import streamlit as st import pandas as pd from sklearn.preprocessing import MinMaxScaler import pickle import Streamlit_functions as sf 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 from datetime import datetime st.set_page_config(layout='wide') load_local_css('styles.css') set_header() st.title("Model Result Overview") 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='Prospects' target='Prospects' # 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 # st.header("") # st.markdown("
MMM Readout for Selected Period
", unsafe_allow_html=True) #### Input Select Start and End Date # Create two columns for start date and end date input col1, col2 = st.columns(2) # now = datetime.now() # us_format = now.strftime("%m/%d/%Y") with col1: default_date = datetime(2024, 1, 28) start_date = st.date_input("Start Date: ",value=default_date) with col2: default_date = datetime(2024, 2, 24) end_date = st.date_input("End Date: ",value = default_date) # col1, col2 = st.columns(2) # with col1: # fig = sf.pie_spend(start_date,end_date) # st.plotly_chart(fig,use_container_width=True) # with col2: # fig = sf.pie_contributions(start_date,end_date) # st.plotly_chart(fig,use_container_width=True) # st.header("Distribution of Spends and Contributions") fig = sf.pie_charts(start_date,end_date) st.plotly_chart(fig,use_container_width=True) # Dropdown menu options options = [ "Month on Month", "Year on Year"] col1, col2 = st.columns(2) # Create a dropdown menu with col1: selected_option = st.selectbox('Select a comparison', options) with col2: st.markdown("""
""",unsafe_allow_html=True) if selected_option == "Month on Month" : st.markdown( f"""
Comparision of current month spends to previous month spends
""", unsafe_allow_html=True ) else : st.markdown( f"""
Comparision of current month spends to the same month in previous year
""", unsafe_allow_html=True ) # Waterfall chart fig = sf.waterfall(start_date,end_date,selected_option) st.plotly_chart(fig,use_container_width=True) # Waterfall table shares_df = sf.shares_df_func(start_date,end_date) st.table(sf.waterfall_table_func(shares_df).style.format("{:.0%}")) ## Channel Contribution Bar Chart st.plotly_chart(sf.channel_contribution(start_date,end_date),use_container_width=True) st.plotly_chart(sf.chanel_spends(start_date,end_date),use_container_width=True) # Format first three rows in percentage format # styled_df = sf.shares_table_func(shares_df) # # styled_df = styled_df.round(0).astype(int) # styled_df.iloc[:3] = (styled_df.iloc[:3]).astype(int) # # Round next two rows to two decimal places # styled_df.iloc[3:5] = styled_df.iloc[3:5].round(0).astype(str) # st.table(styled_df) st.dataframe(sf.shares_table_func(shares_df),use_container_width=True) st.dataframe(sf.eff_table_func(shares_df).style.format({"TOTAL SPEND": "{:,.0f}", "TOTAL SUPPORT": "{:,.0f}", "TOTAL CONTRIBUTION": "{:,.0f}"}),use_container_width=True) ### CPP CHART st.plotly_chart(sf.cpp(start_date,end_date),use_container_width=True) ### Base decomp CHART st.plotly_chart(sf.base_decomp(),use_container_width=True) ### Media decomp CHART st.plotly_chart(sf.media_decomp(),use_container_width=True) # st.write(fig.columns) # st.dataframe(fig) # 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='prospects' # 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("
",unsafe_allow_html=True) # ############################## # st.plotly_chart(create_contribution_pie(scenario),use_container_width=True) # st.markdown("
",unsafe_allow_html=True) # ################################3 # st.plotly_chart(create_contribuion_stacked_plot(scenario),use_container_width=True) # st.markdown("
",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("
",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) # st.header("") # st.markdown("
MMM Readout for Selected Period
", unsafe_allow_html=True) # #### Input Select Start and End Date # # Create two columns for start date and end date input # col1, col2 = st.columns(2) # with col1: # start_date = st.date_input("Start Date: ") # with col2: # end_date = st.date_input("End Date: ") # # Dropdown menu options # options = [ # "Month on Month", # "Year on Year"] # col1, col2 = st.columns(2) # # Create a dropdown menu # with col1: # selected_option = st.selectbox('Select a comparison', options) # with col2: # st.write("") # # Waterfall chart # fig = sf.waterfall(start_date,end_date,selected_option) # st.plotly_chart(fig) # # Waterfall table # shares_df = sf.shares_df_func(start_date,end_date) # st.table(sf.waterfall_table_func(shares_df).style.format("{:.0%}")) # ## Channel Contribution Bar Chart # st.plotly_chart(sf.channel_contribution(start_date,end_date)) # # Format first three rows in percentage format # # styled_df = sf.shares_table_func(shares_df) # # # styled_df = styled_df.round(0).astype(int) # # styled_df.iloc[:3] = (styled_df.iloc[:3]).astype(int) # # # Round next two rows to two decimal places # # styled_df.iloc[3:5] = styled_df.iloc[3:5].round(0).astype(str) # # st.table(styled_df) # st.dataframe(sf.shares_table_func(shares_df)) # st.dataframe(sf.eff_table_func(shares_df)) # ### CPP CHART # st.plotly_chart(sf.cpp(start_date,end_date)) # ### Base decomp CHART # st.plotly_chart(sf.base_decomp()) # ### Media decomp CHART # st.plotly_chart(sf.media_decomp())