["project_dct"] = pickle.load(f)
                st.rerun()
            except Exception as e:
                st.warning(
                    "Something went wrong Unable to load saved details / data is lost due to app refresh. Please go to Home page and create a new project."
                )
                st.stop()


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


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

import os
import json
import glob
import pickle
import streamlit as st


# Function to get panels names
def get_panels_names(file_selected):
    raw_data_mmm_df = pd.read_excel(file_selected, sheet_name="RAW DATA MMM")

    if "Panel" in raw_data_mmm_df.columns:
        panel = list(set(raw_data_mmm_df["Panel"]))
    elif "panel_1" in raw_data_mmm_df.columns:
        panel = list(set(raw_data_mmm_df["panel_1"]))
    else:
        panel = ["Aggregated"]

    return panel


# Function to get metrics names
def get_metrics_names(directory):
    # Create a list to hold the final parts of the filenames
    last_portions = []

    # Patterns to match Excel files (.xlsx and .xls) that contain @#
    patterns = [
        os.path.join(directory, "*@#*.xlsx"),
        os.path.join(directory, "*@#*.xls"),
    ]

    # Process each pattern
    for pattern in patterns:
        files = glob.glob(pattern)

        # Extracting the last portion after @# for each file
        for file in files:
            base_name = os.path.basename(file)
            last_portion = base_name.split("@#")[-1]
            last_portion = last_portion.replace(".xlsx", "").replace(
                ".xls", ""
            )  # Removing extensions
            last_portions.append(last_portion)

    return last_portions


# Function to load the original and modified JSON files into dictionaries
def load_json_files(original_path, modified_path):
    try:
        with open(original_path, "r") as json_file:
            original_data = json.load(json_file)
        print("Original RCS data loaded successfully.")
        with open(modified_path, "r") as json_file:
            modified_data = json.load(json_file)
        print("Modified RCS data loaded successfully.")
    except:
        st.toast("Failed to Load/Update. Tool reset to default settings.", icon="⚠️")
        # Define the paths to the RCS data files
        original_json_file_path = os.path.join(
            st.session_state["project_path"], "rcs_data_original.json"
        )
        modified_json_file_path = os.path.join(
            st.session_state["project_path"], "rcs_data_modified.json"
        )

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

        # Remove the modified data file if it exists
        if os.path.exists(modified_json_file_path):
            os.remove(modified_json_file_path)

        # Generate a new file and try again
        generate_rcs_data(original_path, modified_path)
        original_data, modified_data = load_json_files(original_path, modified_path)

    return original_data, modified_data


# Function to format name
def name_formating(name):
    # Replace underscores with spaces
    name_mod = name.replace("_", " ")

    # Capitalize the first letter of each word
    name_mod = name_mod.title()

    return name_mod


# Function to load the original and modified pickle files into dictionaries
def load_pickle_files(original_path, modified_path):
    try:
        # Load the original data from the pickle file
        with open(original_path, "rb") as pickle_file:
            original_data = pickle.load(pickle_file)
            print("Original scenario data loaded successfully from pickle file.")

        # Load the modified data from the pickle file
        with open(modified_path, "rb") as pickle_file:
            modified_data = pickle.load(pickle_file)
            print("Modified scenario data loaded successfully from pickle file.")
    except:
        st.toast("Failed to Load/Update. Tool reset to default settings.", icon="⚠️")
        # Define the paths to the scenario files
        original_pickle_file_path = os.path.join(
            st.session_state["project_path"], "scenario_data_original.pkl"
        )
        modified_pickle_file_path = os.path.join(
            st.session_state["project_path"], "scenario_data_modified.pkl"
        )

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

        # Remove the modified data file if it exists
        if os.path.exists(modified_pickle_file_path):
            os.remove(modified_pickle_file_path)

        # Generate a new file and try again
        generate_rcs_data(original_path, modified_path)
        original_data, modified_data = load_pickle_files(original_path, modified_path)

    return original_data, modified_data


# Function to generate RCS data and store it as JSON files
def generate_rcs_data(original_path, modified_path):
    # Define the directory where the metrics data is located
    directory = os.path.join(st.session_state["project_path"], "metrics_level_data")

    # Retrieve the list of all metric names from the specified directory
    metrics_list = get_metrics_names(directory)

    # Dictionary to store RCS data for all metrics and their respective panels
    all_rcs_data_original = {}
    all_rcs_data_modified = {}

    # Iterate over each metric in the metrics list
    for metric in metrics_list:
        # Define the path to the Excel file corresponding to the current metric
        file_selected = f"metrics_level_data/data_test_overview_panel@#{metric}.xlsx"
        file_selected_path = os.path.join(
            st.session_state["project_path"], file_selected
        )

        # Retrieve the list of panel names from the current metric's Excel file
        panel_list = get_panels_names(file_selected_path)

        # Check if "rcs_data_modified.json" exist
        if os.path.exists(modified_path):
            with open(modified_path, "r") as json_file:
                modified_data = json.load(json_file)

        # Iterate over each panel in the panel list
        for panel in panel_list:
            # Initialize the original RCS data for the current panel and metric
            rcs_dict_original, scenario = initialize_data(
                panel=panel if panel != "Aggregated" else None,
                target_file=file_selected_path,
                updated_rcs={},
                metrics=metric,
            )

            # Ensure the dictionary has the metric as a key for original data
            if metric not in all_rcs_data_original:
                all_rcs_data_original[metric] = {}

            # Store the original RCS data under the corresponding panel for the current metric
            all_rcs_data_original[metric][panel] = rcs_dict_original

            # Ensure the dictionary has the metric as a key for modified data
            if metric not in all_rcs_data_modified:
                all_rcs_data_modified[metric] = {}

            # Store the modified RCS data under the corresponding panel for the current metric
            for channel in rcs_dict_original:
                all_rcs_data_modified[metric][panel] = all_rcs_data_modified[
                    metric
                ].get(panel, {})

                try:
                    updated_rcs_dict = modified_data[metric][panel][channel]
                except:
                    updated_rcs_dict = {
                        "K": rcs_dict_original[channel]["K"],
                        "b": rcs_dict_original[channel]["b"],
                        "a": rcs_dict_original[channel]["a"],
                        "x0": rcs_dict_original[channel]["x0"],
                    }

                all_rcs_data_modified[metric][panel][channel] = updated_rcs_dict

    # Write the original RCS data to a JSON file
    with open(original_path, "w") as json_file:
        json.dump(all_rcs_data_original, json_file, indent=4)

    # Write the modified RCS data to a separate JSON file
    with open(modified_path, "w") as json_file:
        json.dump(all_rcs_data_modified, json_file, indent=4)


# Function to generate scenario data and store it as pickle files
def generate_scenario_data(original_path, modified_path):
    # Define the directory where the metrics data is located
    directory = os.path.join(st.session_state["project_path"], "metrics_level_data")

    # Retrieve the list of all metric names from the specified directory
    metrics_list = get_metrics_names(directory)

    # Dictionary to store scenario data for all metrics and their respective panels
    all_scenario_data_original = {}
    all_scenario_data_modified = {}

    # Iterate over each metric in the metrics list
    for metric in metrics_list:
        # Define the path to the Excel file corresponding to the current metric
        file_selected = f"metrics_level_data/data_test_overview_panel@#{metric}.xlsx"
        file_selected_path = os.path.join(
            st.session_state["project_path"], file_selected
        )

        # Retrieve the list of panel names from the current metric's Excel file
        panel_list = get_panels_names(file_selected_path)

        # Check if "scenario_data_modified.pkl" exist
        if os.path.exists(modified_path):
            with open(modified_path, "rb") as pickle_file:
                modified_data = pickle.load(pickle_file)

        # Iterate over each panel in the panel list
        for panel in panel_list:
            # Initialize the original scenario data for the current panel and metric
            rcs_dict_original, scenario = initialize_data(
                panel=panel if panel != "Aggregated" else None,
                target_file=file_selected_path,
                updated_rcs={},
                metrics=metric,
            )

            # Ensure the dictionary has the metric as a key for original data
            if metric not in all_scenario_data_original:
                all_scenario_data_original[metric] = {}

            # Store the original scenario data under the corresponding panel for the current metric
            all_scenario_data_original[metric][panel] = class_convert_to_dict(scenario)

            # Ensure the dictionary has the metric as a key for modified data
            if metric not in all_scenario_data_modified:
                all_scenario_data_modified[metric] = {}

            # Store the modified scenario data under the corresponding panel for the current metric
            try:
                all_scenario_data_modified[metric][panel] = modified_data[metric][panel]
            except:
                all_scenario_data_modified[metric][panel] = class_convert_to_dict(
                    scenario
                )

    # Write the original RCS data to a pickle file
    with open(original_path, "wb") as pickle_file:
        pickle.dump(all_scenario_data_original, pickle_file)

    # Write the modified RCS data to a separate pickle file
    with open(modified_path, "wb") as pickle_file:
        pickle.dump(all_scenario_data_modified, pickle_file)


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