Update utilities.py

utilities.py

@@ -1,965 +1,965 @@
-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(
-    panel=None, target_file=r"metrics_level_data\Overview_data_test_panel@#revenue.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')
-
-    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
-    if panel is not None and panel != "Total Market":
-        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 == "Total Market":
-        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)
-
-        ## 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())
-        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")[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('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
-
-    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):
-
-    # 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 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("[email protected]", "jgydhpfusuremcol")
-    s.sendmail("[email protected]", email, message)
-    s.quit()
-
-
-if __name__ == "__main__":
-    initialize_data()
+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(
+    panel=None, target_file="Overview_data_test_panel@#revenue.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')
+
+    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
+    if panel is not None and panel != "Total Market":
+        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 == "Total Market":
+        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)
+
+        ## 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())
+        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")[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('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
+
+    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):
+
+    # 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 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("[email protected]", "jgydhpfusuremcol")
+    s.sendmail("[email protected]", email, message)
+    s.quit()
+
+
+if __name__ == "__main__":
+    initialize_data()