utilities.py

from numerize.numerize 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 = '$'

import streamlit_authenticator as stauth


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 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}/ALDI_2017.png", "rb")

contents = file_.read()

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

file_.close()


DATA_PATH = "./data"

IMAGES_PATH = "./data/images_224_224"


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}/ALDI_2017.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>""",
        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 initialize_data(
    target_file, panel=None,  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')

    excel = pd.read_excel(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
    
    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 ...
    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",
    ]
    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"]))

    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)

        params = pd.read_excel("response_curves_parameters.xlsx",index_col = "channel")
        param_dicts = {col: params[col].to_dict() for col in params.columns}
        response_curves[inp_col] = {
            "Kd": param_dicts["Kd"][inp_col],
            "n": param_dicts["n"][inp_col],
            "x_min": param_dicts["x_min"][inp_col],
            "x_max": param_dicts["x_max"][inp_col],
            "y_min": param_dicts["y_min"][inp_col],
            "y_max": param_dicts["y_max"][inp_col]
        }

        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]

        # # print(response_curves)
        ## conversion rates
        spend_col = [
            _col
            for _col in spend_df.columns
            if _col.startswith(inp_col.rsplit("_", 1)[0])
        ][0]
        # # print(spend_col)
        # # print('## printing spendssss')
        # # print(spend_col)
        conv = (
            spend_df.set_index("Week")[spend_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(conv_rates)
        ### 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,
            sales= y.copy(),
            # conversion_rate = np.mean(list(conv_rates[inp_col].values())),
            conversion_rate=conv_rates[inp_col],
            response_curve_type="hill-eq",
            response_curve_params={
                "Kd": param_dicts["Kd"][inp_col],
                "n": param_dicts["n"][inp_col],
                "x_min": param_dicts["x_min"][inp_col],
                "x_max": param_dicts["x_max"][inp_col],
                "y_min": param_dicts["y_min"][inp_col],
                "y_max": param_dicts["y_max"][inp_col]
            },
            bounds=np.array([-10, 10]),
            channel_bounds_min = 10,
            channel_bounds_max = 10
        )
        channels[inp_col] = channel
        if sales is None:
            sales = channel.actual_sales
        else:
            sales += channel.actual_sales
    # # print(actual_output_dic)
    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
    )
    # # print(other_contributions)
    # # print(correction)
    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

    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
    # if target_file == :
    #     st.session_state["dividing_parameter"] =  
    # else :



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



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 ($)",
            title="Channel spends and Revenue week-wise",
        )
        channel_sales_spends_fig.update_xaxes(showgrid=False)
        channel_sales_spends_fig.update_yaxes(showgrid=False)
    else:
        raw_df = st.session_state["raw_df"]
        df = raw_df.sort_values(by="Date")
        x = df.Date
        scenario = class_from_dict(st.session_state["default_scenario_dict"])
        _sales = 0 #scenario.constant + scenario.correction
        channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
        channel_sales_spends_fig.add_trace(
            go.Bar(
                x=x,
                y=_sales,
                marker_color=color_palette[
                    0
                ],  # You can choose a color from the palette
                name="Revenue",
                hovertemplate="Date:%{x}<br>Revenue:%{y:$.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 format_numbers_f(value, n_decimals=1, include_indicator=False):
    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")
    # st.write(channel_name)
    key_dict = my_dict = {
        "DisplayProspecting" :"Display Prospecting",
        "CableTV" :"Cable TV",
        "SocialProspecting": "Social Prospecting",
        "Connected&OTTTV"  :"Connected & OTTTV",
        "SocialRetargeting" : "Social Retargeting",
        "DigitalPartners" :"Digital Partners",
        "Audio" :"Audio",
        "BroadcastTV": "Broadcast TV",
        "SearchNon-brand": "Search Non-brand",
        "Email" :"Email" ,
        "SearchBrand": "Search Brand",
        "DisplayRetargeting" :  "Display Retargeting" ,
        "\xa0Video":"Video"
    }
    return key_dict[channel_name]


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


if __name__ == "__main__":
    initialize_data()