test1

Model_Result_Overview.py

@@ -1,12 +1,3 @@
-'''
-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
@@ -38,175 +29,175 @@ 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"])
+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
+    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
+    return random_eff_df
 
 
-# def process_train_and_test(train, test, features, panel_col, target_col):
-#     X1 = train[features]
+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)
+    ss = MinMaxScaler()
+    X1 = pd.DataFrame(ss.fit_transform(X1), columns=X1.columns)
 
-#     X1[panel_col] = train[panel_col]
-#     X1[target_col] = train[target_col]
+    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
+    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)
+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)
+    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
+    return X
 
 
-# target_col='Revenue'
-# target='Revenue'
+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'
+# 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)
+#st.write(media_data)
 
-# is_panel = True 
+is_panel = True 
 
-# # panel_col='markets'
-# date_col = 'date'
-# for k, v in st.session_state.items():
+# 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
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
 
-# authenticator = st.session_state.get('authenticator')
+authenticator = st.session_state.get('authenticator')
 
-# if authenticator is None:
-#     authenticator = load_authenticator()
+if authenticator is None:
+    authenticator = load_authenticator()
     
-# name, authentication_status, username = authenticator.login('Login', 'main')
-# auth_status = st.session_state['authentication_status']
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state['authentication_status']
 
-# if auth_status:
-#     authenticator.logout('Logout', 'main')
+if auth_status:
+    authenticator.logout('Logout', 'main')
     
-#     is_state_initiaized = st.session_state.get('initialized',False)
-#     if not is_state_initiaized:
-#         a=1
+    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")
+    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
+        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
+        return panel
 
-#     def rerun():
-#         st.rerun()
+    def rerun():
+        st.rerun()
 
-#     metrics_selected='revenue'
+    metrics_selected='prospects'
 
-#     file_selected = (
-#             f"Overview_data_test_panel@#{metrics_selected}.xlsx"
-#         )
-#     panel_list = panel_fetch(file_selected)
+    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'
+    if "selected_markets" not in st.session_state:
+        st.session_state['selected_markets']='DMA1'
 
 
-#     st.header('Overview of previous spends')
+    st.header('Overview of previous spends')
 
-#     selected_market= st.selectbox(
-#             "Select Markets",
-#             ["Total Market"] + panel_list
-#         )
+    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))
+    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))
+    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) 
+    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)
+    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.markdown("<hr>",unsafe_allow_html=True)
+    ##############################
 
-#     st.plotly_chart(create_contribution_pie(scenario),use_container_width=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)
-#     #######################################
+    ################################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)
+    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.plotly_chart(create_channel_spends_sales_plot(selected_channel), use_container_width=True)
 
-#     st.markdown("<hr>",unsafe_allow_html=True)
+    st.markdown("<hr>",unsafe_allow_html=True)
 
-# # elif auth_status == False:
-# #     st.error('Username/Password is incorrect')
+# 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)
+# 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)

README.md

@@ -1,5 +1,5 @@
 ---
-title: Simulator-UOPX
+title: UOPX
 emoji: 🏆
 colorFrom: indigo
 colorTo: pink

Test/scenario_test_df.csv

@@ -1,105 +1,105 @@
 other_contributions,correction,sales
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pages/8_Scenario_Planner.py

@@ -6,7 +6,7 @@ from collections import OrderedDict
 from plotly.subplots import make_subplots
 import plotly.graph_objects as go
 from utilities import (
-    format_numbers,
+    format_numbers,format_numbers_f,
     load_local_css,
     set_header,
     initialize_data,
@@ -22,12 +22,13 @@ from yaml import SafeLoader
 import re
 import pandas as pd
 import plotly.express as px
-
+import response_curves_model_quality as rc
 
 st.set_page_config(layout="wide")
 load_local_css("styles.css")
 set_header()
 
+
 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
@@ -170,7 +171,7 @@ def update_all_spends_abs_slider():
 #     actual_spends = _scenario.actual_total_spends
 #     if validate_input(st.session_state["total_spends_change_abs_slider"]):
 #         print("#" * 100)
-#         print(st.session_state["total_spends_change_abs_slider"])
+#         print(st.session_state["total_spends_change_abs_slider"])C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulatorAldi\pages\8_Scenario_Planner.py
 #         print("#" * 100)
 
 #         modified_spends = extract_number_for_string(
@@ -493,192 +494,40 @@ def debug_temp(x_test, power, K, b, a, x0):
 
 
 # @st.cache
-def plot_response_curves():
-    cols = 4
-    rows = (
-        len(channels_list) // cols
-        if len(channels_list) % cols == 0
-        else len(channels_list) // cols + 1
-    )
-    rcs = st.session_state["rcs"]
-    shapes = []
-    fig = make_subplots(rows=rows, cols=cols, subplot_titles=channels_list)
+def plot_response_curves(summary_df_sorted):
+    # cols = 3
+    # rows = (
+    #     len(channels_list) // cols
+    #     if len(channels_list) % cols == 0
+    #     else len(channels_list) // cols + 1
+    # )
+    # rcs = st.session_state["rcs"]
+    # shapes = []
+    # fig = make_subplots(rows=rows, cols=cols, subplot_titles=channels_list)
+    channel_cols = [
+    'BroadcastTV',
+    'CableTV',
+    'Connected&OTTTV',
+    'DisplayProspecting',
+    'DisplayRetargeting',
+        'Video',
+    'SocialProspecting',
+    'SocialRetargeting',
+    'SearchBrand',
+    'SearchNon-brand',
+    'DigitalPartners',
+    'Audio',
+    'Email']
+    summary_df_sorted.index = summary_df_sorted["Channel_name"]
     for i in range(0, len(channels_list)):
         col = channels_list[i]
-        x_actual = st.session_state["scenario"].channels[col].actual_spends
-        # x_modified = st.session_state["scenario"].channels[col].modified_spends
-
-        power = np.ceil(np.log(x_actual.max()) / np.log(10)) - 3
-
-        K = rcs[col]["K"]
-        b = rcs[col]["b"]
-        a = rcs[col]["a"]
-        x0 = rcs[col]["x0"]
-
-        x_plot = np.linspace(0, 5 * x_actual.sum(), 50)
-
-        x, y, marginal_roi = [], [], []
-        for x_p in x_plot:
-            x.append(x_p * x_actual / x_actual.sum())
-
-        for index in range(len(x_plot)):
-            y.append(s_curve(x[index] / 10**power, K, b, a, x0))
-
-        for index in range(len(x_plot)):
-            marginal_roi.append(
-                a * y[index] * (1 - y[index] / np.maximum(K, np.finfo(float).eps))
-            )
-
-        x = (
-            np.sum(x, axis=1)
-            * st.session_state["scenario"].channels[col].conversion_rate
-        )
-        y = np.sum(y, axis=1)
-        marginal_roi = (
-            np.average(marginal_roi, axis=1)
-            / st.session_state["scenario"].channels[col].conversion_rate
-        )
-
-        roi = y / np.maximum(x, np.finfo(float).eps)
-
-        fig.add_trace(
-            go.Scatter(
-                x=x,
-                y=y,
-                name=col,
-                customdata=np.stack((roi, marginal_roi), axis=-1),
-                hovertemplate="Spend:%{x:$.2s}<br>Sale:%{y:$.2s}<br>ROI:%{customdata[0]:.3f}<br>MROI:%{customdata[1]:.3f}",
-                line=dict(color="blue"),
-            ),
-            row=1 + (i) // cols,
-            col=i % cols + 1,
-        )
-
-        x_optimal = (
-            st.session_state["scenario"].channels[col].modified_total_spends
-            * st.session_state["scenario"].channels[col].conversion_rate
-        )
-        y_optimal = st.session_state["scenario"].channels[col].modified_total_sales
-
-        # if col == "Paid_social_others":
-        #     debug_temp(x_optimal * x_actual / x_actual.sum(), power, K, b, a, x0)
-
-        fig.add_trace(
-            go.Scatter(
-                x=[x_optimal],
-                y=[y_optimal],
-                name=col,
-                legendgroup=col,
-                showlegend=False,
-                marker=dict(color=["black"]),
-            ),
-            row=1 + (i) // cols,
-            col=i % cols + 1,
-        )
-
-        shapes.append(
-            go.layout.Shape(
-                type="line",
-                x0=0,
-                y0=y_optimal,
-                x1=x_optimal,
-                y1=y_optimal,
-                line_width=1,
-                line_dash="dash",
-                line_color="black",
-                xref=f"x{i+1}",
-                yref=f"y{i+1}",
-            )
-        )
-
-        shapes.append(
-            go.layout.Shape(
-                type="line",
-                x0=x_optimal,
-                y0=0,
-                x1=x_optimal,
-                y1=y_optimal,
-                line_width=1,
-                line_dash="dash",
-                line_color="black",
-                xref=f"x{i+1}",
-                yref=f"y{i+1}",
-            )
-        )
-
-        start_value, end_value, left_value, right_value = find_segment_value(
-            x,
-            roi,
-            marginal_roi,
-        )
-
-        # Adding background colors
-        y_max = y.max() * 1.3  # 30% extra space above the max
-
-        # Yellow region
-        shapes.append(
-            go.layout.Shape(
-                type="rect",
-                x0=start_value,
-                y0=0,
-                x1=left_value,
-                y1=y_max,
-                line=dict(width=0),
-                fillcolor="rgba(255, 255, 0, 0.3)",
-                layer="below",
-                xref=f"x{i+1}",
-                yref=f"y{i+1}",
-            )
-        )
-
-        # Green region
-        shapes.append(
-            go.layout.Shape(
-                type="rect",
-                x0=left_value,
-                y0=0,
-                x1=right_value,
-                y1=y_max,
-                line=dict(width=0),
-                fillcolor="rgba(0, 255, 0, 0.3)",
-                layer="below",
-                xref=f"x{i+1}",
-                yref=f"y{i+1}",
-            )
-        )
-
-        # Red region
-        shapes.append(
-            go.layout.Shape(
-                type="rect",
-                x0=right_value,
-                y0=0,
-                x1=end_value,
-                y1=y_max,
-                line=dict(width=0),
-                fillcolor="rgba(255, 0, 0, 0.3)",
-                layer="below",
-                xref=f"x{i+1}",
-                yref=f"y{i+1}",
-            )
-        )
-
-    fig.update_layout(
-        # height=1000,
-        # width=1000,
-        title_text=f"Response Curves (X: Spends Vs Y: {target})",
-        showlegend=False,
-        shapes=shapes,
-    )
-    fig.update_annotations(font_size=10)
-    # fig.update_xaxes(title="Spends")
-    # fig.update_yaxes(title=target)
-    fig.update_yaxes(
-        gridcolor="rgba(136, 136, 136, 0.5)", gridwidth=0.5, griddash="dash"
-    )
-
-    return fig
-
+        if col == "Panel":
+            continue
+        st.write(col)
+        x_modified = summary_df_sorted["Optimized_spend"][col]/104
+        y_modified = summary_df_sorted["New_sales"][col]/104
+        st.plotly_chart(rc.response_curves(col,x_modified,y_modified))
+        
 
 # @st.cache
 # def plot_response_curves():
@@ -849,7 +698,7 @@ def name_formating(channel_name):
     return name_mod
 
 
-
+@st.experimental_memo(show_spinner=False)
 def panel_fetch(file_selected):
     raw_data_mmm_df = pd.read_excel(file_selected, sheet_name="RAW DATA MMM")
 
@@ -882,118 +731,174 @@ def reset_inputs():
 
 if auth_status == True:
     authenticator.logout("Logout", "main")
-
-    st.header("Simulation")
-    with st.expander('Optimized Spends Overview'):
-        if st.button('Refresh'):
-            st.rerun()
-
-        import plotly.graph_objects as go
-        from plotly.subplots import make_subplots
-
-        # Define light colors for bars
-        import plotly.graph_objects as go
-        from plotly.subplots import make_subplots
-
-        st.empty()
-        #st.header('Model Result Analysis')
-        spends_data=pd.read_excel('Overview_data_test.xlsx')
-
-        with open('summary_df.pkl', 'rb') as file:
-            summary_df_sorted = pickle.load(file)
-        #st.write(summary_df_sorted)
-
-       # selected_scenario= st.selectbox('Select Saved Scenarios',['S1','S2']) 
-        summary_df_sorted=summary_df_sorted.sort_values(by=['Optimized_spend'],ascending=False)
-        summary_df_sorted['old_roi']=summary_df_sorted['Old_sales']/summary_df_sorted['Actual_spend']
-        summary_df_sorted['new_roi']=summary_df_sorted['New_sales']/summary_df_sorted['Optimized_spend']
-
-        total_actual_spend = summary_df_sorted['Actual_spend'].sum()
-        total_optimized_spend = summary_df_sorted['Optimized_spend'].sum()
-
-        actual_spend_percentage = (summary_df_sorted['Actual_spend'] / total_actual_spend) * 100
-        optimized_spend_percentage = (summary_df_sorted['Optimized_spend'] / total_optimized_spend) * 100
-
-
-
-        light_blue = 'rgba(0, 31, 120, 0.7)'
-        light_orange = 'rgba(0, 181, 219, 0.7)'
-        light_green = 'rgba(240, 61, 20, 0.7)'
-        light_red = 'rgba(250, 110, 10, 0.7)'
-        light_purple = 'rgba(255, 191, 69, 0.7)'
-
-
-        # Create subplots with one row and two columns
-        fig = make_subplots(rows=1, cols=3, subplot_titles=("Actual vs. Optimized Spend", "Actual vs. Optimized Contribution", "Actual vs. Optimized ROI"))
-
-        # Add actual vs optimized spend bars
-
-        
-        fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Actual_spend'], name='Actual',
-                             text=summary_df_sorted['Actual_spend'].apply(format_number) + ' '+' (' + actual_spend_percentage.round(2).astype(str) + '%)',
-                               marker_color=light_blue, orientation='h'), 
-                             row=1,
-                               col=1)
-        
+    st.header("Scenario Planner")
+    def scenario_planner_plots():
         
-        fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Optimized_spend'], name='Optimized',
-                             text=summary_df_sorted['Optimized_spend'].apply(format_number) + ' (' + optimized_spend_percentage.round(2).astype(str) + '%)',
-                               marker_color=light_orange, 
-                               orientation='h'),
-                                 row=1, 
-                                 col=1)
-        
-        fig.update_xaxes(title_text="Amount", row=1, col=1)
-
-        # Add actual vs optimized Contribution
-        fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['New_sales'],
-                              name='Optimized Contribution',text=summary_df_sorted['New_sales'].apply(format_number), 
-                              marker_color=light_orange, orientation='h',showlegend=False), row=1, col=2)
-        
-        fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Old_sales'], 
-                             name='Actual Contribution',text=summary_df_sorted['Old_sales'].apply(format_number), 
-                             marker_color=light_blue, orientation='h',showlegend=False), row=1, col=2)
-        
-  
-        fig.update_xaxes(title_text="Contribution", row=1, col=2) 
-
-        # Add actual vs optimized ROI bars
+        with st.expander('Optimized Spends Overview'):
+            # if st.button('Refresh'):
+            #     st.experimental_rerun()
+
+            import plotly.graph_objects as go
+            from plotly.subplots import make_subplots
+
+            # Define light colors for bars
+            import plotly.graph_objects as go
+            from plotly.subplots import make_subplots
+
+            st.empty()
+            #st.header('Model Result Analysis')
+            spends_data=pd.read_excel('Overview_data_test.xlsx')
+
+            with open('summary_df.pkl', 'rb') as file:
+                summary_df_sorted = pickle.load(file)
+            #st.write(summary_df_sorted)
+
+            # selected_scenario= st.selectbox('Select Saved Scenarios',['S1','S2']) 
+            summary_df_sorted=summary_df_sorted.sort_values(by=['Optimized_spend'],ascending=False)
+            summary_df_sorted['old_efficiency']=(summary_df_sorted['Old_sales']/summary_df_sorted['Old_sales'].sum())/(summary_df_sorted['Actual_spend']/summary_df_sorted['Actual_spend'].sum())
+            summary_df_sorted['new_efficiency']=(summary_df_sorted['New_sales']/summary_df_sorted['New_sales'].sum())/(summary_df_sorted['Optimized_spend']/summary_df_sorted['Optimized_spend'].sum())
+
+            summary_df_sorted['old_roi']=summary_df_sorted['Old_sales']/summary_df_sorted['Actual_spend']
+            summary_df_sorted['new_roi']=summary_df_sorted['New_sales']/summary_df_sorted['Optimized_spend']
+
+            total_actual_spend = summary_df_sorted['Actual_spend'].sum()
+            total_optimized_spend = summary_df_sorted['Optimized_spend'].sum()
+
+            actual_spend_percentage = (summary_df_sorted['Actual_spend'] / total_actual_spend) * 100
+            optimized_spend_percentage = (summary_df_sorted['Optimized_spend'] / total_optimized_spend) * 100
+
+
+
+            light_blue = 'rgba(0, 31, 120, 0.7)'
+            light_orange = 'rgba(0, 181, 219, 0.7)'
+            light_green = 'rgba(240, 61, 20, 0.7)'
+            light_red = 'rgba(250, 110, 10, 0.7)'
+            light_purple = 'rgba(255, 191, 69, 0.7)'
+
+
+            # # Create subplots with one row and two columns
+            # fig = make_subplots(rows=3, cols=1, subplot_titles=("Actual vs. Optimized Spend", "Actual vs. Optimized Contribution", "Actual vs. Optimized ROI"))
+
+            # # Add actual vs optimized spend bars
+
+            
+            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Actual_spend'], name='Actual',
+            #                      text=summary_df_sorted['Actual_spend'].apply(format_number) + ' '+' (' + actual_spend_percentage.round(2).astype(str) + '%)',
+            #                        marker_color=light_blue, orientation='h'), 
+            #                      row=1,
+            #                        col=1)
+            
+            
+            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Optimized_spend'], name='Optimized',
+            #                      text=summary_df_sorted['Optimized_spend'].apply(format_number) + ' (' + optimized_spend_percentage.round(2).astype(str) + '%)',
+            #                        marker_color=light_orange, 
+            #                        orientation='h'),
+            #                          row=1, 
+            #                          col=1)
+            
+            # fig.update_xaxes(title_text="Amount", row=1, col=1)
+
+            # # Add actual vs optimized Contribution
+            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['New_sales'],
+            #                       name='Optimized Contribution',text=summary_df_sorted['New_sales'].apply(format_number), 
+            #                       marker_color=light_orange, orientation='h',showlegend=False), row=2, col=1)
+            
+            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['Old_sales'], 
+            #                      name='Actual Contribution',text=summary_df_sorted['Old_sales'].apply(format_number), 
+            #                      marker_color=light_blue, orientation='h',showlegend=False), row=2, col=1)
+            
+    
+            # fig.update_xaxes(title_text="Contribution", row=2, col=1) 
+
+            # # Add actual vs optimized ROI bars
+                    
+            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['new_roi'], 
+            #                      name='Optimized ROI',text=summary_df_sorted['new_roi'].apply(format_number) , 
+            #                      marker_color=light_orange, orientation='h',showlegend=False), row=3, col=1)
+            
+            # fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['old_roi'], 
+            #                      name='Actual ROI', text=summary_df_sorted['old_roi'].apply(format_number) ,
+            #                        marker_color=light_blue, orientation='h',showlegend=False), row=3, col=1)
+
+            # fig.update_xaxes(title_text="ROI", row=3, col=1)
+
+            # # Update layout
+            # fig.update_layout(title_text="Actual vs. Optimized Metrics for Media Channels",
+            #                    showlegend=True, yaxis=dict(title='Media Channels', autorange="reversed"))
+
+            # st.plotly_chart(fig,use_container_width=True)
+
+            # Create subplots with one row and two columns
+            fig = go.Figure()
+            # Add actual vs optimized spend bars
+
+            
+            fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Actual_spend'], name='Actual',
+                                text=summary_df_sorted['Actual_spend'].apply(format_number) + ' '
+                                #  + 
+                                #  ' '+
+                                # '</br> (' + actual_spend_percentage.astype(int).astype(str) + '%)'
+                                ,textposition='outside',#textfont=dict(size=30), 
+                                marker_color=light_blue))
+            
+            
+            fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Optimized_spend'], name='Optimized',
+                                text=summary_df_sorted['Optimized_spend'].apply(format_number) + ' '
+                                #    + 
+                                #  '</br> (' + optimized_spend_percentage.astype(int).astype(str) + '%)'
+                                ,textposition='outside',#textfont=dict(size=30), 
+                                marker_color=light_orange))
+            
+            fig.update_xaxes(title_text="Channels")
+            fig.update_yaxes(title_text="Spends ($)")
+            fig.update_layout(
+                title = "Actual vs. Optimized Spends",
+                        margin=dict(t=40, b=40, l=40, r=40)
+                    )
+            
+            st.plotly_chart(fig,use_container_width=True)
+
+            # Add actual vs optimized Contribution
+            fig = go.Figure()
+            fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['Old_sales'], 
+                                name='Actual Contribution',text=summary_df_sorted['Old_sales'].apply(format_number),textposition='outside', 
+                                marker_color=light_blue,showlegend=True))
+            
+            fig.add_trace(go.Bar(x=summary_df_sorted['Channel_name'], y=summary_df_sorted['New_sales'],
+                                name='Optimized Contribution',text=summary_df_sorted['New_sales'].apply(format_number),textposition='outside', 
+                                marker_color=light_orange, showlegend=True))
+            
+            
+    
+            fig.update_yaxes(title_text="Contribution")
+            fig.update_xaxes(title_text="Channels") 
+            fig.update_layout(
+                title = "Actual vs. Optimized Contributions",
+                        margin=dict(t=40, b=40, l=40, r=40)
+                        # yaxis=dict(range=[0, 0.002]),
+                    )
+            st.plotly_chart(fig,use_container_width=True)
+
+            # Add actual vs optimized Efficiency bars
+            fig = go.Figure()
+            summary_df_sorted_p =  summary_df_sorted[summary_df_sorted['Channel_name']!="Panel"]
+            fig.add_trace(go.Bar(x=summary_df_sorted_p['Channel_name'], y=summary_df_sorted_p['old_efficiency'], 
+                                name='Actual Efficiency', text=summary_df_sorted_p['old_efficiency'].apply(format_number) ,textposition='outside',
+                                marker_color=light_blue,showlegend=True))
+            fig.add_trace(go.Bar(x=summary_df_sorted_p['Channel_name'], y=summary_df_sorted_p['new_efficiency'], 
+                                name='Optimized Efficiency',text=summary_df_sorted_p['new_efficiency'].apply(format_number),textposition='outside' , 
+                                marker_color=light_orange,showlegend=True))
+            
+            fig.update_xaxes(title_text="Channels")
+            fig.update_yaxes(title_text="ROI")
+            fig.update_layout(
+                title = "Actual vs. Optimized ROI",
+                        margin=dict(t=40, b=40, l=40, r=40),
+                        # yaxis=dict(range=[0, 0.002]),
+                    )
                 
-        fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['new_roi'], 
-                             name='Optimized ROI',text=summary_df_sorted['new_roi'].apply(format_number) , 
-                             marker_color=light_orange, orientation='h',showlegend=False), row=1, col=3)
-        
-        fig.add_trace(go.Bar(y=summary_df_sorted['Channel_name'], x=summary_df_sorted['old_roi'], 
-                             name='Actual ROI', text=summary_df_sorted['old_roi'].apply(format_number) ,
-                               marker_color=light_blue, orientation='h',showlegend=False), row=1, col=3)
-
-        fig.update_xaxes(title_text="ROI", row=1, col=3)
-
-        # Update layout
-        fig.update_layout(title_text="Actual vs. Optimized Metrics for Media Channels",
-                           showlegend=True, yaxis=dict(title='Media Channels', autorange="reversed"))
-
-        st.plotly_chart(fig,use_container_width=True)
+            st.plotly_chart(fig,use_container_width=True)
 
-        # fig = make_subplots(rows=1, cols=2, subplot_titles=("Actual Spend", "Optimized Spend"), specs=[[{'type': 'domain'}, {'type': 'domain'}]])
-
-        # # Actual spend donut chart
-        # fig.add_trace(go.Pie(labels=summary_df_sorted['Channel_name'],
-        #                       values=summary_df_sorted['Actual_spend'], name='Actual Spend', hole=0.3, 
-        #                       marker_colors=[light_blue, light_orange, light_green, light_red, light_purple]), row=1, col=1)
-
-        # # Optimized spend donut chart
-        # fig.add_trace(go.Pie(labels=summary_df_sorted['Channel_name'], 
-        #                      values=summary_df_sorted['Optimized_spend'], name='Optimized Spend', hole=0.3, 
-        #                      arker_colors=[light_blue, light_orange, light_green, light_red, light_purple]), row=1, col=2)
-
-        # # Update layout
-        # fig.update_layout(title_text="Actual vs. Optimized Spend Distribution")
-
-        # # Show plot
-        # st.plotly_chart(fig, use_container_width=True)
-
-    #col1, col2 = st.columns([1, 1])
 
     # Response Metrics
     directory = "metrics_level_data"
@@ -1018,14 +923,16 @@ if auth_status == True:
     # Panel List
     panel_list = panel_fetch(file_selected)
 
-    # Panel Selected
-    panel_selected = st.selectbox(
-        "Markets",
-        ["Total Market"] + panel_list,
-        index=0,
-        on_change=reset_inputs,
-    )
+    # # Panel Selected
+    # panel_selected = st.selectbox(
+    #     "Markets",
+    #     ["Total Market"] + panel_list,
+    #     index=0,
+    #     on_change=reset_inputs,
+    # )
     
+    # st.write(panel_selected)
+    panel_selected = "Total Market"
     st.session_state['selected_markets']=panel_selected
 
     if "update_rcs" in st.session_state:
@@ -1058,6 +965,15 @@ if auth_status == True:
         st.session_state["initialized"] = True
         st.session_state["first_time"] = False
 
+    # initialize_data(
+    #             panel=panel_selected,
+    #             target_file=file_selected,
+    #             updated_rcs=updated_rcs,
+    #             metrics=metrics_selected,
+    #         )
+    # st.session_state["initialized"] = True
+    # st.session_state["first_time"] = False
+
     # Channels List
     channels_list = st.session_state["channels_list"]
 
@@ -1108,7 +1024,7 @@ if auth_status == True:
     with sub_header[1]:
         st.metric(
             label=target,
-            value=format_numbers(
+            value=format_numbers_f(
                 float(_scenario.actual_total_sales)
             ),
         )
@@ -1123,7 +1039,7 @@ if auth_status == True:
     with sub_header[3]:
         st.metric(
             label=target,
-            value=format_numbers(
+            value=format_numbers_f(
                 float(_scenario.modified_total_sales)
             ),
             delta=numerize(_scenario.delta_sales, 1),
@@ -1169,8 +1085,11 @@ if auth_status == True:
             st.button(
                 "Reset",
                 on_click=reset_scenario,
-                args=(panel_selected, file_selected, updated_rcs)
+                args=(panel_selected, file_selected, updated_rcs),
+                # use_container_width=True,
             )
+            # st.write(target)
+
 
         _columns2 = st.columns((2, 2, 2))
         if st.session_state["optimization_key"] == "Media Spends":
@@ -1206,8 +1125,9 @@ if auth_status == True:
                 #     key="total_spends_change_abs_slider",
                 #     on_change=update_all_spends_abs_slider,
                 # )
-
+        
         elif st.session_state["optimization_key"] == target:
+            # st.write(target)
             with _columns2[0]:
                 sales_input = st.text_input(
                     "Absolute",
@@ -1227,9 +1147,13 @@ if auth_status == True:
             with _columns2[2]:
                 min_value = round(_scenario.actual_total_sales * 0.5)
                 max_value = round(_scenario.actual_total_sales * 1.5)
+                st.write(min_value)
+                st.write(max_value)
+                # for value in range(min_value, max_value + 1, int(100)):
+                #     st.write(numerize(value, 1))
                 st.session_state["total_sales_change_abs_slider_options"] = [
                     numerize(value, 1)
-                    for value in range(min_value, max_value + 1, int(1e5))
+                    for value in range(min_value, max_value + 1, int(100))
                 ]
 
                 st.select_slider(
@@ -1237,6 +1161,7 @@ if auth_status == True:
                     options=st.session_state["total_sales_change_abs_slider_options"],
                     key="total_sales_change_abs_slider",
                     on_change=update_sales_abs_slider,
+                    # value=numerize(min_value, 1)
                 )
 
         if (
@@ -1261,7 +1186,7 @@ if auth_status == True:
             "Optimize",
             on_click=optimize,
             args=(st.session_state["optimization_key"], status_placeholder),
-            use_container_width=True,
+            # use_container_width=True,
         )
 
         st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)
@@ -1289,6 +1214,7 @@ if auth_status == True:
                 "Old_sales":[]
             }
         for i, channel_name in enumerate(channels_list):
+            # st.write(channel_name)
             _channel_class = st.session_state["scenario"].channels[channel_name]
             _columns = st.columns((2.5, 1.5, 1.5, 1.5, 1))
             with _columns[0]:
@@ -1360,7 +1286,7 @@ if auth_status == True:
 
                 st.metric(
                     target,
-                    format_numbers(current_channel_sales),
+                    format_numbers_f(current_channel_sales),
                     delta=numerize(sales_delta, 1),
                     label_visibility="collapsed",
                 )
@@ -1393,7 +1319,13 @@ if auth_status == True:
             col = channels_list[i]
             x_actual = st.session_state["scenario"].channels[col].actual_spends
             x_modified = st.session_state["scenario"].channels[col].modified_spends
-
+            # x_modified_total = 0 
+            # for c in channels_list:
+            #     # st.write(c)
+            #     # st.write(st.session_state["scenario"].channels[c].modified_spends)
+            #     x_modified_total = x_modified_total + st.session_state["scenario"].channels[c].modified_spends.sum()
+            # st.write(x_modified_total)
+            
             x_total = x_modified.sum()
             power = np.ceil(np.log(x_actual.max()) / np.log(10)) - 3
 
@@ -1438,9 +1370,13 @@ if auth_status == True:
             )
 
             roi = y / np.maximum(x, np.finfo(float).eps)
+            # roi = (y/np.sum(y))/(x/np.sum(x))
+            # st.write(x)
+            # st.write(y)
+            # st.write(roi)
 
-            #st.write(roi[-1])
-
+            # st.write(roi[-1])
+            
             roi_current, marginal_roi_current = roi[-1], marginal_roi[-1]
             x, y, roi, marginal_roi = (
                 x[:-1],
@@ -1449,6 +1385,8 @@ if auth_status == True:
                 marginal_roi[:-1],
             )  # Drop data for current spends
 
+            # roi_current = 
+
             start_value, end_value, left_value, right_value = find_segment_value(
                 x,
                 roi,
@@ -1465,6 +1403,25 @@ if auth_status == True:
                 current_channel_spends,
             )
 
+            summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
+            # st.dataframe(summary_df)
+            summary_df.drop_duplicates(subset="Channel_name", keep="last", inplace=True)
+            # st.dataframe(summary_df)
+
+            summary_df_sorted = summary_df.sort_values(by="Delta", ascending=False)
+            summary_df_sorted["Delta_percent"] = np.round(
+                ((summary_df_sorted["Optimized_spend"] / summary_df_sorted["Actual_spend"]) - 1)
+                * 100,
+                2,
+            )
+
+            summary_df_sorted=summary_df_sorted.sort_values(by=['Optimized_spend'],ascending=False)
+            summary_df_sorted['old_efficiency']=(summary_df_sorted['Old_sales']/summary_df_sorted['Old_sales'].sum())/(summary_df_sorted['Actual_spend']/summary_df_sorted['Actual_spend'].sum())
+            summary_df_sorted['new_efficiency']=(summary_df_sorted['New_sales']/summary_df_sorted['New_sales'].sum())/(summary_df_sorted['Optimized_spend']/summary_df_sorted['Optimized_spend'].sum())
+            
+            a = (summary_df_sorted[summary_df_sorted['Channel_name']== col]).reset_index()['new_efficiency'][0]
+            # st.write(a)
+
             with bin_placeholder:
                 st.markdown(
                     f"""
@@ -1475,41 +1432,33 @@ if auth_status == True:
                         text-align: center;
                         color: #006EC0;
                         ">
-                        <p style="margin: 0; font-size: 20px;">ROI: {round(roi_current,1)}</p>
-                        <p style="margin: 0; font-size: 20px;">Marginal ROI: {round(marginal_roi_current,1)}</p>
+                        <p style="margin: 0; font-size: 20px;">Efficiency: {round(a,2)}</p>
+                        <!--<p style="margin: 0; font-size: 20px;">Marginal ROI: {round(marginal_roi_current,1)}</p>-->
                     </div>
                     """,
                     unsafe_allow_html=True,
                 )
 
-    # with st.expander("See Response Curves", expanded=True):
-    #     fig = plot_response_curves()
-    #     st.plotly_chart(fig, use_container_width=True)
-
-    # _columns = st.columns(2)
-    # # with _columns[0]:
-    # st.subheader("Save Scenario")
-    # scenario_name = st.text_input(
-    #     "Scenario name",
-    #     key="scenario_input",
-    #     placeholder="Scenario name",
-    #     label_visibility="collapsed",
-    # )
-    # st.button(
-    #     "Save",
-    #     on_click=lambda: save_scenario(scenario_name),
-    #     disabled=len(st.session_state["scenario_input"]) == 0,use_container_width=True
-    # )
+    with st.expander("See Response Curves", expanded=True):
+        fig = plot_response_curves(summary_df_sorted)
+        # st.plotly_chart(rc.response_curves(col))
+        # st.plotly_chart(fig, use_container_width=True)
 
-    # summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
-    # summary_df.drop_duplicates(subset="Channel_name", keep="last", inplace=True)
+    summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
+    # st.dataframe(summary_df)
+    summary_df.drop_duplicates(subset="Channel_name", keep="last", inplace=True)
+    # st.dataframe(summary_df)
 
-    # summary_df_sorted = summary_df.sort_values(by="Delta", ascending=False)
-    # summary_df_sorted["Delta_percent"] = np.round(
-    #     ((summary_df_sorted["Optimized_spend"] / summary_df_sorted["Actual_spend"]) - 1)
-    #     * 100,
-    #     2,
-    # )
+    summary_df_sorted = summary_df.sort_values(by="Delta", ascending=False)
+    summary_df_sorted["Delta_percent"] = np.round(
+        ((summary_df_sorted["Optimized_spend"] / summary_df_sorted["Actual_spend"]) - 1)
+        * 100,
+        2,
+    )
+
+    
+
+    
 
     with open("summary_df.pkl", "wb") as f:
         pickle.dump(summary_df_sorted, f)
@@ -1525,6 +1474,24 @@ if auth_status == True:
         #     fig=summary_plot(summary_df_sorted, x='Optimized_spend', y='Channel_name', title='Planned Spend', text_column='Optimized_spend')
         #     st.plotly_chart(fig,use_container_width=True)
 
+    scenario_planner_plots()
+
+    _columns = st.columns(2)
+    # with _columns[0]:
+    st.subheader("Save Scenario")
+    scenario_name = st.text_input(
+        "Scenario name",
+        key="scenario_input",
+        placeholder="Scenario name",
+        label_visibility="collapsed",
+    )
+    st.button(
+        "Save",
+        on_click=lambda: save_scenario(scenario_name),
+        disabled=len(st.session_state["scenario_input"]) == 0,#use_container_width=True
+    )
+
+
 
 elif auth_status == False:
     st.error("Username/Password is incorrect")

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d1b0b3954664cb2e6a7ed10e74ff599cc25479153bb9ee953a602ce03710de55
-size 2269
+oid sha256:41027251e75b11465e5cec329b14a8e35d4791aa6fdbbc948a0f32b4c1acc286
+size 1886

utilities.py

@@ -931,6 +931,11 @@ def format_numbers(value, n_decimals=1, include_indicator=True):
     else:
         return f"{numerize(value,n_decimals)}"
 
+def format_numbers_f(value, n_decimals=1, include_indicator=False):
+    if include_indicator:
+        return f"{CURRENCY_INDICATOR} {numerize(value,n_decimals)}"
+    else:
+        return f"{numerize(value,n_decimals)}"
 
 def decimal_formater(num_string, n_decimals=1):
     parts = num_string.split(".")

utilities_with_panel.py

@@ -82,15 +82,15 @@ def nav_page(page_name, timeout_secs=3):
 #                     <img src="https://assets-global.website-files.com/64c8fffb0e95cbc525815b79/64df84637f83a891c1473c51_Vector%20(Stroke).svg   ">
 #             </div>""", unsafe_allow_html=True)
 
-path = os.path.dirname(__file__)
+# path = os.path.dirname(__file__)
 
-file_ = open(f"{path}/mastercard_logo.png", "rb")
+# file_ = open(f"{path}/mastercard_logo.png", "rb")
 
-contents = file_.read()
+# contents = file_.read()
 
-data_url = base64.b64encode(contents).decode("utf-8")
+# data_url = base64.b64encode(contents).decode("utf-8")
 
-file_.close()
+# file_.close()
 
  
 
@@ -136,7 +136,7 @@ def load_local_css(file_name):
 #             </div>""", unsafe_allow_html=True)
 path1 = os.path.dirname(__file__)
 
-file_1 = open(f"{path}/ALDI_2017.png", "rb")
+file_1 = open(f"ALDI_2017.png", "rb")
 
 contents1 = file_1.read()
 
@@ -723,9 +723,9 @@ def create_channel_summary(scenario):
 
     actual_summary_df = pd.DataFrame([summary_columns, actual_spends_rows, actual_sales_rows, actual_roi_rows]).T
 
-    actual_summary_df.columns = ['Channel', 'Spends', 'Revenue', 'ROI']
+    actual_summary_df.columns = ['Channel', 'Spends', 'Prospects', 'ROI']
 
-    actual_summary_df['Revenue'] = actual_summary_df['Revenue'].map(lambda x: str(x)[1:])
+    actual_summary_df['Prospects'] = actual_summary_df['Prospects'].map(lambda x: str(x)[1:])
 
     return actual_summary_df
 
@@ -771,7 +771,7 @@ def create_contribution_pie(scenario):
     light_purple = 'rgba(255, 191, 69, 0.7)'
 
     colors_map = {col:color for col,color in zip(st.session_state['channels_list'],plotly.colors.n_colors(plotly.colors.hex_to_rgb('#BE6468'), plotly.colors.hex_to_rgb('#E7B8B7'),23))}
-    total_contribution_fig = make_subplots(rows=1, cols=2,subplot_titles=['Media Spends','Revenue Contribution'],specs=[[{"type": "pie"}, {"type": "pie"}]])
+    total_contribution_fig = make_subplots(rows=1, cols=2,subplot_titles=['Media Spends','Prospects Contribution'],specs=[[{"type": "pie"}, {"type": "pie"}]])
     total_contribution_fig.add_trace(
                 go.Pie(labels=[channel_name_formating(channel_name) for channel_name in st.session_state['channels_list']] + ['Non Media'],
                     values= [round(scenario.channels[channel_name].actual_total_spends * scenario.channels[channel_name].conversion_rate,1) for channel_name in st.session_state['channels_list']] + [0],
@@ -893,7 +893,7 @@ def create_contribution_pie(scenario):
 #     return total_contribution_fig
 
 def create_contribuion_stacked_plot(scenario):
-    weekly_contribution_fig = make_subplots(rows=1, cols=2, subplot_titles=['Spends', 'Revenue'], specs=[[{"type": "bar"}, {"type": "bar"}]])
+    weekly_contribution_fig = make_subplots(rows=1, cols=2, subplot_titles=['Spends', 'Prospects'], specs=[[{"type": "bar"}, {"type": "bar"}]])
     raw_df = st.session_state['raw_df']
     df = raw_df.sort_values(by='Date')
     x = df.Date
@@ -916,7 +916,7 @@ def create_contribuion_stacked_plot(scenario):
             x=x,
             y=scenario.channels[channel_name].actual_sales,
             name=channel_name_formating(channel_name),
-            hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
+            hovertemplate="Date:%{x}<br>Prospects:%{y:$.2s}",
             legendgroup=channel_name,
             showlegend=False,
             marker_color=color,
@@ -931,7 +931,7 @@ def create_contribuion_stacked_plot(scenario):
         x=x,
         y=scenario.constant + scenario.correction,
         name='Non Media',
-        hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
+        hovertemplate="Date:%{x}<br>Prospects:%{y:$.2s}",
         marker_color=color_palette[-1],
     ), row=1, col=2)
 
@@ -950,8 +950,8 @@ def create_channel_spends_sales_plot(channel):
             x=x,
             y=_sales,
             marker_color=color_palette[1],  # You can choose a color from the palette
-            name='Revenue',
-            hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
+            name='Prospects',
+            hovertemplate="Date:%{x}<br>Prospects:%{y:$.2s}",
         ), secondary_y=False)
         
         channel_sales_spends_fig.add_trace(go.Scatter(
@@ -962,7 +962,7 @@ def create_channel_spends_sales_plot(channel):
             hovertemplate="Date:%{x}<br>Spend:%{y:$.2s}",
         ), secondary_y=True)
         
-        channel_sales_spends_fig.update_layout(xaxis_title='Date', yaxis_title='Revenue', yaxis2_title='Spends ($)', title='Channel spends and Revenue week-wise')
+        channel_sales_spends_fig.update_layout(xaxis_title='Date', yaxis_title='Prospects', yaxis2_title='Spends ($)', title='Channel spends and Prospects week-wise')
         channel_sales_spends_fig.update_xaxes(showgrid=False)
         channel_sales_spends_fig.update_yaxes(showgrid=False)
     else:
@@ -976,11 +976,11 @@ def create_channel_spends_sales_plot(channel):
             x=x,
             y=_sales,
             marker_color=color_palette[0],  # You can choose a color from the palette
-            name='Revenue',
-            hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
+            name='Prospects',
+            hovertemplate="Date:%{x}<br>Prospects:%{y:$.2s}",
         ), secondary_y=False)
         
-        channel_sales_spends_fig.update_layout(xaxis_title='Date', yaxis_title='Revenue', yaxis2_title='Spends ($)', title='Channel spends and Revenue week-wise')
+        channel_sales_spends_fig.update_layout(xaxis_title='Date', yaxis_title='Prospects', yaxis2_title='Spends ($)', title='Channel spends and Prospects week-wise')
         channel_sales_spends_fig.update_xaxes(showgrid=False)
         channel_sales_spends_fig.update_yaxes(showgrid=False)