Model_Result_Overview.py

'''
MMO Build Sprint 3
additions : contributions calculated using tuned Mixed LM model
pending : contributions calculations using - 1. not tuned Mixed LM model, 2. tuned OLS model, 3. not tuned OLS model

MMO Build Sprint 4
additions : response metrics selection
pending : contributions calculations using - 1. not tuned Mixed LM model, 2. tuned OLS model, 3. not tuned OLS model
'''

import streamlit as st
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import pickle

from utilities import load_authenticator

from utilities_with_panel import (set_header,
                                  overview_test_data_prep_panel,
                                  overview_test_data_prep_nonpanel,
                                  initialize_data,
                                  load_local_css,
                                  create_channel_summary,
                                  create_contribution_pie,
                                  create_contribuion_stacked_plot,
                                  create_channel_spends_sales_plot,
                                  format_numbers,
                                  channel_name_formating)

import plotly.graph_objects as go
import streamlit_authenticator as stauth
import yaml
from yaml import SafeLoader
import time

st.set_page_config(layout='wide')
load_local_css('styles.css')
set_header()


# def get_random_effects(media_data, panel_col, mdf):
#     random_eff_df = pd.DataFrame(columns=[panel_col, "random_effect"])

#     for i, market in enumerate(media_data[panel_col].unique()):
#         print(i, end='\r')
#         intercept = mdf.random_effects[market].values[0]
#         random_eff_df.loc[i, 'random_effect'] = intercept
#         random_eff_df.loc[i, panel_col] = market

#     return random_eff_df


# def process_train_and_test(train, test, features, panel_col, target_col):
#     X1 = train[features]

#     ss = MinMaxScaler()
#     X1 = pd.DataFrame(ss.fit_transform(X1), columns=X1.columns)

#     X1[panel_col] = train[panel_col]
#     X1[target_col] = train[target_col]

#     if test is not None:
#         X2 = test[features]
#         X2 = pd.DataFrame(ss.transform(X2), columns=X2.columns)
#         X2[panel_col] = test[panel_col]
#         X2[target_col] = test[target_col]
#         return X1, X2
#     return X1

# def mdf_predict(X_df, mdf, random_eff_df) :
#     X=X_df.copy()
#     X=pd.merge(X, random_eff_df[[panel_col,'random_effect']], on=panel_col, how='left')
#     X['pred_fixed_effect'] = mdf.predict(X)

#     X['pred'] = X['pred_fixed_effect'] + X['random_effect']
#     X.to_csv('Test/merged_df_contri.csv',index=False)
#     X.drop(columns=['pred_fixed_effect', 'random_effect'], inplace=True)

#     return X


# target_col='Revenue'
# target='Revenue'

# # is_panel=False
# # is_panel = st.session_state['is_panel']
# #panel_col = [col.lower().replace('.','_').replace('@','_').replace(" ", "_").replace('-', '').replace(':', '').replace("__", "_") for col in  st.session_state['bin_dict']['Panel Level 1']  ] [0]# set the panel column
# panel_col='Panel'
# date_col = 'date'

# #st.write(media_data)

# is_panel = True 

# # panel_col='markets'
# date_col = 'date'
# for k, v in st.session_state.items():

#     if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
#         st.session_state[k] = v

# authenticator = st.session_state.get('authenticator')

# if authenticator is None:
#     authenticator = load_authenticator()
    
# name, authentication_status, username = authenticator.login('Login', 'main')
# auth_status = st.session_state['authentication_status']

# if auth_status:
#     authenticator.logout('Logout', 'main')
    
#     is_state_initiaized = st.session_state.get('initialized',False)
#     if not is_state_initiaized:
#         a=1

#     def panel_fetch(file_selected):
#         raw_data_mmm_df = pd.read_excel(file_selected, sheet_name="RAW DATA MMM")

#         if "Panel" in raw_data_mmm_df.columns:
#             panel = list(set(raw_data_mmm_df["Panel"]))
#         else:
#             raw_data_mmm_df = None
#             panel = None

#         return panel

#     def rerun():
#         st.rerun()

#     metrics_selected='revenue'

#     file_selected = (
#             f"Overview_data_test_panel@#{metrics_selected}.xlsx"
#         )
#     panel_list = panel_fetch(file_selected)

#     if "selected_markets" not in st.session_state:
#         st.session_state['selected_markets']='DMA1'


#     st.header('Overview of previous spends')

#     selected_market= st.selectbox(
#             "Select Markets",
#             ["Total Market"] + panel_list
#         )



#     initialize_data(target_col,selected_market)
#     scenario = st.session_state['scenario']
#     raw_df = st.session_state['raw_df']
#     # st.write(scenario.actual_total_spends)
#     # st.write(scenario.actual_total_sales)
#     columns = st.columns((1,1,3))

#     with columns[0]:
#         st.metric(label='Spends', value=format_numbers(float(scenario.actual_total_spends)))
#     ###print(f"##################### {scenario.actual_total_sales} ##################")
#     with columns[1]:
#         st.metric(label=target, value=format_numbers(float(scenario.actual_total_sales),include_indicator=False))


#     actual_summary_df = create_channel_summary(scenario)
#     actual_summary_df['Channel'] = actual_summary_df['Channel'].apply(channel_name_formating) 

#     columns = st.columns((2,1))
#     #with columns[0]:
#     with st.expander('Channel wise overview'):
#         st.markdown(actual_summary_df.style.set_table_styles(
#         [{
#             'selector': 'th',
#             'props': [('background-color', '#FFFFF')]
#         },
#             {
#             'selector' : 'tr:nth-child(even)',
#             'props' : [('background-color', '#FFFFF')]
#             }]).to_html(), unsafe_allow_html=True)
            
#     st.markdown("<hr>",unsafe_allow_html=True)
#     ##############################

#     st.plotly_chart(create_contribution_pie(scenario),use_container_width=True)
#     st.markdown("<hr>",unsafe_allow_html=True)


#     ################################3
#     st.plotly_chart(create_contribuion_stacked_plot(scenario),use_container_width=True)
#     st.markdown("<hr>",unsafe_allow_html=True)
#     #######################################

#     selected_channel_name = st.selectbox('Channel', st.session_state['channels_list'] + ['non media'], format_func=channel_name_formating)
#     selected_channel = scenario.channels.get(selected_channel_name,None)

#     st.plotly_chart(create_channel_spends_sales_plot(selected_channel), use_container_width=True)

#     st.markdown("<hr>",unsafe_allow_html=True)

# # elif auth_status == False:
# #     st.error('Username/Password is incorrect')
    
# # if auth_status != True:
# #     try:
# #         username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
# #         if username_forgot_pw:
# #             st.success('New password sent securely')
# #             # Random password to be transferred to user securely
# #         elif username_forgot_pw == False:
# #             st.error('Username not found')
# #     except Exception as e:
# #         st.error(e)