m1

Model_Result_Overview.py

@@ -4,7 +4,7 @@ import pandas as pd
 from sklearn.preprocessing import MinMaxScaler
 import pickle
 import Streamlit_functions as sf
-from utilities import load_authenticator
+from utilities import (load_authenticator)
 
 from utilities_with_panel import (set_header,
                                   overview_test_data_prep_panel,
@@ -23,7 +23,7 @@ import streamlit_authenticator as stauth
 import yaml
 from yaml import SafeLoader
 import time
-from datetime import datetime
+from datetime import datetime,timedelta
 
 st.set_page_config(layout='wide')
 load_local_css('styles.css')
@@ -116,14 +116,21 @@ if auth_status:
 
     # now = datetime.now()
     # us_format = now.strftime("%m/%d/%Y")
-                                                                    
+
+    # Define the minimum and maximum dates
+    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:
-        default_date = datetime(2024, 1, 28)
-        start_date = st.date_input("Start Date: ",value=default_date)
+        start_date = st.date_input("Start Date: ",value=default_date1,min_value=min_date,
+                                    max_value=max_date)
         
     with col2:
-        default_date = datetime(2024, 2, 24)
-        end_date = st.date_input("End Date: ",value = default_date)
+        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:
@@ -137,6 +144,12 @@ if auth_status:
     st.plotly_chart(fig,use_container_width=True)
 
     # Dropdown menu options
+    st.markdown("<h1 style='font-size:28px;'>Change in MMM Estimated Prospect Contributions</h1>", unsafe_allow_html=True)
+    # st.header("Change in MMM Estimated Prospect Contributions")
+
+
+
+
     options = [
         "Month on Month",
         "Year on Year"]
@@ -166,11 +179,38 @@ if auth_status:
                 unsafe_allow_html=True
             )
         # Waterfall chart
-    fig = sf.waterfall(start_date,end_date,selected_option)
+    
+    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)
+
+    fig = sf.waterfall(waterfall_start_date,waterfall_end_date,selected_option)
     st.plotly_chart(fig,use_container_width=True)
         
     # Waterfall table
-    shares_df = sf.shares_df_func(start_date,end_date)
+    shares_df = sf.shares_df_func(waterfall_start_date,waterfall_end_date)
     st.table(sf.waterfall_table_func(shares_df).style.format("{:.0%}"))
 
     ## Channel Contribution Bar Chart

Streamlit_functions.py

@@ -106,6 +106,13 @@ contribution_cols = [
  'Audio_Prospects',
  'Email_Prospects']
 
+def get_date_range():
+    return df['Date'].min(),df['Date'].max()+ timedelta(days=7)
+
+def get_default_dates():
+    return df['Date'].max()- timedelta(days=21),df['Date'].max()+ timedelta(days=6)
+
+
 def pie_charts(start_date,end_date):
     start_date = pd.to_datetime(start_date)
     end_date = pd.to_datetime(end_date)
@@ -129,7 +136,7 @@ def pie_charts(start_date,end_date):
                          hoverinfo='label+percent',
                         textinfo= 'label+percent',
                         showlegend= False,textfont=dict(size =10),
-                        # title="Distribution of Spends"
+                        title="Distribution of Spends"
                         ), 1, 1)
 
     fig.add_trace(go.Pie(labels=channels, 
@@ -139,10 +146,35 @@ def pie_charts(start_date,end_date):
                         textinfo= 'label+percent',
                         showlegend= False,
                         textfont=dict(size = 10),
-                        # title = "Distribution of Contributions"
+                        title = "Distribution of Prospect Contributions"
                         ), 1, 2)
+    # fig.update_layout(
+    #     title="Distribution Of Spends And Prospect Contributions"
+    # )
+
     fig.update_layout(
-        title="Distribution Of Spends And Contributions"
+        # title="Distribution Of Spends"
+        title={
+                    'text': "Distribution Of Spends And Prospects",
+                    'font': {
+                    'size': 24,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            }
+            
+    )
+
+    fig.add_annotation(
+        text=f"{start_date.strftime('%m-%d-%Y')} to {end_date.strftime('%m-%d-%Y')}",
+        x=0,
+        y=1.15,
+        xref="x domain",
+        yref="y domain",
+        showarrow=False,
+        font=dict(size=18),
+        # align='left'
     )
 
     return fig
@@ -166,8 +198,29 @@ def pie_spend(start_date,end_date):
     )])
 
     # Customize the layout
-    fig.update_layout(
-        title="Distribution Of Spends"
+    # fig.update_layout(
+    #     # title="Distribution Of Spends"
+    #     title={
+    #                 'text': "Distribution Of Spends",
+    #                 'font': {
+    #                 'size': 24,
+    #                 'family': 'Arial',
+    #                 'color': 'black',
+    #                 # 'bold': True
+    #             }
+    #         }
+            
+    # )
+
+    fig.add_annotation(
+        text=f"Distribution Of Spends </br> {start_date.strftime('%m-%d-%Y')} to {end_date.strftime('%m-%d-%Y')}",
+        x=0,
+        y=1.15,
+        xref="x domain",
+        yref="y domain",
+        showarrow=False,
+        font=dict(size=18),
+        # align='left'
     )
 
     # Show the figure
@@ -222,6 +275,8 @@ def waterfall(start_date,end_date,btn_chart):
         end_date_prev = start_date_prev +timedelta(weeks=4) +timedelta(days=-1) 
         
 
+    # if start_date_prev < df['Date'].min() : 
+    #     return "a"
 
     prev_data = df[(df['Date'] >= start_date_prev) & (df['Date'] <= end_date_prev)]
     cur_data = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
@@ -319,8 +374,7 @@ def waterfall(start_date,end_date,btn_chart):
         # )
     else :
         fig.update_layout(
-            title="Change In MMM Estimated Prospect Contribution "
-            ,showlegend=False,
+            showlegend=False,
             # plot_bgcolor='black',
             # paper_bgcolor='black',
             # font=dict(color='white'),  # Changing font color to white for better contrast
@@ -453,10 +507,21 @@ def channel_contribution(start_date,end_date):
 
     # Updating layout for better visualization
     fig.update_layout(
-        title=f"Media Contribution",
+        
+        # title=f"Media Contribution",
         # plot_bgcolor='black',
         # paper_bgcolor='black',
         # font=dict(color='white'),  # Changing font color to white for better contrast
+        title=
+            {
+                    'text': "Media Contribution",
+                    'font': {
+                    'size': 28,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            },
         xaxis=dict(
             showgrid=False,
             gridcolor='gray',  # Setting x-axis gridline color to gray
@@ -470,6 +535,8 @@ def channel_contribution(start_date,end_date):
             zeroline=False,  # Hiding the y-axis zero line
         )
     )
+    
+            
     fig.add_annotation(
         text=f"{cur_data['Date'].min().strftime('%m-%d-%Y')} to {cur_data['Date'].max().strftime('%m-%d-%Y')}",
         x=0,
@@ -511,10 +578,20 @@ def chanel_spends(start_date,end_date):
 
     # Updating layout for better visualization
     fig.update_layout(
-        title=f"Media Spends",
+        # title=f"Media Spends",
         # plot_bgcolor='black',
         # paper_bgcolor='black',
         # font=dict(color='white'),  # Changing font color to white for better contrast
+        title=
+            {
+                    'text': "Media Spends",
+                    'font': {
+                    'size': 28,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            },
         xaxis=dict(
             showgrid=False,
             gridcolor='gray',  # Setting x-axis gridline color to gray
@@ -634,8 +711,18 @@ def cpp(start_date,end_date):
 
     # Update layout for better visualization
     fig.update_layout(
-        title=f"CPP Distribution"
-        ,
+        # title=f"CPP Distribution"
+        # ,
+        title=
+            {
+                    'text': "CPP Distribution",
+                    'font': {
+                    'size': 28,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            },
         # plot_bgcolor='black',
         # paper_bgcolor='black',
         # font=dict(color='white'),  # Changing font color to white for better contrast
@@ -683,12 +770,22 @@ def base_decomp():
 
     # Update layout for better visualization
     fig.update_layout(
-        title=f"Base Decomposition"
+        # title=f"Base Decomposition"
         # <br>{cur_data['Date'].min().strftime('%m-%d-%Y')} to {cur_data['Date'].max().strftime('%m-%d-%Y')}"
-        ,
+        # ,
         # plot_bgcolor='black',
         # paper_bgcolor='black',
         # font=dict(color='white'),  # Changing font color to white for better contrast
+        title=
+            {
+                    'text': "Base Decomposition",
+                    'font': {
+                    'size': 28,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            },
         xaxis=dict(
             showgrid=False,
             gridcolor='gray',  # Setting x-axis gridline color to gray
@@ -703,7 +800,16 @@ def base_decomp():
         ),
         hovermode='x'  # Show hover info for all lines at a single point
     )
-
+    fig.add_annotation(
+        text=f"{base_decomp_df['Date'].min().strftime('%m-%d-%Y')} to {(base_decomp_df['Date'].max()+timedelta(days=6)).strftime('%m-%d-%Y')}",
+        x=0,
+        y=1.15,
+        xref="x domain",
+        yref="y domain",
+        showarrow=False,
+        font=dict(size=16),
+        # align='left'
+    )
     return fig
 
 def media_decomp():
@@ -776,7 +882,17 @@ def media_decomp():
 
     # Updating layout for better visualization
     fig.update_layout(
-        title=f"Media Decomposition",# <br>{cur_data['Date'].min().strftime('%m-%d-%Y')} to {cur_data['Date'].max().strftime('%m-%d-%Y')}",
+        # title=f"Media Decomposition",# <br>{cur_data['Date'].min().strftime('%m-%d-%Y')} to {cur_data['Date'].max().strftime('%m-%d-%Y')}",
+        title=
+            {
+                    'text': "Media Decomposition",
+                    'font': {
+                    'size': 28,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            },
         # plot_bgcolor='black',
         # paper_bgcolor='black',
         # font=dict(color='white'),  # Changing font color to white for better contrast
@@ -793,7 +909,16 @@ def media_decomp():
             zeroline=False,  # Hiding the y-axis zero line
         )
     )
-
+    fig.add_annotation(
+        text=f"{media_decomp_df['Date'].min().strftime('%m-%d-%Y')} to {(media_decomp_df['Date'].max()+timedelta(days=6)).strftime('%m-%d-%Y')}",
+        x=0,
+        y=1.15,
+        xref="x domain",
+        yref="y domain",
+        showarrow=False,
+        font=dict(size=16),
+        # align='left'
+    )
     return fig
 
 def mmm_model_quality():
@@ -807,7 +932,16 @@ def mmm_model_quality():
 
     # Update layout for better visualization
     fig.update_layout(
-        title=f"Model Predicted v/s Actual Prospects"
+        title={
+                    'text': "Model Predicted v/s Actual Prospects",
+                    'font': {
+                    'size': 24,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            }
+        # title=f"Model Predicted v/s Actual Prospects"
         ,
         # plot_bgcolor='black',
         # paper_bgcolor='black',
@@ -898,12 +1032,23 @@ def elasticity(media_df):
         orientation='h',  # Setting the orientation to horizontal
         marker_color='rgba(75, 136, 257, 1)',  
         text= media_df['coeff'].round(2),
-        textposition="auto"
+        textposition="outside"
     ))
 
     # Updating layout for better visualization
     fig.update_layout(
-        title="Media And Baseline Elasticity",
+        title={
+                    'text': "Media And Baseline Elasticity",
+                    'font': {
+                    'size': 24,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            }
+            
+            ,
+        # title="Media And Baseline Elasticity",
         xaxis=dict(
             title="Elasticity (coefficient)",
             showgrid=True,
@@ -939,12 +1084,22 @@ def half_life(media_df):
         orientation='h',  # Setting the orientation to horizontal
         marker_color='rgba(75, 136, 257, 1)',  
         text= media_df['coeff'].round(2),
-        textposition="auto"
+        textposition="outside"
     ))
 
     # Updating layout for better visualization
     fig.update_layout(
-        title="Media Half-life",
+        title={
+                    'text': "Media Half-life",
+                    'font': {
+                    'size': 24,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            }
+            
+            ,
         xaxis=dict(
             title="Weeks",
             showgrid=True,
@@ -1009,7 +1164,11 @@ def model_metrics_table_func():
     # model_metrics_df.index = model_metrics_df["R-squared"]
     # model_metrics_df = model_metrics_df.drop(columns=["R-squared"])
     model_metrics_df2 = pd.DataFrame(model_metrics_df.values,columns=["R-squared","Adjusted R-squared","MAPE","AIC","BIC"] )
-    model_metrics_df2 = model_metrics_df2.round(2)
+
+    # model_metrics_df2 = model_metrics_df2.round(2)
+    model_metrics_df2["R-squared"] = model_metrics_df2["R-squared"].apply(lambda x: "{:.2%}".format(x))
+    model_metrics_df2["Adjusted R-squared"] = model_metrics_df2["Adjusted R-squared"].apply(lambda x: "{:.2%}".format(x))
+    model_metrics_df2["MAPE"] = (model_metrics_df2["MAPE"]/100).apply(lambda x: "{:.2%}".format(x))
     model_metrics_df2["AIC"] = model_metrics_df2["AIC"].round(0)
     model_metrics_df2["BIC"] = model_metrics_df2["BIC"].round(0)
     model_metrics_df2.index = [" "]
@@ -1062,16 +1221,20 @@ def scenario_spend_forecasting(delta_df,start_date,end_date):
     df_modified = delta_df.merge(key_df,on = "Channel_name",how = "inner")
     df_modified2 = df_modified.merge(data1,on = "Channels",how ="outer")
     # df_modified2["Forecasted Spends"] =( df_modified2["last_year_spends"]*(1+df_modified2["Delta_percent"]/100)).astype(int)
-    df_modified2["Forecasted Spends"] =( df_modified2["last_year_spends"]*(1+df_modified2["Delta_percent"]/100)).astype(int)
-
+    df_modified2["Forecasted Spends"] =( df_modified2["last_year_spends"]*(1+df_modified2["Delta_percent"]/100)).apply(lambda x: "${:,.0f}".format(x))
     df_modified2.index = df_modified2["Channels"]
-    df_modified2["Spend Change"] = df_modified2["Delta_percent"].astype(int)
+    df_modified2["Spend Change"] = (df_modified2["Delta_percent"]/100).apply(lambda x: "{:.0%}".format(x))
     # df_modified2["Forecasted Spends"] = df_modified2["Forecasted Spends"].astype(int)
-    df_modified2["Last Year Spends"] = df_modified2["last_year_spends"].astype(int)
+    df_modified2["Last Year Spends"] = df_modified2["last_year_spends"].apply(lambda x: "${:,.0f}".format(x))
     df_modified3 = df_modified2[["Last Year Spends","Forecasted Spends","Spend Change"]].transpose()
     # df_modified2["forecasted_spends"] = 
     # # df_modified = delta_percent
     # # df_modified["Optimised Spends"] = df_modified["Current Spends"]*
+    df_modified3 = df_modified3[['BROADCAST TV', 'CABLE TV',
+       'CONNECTED & OTT TV', 'VIDEO', 'DISPLAY PROSPECTING',
+       'DISPLAY RETARGETING', 'SOCIAL PROSPECTING', 'SOCIAL RETARGETING',
+       'SEARCH BRAND', 'SEARCH NON-BRAND', 'DIGITAL PARTNERS', 'AUDIO',
+       'EMAIL']]
 
     return df_modified3
 
@@ -1152,96 +1315,23 @@ def scenario_spend_forecasting2(delta_df,start_date,end_date):
     
     data2["Month year"] = data2["Month"].apply(get_month_name) + ' ' +(data2["Date"].dt.year+1).astype(str)   
     print(data2.columns)
-    data2 = data2[['Month year' ,'Date', 'Date2', 'BROADCAST TV', 'CABLE TV',
+    data2 = data2[['Month year' ,'BROADCAST TV', 'CABLE TV',
        'CONNECTED & OTT TV', 'VIDEO', 'DISPLAY PROSPECTING',
        'DISPLAY RETARGETING', 'SOCIAL PROSPECTING', 'SOCIAL RETARGETING',
        'SEARCH BRAND', 'SEARCH NON-BRAND', 'DIGITAL PARTNERS', 'AUDIO',
        'EMAIL']]
-    data2.columns = ['Month          ','Base Data Start Date', 'Base Data End Date', 'BROADCAST TV', 'CABLE TV',
+    data2.columns = ['Month          ','BROADCAST TV', 'CABLE TV',
        'CONNECTED & OTT TV', 'VIDEO', 'DISPLAY PROSPECTING',
        'DISPLAY RETARGETING', 'SOCIAL PROSPECTING', 'SOCIAL RETARGETING',
        'SEARCH BRAND', 'SEARCH NON-BRAND', 'DIGITAL PARTNERS', 'AUDIO',
        'EMAIL']
-    data2['Base Data Start Date'] = data2['Base Data Start Date'].dt.date
-    data2['Base Data End Date'] = data2['Base Data End Date'].dt.date
-    #.transpose()
-    # st.dataframe(data2)
-    # st.dataframe(data1)
-    # months_list = cur_data["Month"].unique()
-    # data1["Channels"]=data1.index
-    # df_modified = delta_df.merge(key_df,on = "Channel_name",how = "inner")
-    # df_modified2 = df_modified.merge(data1,on = "Channels",how ="outer")
-    # df_modified2.index = df_modified2["Channels"]
-
-    # data3 = pd.DataFrame(index = data1.index)
-    # for c in months_list:
-    #     data3[c] = df_modified2[c]*(1+df_modified2["Delta_percent"]/100)
-    
-    # df1 = df_modified2[months_list].transpose()
-    # df1["Metrics"] = "Last Year Spends"
-    
-    # data3 = data3.transpose()
-    # data3 = data3.astype(int)
-    # # data2.index = data2["Date_diff_months"]
-    # data2.columns = ["start date","end date"]
-    # data3["start date"] = data2["start date"].dt.date
-    # data3["end date"] = data2["end date"].dt.date
-    # data3["Month"] = data3.index
-    # cols = ["Month","start date","end date",'BROADCAST TV',
-    #         'CABLE TV',
-    #         'CONNECTED & OTT TV',
-    #         'VIDEO',
-    #         'DISPLAY PROSPECTING',
-    #         'DISPLAY RETARGETING',
-    #         'SOCIAL PROSPECTING',
-    #         'SOCIAL RETARGETING',
-    #         'SEARCH BRAND',
-    #         'SEARCH NON-BRAND',
-    #         'DIGITAL PARTNERS',
-    #         'AUDIO',
-    #         'EMAIL']
-    # data3["Metrics"] = "Forecasted Year Spends"
-
-    # df2 = df_modified2["Delta_percent"].transpose()
-    # df2["Metrics"] = "Percent Change"
-    # df_modified2["last_year_spends"] = 
-
-    # data3 = pd.DataFrame(index = data1.index)
-    # for c in months_list:
-    #     for idx in data3.index:
-    #         data3[c][idx] = df_modified2[c][idx]*df_modified2["Delta_percent"]
-
     
-    # data1 = data1[['Date',"Date2",'BROADCAST TV',
-    #     'CABLE TV',
-    #     'CONNECTED & OTT TV',
-    #     'VIDEO','DISPLAY PROSPECTING',
-    #     'DISPLAY RETARGETING',
-    #     'SOCIAL PROSPECTING',
-    #     'SOCIAL RETARGETING',
-    #     'SEARCH BRAND',
-    #     'SEARCH NON-BRAND',
-    #     'DIGITAL PARTNERS',
-    #     'AUDIO',
-    #     'EMAIL',
-    #     ]]
-    # data1[channels] = data1[channels].astype(int)
-    # data1["Date"] = data1["Date"].dt.date
-    # data1["Date2"] = data1["Date2"].dt.date
-    # # pd.DataFrame(cur_data[channels].groupby("Date_diff_months").sum().transpose()).reset_index()
-    # # # data1.columns = ["Channels","last_year_spends"]
-
-    # # df_modified = delta_df.merge(key_df,on = "Channel_name",how = "inner")
-    # # df_modified2 = df_modified.merge(data1,on = "Channels",how ="outer")
-    # # df_modified2["Forecasted Spends"] =( df_modified2["last_year_spends"]*(1+df_modified2["Delta_percent"]/100)).apply(numerize)
-    # # df_modified2.index = df_modified2["Channels"]
-    # # df_modified2["Spend Change"] = df_modified2["Delta_percent"]
-    # # df_modified2["Last Year Spends"] = df_modified2["last_year_spends"].apply(numerize)
-    # # df_modified3 = df_modified2[["Last Year Spends","Forecasted Spends","Spend Change"]].transpose()
-    # # # df_modified2["forecasted_spends"] = 
-    # # # # df_modified = delta_percent
-    # # # # df_modified["Optimised Spends"] = df_modified["Current Spends"]*
-    # # spend_cols1 = pd.DataFrame(spend_cols)[0].to_list()
     data2.set_index('Month          ', inplace=True)
+    for c in ['BROADCAST TV', 'CABLE TV',
+       'CONNECTED & OTT TV', 'VIDEO', 'DISPLAY PROSPECTING',
+       'DISPLAY RETARGETING', 'SOCIAL PROSPECTING', 'SOCIAL RETARGETING',
+       'SEARCH BRAND', 'SEARCH NON-BRAND', 'DIGITAL PARTNERS', 'AUDIO',
+       'EMAIL']:
+        data2[c] = data2[c].apply(lambda x: "${:,.0f}".format(x))
     return data2
 

pages/1_Model_Quality.py

@@ -16,14 +16,16 @@ st.plotly_chart(sf.mmm_model_quality(),use_container_width=True)
     
 media_df = sf.media_data()
     # Create two columns for start date and end date input
-col1, col2 = st.columns(2)
-
-st.dataframe(sf.model_metrics_table_func(),hide_index  = True,use_container_width=True) 
+col1, col2 , col3 = st.columns([1,0.01,1])
+df1 = sf.model_metrics_table_func()
+st.dataframe(df1,hide_index  = True,use_container_width=True) 
 
-st.plotly_chart(sf.elasticity_and_media(media_df))
+# st.plotly_chart(sf.elasticity_and_media(media_df))
 with col1:
     st.plotly_chart(sf.elasticity(media_df))
 with col2:
+    st.write("")
+with col3:
     st.plotly_chart(sf.half_life(media_df))
     
 

pages/2_Scenario_Planner.py

@@ -48,6 +48,34 @@ for k, v in st.session_state.items():
 # ======================= Functions ====================== #
 # ======================================================== #
 
+def first_day_of_next_year(date):
+    next_year = date.year + 1
+    first_day = datetime(next_year, 1, 1).date()
+    
+    # Calculate the last day of the next year
+    last_day = (first_day + relativedelta(years=1, days=-1))
+    
+    return first_day, last_day
+
+def first_day_of_next_quarter(date):
+    current_quarter = (date.month - 1) // 3 + 1
+    next_quarter_first_month = ((current_quarter % 4) * 3) + 1
+    next_quarter_year = date.year if next_quarter_first_month > 1 else date.year + 1
+    # Ensure month is within valid range
+    if next_quarter_first_month < 1 or next_quarter_first_month > 12:
+        raise ValueError("Calculated month is out of range: {}".format(next_quarter_first_month))
+    # st.write(next_quarter_first_month)
+    first_day_next_quarter = datetime(next_quarter_year, next_quarter_first_month, 1).date()
+    last_day_next_quarter = (first_day_next_quarter + relativedelta(months=3)) - relativedelta(days=1)
+    
+    return first_day_next_quarter, last_day_next_quarter
+
+
+def first_day_of_next_month(date):
+    next_month_date = date + relativedelta(months=1)
+    first_day_next_month = next_month_date.replace(day=1)
+    last_day_next_month = (first_day_next_month + relativedelta(months=1)) - relativedelta(days=1)
+    return first_day_next_month, last_day_next_month
 
 def optimize(key, status_placeholder):
     """
@@ -984,6 +1012,9 @@ if auth_status == True:
                 )
             st.session_state["initialized"] = True
             st.session_state["first_time"] = False
+            save_scenario("current scenario")
+
+
 
         # initialize_data(
         #             panel=panel_selected,
@@ -1894,7 +1925,14 @@ if auth_status == True:
         scenario_planner_plots()
 
         with st.expander ("View Forecasted spends"):
-            col1, col2, col3 = st.columns([1,1,0.75])
+            # st.write("Select Time Period")
+            
+            options = ["Next Month","Next Quarter","Next Year","Custom Time Period"]
+
+            # # Create the radio button
+            forecast_btn_op = st.radio("Select Time Period", options)
+
+            
             # List of 12 months
             months_start = ["January", "February", "March", "April", "May", "June",
                         "July", "August", "September", "October", "November", "December"]
@@ -1902,74 +1940,85 @@ if auth_status == True:
             months_end = ["January", "February", "March", "April", "May", "June",
                         "July", "August", "September", "October", "November", "December"]
             years_end = range(2022,2025)
-            with col1:
-                from datetime import date
-                st.write ("Select Start Time Period")
-                sc1,sc2 = st.columns([1,1])
-                
-                with sc1:
-                # Create a dropdown (selectbox) for months
-                    start_date_mon = st.selectbox("Select Start Month:", months_start)
-                with sc2:
-                    start_date_year = st.selectbox("Select Start Year:", years_start,index=2)
-                start_date1 = date(start_date_year, months_start.index(start_date_mon)+1, 1)-  relativedelta(years=1)
-                # st.write(start_date1)
-                # default_Month = "January" 
-                # start_date_mon = st.text_input("Select Start Month: ",value=default_Month)
+
+            if forecast_btn_op == "Custom Time Period":
+                col1, col2, col3 = st.columns([1,1,0.75])
+
+                with col1:
+                    from datetime import date
+                    st.write ("Select Start Time Period")
+                    sc1,sc2 = st.columns([1,1])
+                    
+                    with sc1:
+                    # Create a dropdown (selectbox) for months
+                        start_date_mon = st.selectbox("Select Start Month:", months_start)
+                    with sc2:
+                        start_date_year = st.selectbox("Select Start Year:", years_start,index=2)
+                    start_date1 = date(start_date_year, months_start.index(start_date_mon)+1, 1)
+                    # -  relativedelta(years=1)
+                    # st.write(start_date1)
+                    # default_Month = "January" 
+                    # start_date_mon = st.text_input("Select Start Month: ",value=default_Month)
+                    
+                    # default_Year = 2024 
+                    # start_date_year = st.number_input("Select Start Year: ",value=default_Year)
+
+                with col2:
+                    st.write ("Select End Time Period")
+                    ec1,ec2 = st.columns([1,1])
+                    with ec1:
+                        end_date_mon = st.selectbox("Select End Month:", months_end,index=1)
+                    with ec2:
+                        end_date_year = st.selectbox("Select End Year:", years_end,index=2)
+                    end_date1 = date(end_date_year, months_end.index(end_date_mon)+1, 1)+ relativedelta(months=1) - relativedelta(days=1)
+                    # -  relativedelta(years=1)
+                    # st.write(end_date1)
+                    # default_Month = "February" 
+                    # end_date_mon = st.text_input("Select End Month: ",value=default_Month)
+                    
+                    # default_Year = 2024 
+                    # end_date_year = st.number_input("Select End Year: ",value=default_Year)
+                    # end_date1 = st.date_input("Select End Date: ",value=default_date) -  relativedelta(years=1)    
+            elif forecast_btn_op == 'Next Month':
+                # current_date = datetime.now()
+                # start_date1  = current_date-  relativedelta(years=1)
+                # end_date1 = current_date + relativedelta(months=1)-  relativedelta(years=1)
+                start_date1,end_date1 = first_day_of_next_month(datetime.now())
+                # start_date1 = start_date1-  relativedelta(years=1)
+                # end_date1 = end_date1 - relativedelta(years=1)
+            elif forecast_btn_op == 'Next Quarter':
+                # current_date = datetime.now()
+                # start_date1  = current_date-  relativedelta(years=1)
+                # end_date1 = current_date + relativedelta(months = 3)-  relativedelta(years=1)
+                start_date1,end_date1 = first_day_of_next_quarter(datetime.now())
+                # start_date1 = start_date1-  relativedelta(years=1)
+                # end_date1 = end_date1 - relativedelta(years=1)
+            elif forecast_btn_op == 'Next Year':
+                # current_date = datetime.now()
+                # start_date1  = current_date-  relativedelta(years=1)
+                # end_date1 = current_date + relativedelta(months = 12)-  relativedelta(years=1)
+                start_date1,end_date1 = first_day_of_next_year(datetime.now())
+                # start_date1 = start_date1-  relativedelta(years=1)
+                # end_date1 = end_date1 - relativedelta(years=1)
+            st.write(f"Forecasted Spends Time Period : {start_date1.strftime('%m-%d-%Y')} to {end_date1.strftime('%m-%d-%Y')}")
+            if end_date1 < start_date1 :
+                st.error("End date cannot be less than start date")
+            forecasted_table_df2 = pd.DataFrame()
+            try:
                 
-                # default_Year = 2024 
-                # start_date_year = st.number_input("Select Start Year: ",value=default_Year)
-
-            with col2:
-                st.write ("Select End Time Period")
-                ec1,ec2 = st.columns([1,1])
-                with ec1:
-                    end_date_mon = st.selectbox("Select End Month:", months_end,index=1)
-                with ec2:
-                    end_date_year = st.selectbox("Select End Year:", years_end,index=2)
-                end_date1 = date(end_date_year, months_end.index(end_date_mon)+2, 1)-  relativedelta(years=1)
-                # st.write(end_date1)
-                # default_Month = "February" 
-                # end_date_mon = st.text_input("Select End Month: ",value=default_Month)
+                st.write("Forecasted Spends wrt. Channels ")
+                forecasted_table_df = sf.scenario_spend_forecasting(summary_df_sorted,start_date1-  relativedelta(years=1),end_date1-  relativedelta(years=1))
                 
-                # default_Year = 2024 
-                # end_date_year = st.number_input("Select End Year: ",value=default_Year)
-                # end_date1 = st.date_input("Select End Date: ",value=default_date) -  relativedelta(years=1)
-
-            with col3:
-                current_date = datetime.now()
-                c1, c2, c3 = st.columns(3)
-                with c1:
-                    if st.button('Next Month'):
-                        start_date1  = current_date-  relativedelta(years=1)
-                        end_date1 = current_date + relativedelta(months=1)-  relativedelta(years=1)
-                with c2:
-                    if st.button('Next Quarter'):
-                        start_date1  = current_date-  relativedelta(years=1)
-                        end_date1 = current_date + relativedelta(months = 3)-  relativedelta(years=1)
-                with c3:
-                    if st.button('Next Year'):
-                        start_date1  = current_date-  relativedelta(years=1)
-                        end_date1 = current_date + relativedelta(months = 12)-  relativedelta(years=1)
-
-            forecasted_table_df = sf.scenario_spend_forecasting(summary_df_sorted,start_date1,end_date1)
-            st.dataframe(forecasted_table_df)
-
-            forecasted_table_df2 = sf.scenario_spend_forecasting2(summary_df_sorted,start_date1,end_date1)
-            st.dataframe(forecasted_table_df2)
-            
-            st.markdown("""
-                <style>
-                .yellow-container {
-                    background-color: #FFFF99;
-                    border: 1px solid #FFD700;
-                    padding: 10px;
-                    border-radius: 5px;
-                    margin-bottom: 10px;
-                }
-                </style>
-                """, unsafe_allow_html=True)
-            
+                forecasted_table_df2 = sf.scenario_spend_forecasting2(summary_df_sorted,start_date1-  relativedelta(years=1),end_date1-  relativedelta(years=1))
+                st.dataframe(forecasted_table_df)
+
+                st.write("Monthly Breakdown Of Forecasted Spends wrt. Channels ")
+                st.dataframe(forecasted_table_df2)
+
+
+            except:
+                st.warning("Please make sure the base data is updated")
+
             def save_report_forecast(forecasted_table_df,forecasted_table_df2):
                 
                 # Convert the DataFrame to an Excel file in memory
@@ -1980,24 +2029,28 @@ if auth_status == True:
                 # Seek to the beginning of the BytesIO buffer
                 excel_file.seek(0)
                 return excel_file
-
+                
             st.subheader("Download Report")
             report_name = st.text_input(
-                "Report name",
-                key="report_input",
-                placeholder="Report name",
-                label_visibility="collapsed",
-            )
-
+                    "Report name",
+                    key="report_input",
+                    placeholder="Report name",
+                    label_visibility="collapsed",
+                )
 
+            
             st.download_button(
-                "Download Report",
-                data = save_report_forecast(forecasted_table_df,forecasted_table_df2),
-                file_name = report_name+".xlsx",
-                mime="application/vnd.ms-excel",
-                # on_click=lambda: save_report_forecast(forecasted_table_df,report_name),
-                disabled=len(st.session_state["report_input"]) == 0,#use_container_width=True
-                )
+                    "Download Report",
+                    data = save_report_forecast(forecasted_table_df,forecasted_table_df2),
+                    file_name = report_name+".xlsx",
+                    mime="application/vnd.ms-excel",
+                    # on_click=lambda: save_report_forecast(forecasted_table_df,report_name),
+                    disabled=len(st.session_state["report_input"]) == 0,#use_container_width=True
+                    )
+            
+            
+
+            
 
             # filename = st.text_input("Save Report: ",placeholder="Report name")
             # if st.button("Download Report",disabled= (filename != "Report name")):

pages/3_Saved_Scenarios.py

@@ -109,7 +109,7 @@ def plot_comparison_chart(df,metric):
             y=df[column],
             name=column,
             text=df[column].apply(numerize),  # Adding text for each point
-            textposition='outside', 
+            textposition='auto', 
             hoverinfo='x+y+text',
         ))
 
@@ -465,8 +465,8 @@ if auth_status == True:
             ]).to_html(),unsafe_allow_html=True)
         st.markdown("<br><br>", unsafe_allow_html=True)
         
-    with st.expander('Scenario comparison'):
-            st.header("Scenario comparison")
+    with st.expander('Scenario Comparison'):
+            st.header("Scenario Comparison")
             if len(scenarios_to_compare)== 0:
                 st.write("")
             else:

response_curves_model_quality.py

@@ -434,7 +434,7 @@ for i in range(1,13):
     plotly_data = plotly_data.merge(pdf,on = ["Date","MAT"],how = "left")
     
 def response_curves(channel,x_modified,y_modified):
-
+    
     # Initialize the Plotly figure
     fig = go.Figure()
 
@@ -447,10 +447,10 @@ def response_curves(channel,x_modified,y_modified):
     #     mode='markers',
     #     name=x_col.replace('_Spends', '')
     # ))
-
+    plotly_data1 = plotly_data[plotly_data["MAT"]!="ext"]
     fig.add_trace(go.Scatter(
-        x=plotly_data.sort_values(by=x_col, ascending=True)[x_col],
-        y=plotly_data.sort_values(by=x_col, ascending=True)[y_col],
+        x=plotly_data1.sort_values(by=x_col, ascending=True)[x_col],
+        y=plotly_data1.sort_values(by=x_col, ascending=True)[y_col],
         mode='lines',
         marker=dict(color = 'blue'),
         name=x_col.replace('_Spends', '')
@@ -458,10 +458,11 @@ def response_curves(channel,x_modified,y_modified):
     
     plotly_data2 = plotly_data.copy()
     plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
+    plotly_data2 = plotly_data2[plotly_data2["MAT"]!="ext"]
     # .dropna(subset=[x_col]).reset_index(inplace = True)
     fig.add_trace(go.Scatter(
-        x=plotly_data2[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col],
-        y=plotly_data2[plotly_data2['Date'] == plotly_data2['Date'].max()][y_col],
+        x=np.array(plotly_data2[x_col].mean()),
+        y=np.array(plotly_data2[y_col].mean()),
         mode='markers',
         marker=dict(
         size=13  # Adjust the size value to make the markers larger or smaller
@@ -483,7 +484,16 @@ def response_curves(channel,x_modified,y_modified):
 
     # Update layout with titles
     fig.update_layout(
-        title=channel_name_formating(channel)+' Response Curve',
+         title={
+                    'text': channel_name_formating(channel)+' Response Curve',
+                    'font': {
+                    'size': 24,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            },
+        # title=channel_name_formating(channel)+' Response Curve',
         xaxis_title='Weekly Spends',
         yaxis_title='Prospects'
     )

response_curves_model_quality_base.py

@@ -244,23 +244,34 @@ def response_curves(channel,chart_typ):
 
     
     plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
+    plotly_data2 = plotly_data2[plotly_data2["MAT"]!="ext"]
+    # print(plotly_data2[x_col].mean(),plotly_data2[y_col].mean())
     # import steamlit as st
     # st.dataframe()    
     fig.add_trace(go.Scatter(
-        x=plotly_data2[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col],
-        y=plotly_data2[plotly_data2['Date'] == plotly_data2['Date'].max()][y_col],
+        x=np.array(plotly_data2[x_col].mean()),
+        y=np.array(plotly_data2['Fit_Data_'+channel].mean()),
         mode='markers',
         marker=dict(
         size=13  # Adjust the size value to make the markers larger or smaller
         , color = 'green'
         ),
-        name="Current Spends"
+        name="Average Weekly Spends"
     ))
 
     # Update layout with titles
     fig.update_layout(
         width=700, height=500,
-        title=channel_name_formating(channel)+' Response Curve',
+         title={
+                    'text': channel_name_formating(channel)+' Response Curve',
+                    'font': {
+                    'size': 24,
+                    'family': 'Arial',
+                    'color': 'black',
+                    # 'bold': True
+                }
+            },
+        # title=channel_name_formating(channel)+' Response Curve',
         xaxis_title='Weekly Spends',
         yaxis_title='Prospects'
     )

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9b4eac3b946dbc2110dc592ef0ca72a418abaeddfca33d0f2c8dee3b2f4d3246
+oid sha256:ea149846bb9c8341d1a26a5e74dfbfea9df62ce4a7bd05ff07922a9169932a5d
 size 1822