Model_Result_Overview.py

# import subprocess
# import sys

# def install_conda_package(package_name, channel=None):
#     try:
#         if channel:
#             subprocess.check_call([sys.executable, "-m", "conda", "install", "-c", channel, package_name, "-y"])
#         else:
#             subprocess.check_call([sys.executable, "-m", "conda", "install", package_name, "-y"])
#     except subprocess.CalledProcessError as e:
#         # print(f"Failed to install {package_name}: {e}")

# # Example usage
# install_conda_package("plotly-orca", channel="plotly")

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
from pptx import Presentation
from pptx.util import Inches
from io import BytesIO
import plotly.io as pio
import response_curves_model_quality as rc1
st.set_page_config(layout='wide')
load_local_css('styles.css')
set_header()

st.title("Model Result Overview")
def add_plotly_chart_to_slide(slide, fig, left, top, width, height):
    img_stream = BytesIO()
    pio.write_image(fig, img_stream, format='png')
    slide.shapes.add_picture(img_stream, left, top, width, height)



def save_table(df,prs):
    # Add a blank slide
    slide = prs.slides.add_slide(prs.slide_layouts[6])
    
    rows, cols = df.shape[0] + 1, df.shape[1]  # +1 for the header row
    table = slide.shapes.add_table(rows, cols, Inches(1), Inches(1), Inches(10), Inches(7)).table

    # Set the header row
    for col_idx, col_name in enumerate(df.columns):
        table.cell(0, col_idx).text = col_name

    # Add the DataFrame rows to the table
    for row_idx, row in df.iterrows():
        for col_idx, value in enumerate(row):
            # # # print(value)
            if isinstance(value, int):
                table.cell(row_idx + 1, col_idx).text = str(value)


def save_ppt_file(fig1,fig2,fig3,fig4,fig6,fig7,figw,start_date,end_date,shares_df1,shares_df2):
    # Initialize PowerPoint presentation
    prs = Presentation()

    # save_table(shares_df1,prs)
    # save_table(shares_df2,prs)  
    # Slide 1: Model Quality with Chart
    slide_1 = prs.slides.add_slide(prs.slide_layouts[6])
    # title_1 = slide_1.shapes.title
    # title_1.text = "Distribution Of Spends And Revenue"
    # Add the Plotly chart to the slide
    add_plotly_chart_to_slide(slide_1, sf.pie_contributions(start_date,end_date),  Inches(0.25), Inches(0.25), width=Inches(9.25), height=Inches(6.75))
    add_plotly_chart_to_slide(prs.slides.add_slide(prs.slide_layouts[6]), sf.pie_spend(start_date,end_date),  Inches(0.25), Inches(0.25), width=Inches(9.25), height=Inches(6.75))
    # Slide 2: Media Data Elasticity
    slide_2 = prs.slides.add_slide(prs.slide_layouts[6])
    # title_2 = slide_2.shapes.title
    # title_2.text = "Media Contribution"
    add_plotly_chart_to_slide(slide_2, fig2,  Inches(0.25), Inches(0.25), width=Inches(9.25), height=Inches(6.75))
    slide_3 = prs.slides.add_slide(prs.slide_layouts[6])
    # title_3 = slide_3.shapes.title
    # title_3.text = "Media Spends"
    add_plotly_chart_to_slide(slide_3, fig3,  Inches(0.25), Inches(0.25), width=Inches(9.25), height=Inches(6.75))
    slide_4 = prs.slides.add_slide(prs.slide_layouts[6])
    # title_4 = slide_4.shapes.title
    # title_4.text = "CPP Distribution"
    add_plotly_chart_to_slide(slide_4, fig4,  Inches(0.25), Inches(0.25), width=Inches(9.25), height=Inches(6.75))

    if figw != None:
        slide_5 = prs.slides.add_slide(prs.slide_layouts[6])
        # title_5 = slide_5.shapes.title
        # title_5.text = "Change in MMM Estimated Revenue Contributions"
        figw.update_layout(
        # title="Distribution Of Spends"
        title={
                    'text': "Change In MMM Estimated Revenue Contribution",
                    'font': {
                    'size': 24,
                    'family': 'Arial',
                    'color': 'black',
                    # 'bold': True
                }
            }
            
    )
        add_plotly_chart_to_slide(slide_5, figw, Inches(0.25), Inches(0.25), width=Inches(9.25), height=Inches(6.75))
    else :
        slide_5 = prs.slides.add_slide(prs.slide_layouts[5])
        title_5 = slide_5.shapes.title
        title_5.text = "Change in MMM Estimated Revenue Contributions"

    slide_6 = prs.slides.add_slide(prs.slide_layouts[6])
    # title_6 = slide_6.shapes.title
    # title_6.text = "Base Decomposition"
    add_plotly_chart_to_slide(slide_6, fig6, Inches(0.25), Inches(0.25), width=Inches(9.25), height=Inches(6.75))

    slide_7 = prs.slides.add_slide(prs.slide_layouts[6])
    # title_7 = slide_7.shapes.title
    # title_7.text = "Media Decomposition"
    add_plotly_chart_to_slide(slide_7, fig7,  Inches(0.25), Inches(0.25), width=Inches(9.25), height=Inches(6.75))

    




    # prs.save('MMM_Model_Result Overview.pptx')

    # # print("PowerPoint slides created successfully.")

    # Save to a BytesIO object
    ppt_stream = BytesIO()
    prs.save(ppt_stream)
    ppt_stream.seek(0)
    
    return ppt_stream.getvalue()

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 Revenue 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")
        fig1 = sf.pie_charts(start_date,end_date)
        st.plotly_chart(fig1,use_container_width=True)

         ## Channel Contribution Bar Chart
        fig2 =sf.channel_contribution(start_date,end_date) 
        st.plotly_chart(fig2,use_container_width=True)
        fig3 = sf.chanel_spends(start_date,end_date)
        st.plotly_chart(fig3,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)
        shares_df1 = sf.shares_table_func(shares_df)

        if "Effectiveness" in shares_df1.index:
            shares_df1 = shares_df1.drop(index="Effectiveness")

        st.dataframe(shares_df1,use_container_width=True)
        shares_df2 = sf.eff_table_func(shares_df)
        # 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
        fig4 = sf.cpp(start_date,end_date)
        st.plotly_chart(fig4,use_container_width=True)
    
    with st.expander("View Change in MMM Estimated Revenue Contributions Analysis"):
        data_selection_type = st.radio("Select Input Type",["Compare Monthly Change", "Compare Custom Range"])
        waterfall_start_date,waterfall_end_date = start_date,end_date
        # Dropdown menu options
        st.markdown("<h1 style='font-size:28px;'>Change in MMM Estimated Revenue Contributions</h1>", 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("""</br>""",unsafe_allow_html=True)
                if selected_option == "Month on Month" :
                    st.write("######")
                    st.markdown(
                        f"""

                        <div style="padding: 5px; border-radius: 5px; background-color: #FFFFE0; width: fit-content; display: inline-block;">

                            <strong> Comparision of current month spends to previous month spends</strong>

                        </div>

                        """,
                        unsafe_allow_html=True
                    )
                else :
                    st.markdown(
                        f"""

                        <div style="padding: 5px; border-radius: 5px; background-color: #FFFFE0; width: fit-content; display: inline-block;">

                            <strong> Comparision of current month spends to the same month in previous year</strong>

                        </div>

                        """,
                        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
        fig6 = sf.base_decomp()
        st.plotly_chart(fig6,use_container_width=True)

        ### Media decomp CHART
        fig7 = sf.media_decomp()
        st.plotly_chart(fig7,use_container_width=True)

    if st.button("Prepare Download Of Analysis"):
        ppt_file =  save_ppt_file(fig1,fig2,fig3,fig4,fig6,fig7,figw,start_date,end_date,shares_df1,shares_df2)
        # Add a download button
        st.download_button(
            label="Download Analysis",
            data=ppt_file,
            file_name="MMM_Model_Result Overview.pptx",
            mime="application/vnd.openxmlformats-officedocument.presentationml.presentation"
        )