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,timedelta
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
with st.expander("View Channel Wise Spend And Prospect Analysis "):
# Create two columns for start date and end date input
col1, col2 = st.columns(2)
min_date,max_date = sf.get_date_range()
# st.write(min_date,max_date)
# min_date = datetime(2023, 1, 1)
# max_date = datetime(2024, 12, 31)
default_date1,default_date2 = sf.get_default_dates()
# st.write(default_date1,default_date2)
with col1:
start_date = st.date_input("Start Date: ",value=default_date1,min_value=min_date,
max_value=max_date)
with col2:
end_date = st.date_input("End Date: ",value = default_date2,min_value=min_date,
max_value=max_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)
## 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)
shares_df = sf.shares_df_func(start_date,end_date)
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)
data_selection_type = st.radio("Select Input Type",["Compare Monthly Change", "Compare Custom Range"])
waterfall_start_date,waterfall_end_date = start_date,end_date
with st.expander("View Change in MMM Estimated Prospect Contributions Analysis"):
# Dropdown menu options
st.markdown("Change in MMM Estimated Prospect Contributions
", unsafe_allow_html=True)
if data_selection_type == "Compare Monthly Change":
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
def get_month_year_list(start_date, end_date):
# Generate a range of dates from start_date to end_date with a monthly frequency
dates = pd.date_range(start=start_date, end=end_date, freq='MS') # 'MS' is month start frequency
# Extract month and year from each date and create a list of tuples
month_year_list = [(date.month, date.year) for date in dates]
return month_year_list
def get_start_end_dates(month, year):
start_date = datetime(year, month, 1).date()
if month == 12:
end_date = datetime(year + 1, 1, 1).date() - timedelta(days=1)
else:
end_date = datetime(year, month + 1, 1).date() - timedelta(days=1)
return start_date, end_date
month_year_list = get_month_year_list(start_date, end_date)
dropdown_options = [f"{date.strftime('%B %Y')}" for date in pd.date_range(start=start_date, end=end_date, freq='MS')]
waterfall_option = st.selectbox("Select a month:", dropdown_options)
waterfall_date = datetime.strptime(waterfall_option, "%B %Y")
waterfall_month = waterfall_date.month
waterfall_year = waterfall_date.year
waterfall_start_date, waterfall_end_date = get_start_end_dates(waterfall_month, waterfall_year)
# st.write("abc")
# figw = sf.waterfall(waterfall_start_date,waterfall_end_date)
figw= sf.waterfall(waterfall_start_date,waterfall_end_date,selected_option)
st.plotly_chart(figw,use_container_width=True)
elif data_selection_type == "Compare Custom Range":
col1, col2 = st.columns(2)
min_date,max_date = sf.get_date_range()
with col1:
st.write("Select Time Period 1")
sc1,sc2 = st.columns(2)
with sc1:
waterfall_start_date1 = st.date_input("Start Date 1: ",value=start_date,min_value=min_date,
max_value=max_date)
with sc2:
waterfall_end_date1 = st.date_input("End Date 1: ",value = end_date,min_value=min_date,
max_value=max_date)
with col2:
st.write("Select Time Period 2")
ec1,ec2 = st.columns(2)
with ec1:
waterfall_start_date2 = st.date_input("Start Date 2: ",value=end_date-timedelta(days = -1),min_value=min_date,
max_value=max_date)
with ec2:
diff = min((start_date-end_date).days,-30)
waterfall_end_date2 = st.date_input("End Date 2: ",value = start_date,min_value=min_date,
max_value=max_date)
try:
figw= sf.waterfall2(waterfall_start_date1,waterfall_end_date1,waterfall_start_date2,waterfall_end_date2)
st.plotly_chart(figw,use_container_width=True)
except:
st.warning("Previous data does not exist")
# Waterfall table
# shares_df = sf.shares_df_func(waterfall_start_date,waterfall_end_date)
st.table(sf.waterfall_table_func(shares_df).style.format("{:.0%}"))
with st.expander("View Decomposition Analysis"):
### 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)