Update utilities_with_panel.py

utilities_with_panel.py

@@ -1,1025 +1,1025 @@
-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 streamlit_authenticator as stauth
-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 = ['#001f78', '#00b5db', '#f03d14', '#fa6e0a', '#ffbf45']
-
-
-CURRENCY_INDICATOR = '$'
-
-def load_authenticator():
-    with open('config.yaml') as file:
-        config = yaml.load(file, Loader=SafeLoader)
-        st.session_state['config'] = config
-    authenticator = stauth.Authenticate(
-        config['credentials'],
-        config['cookie']['name'],
-        config['cookie']['key'],
-        config['cookie']['expiry_days'],
-        config['preauthorized']
-    )
-    st.session_state['authenticator'] = authenticator
-    return authenticator
-
-def nav_page(page_name, timeout_secs=3):
-    nav_script = """
-        <script type="text/javascript">
-            function attempt_nav_page(page_name, start_time, timeout_secs) {
-                var links = window.parent.document.getElementsByTagName("a");
-                for (var i = 0; i < links.length; i++) {
-                    if (links[i].href.toLowerCase().endsWith("/" + page_name.toLowerCase())) {
-                        links[i].click();
-                        return;
-                    }
-                }
-                var elasped = new Date() - start_time;
-                if (elasped < timeout_secs * 1000) {
-                    setTimeout(attempt_nav_page, 100, page_name, start_time, timeout_secs);
-                } else {
-                    alert("Unable to navigate to page '" + page_name + "' after " + timeout_secs + " second(s).");
-                }
-            }
-            window.addEventListener("load", function() {
-                attempt_nav_page("%s", new Date(), %d);
-            });
-        </script>
-    """ % (page_name, timeout_secs)
-    html(nav_script)
-
-
-# def load_local_css(file_name):
-#     with open(file_name) as f:
-#         st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
-
-
-# def set_header():
-#     return st.markdown(f"""<div class='main-header'>
-#                     <h1>MMM LiME</h1>
-#                     <img src="https://assets-global.website-files.com/64c8fffb0e95cbc525815b79/64df84637f83a891c1473c51_Vector%20(Stroke).svg   ">
-#             </div>""", unsafe_allow_html=True)
-
-path = os.path.dirname(__file__)
-
-file_ = open(f"{path}/mastercard_logo.png", "rb")
-
-contents = file_.read()
-
-data_url = base64.b64encode(contents).decode("utf-8")
-
-file_.close()
-
- 
-
-DATA_PATH = './data'
-
-IMAGES_PATH = './data/images_224_224'
-
-# New - Sprint 2
-if 'bin_dict' not in st.session_state:
-    
-    with open("data_import.pkl", "rb") as f:
-        data = pickle.load(f)
-
-        st.session_state['bin_dict'] = data["bin_dict"]
-
-# panel_col = [col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in  st.session_state['bin_dict']['Panel Level 1']  ] [0]# set the panel column
-
-panel_col="Panel"
-
-is_panel = True if len(panel_col)>0 else False
-
-date_col='Date'
-#is_panel = False # flag if set to true - do panel level response curves
-
-def load_local_css(file_name):
-
-    with open(file_name) as f:
-
-        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
-
- 
-
- 
-
-# def set_header():
-
-#     return st.markdown(f"""<div class='main-header'>
-
-#                     <h1>H & M Recommendations</h1>
-
-#                     <img src="data:image;base64,{data_url}", alt="Logo">
-
-#             </div>""", unsafe_allow_html=True)
-path1 = os.path.dirname(__file__)
-
-file_1 = open(f"{path}/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 overview_test_data_prep_panel(X, df, spends_X, date_col, panel_col, target_col):
-    '''
-        function to create the data which is used in initialize data fn
-        X : X test with contributions
-        df : originally uploaded data (media data) which has raw vars
-        spends_X : spends of dates in X test
-    '''
-
-    # define channels
-    channels = {'paid_search': ['paid_search_impressions', 'paid_search_clicks'],
-
-                'fb_level_achieved_tier_1': ['fb_level_achieved_tier_1_impressions'], #, 'fb:_level_achieved_-_tier_1_clicks'],
-
-                'fb_level_achieved_tier_2': ['fb:_level_achieved_tier_2_impressions',
-                                             'fb_level_achieved_tier_2_clicks'],
-
-                 'paid_social_others' : ['paid_social_others_impressions', 'paid_social_others_clicks'],
-
-                'ga_app': ['ga_app_impressions', 'ga_app_clicks'],
-
-                'digital_tactic_others': ['digital_tactic_others_impressions', 'digital_tactic_others_clicks'],
-
-                'kwai': ['kwai_impressions', 'kwai_clicks'],
-
-                'programmatic': ['programmatic_impressions', 'programmatic_clicks'],
-
-                 # 'affiliates':['affiliates_clicks'],
-                 #
-                 # "indicacao":['indicacao_clicks'],
-                 #
-                 # "infleux":['infleux_clicks'],
-                 #
-                 # "influencer":['influencer_clicks']
-                }
-
-    channel_list = list(channels.keys())
-
-    # map transformed variable to raw variable name & channel name
-    # mapping eg : paid_search_clicks_lag_2 (transformed var) --> paid_search_clicks (raw var) --> paid_search (channel)
-    variables = {}
-    channel_and_variables = {}
-    new_variables = {}
-    new_channels_and_variables = {}
-
-    for transformed_var in [col for col in
-                            X.drop(columns=[date_col, panel_col, target_col, 'pred', 'panel_effect']).columns if
-                            "_contr" not in col]:
-        if len([col for col in df.columns if col in transformed_var]) == 1:
-            raw_var = [col for col in df.columns if col in transformed_var][0]
-            variables[transformed_var] = raw_var
-            channel_and_variables[raw_var] = [channel for channel, raw_vars in channels.items() if raw_var in raw_vars][
-                0]
-        else:
-            new_variables[transformed_var] = transformed_var
-            new_channels_and_variables[transformed_var] = 'base'
-
-    # Raw DF
-    raw_X = pd.merge(X[[date_col, panel_col]], df[[date_col, panel_col] + list(variables.values())], how='left',
-                     on=[date_col, panel_col])
-    assert len(raw_X) == len(X)
-
-    raw_X_cols = []
-    for i in raw_X.columns:
-        if i in channel_and_variables.keys():
-            raw_X_cols.append(channel_and_variables[i])
-        else:
-            raw_X_cols.append(i)
-    raw_X.columns = raw_X_cols
-
-    # Contribution DF
-    contr_X = X[[date_col, panel_col, 'panel_effect'] + [col for col in X.columns if
-                                                         "_contr" in col and "sum_" not in col]].copy()
-    new_variables = [col for col in contr_X.columns if
-                     "_flag" in col.lower() or "trend" in col.lower() or "sine" in col.lower()]
-    if len(new_variables) > 0:
-        contr_X['const'] = contr_X[['panel_effect'] + new_variables].sum(axis=1)
-        contr_X.drop(columns=['panel_effect'], inplace=True)
-        contr_X.drop(columns=new_variables, inplace=True)
-    else:
-        contr_X.rename(columns={'panel_effect': 'const'}, inplace=True)
-
-    new_contr_X_cols = []
-    for col in contr_X.columns:
-        col_clean = col.replace("_contr", "")
-        new_contr_X_cols.append(col_clean)
-    contr_X.columns = new_contr_X_cols
-
-    contr_X_cols = []
-    for i in contr_X.columns:
-        if i in variables.keys():
-            contr_X_cols.append(channel_and_variables[variables[i]])
-        else:
-            contr_X_cols.append(i)
-    contr_X.columns = contr_X_cols
-
-    # Spends DF
-    spends_X.columns = [col.replace("_cost", "") for col in spends_X.columns]
-
-    raw_X.rename(columns={"date": "Date"}, inplace=True)
-    contr_X.rename(columns={"date": "Date"}, inplace=True)
-    spends_X.rename(columns={'date': 'Week'}, inplace=True)
-
-    # Create excel
-    file_name = "data_test_overview_panel_#" + target_col + ".xlsx"
-    with pd.ExcelWriter(file_name) as writer:
-        raw_X.to_excel(writer, sheet_name="RAW DATA MMM", index=False)
-        contr_X.to_excel(writer, sheet_name="CONTRIBUTION MMM", index=False)
-        spends_X.to_excel(writer, sheet_name="SPEND INPUT", index=False)
-
-
-def overview_test_data_prep_nonpanel(X, df, spends_X, date_col, target_col):
-    '''
-        function to create the data which is used in initialize data fn
-        X : X test with contributions
-        df : originally uploaded data (media data) which has raw vars
-        spends_X : spends of dates in X test
-    '''
-    # define channels
-    channels = {'paid_search': ['paid_search_impressions', 'paid_search_clicks'],
-
-                'fb_level_achieved_tier_1': ['fb_level_achieved_tier_1_impressions', 'fb_level_achieved_tier_1_clicks'],
-
-                'fb_level_achieved_tier_2': ['fb_level_achieved_tier_2_impressions',
-                                             'fb_level_achieved_tier_2_clicks'],
-
-                 'paid_social_others' : ['paid_social_others_impressions', 'paid_social_others_clicks'],
-
-                'ga_app_will_and_cid_pequena_baixo_risco': ['ga_app_will_and_cid_pequena_baixo_risco_impressions', 'ga_app_will_and_cid_pequena_baixo_risco_clicks'],
-
-                'digital_tactic_others': ['digital_tactic_others_impressions', 'digital_tactic_others_clicks'],
-
-                'kwai': ['kwai_impressions', 'kwai_clicks'],
-
-                'programmatic': ['programmatic_impressions', 'programmatic_clicks'],
-
-                 'affiliates':['affiliates_clicks', 'affiliates_impressions'],
-
-                 "indicacao":['indicacao_clicks', 'indicacao_impressions'],
-
-                 "infleux":['infleux_clicks', 'infleux_impressions'],
-
-                 "influencer":['influencer_clicks', 'influencer_impressions']
-                }
-
-    channel_list = list(channels.keys())
-
-    # map transformed variable to raw variable name & channel name
-    # mapping eg : paid_search_clicks_lag_2 (transformed var) --> paid_search_clicks (raw var) --> paid_search (channel)
-    variables = {}
-    channel_and_variables = {}
-    new_variables = {}
-    new_channels_and_variables = {}
-
-    cols_to_del = list(set([date_col, target_col, 'pred']).intersection((set(X.columns))))
-    for transformed_var in [col for col in
-                            X.drop(columns=cols_to_del).columns if
-                            "_contr" not in col]: # also has 'const'
-        if len([col for col in df.columns if col in transformed_var]) == 1: # col is raw var
-            raw_var = [col for col in df.columns if col in transformed_var][0]
-            variables[transformed_var] = raw_var
-            channel_and_variables[raw_var] = [channel for channel, raw_vars in channels.items() if raw_var in raw_vars][0]
-        else: # when no corresponding raw var then base
-            new_variables[transformed_var] = transformed_var
-            new_channels_and_variables[transformed_var] = 'base'
-
-    # Raw DF
-    raw_X = pd.merge(X[[date_col]], df[[date_col] + list(variables.values())], how='left',
-                     on=[date_col])
-    assert len(raw_X) == len(X)
-
-    raw_X_cols = []
-    for i in raw_X.columns:
-        if i in channel_and_variables.keys():
-            raw_X_cols.append(channel_and_variables[i])
-        else:
-            raw_X_cols.append(i)
-    raw_X.columns = raw_X_cols
-
-    # Contribution DF
-    contr_X = X[[date_col] + [col for col in X.columns if "_contr" in col and "sum_" not in col]].copy()
-    # st.write(contr_X.columns)
-    new_variables = [col for col in contr_X.columns if
-                     "_flag" in col.lower() or "trend" in col.lower() or "sine" in col.lower()]
-    if len(new_variables) > 0: # if new vars are available, their contributions should be added to base (called const)
-        contr_X['const_contr'] = contr_X[['const_contr'] + new_variables].sum(axis=1)
-        contr_X.drop(columns=new_variables, inplace=True)
-
-
-    new_contr_X_cols = []
-    for col in contr_X.columns:
-        col_clean = col.replace("_contr", "")
-        new_contr_X_cols.append(col_clean)
-    contr_X.columns = new_contr_X_cols
-
-    contr_X_cols = []
-    for i in contr_X.columns:
-        if i in variables.keys():
-            contr_X_cols.append(channel_and_variables[variables[i]])
-        else:
-            contr_X_cols.append(i)
-    contr_X.columns = contr_X_cols
-
-    # Spends DF
-    spends_X.columns = [col.replace("_cost", "").replace("_spends", '').replace("_spend", "") for col in spends_X.columns]
-
-    raw_X.rename(columns={"date": "Date"}, inplace=True)
-    contr_X.rename(columns={"date": "Date"}, inplace=True)
-    spends_X.rename(columns={'date': 'Week'}, inplace=True)
-
-    # Create excel
-    file_name = "data_test_overview_panel_#" + target_col + ".xlsx"
-    with pd.ExcelWriter(file_name) as writer:
-        raw_X.to_excel(writer, sheet_name="RAW DATA MMM", index=False)
-        contr_X.to_excel(writer, sheet_name="CONTRIBUTION MMM", index=False)
-        spends_X.to_excel(writer, sheet_name="SPEND INPUT", index=False)
-
-
-def initialize_data(target_col,selected_markets):
-    # uopx_conv_rates = {'streaming_impressions' : 0.007,'digital_impressions' : 0.007,'search_clicks' : 0.00719,'tv_impressions' : 0.000173,
-    #                    "digital_clicks":0.005,"streaming_clicks":0.004,'streaming_spends':1,"tv_spends":1,"search_spends":1,
-    #                    "digital_spends":1}
-    #print('State initialized')
-    # excel = pd.read_excel("data_test_overview_panel.xlsx",sheet_name=None)
-    #excel = pd.read_excel(r"metrics_level_data\Overview_data_test_panel@#revenue.xlsx" + target_col + ".xlsx",sheet_name=None)
-    
-    excel = pd.read_excel(r"metrics_level_data\Overview_data_test_panel@#revenue.xlsx",sheet_name=None)
-
-    raw_df = excel['RAW DATA MMM']
-
-    spend_df = excel['SPEND INPUT']
-    contri_df = excel['CONTRIBUTION MMM']
-    
-    #st.write(raw_df)
-    if selected_markets!= "Total Market":
-      
-      raw_df=raw_df[raw_df['Panel']==selected_markets]
-      spend_df=spend_df[spend_df['Panel']==selected_markets]
-      contri_df=contri_df[contri_df['Panel']==selected_markets]
-      
-    else:
-        raw_df=raw_df.groupby('Date').sum().reset_index()
-        spend_df=spend_df.groupby('Week').sum().reset_index()
-        contri_df=contri_df.groupby('Date').sum().reset_index()
-    #Revenue_df = excel['Revenue']
-    
-    ## remove sesonalities, indices etc ... 
-    exclude_columns = ['Date', 'Week','Panel',date_col, panel_col,'Others'
-                  ] 
-
-    # Aggregate all 3 dfs to date level (from date-panel level)
-    raw_df[date_col]=pd.to_datetime(raw_df[date_col])
-    raw_df_aggregations = {c:'sum' for c in raw_df.columns if c not in exclude_columns}
-    raw_df = raw_df.groupby(date_col).agg(raw_df_aggregations).reset_index()
-
-    contri_df[date_col]=pd.to_datetime(contri_df[date_col])
-    contri_df_aggregations = {c:'sum' for c in contri_df.columns if c not in exclude_columns}
-    contri_df = contri_df.groupby(date_col).agg(contri_df_aggregations).reset_index()
-
-    input_df = raw_df.sort_values(by=[date_col])
-
-    output_df = contri_df.sort_values(by=[date_col])
-
-    spend_df['Week'] = pd.to_datetime(spend_df['Week'], format='%Y-%m-%d', errors='coerce')
-    spend_df_aggregations = {c: 'sum' for c in spend_df.columns if c not in exclude_columns}
-    spend_df = spend_df.groupby('Week').agg(spend_df_aggregations).reset_index()
-    # spend_df['Week'] = pd.to_datetime(spend_df['Week'], errors='coerce')
-    # spend_df = spend_df.sort_values(by='Week')
-    
-
-    channel_list = [col for col in input_df.columns if col not in exclude_columns]
-    
-    response_curves = {}
-    mapes = {}
-    rmses = {}
-    upper_limits = {}
-    powers = {}
-    r2 = {}
-    conv_rates = {}
-    output_cols = []
-    channels = {}
-    sales = None
-    dates = input_df.Date.values
-    actual_output_dic = {}
-    actual_input_dic = {}
-
-    # ONLY FOR TESTING
-    # channel_list=['programmatic']
-    infeasible_channels = [c for c in contri_df.select_dtypes(include=['float', 'int']).columns if contri_df[c].sum()<=0]
-    # st.write(infeasible_channels)
-    channel_list=list(set(channel_list)-set(infeasible_channels))
-
-
-    for inp_col in channel_list:
-        #st.write(inp_col)
-
-        # # New - Sprint 2
-        # if is_panel:
-        #     input_df1 = input_df.groupby([date_col]).agg({inp_col:'sum'}).reset_index() # aggregate spends on date
-        #     spends = input_df1[inp_col].values
-        # else :
-        #     spends = input_df[inp_col].values
-        spends = spend_df[inp_col].values
-
-        x = spends.copy()
-        # upper limit for penalty   
-        upper_limits[inp_col] = 2*x.max()
-
-
-
-        # contribution
-        # New - Sprint 2
-        out_col = [_col for _col in output_df.columns if _col.startswith(inp_col)][0]
-        if is_panel :
-            output_df1 = output_df.groupby([date_col]).agg({out_col:'sum'}).reset_index()
-            y = output_df1[out_col].values.copy()
-        else :
-            y = output_df[out_col].values.copy()
-
-        actual_output_dic[inp_col] = y.copy()
-        actual_input_dic[inp_col] = x.copy()
-        ##output cols aggregation
-        output_cols.append(out_col)
-        
-        ## scale the input
-        power = (np.ceil(np.log(x.max()) / np.log(10) )- 3)
-        if power >= 0 :
-            x = x / 10**power
-        
-            
-        x = x.astype('float64')
-        y = y.astype('float64')
-        #print('#printing yyyyyyyyy')
-        #print(inp_col)
-        #print(x.max())
-        #print(y.max())
-        # st.write(y.max(),x.max())
-        print(y.max(),x.max())
-        if y.max()<=0.01:
-            if x.max()<=0.01 :
-                st.write("here-here")
-                bounds = ((0, 0, 0, 0), (3 * 0.01, 1000, 1, 0.01))
-
-            else :
-                st.write("here")
-                bounds = ((0, 0, 0, 0), (3 * 0.01, 1000, 1, 0.01))
-        else :
-            bounds = ((0, 0, 0, 0), (3 * y.max(), 1000, 1, x.max()))
-        #bounds = ((y.max(), 3*y.max()),(0,1000),(0,1),(0,x.max()))
-        params,_ = curve_fit(s_curve,x,y,p0=(2*y.max(),0.01,1e-5,x.max()),
-                                bounds=bounds,
-                                maxfev=int(1e5))
-        mape = (100 * abs(1 - s_curve(x, *params) / y.clip(min=1))).mean()
-        rmse =  np.sqrt(((y - s_curve(x,*params))**2).mean())
-        r2_ = r2_score(y, s_curve(x,*params))
-
-        response_curves[inp_col] = {'K' : params[0], 'b' : params[1], 'a' : params[2], 'x0' : params[3]}
-        mapes[inp_col] = mape
-        rmses[inp_col] = rmse
-        r2[inp_col] = r2_
-        powers[inp_col] = power
-        
-        
-        ## conversion rates
-        spend_col = [_col for _col in spend_df.columns if _col.startswith(inp_col.rsplit('_',1)[0])][0]
-
-        #print('#printing spendssss')
-        #print(spend_col)
-        conv = (spend_df.set_index('Week')[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
-        # st.write(inp_col, channel.actual_sales)
-    # st.write(output_cols)
-    other_contributions = output_df.drop([*output_cols], axis=1).sum(axis=1, numeric_only = True).values
-    correction = output_df.drop(['Date'],axis=1).sum(axis=1).values - (sales + other_contributions)
-
-    scenario_test_df=pd.DataFrame(columns=['other_contributions','correction', 'sales'])
-    scenario_test_df['other_contributions']=other_contributions
-    scenario_test_df['correction']=correction
-    scenario_test_df['sales']=sales
-    scenario_test_df.to_csv("test/scenario_test_df.csv",index=False)
-    output_df.to_csv("test/output_df.csv",index=False)
-
-    scenario = Scenario(name='default', channels=channels, constant=other_contributions, correction = correction)
-    ## setting session variables
-    st.session_state['initialized'] = True
-    st.session_state['actual_df'] = input_df
-    st.session_state['raw_df'] = raw_df
-    st.session_state['contri_df'] = output_df
-    default_scenario_dict = class_to_dict(scenario)
-    st.session_state['default_scenario_dict'] = default_scenario_dict
-    st.session_state['scenario'] = scenario
-    st.session_state['channels_list'] = channel_list
-    st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
-    st.session_state['rcs'] = response_curves
-    st.session_state['powers'] = powers
-    st.session_state['actual_contribution_df'] = pd.DataFrame(actual_output_dic)
-    st.session_state['actual_input_df'] = pd.DataFrame(actual_input_dic)
-    
-    for channel in channels.values():
-        st.session_state[channel.name] = numerize(channel.actual_total_spends * channel.conversion_rate,1)
-    
-    st.session_state['xlsx_buffer'] = io.BytesIO()
-
-    
-    if Path('../saved_scenarios.pkl').exists():
-        with open('../saved_scenarios.pkl','rb') as f:
-            st.session_state['saved_scenarios'] = pickle.load(f)
-    else:
-        st.session_state['saved_scenarios'] = OrderedDict()
-        
-    st.session_state['total_spends_change'] = 0
-    st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
-    st.session_state['disable_download_button'] = True
-    
-# def initialize_data():
-#     # fetch data from excel
-#     output = pd.read_excel('data.xlsx',sheet_name=None)
-#     raw_df = output['RAW DATA MMM']
-#     contribution_df = output['CONTRIBUTION MMM']
-#     Revenue_df = output['Revenue']
-
-#     ## channels to be shows
-#     channel_list = []
-#     for col in raw_df.columns:
-#         if 'click' in col.lower() or 'spend' in col.lower() or 'imp' in col.lower():
-#             ##print(col)
-#             channel_list.append(col)
-#         else:
-#             pass
-    
-#     ## NOTE : Considered only Desktop spends for all calculations
-#     acutal_df = raw_df[raw_df.Region == 'Desktop'].copy()
-#     ## NOTE : Considered one year of data
-#     acutal_df = acutal_df[acutal_df.Date>'2020-12-31']
-#     actual_df = acutal_df.drop('Region',axis=1).sort_values(by='Date')[[*channel_list,'Date']]
-    
-#     ##load response curves
-#     with open('./grammarly_response_curves.json','r') as f:
-#         response_curves = json.load(f)
-        
-#     ## create channel dict for scenario creation
-#     dates = actual_df.Date.values
-#     channels = {}
-#     rcs = {}
-#     constant = 0.
-#     for i,info_dict in enumerate(response_curves):
-#         name = info_dict.get('name')
-#         response_curve_type = info_dict.get('response_curve')
-#         response_curve_params = info_dict.get('params')
-#         rcs[name] = response_curve_params
-#         if name != 'constant':
-#             spends = actual_df[name].values
-#             channel = Channel(name=name,dates=dates,
-#                             spends=spends,
-#                             response_curve_type=response_curve_type,
-#                             response_curve_params=response_curve_params,
-#                             bounds=np.array([-30,30]))
-            
-#             channels[name] = channel
-#         else:
-#             constant = info_dict.get('value',0.) * len(dates)
-            
-#     ## create scenario
-#     scenario = Scenario(name='default', channels=channels, constant=constant)
-#     default_scenario_dict = class_to_dict(scenario)
-    
-
-#     ## setting session variables
-#     st.session_state['initialized'] = True
-#     st.session_state['actual_df'] = actual_df
-#     st.session_state['raw_df'] = raw_df
-#     st.session_state['default_scenario_dict'] = default_scenario_dict
-#     st.session_state['scenario'] = scenario
-#     st.session_state['channels_list'] = channel_list
-#     st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
-#     st.session_state['rcs'] = rcs
-#     for channel in channels.values():
-#         if channel.name not in st.session_state:
-#             st.session_state[channel.name] = float(channel.actual_total_spends)
-    
-#     if 'xlsx_buffer' not in st.session_state:
-#         st.session_state['xlsx_buffer'] = io.BytesIO()
-
-#     ## for saving scenarios
-#     if 'saved_scenarios' not in st.session_state:
-#         if Path('../saved_scenarios.pkl').exists():
-#             with open('../saved_scenarios.pkl','rb') as f:
-#                 st.session_state['saved_scenarios'] = pickle.load(f)
-        
-#         else:
-#             st.session_state['saved_scenarios'] = OrderedDict()
-
-#     if 'total_spends_change' not in st.session_state:
-#         st.session_state['total_spends_change'] = 0
-        
-#     if 'optimization_channels' not in st.session_state:
-#         st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
-    
-#     if 'disable_download_button' not in st.session_state:
-#         st.session_state['disable_download_button'] = True
-def create_channel_summary(scenario):
-    summary_columns = []
-
-    actual_spends_rows = []
-
-    actual_sales_rows = []
-
-    actual_roi_rows = []
-
-    for channel in scenario.channels.values():
-
-        name_mod = channel.name.replace('_', ' ')
-
-        if name_mod.lower().endswith(' imp'):
-            name_mod = name_mod.replace('Imp', ' Impressions')
-
-        print(name_mod, channel.actual_total_spends, channel.conversion_rate,
-              channel.actual_total_spends * channel.conversion_rate)
-
-        summary_columns.append(name_mod)
-
-        actual_spends_rows.append(format_numbers(float(channel.actual_total_spends * channel.conversion_rate)))
-
-        actual_sales_rows.append(format_numbers((float(channel.actual_total_sales))))
-
-        actual_roi_rows.append(decimal_formater(
-            format_numbers((channel.actual_total_sales) / (channel.actual_total_spends * channel.conversion_rate),
-                           include_indicator=False, n_decimals=4), n_decimals=4))
-
-    actual_summary_df = pd.DataFrame([summary_columns, actual_spends_rows, actual_sales_rows, actual_roi_rows]).T
-
-    actual_summary_df.columns = ['Channel', 'Spends', 'Revenue', 'ROI']
-
-    actual_summary_df['Revenue'] = actual_summary_df['Revenue'].map(lambda x: str(x)[1:])
-
-    return actual_summary_df
-
-
-# def create_channel_summary(scenario):
-#
-#     # Provided data
-#     data = {
-#         'Channel': ['Paid Search', 'Ga will cid baixo risco', 'Digital tactic others', 'Fb la tier 1', 'Fb la tier 2', 'Paid social others', 'Programmatic', 'Kwai', 'Indicacao', 'Infleux', 'Influencer'],
-#         'Spends': ['$ 11.3K', '$ 155.2K', '$ 50.7K', '$ 125.4K', '$ 125.2K', '$ 105K', '$ 3.3M', '$ 47.5K', '$ 55.9K', '$ 632.3K', '$ 48.3K'],
-#         'Revenue': ['558.0K', '3.5M', '5.2M', '3.1M', '3.1M', '2.1M', '20.8M', '1.6M', '728.4K', '22.9M', '4.8M']
-#     }
-#
-#     # Create DataFrame
-#     df = pd.DataFrame(data)
-#
-#     # Convert currency strings to numeric values
-#     df['Spends'] = df['Spends'].replace({'\$': '', 'K': '*1e3', 'M': '*1e6'}, regex=True).map(pd.eval).astype(int)
-#     df['Revenue'] = df['Revenue'].replace({'\$': '', 'K': '*1e3', 'M': '*1e6'}, regex=True).map(pd.eval).astype(int)
-#
-#     # Calculate ROI
-#     df['ROI'] = ((df['Revenue'] - df['Spends']) / df['Spends'])
-#
-#     # Format columns
-#     format_currency = lambda x: f"${x:,.1f}"
-#     format_roi = lambda x: f"{x:.1f}"
-#
-#     df['Spends'] = ['$ 11.3K', '$ 155.2K', '$ 50.7K', '$ 125.4K', '$ 125.2K', '$ 105K', '$ 3.3M', '$ 47.5K', '$ 55.9K', '$ 632.3K', '$ 48.3K']
-#     df['Revenue'] =  ['$ 536.3K', '$ 3.4M', '$ 5M', '$ 3M', '$ 3M', '$ 2M', '$ 20M', '$ 1.5M', '$ 7.1M', '$ 22M', '$ 4.6M']
-#     df['ROI'] = df['ROI'].apply(format_roi)
-#
-#     return df
-
-
-#@st.cache_data()
-def create_contribution_pie(scenario):
-    #c1f7dc
-    
-    light_blue = 'rgba(0, 31, 120, 0.7)'
-    light_orange = 'rgba(0, 181, 219, 0.7)'
-    light_green = 'rgba(240, 61, 20, 0.7)'
-    light_red = 'rgba(250, 110, 10, 0.7)'
-    light_purple = 'rgba(255, 191, 69, 0.7)'
-
-    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=['Media Spends','Revenue Contribution'],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_colors=[light_blue, light_orange, light_green, light_red, light_purple],
-                        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='', uniformtext_mode='hide')
-    return total_contribution_fig
-
-#@st.cache_data()
-
-# 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:$.2s}"), row=1, col=2)
-#     weekly_contribution_fig.update_layout(barmode='stack', title='Channel contribuion by week', xaxis_title='Date')
-#     weekly_contribution_fig.update_xaxes(showgrid=False)
-#     weekly_contribution_fig.update_yaxes(showgrid=False)
-#     return weekly_contribution_fig
-
-# @st.cache_data(allow_output_mutation=True)
-# def create_channel_spends_sales_plot(channel):
-#     if channel is not None:
-#         x = channel.dates
-#         _spends = channel.actual_spends * channel.conversion_rate
-#         _sales = channel.actual_sales
-#         channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
-#         channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}<br>Revenue:%{y:$.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[1],  # You can choose a color from the palette
-            name='Revenue',
-            hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
-        ), secondary_y=False)
-        
-        channel_sales_spends_fig.add_trace(go.Scatter(
-            x=x,
-            y=_spends,
-            line=dict(color=color_palette[3]),  # You can choose another color from the palette
-            name='Spends',
-            hovertemplate="Date:%{x}<br>Spend:%{y:$.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("geethu4444@gmail.com", "jgydhpfusuremcol")
-    s.sendmail("geethu4444@gmail.com", 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 streamlit_authenticator as stauth
+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 = ['#001f78', '#00b5db', '#f03d14', '#fa6e0a', '#ffbf45']
+
+
+CURRENCY_INDICATOR = '$'
+
+def load_authenticator():
+    with open('config.yaml') as file:
+        config = yaml.load(file, Loader=SafeLoader)
+        st.session_state['config'] = config
+    authenticator = stauth.Authenticate(
+        config['credentials'],
+        config['cookie']['name'],
+        config['cookie']['key'],
+        config['cookie']['expiry_days'],
+        config['preauthorized']
+    )
+    st.session_state['authenticator'] = authenticator
+    return authenticator
+
+def nav_page(page_name, timeout_secs=3):
+    nav_script = """
+        <script type="text/javascript">
+            function attempt_nav_page(page_name, start_time, timeout_secs) {
+                var links = window.parent.document.getElementsByTagName("a");
+                for (var i = 0; i < links.length; i++) {
+                    if (links[i].href.toLowerCase().endsWith("/" + page_name.toLowerCase())) {
+                        links[i].click();
+                        return;
+                    }
+                }
+                var elasped = new Date() - start_time;
+                if (elasped < timeout_secs * 1000) {
+                    setTimeout(attempt_nav_page, 100, page_name, start_time, timeout_secs);
+                } else {
+                    alert("Unable to navigate to page '" + page_name + "' after " + timeout_secs + " second(s).");
+                }
+            }
+            window.addEventListener("load", function() {
+                attempt_nav_page("%s", new Date(), %d);
+            });
+        </script>
+    """ % (page_name, timeout_secs)
+    html(nav_script)
+
+
+# def load_local_css(file_name):
+#     with open(file_name) as f:
+#         st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+
+# def set_header():
+#     return st.markdown(f"""<div class='main-header'>
+#                     <h1>MMM LiME</h1>
+#                     <img src="https://assets-global.website-files.com/64c8fffb0e95cbc525815b79/64df84637f83a891c1473c51_Vector%20(Stroke).svg   ">
+#             </div>""", unsafe_allow_html=True)
+
+path = os.path.dirname(__file__)
+
+file_ = open(f"{path}/mastercard_logo.png", "rb")
+
+contents = file_.read()
+
+data_url = base64.b64encode(contents).decode("utf-8")
+
+file_.close()
+
+ 
+
+DATA_PATH = './data'
+
+IMAGES_PATH = './data/images_224_224'
+
+# New - Sprint 2
+if 'bin_dict' not in st.session_state:
+    
+    with open("data_import.pkl", "rb") as f:
+        data = pickle.load(f)
+
+        st.session_state['bin_dict'] = data["bin_dict"]
+
+# panel_col = [col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in  st.session_state['bin_dict']['Panel Level 1']  ] [0]# set the panel column
+
+panel_col="Panel"
+
+is_panel = True if len(panel_col)>0 else False
+
+date_col='Date'
+#is_panel = False # flag if set to true - do panel level response curves
+
+def load_local_css(file_name):
+
+    with open(file_name) as f:
+
+        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+ 
+
+ 
+
+# def set_header():
+
+#     return st.markdown(f"""<div class='main-header'>
+
+#                     <h1>H & M Recommendations</h1>
+
+#                     <img src="data:image;base64,{data_url}", alt="Logo">
+
+#             </div>""", unsafe_allow_html=True)
+path1 = os.path.dirname(__file__)
+
+file_1 = open(f"{path}/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 overview_test_data_prep_panel(X, df, spends_X, date_col, panel_col, target_col):
+    '''
+        function to create the data which is used in initialize data fn
+        X : X test with contributions
+        df : originally uploaded data (media data) which has raw vars
+        spends_X : spends of dates in X test
+    '''
+
+    # define channels
+    channels = {'paid_search': ['paid_search_impressions', 'paid_search_clicks'],
+
+                'fb_level_achieved_tier_1': ['fb_level_achieved_tier_1_impressions'], #, 'fb:_level_achieved_-_tier_1_clicks'],
+
+                'fb_level_achieved_tier_2': ['fb:_level_achieved_tier_2_impressions',
+                                             'fb_level_achieved_tier_2_clicks'],
+
+                 'paid_social_others' : ['paid_social_others_impressions', 'paid_social_others_clicks'],
+
+                'ga_app': ['ga_app_impressions', 'ga_app_clicks'],
+
+                'digital_tactic_others': ['digital_tactic_others_impressions', 'digital_tactic_others_clicks'],
+
+                'kwai': ['kwai_impressions', 'kwai_clicks'],
+
+                'programmatic': ['programmatic_impressions', 'programmatic_clicks'],
+
+                 # 'affiliates':['affiliates_clicks'],
+                 #
+                 # "indicacao":['indicacao_clicks'],
+                 #
+                 # "infleux":['infleux_clicks'],
+                 #
+                 # "influencer":['influencer_clicks']
+                }
+
+    channel_list = list(channels.keys())
+
+    # map transformed variable to raw variable name & channel name
+    # mapping eg : paid_search_clicks_lag_2 (transformed var) --> paid_search_clicks (raw var) --> paid_search (channel)
+    variables = {}
+    channel_and_variables = {}
+    new_variables = {}
+    new_channels_and_variables = {}
+
+    for transformed_var in [col for col in
+                            X.drop(columns=[date_col, panel_col, target_col, 'pred', 'panel_effect']).columns if
+                            "_contr" not in col]:
+        if len([col for col in df.columns if col in transformed_var]) == 1:
+            raw_var = [col for col in df.columns if col in transformed_var][0]
+            variables[transformed_var] = raw_var
+            channel_and_variables[raw_var] = [channel for channel, raw_vars in channels.items() if raw_var in raw_vars][
+                0]
+        else:
+            new_variables[transformed_var] = transformed_var
+            new_channels_and_variables[transformed_var] = 'base'
+
+    # Raw DF
+    raw_X = pd.merge(X[[date_col, panel_col]], df[[date_col, panel_col] + list(variables.values())], how='left',
+                     on=[date_col, panel_col])
+    assert len(raw_X) == len(X)
+
+    raw_X_cols = []
+    for i in raw_X.columns:
+        if i in channel_and_variables.keys():
+            raw_X_cols.append(channel_and_variables[i])
+        else:
+            raw_X_cols.append(i)
+    raw_X.columns = raw_X_cols
+
+    # Contribution DF
+    contr_X = X[[date_col, panel_col, 'panel_effect'] + [col for col in X.columns if
+                                                         "_contr" in col and "sum_" not in col]].copy()
+    new_variables = [col for col in contr_X.columns if
+                     "_flag" in col.lower() or "trend" in col.lower() or "sine" in col.lower()]
+    if len(new_variables) > 0:
+        contr_X['const'] = contr_X[['panel_effect'] + new_variables].sum(axis=1)
+        contr_X.drop(columns=['panel_effect'], inplace=True)
+        contr_X.drop(columns=new_variables, inplace=True)
+    else:
+        contr_X.rename(columns={'panel_effect': 'const'}, inplace=True)
+
+    new_contr_X_cols = []
+    for col in contr_X.columns:
+        col_clean = col.replace("_contr", "")
+        new_contr_X_cols.append(col_clean)
+    contr_X.columns = new_contr_X_cols
+
+    contr_X_cols = []
+    for i in contr_X.columns:
+        if i in variables.keys():
+            contr_X_cols.append(channel_and_variables[variables[i]])
+        else:
+            contr_X_cols.append(i)
+    contr_X.columns = contr_X_cols
+
+    # Spends DF
+    spends_X.columns = [col.replace("_cost", "") for col in spends_X.columns]
+
+    raw_X.rename(columns={"date": "Date"}, inplace=True)
+    contr_X.rename(columns={"date": "Date"}, inplace=True)
+    spends_X.rename(columns={'date': 'Week'}, inplace=True)
+
+    # Create excel
+    file_name = "data_test_overview_panel_#" + target_col + ".xlsx"
+    with pd.ExcelWriter(file_name) as writer:
+        raw_X.to_excel(writer, sheet_name="RAW DATA MMM", index=False)
+        contr_X.to_excel(writer, sheet_name="CONTRIBUTION MMM", index=False)
+        spends_X.to_excel(writer, sheet_name="SPEND INPUT", index=False)
+
+
+def overview_test_data_prep_nonpanel(X, df, spends_X, date_col, target_col):
+    '''
+        function to create the data which is used in initialize data fn
+        X : X test with contributions
+        df : originally uploaded data (media data) which has raw vars
+        spends_X : spends of dates in X test
+    '''
+    # define channels
+    channels = {'paid_search': ['paid_search_impressions', 'paid_search_clicks'],
+
+                'fb_level_achieved_tier_1': ['fb_level_achieved_tier_1_impressions', 'fb_level_achieved_tier_1_clicks'],
+
+                'fb_level_achieved_tier_2': ['fb_level_achieved_tier_2_impressions',
+                                             'fb_level_achieved_tier_2_clicks'],
+
+                 'paid_social_others' : ['paid_social_others_impressions', 'paid_social_others_clicks'],
+
+                'ga_app_will_and_cid_pequena_baixo_risco': ['ga_app_will_and_cid_pequena_baixo_risco_impressions', 'ga_app_will_and_cid_pequena_baixo_risco_clicks'],
+
+                'digital_tactic_others': ['digital_tactic_others_impressions', 'digital_tactic_others_clicks'],
+
+                'kwai': ['kwai_impressions', 'kwai_clicks'],
+
+                'programmatic': ['programmatic_impressions', 'programmatic_clicks'],
+
+                 'affiliates':['affiliates_clicks', 'affiliates_impressions'],
+
+                 "indicacao":['indicacao_clicks', 'indicacao_impressions'],
+
+                 "infleux":['infleux_clicks', 'infleux_impressions'],
+
+                 "influencer":['influencer_clicks', 'influencer_impressions']
+                }
+
+    channel_list = list(channels.keys())
+
+    # map transformed variable to raw variable name & channel name
+    # mapping eg : paid_search_clicks_lag_2 (transformed var) --> paid_search_clicks (raw var) --> paid_search (channel)
+    variables = {}
+    channel_and_variables = {}
+    new_variables = {}
+    new_channels_and_variables = {}
+
+    cols_to_del = list(set([date_col, target_col, 'pred']).intersection((set(X.columns))))
+    for transformed_var in [col for col in
+                            X.drop(columns=cols_to_del).columns if
+                            "_contr" not in col]: # also has 'const'
+        if len([col for col in df.columns if col in transformed_var]) == 1: # col is raw var
+            raw_var = [col for col in df.columns if col in transformed_var][0]
+            variables[transformed_var] = raw_var
+            channel_and_variables[raw_var] = [channel for channel, raw_vars in channels.items() if raw_var in raw_vars][0]
+        else: # when no corresponding raw var then base
+            new_variables[transformed_var] = transformed_var
+            new_channels_and_variables[transformed_var] = 'base'
+
+    # Raw DF
+    raw_X = pd.merge(X[[date_col]], df[[date_col] + list(variables.values())], how='left',
+                     on=[date_col])
+    assert len(raw_X) == len(X)
+
+    raw_X_cols = []
+    for i in raw_X.columns:
+        if i in channel_and_variables.keys():
+            raw_X_cols.append(channel_and_variables[i])
+        else:
+            raw_X_cols.append(i)
+    raw_X.columns = raw_X_cols
+
+    # Contribution DF
+    contr_X = X[[date_col] + [col for col in X.columns if "_contr" in col and "sum_" not in col]].copy()
+    # st.write(contr_X.columns)
+    new_variables = [col for col in contr_X.columns if
+                     "_flag" in col.lower() or "trend" in col.lower() or "sine" in col.lower()]
+    if len(new_variables) > 0: # if new vars are available, their contributions should be added to base (called const)
+        contr_X['const_contr'] = contr_X[['const_contr'] + new_variables].sum(axis=1)
+        contr_X.drop(columns=new_variables, inplace=True)
+
+
+    new_contr_X_cols = []
+    for col in contr_X.columns:
+        col_clean = col.replace("_contr", "")
+        new_contr_X_cols.append(col_clean)
+    contr_X.columns = new_contr_X_cols
+
+    contr_X_cols = []
+    for i in contr_X.columns:
+        if i in variables.keys():
+            contr_X_cols.append(channel_and_variables[variables[i]])
+        else:
+            contr_X_cols.append(i)
+    contr_X.columns = contr_X_cols
+
+    # Spends DF
+    spends_X.columns = [col.replace("_cost", "").replace("_spends", '').replace("_spend", "") for col in spends_X.columns]
+
+    raw_X.rename(columns={"date": "Date"}, inplace=True)
+    contr_X.rename(columns={"date": "Date"}, inplace=True)
+    spends_X.rename(columns={'date': 'Week'}, inplace=True)
+
+    # Create excel
+    file_name = "data_test_overview_panel_#" + target_col + ".xlsx"
+    with pd.ExcelWriter(file_name) as writer:
+        raw_X.to_excel(writer, sheet_name="RAW DATA MMM", index=False)
+        contr_X.to_excel(writer, sheet_name="CONTRIBUTION MMM", index=False)
+        spends_X.to_excel(writer, sheet_name="SPEND INPUT", index=False)
+
+
+def initialize_data(target_col,selected_markets):
+    # uopx_conv_rates = {'streaming_impressions' : 0.007,'digital_impressions' : 0.007,'search_clicks' : 0.00719,'tv_impressions' : 0.000173,
+    #                    "digital_clicks":0.005,"streaming_clicks":0.004,'streaming_spends':1,"tv_spends":1,"search_spends":1,
+    #                    "digital_spends":1}
+    #print('State initialized')
+    # excel = pd.read_excel("data_test_overview_panel.xlsx",sheet_name=None)
+    #excel = pd.read_excel("Overview_data_test_panel@#revenue.xlsx" + target_col + ".xlsx",sheet_name=None)
+    
+    excel = pd.read_excel("Overview_data_test_panel@#revenue.xlsx",sheet_name=None)
+
+    raw_df = excel['RAW DATA MMM']
+
+    spend_df = excel['SPEND INPUT']
+    contri_df = excel['CONTRIBUTION MMM']
+    
+    #st.write(raw_df)
+    if selected_markets!= "Total Market":
+      
+      raw_df=raw_df[raw_df['Panel']==selected_markets]
+      spend_df=spend_df[spend_df['Panel']==selected_markets]
+      contri_df=contri_df[contri_df['Panel']==selected_markets]
+      
+    else:
+        raw_df=raw_df.groupby('Date').sum().reset_index()
+        spend_df=spend_df.groupby('Week').sum().reset_index()
+        contri_df=contri_df.groupby('Date').sum().reset_index()
+    #Revenue_df = excel['Revenue']
+    
+    ## remove sesonalities, indices etc ... 
+    exclude_columns = ['Date', 'Week','Panel',date_col, panel_col,'Others'
+                  ] 
+
+    # Aggregate all 3 dfs to date level (from date-panel level)
+    raw_df[date_col]=pd.to_datetime(raw_df[date_col])
+    raw_df_aggregations = {c:'sum' for c in raw_df.columns if c not in exclude_columns}
+    raw_df = raw_df.groupby(date_col).agg(raw_df_aggregations).reset_index()
+
+    contri_df[date_col]=pd.to_datetime(contri_df[date_col])
+    contri_df_aggregations = {c:'sum' for c in contri_df.columns if c not in exclude_columns}
+    contri_df = contri_df.groupby(date_col).agg(contri_df_aggregations).reset_index()
+
+    input_df = raw_df.sort_values(by=[date_col])
+
+    output_df = contri_df.sort_values(by=[date_col])
+
+    spend_df['Week'] = pd.to_datetime(spend_df['Week'], format='%Y-%m-%d', errors='coerce')
+    spend_df_aggregations = {c: 'sum' for c in spend_df.columns if c not in exclude_columns}
+    spend_df = spend_df.groupby('Week').agg(spend_df_aggregations).reset_index()
+    # spend_df['Week'] = pd.to_datetime(spend_df['Week'], errors='coerce')
+    # spend_df = spend_df.sort_values(by='Week')
+    
+
+    channel_list = [col for col in input_df.columns if col not in exclude_columns]
+    
+    response_curves = {}
+    mapes = {}
+    rmses = {}
+    upper_limits = {}
+    powers = {}
+    r2 = {}
+    conv_rates = {}
+    output_cols = []
+    channels = {}
+    sales = None
+    dates = input_df.Date.values
+    actual_output_dic = {}
+    actual_input_dic = {}
+
+    # ONLY FOR TESTING
+    # channel_list=['programmatic']
+    infeasible_channels = [c for c in contri_df.select_dtypes(include=['float', 'int']).columns if contri_df[c].sum()<=0]
+    # st.write(infeasible_channels)
+    channel_list=list(set(channel_list)-set(infeasible_channels))
+
+
+    for inp_col in channel_list:
+        #st.write(inp_col)
+
+        # # New - Sprint 2
+        # if is_panel:
+        #     input_df1 = input_df.groupby([date_col]).agg({inp_col:'sum'}).reset_index() # aggregate spends on date
+        #     spends = input_df1[inp_col].values
+        # else :
+        #     spends = input_df[inp_col].values
+        spends = spend_df[inp_col].values
+
+        x = spends.copy()
+        # upper limit for penalty   
+        upper_limits[inp_col] = 2*x.max()
+
+
+
+        # contribution
+        # New - Sprint 2
+        out_col = [_col for _col in output_df.columns if _col.startswith(inp_col)][0]
+        if is_panel :
+            output_df1 = output_df.groupby([date_col]).agg({out_col:'sum'}).reset_index()
+            y = output_df1[out_col].values.copy()
+        else :
+            y = output_df[out_col].values.copy()
+
+        actual_output_dic[inp_col] = y.copy()
+        actual_input_dic[inp_col] = x.copy()
+        ##output cols aggregation
+        output_cols.append(out_col)
+        
+        ## scale the input
+        power = (np.ceil(np.log(x.max()) / np.log(10) )- 3)
+        if power >= 0 :
+            x = x / 10**power
+        
+            
+        x = x.astype('float64')
+        y = y.astype('float64')
+        #print('#printing yyyyyyyyy')
+        #print(inp_col)
+        #print(x.max())
+        #print(y.max())
+        # st.write(y.max(),x.max())
+        print(y.max(),x.max())
+        if y.max()<=0.01:
+            if x.max()<=0.01 :
+                st.write("here-here")
+                bounds = ((0, 0, 0, 0), (3 * 0.01, 1000, 1, 0.01))
+
+            else :
+                st.write("here")
+                bounds = ((0, 0, 0, 0), (3 * 0.01, 1000, 1, 0.01))
+        else :
+            bounds = ((0, 0, 0, 0), (3 * y.max(), 1000, 1, x.max()))
+        #bounds = ((y.max(), 3*y.max()),(0,1000),(0,1),(0,x.max()))
+        params,_ = curve_fit(s_curve,x,y,p0=(2*y.max(),0.01,1e-5,x.max()),
+                                bounds=bounds,
+                                maxfev=int(1e5))
+        mape = (100 * abs(1 - s_curve(x, *params) / y.clip(min=1))).mean()
+        rmse =  np.sqrt(((y - s_curve(x,*params))**2).mean())
+        r2_ = r2_score(y, s_curve(x,*params))
+
+        response_curves[inp_col] = {'K' : params[0], 'b' : params[1], 'a' : params[2], 'x0' : params[3]}
+        mapes[inp_col] = mape
+        rmses[inp_col] = rmse
+        r2[inp_col] = r2_
+        powers[inp_col] = power
+        
+        
+        ## conversion rates
+        spend_col = [_col for _col in spend_df.columns if _col.startswith(inp_col.rsplit('_',1)[0])][0]
+
+        #print('#printing spendssss')
+        #print(spend_col)
+        conv = (spend_df.set_index('Week')[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
+        # st.write(inp_col, channel.actual_sales)
+    # st.write(output_cols)
+    other_contributions = output_df.drop([*output_cols], axis=1).sum(axis=1, numeric_only = True).values
+    correction = output_df.drop(['Date'],axis=1).sum(axis=1).values - (sales + other_contributions)
+
+    scenario_test_df=pd.DataFrame(columns=['other_contributions','correction', 'sales'])
+    scenario_test_df['other_contributions']=other_contributions
+    scenario_test_df['correction']=correction
+    scenario_test_df['sales']=sales
+    scenario_test_df.to_csv("test/scenario_test_df.csv",index=False)
+    output_df.to_csv("test/output_df.csv",index=False)
+
+    scenario = Scenario(name='default', channels=channels, constant=other_contributions, correction = correction)
+    ## setting session variables
+    st.session_state['initialized'] = True
+    st.session_state['actual_df'] = input_df
+    st.session_state['raw_df'] = raw_df
+    st.session_state['contri_df'] = output_df
+    default_scenario_dict = class_to_dict(scenario)
+    st.session_state['default_scenario_dict'] = default_scenario_dict
+    st.session_state['scenario'] = scenario
+    st.session_state['channels_list'] = channel_list
+    st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+    st.session_state['rcs'] = response_curves
+    st.session_state['powers'] = powers
+    st.session_state['actual_contribution_df'] = pd.DataFrame(actual_output_dic)
+    st.session_state['actual_input_df'] = pd.DataFrame(actual_input_dic)
+    
+    for channel in channels.values():
+        st.session_state[channel.name] = numerize(channel.actual_total_spends * channel.conversion_rate,1)
+    
+    st.session_state['xlsx_buffer'] = io.BytesIO()
+
+    
+    if Path('../saved_scenarios.pkl').exists():
+        with open('../saved_scenarios.pkl','rb') as f:
+            st.session_state['saved_scenarios'] = pickle.load(f)
+    else:
+        st.session_state['saved_scenarios'] = OrderedDict()
+        
+    st.session_state['total_spends_change'] = 0
+    st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+    st.session_state['disable_download_button'] = True
+    
+# def initialize_data():
+#     # fetch data from excel
+#     output = pd.read_excel('data.xlsx',sheet_name=None)
+#     raw_df = output['RAW DATA MMM']
+#     contribution_df = output['CONTRIBUTION MMM']
+#     Revenue_df = output['Revenue']
+
+#     ## channels to be shows
+#     channel_list = []
+#     for col in raw_df.columns:
+#         if 'click' in col.lower() or 'spend' in col.lower() or 'imp' in col.lower():
+#             ##print(col)
+#             channel_list.append(col)
+#         else:
+#             pass
+    
+#     ## NOTE : Considered only Desktop spends for all calculations
+#     acutal_df = raw_df[raw_df.Region == 'Desktop'].copy()
+#     ## NOTE : Considered one year of data
+#     acutal_df = acutal_df[acutal_df.Date>'2020-12-31']
+#     actual_df = acutal_df.drop('Region',axis=1).sort_values(by='Date')[[*channel_list,'Date']]
+    
+#     ##load response curves
+#     with open('./grammarly_response_curves.json','r') as f:
+#         response_curves = json.load(f)
+        
+#     ## create channel dict for scenario creation
+#     dates = actual_df.Date.values
+#     channels = {}
+#     rcs = {}
+#     constant = 0.
+#     for i,info_dict in enumerate(response_curves):
+#         name = info_dict.get('name')
+#         response_curve_type = info_dict.get('response_curve')
+#         response_curve_params = info_dict.get('params')
+#         rcs[name] = response_curve_params
+#         if name != 'constant':
+#             spends = actual_df[name].values
+#             channel = Channel(name=name,dates=dates,
+#                             spends=spends,
+#                             response_curve_type=response_curve_type,
+#                             response_curve_params=response_curve_params,
+#                             bounds=np.array([-30,30]))
+            
+#             channels[name] = channel
+#         else:
+#             constant = info_dict.get('value',0.) * len(dates)
+            
+#     ## create scenario
+#     scenario = Scenario(name='default', channels=channels, constant=constant)
+#     default_scenario_dict = class_to_dict(scenario)
+    
+
+#     ## setting session variables
+#     st.session_state['initialized'] = True
+#     st.session_state['actual_df'] = actual_df
+#     st.session_state['raw_df'] = raw_df
+#     st.session_state['default_scenario_dict'] = default_scenario_dict
+#     st.session_state['scenario'] = scenario
+#     st.session_state['channels_list'] = channel_list
+#     st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+#     st.session_state['rcs'] = rcs
+#     for channel in channels.values():
+#         if channel.name not in st.session_state:
+#             st.session_state[channel.name] = float(channel.actual_total_spends)
+    
+#     if 'xlsx_buffer' not in st.session_state:
+#         st.session_state['xlsx_buffer'] = io.BytesIO()
+
+#     ## for saving scenarios
+#     if 'saved_scenarios' not in st.session_state:
+#         if Path('../saved_scenarios.pkl').exists():
+#             with open('../saved_scenarios.pkl','rb') as f:
+#                 st.session_state['saved_scenarios'] = pickle.load(f)
+        
+#         else:
+#             st.session_state['saved_scenarios'] = OrderedDict()
+
+#     if 'total_spends_change' not in st.session_state:
+#         st.session_state['total_spends_change'] = 0
+        
+#     if 'optimization_channels' not in st.session_state:
+#         st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
+    
+#     if 'disable_download_button' not in st.session_state:
+#         st.session_state['disable_download_button'] = True
+def create_channel_summary(scenario):
+    summary_columns = []
+
+    actual_spends_rows = []
+
+    actual_sales_rows = []
+
+    actual_roi_rows = []
+
+    for channel in scenario.channels.values():
+
+        name_mod = channel.name.replace('_', ' ')
+
+        if name_mod.lower().endswith(' imp'):
+            name_mod = name_mod.replace('Imp', ' Impressions')
+
+        print(name_mod, channel.actual_total_spends, channel.conversion_rate,
+              channel.actual_total_spends * channel.conversion_rate)
+
+        summary_columns.append(name_mod)
+
+        actual_spends_rows.append(format_numbers(float(channel.actual_total_spends * channel.conversion_rate)))
+
+        actual_sales_rows.append(format_numbers((float(channel.actual_total_sales))))
+
+        actual_roi_rows.append(decimal_formater(
+            format_numbers((channel.actual_total_sales) / (channel.actual_total_spends * channel.conversion_rate),
+                           include_indicator=False, n_decimals=4), n_decimals=4))
+
+    actual_summary_df = pd.DataFrame([summary_columns, actual_spends_rows, actual_sales_rows, actual_roi_rows]).T
+
+    actual_summary_df.columns = ['Channel', 'Spends', 'Revenue', 'ROI']
+
+    actual_summary_df['Revenue'] = actual_summary_df['Revenue'].map(lambda x: str(x)[1:])
+
+    return actual_summary_df
+
+
+# def create_channel_summary(scenario):
+#
+#     # Provided data
+#     data = {
+#         'Channel': ['Paid Search', 'Ga will cid baixo risco', 'Digital tactic others', 'Fb la tier 1', 'Fb la tier 2', 'Paid social others', 'Programmatic', 'Kwai', 'Indicacao', 'Infleux', 'Influencer'],
+#         'Spends': ['$ 11.3K', '$ 155.2K', '$ 50.7K', '$ 125.4K', '$ 125.2K', '$ 105K', '$ 3.3M', '$ 47.5K', '$ 55.9K', '$ 632.3K', '$ 48.3K'],
+#         'Revenue': ['558.0K', '3.5M', '5.2M', '3.1M', '3.1M', '2.1M', '20.8M', '1.6M', '728.4K', '22.9M', '4.8M']
+#     }
+#
+#     # Create DataFrame
+#     df = pd.DataFrame(data)
+#
+#     # Convert currency strings to numeric values
+#     df['Spends'] = df['Spends'].replace({'\$': '', 'K': '*1e3', 'M': '*1e6'}, regex=True).map(pd.eval).astype(int)
+#     df['Revenue'] = df['Revenue'].replace({'\$': '', 'K': '*1e3', 'M': '*1e6'}, regex=True).map(pd.eval).astype(int)
+#
+#     # Calculate ROI
+#     df['ROI'] = ((df['Revenue'] - df['Spends']) / df['Spends'])
+#
+#     # Format columns
+#     format_currency = lambda x: f"${x:,.1f}"
+#     format_roi = lambda x: f"{x:.1f}"
+#
+#     df['Spends'] = ['$ 11.3K', '$ 155.2K', '$ 50.7K', '$ 125.4K', '$ 125.2K', '$ 105K', '$ 3.3M', '$ 47.5K', '$ 55.9K', '$ 632.3K', '$ 48.3K']
+#     df['Revenue'] =  ['$ 536.3K', '$ 3.4M', '$ 5M', '$ 3M', '$ 3M', '$ 2M', '$ 20M', '$ 1.5M', '$ 7.1M', '$ 22M', '$ 4.6M']
+#     df['ROI'] = df['ROI'].apply(format_roi)
+#
+#     return df
+
+
+#@st.cache_data()
+def create_contribution_pie(scenario):
+    #c1f7dc
+    
+    light_blue = 'rgba(0, 31, 120, 0.7)'
+    light_orange = 'rgba(0, 181, 219, 0.7)'
+    light_green = 'rgba(240, 61, 20, 0.7)'
+    light_red = 'rgba(250, 110, 10, 0.7)'
+    light_purple = 'rgba(255, 191, 69, 0.7)'
+
+    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=['Media Spends','Revenue Contribution'],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_colors=[light_blue, light_orange, light_green, light_red, light_purple],
+                        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='', uniformtext_mode='hide')
+    return total_contribution_fig
+
+#@st.cache_data()
+
+# 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:$.2s}"), row=1, col=2)
+#     weekly_contribution_fig.update_layout(barmode='stack', title='Channel contribuion by week', xaxis_title='Date')
+#     weekly_contribution_fig.update_xaxes(showgrid=False)
+#     weekly_contribution_fig.update_yaxes(showgrid=False)
+#     return weekly_contribution_fig
+
+# @st.cache_data(allow_output_mutation=True)
+# def create_channel_spends_sales_plot(channel):
+#     if channel is not None:
+#         x = channel.dates
+#         _spends = channel.actual_spends * channel.conversion_rate
+#         _sales = channel.actual_sales
+#         channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
+#         channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}<br>Revenue:%{y:$.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[1],  # You can choose a color from the palette
+            name='Revenue',
+            hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
+        ), secondary_y=False)
+        
+        channel_sales_spends_fig.add_trace(go.Scatter(
+            x=x,
+            y=_spends,
+            line=dict(color=color_palette[3]),  # You can choose another color from the palette
+            name='Spends',
+            hovertemplate="Date:%{x}<br>Spend:%{y:$.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("geethu4444@gmail.com", "jgydhpfusuremcol")
+    s.sendmail("geethu4444@gmail.com", email, message)
+    s.quit()
+
+if __name__ == "__main__":
+    initialize_data()