Email

Streamlit_functions.py

@@ -154,8 +154,9 @@ def pie_charts(start_date,end_date):
                          hoverinfo='label+percent',
                         textinfo= 'label+percent',
                         showlegend= False,textfont=dict(size =10),
-                        title="Distribution of Spends"
-                        , marker=dict(colors=colors)
+                        title="Distribution of Spends",
+                        texttemplate='%{label}: %{percent:.1%}',
+                        marker=dict(colors=colors)
                         ), 1, 1)
 
     fig.add_trace(go.Pie(labels=channels, 
@@ -165,7 +166,8 @@ def pie_charts(start_date,end_date):
                         textinfo= 'label+percent',
                         showlegend= False,
                         textfont=dict(size = 10),
-                        title = "Distribution of Revenue Contributions", marker=dict(colors=colors)
+                        title = "Distribution of Revenue Contributions", marker=dict(colors=colors),
+                        texttemplate='%{label}: %{percent:.1%}',
                         ), 1, 2)
     # fig.update_layout(
     #     title="Distribution Of Spends And Revenue Contributions"
@@ -261,69 +263,73 @@ def pie_spend(start_date,end_date):
 
     # Show the figure
     return fig
-def pie_contributions(start_date,end_date):
-    colors = ['#ff2b2b',  # Pastel Peach
-    '#0068c9',  # Pastel Blue
-    '#83c9ff',  # Pastel Pink
+
+# def pie_contributions(start_date,end_date):
+#     colors = ['#ff2b2b',  # Pastel Peach
+#     '#0068c9',  # Pastel Blue
+#     '#83c9ff',  # Pastel Pink
     
-    '#ffabab',  # Pastel Purple
-    '#29b09d',  # Pastel Green
-    '#7defa1',  # Pastel Yellow
-    '#ff8700',  # Pastel Gray
-    '#ffd16a',  # Pastel Red
-    '#6d3fc0',  # Pastel Rose
-    '#d5dae5',  # Pastel Lavender
-    '#309bff',  # Pastel Mauve
-    '#e9f5ff',  # Pastel Beige
-    '#BEBADA'   # Pastel Lilac
-    ]
-    start_date = pd.to_datetime(start_date)
-    end_date = pd.to_datetime(end_date)
-    cur_data = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
-    data = pd.DataFrame(cur_data[contribution_cols].sum().transpose())
-    data.index = channels
-    data.columns = ["p"]
-    # Create a pie chart with custom options
-    fig = go.Figure(data=[go.Pie(
-        labels=channels, 
-        values=data["p"],#ype(str)+'<br>'+data.index,
-        hoverinfo='label+percent',
-        textinfo= 'label+percent',
-        textposition='auto',
-        showlegend= False,
-        textfont=dict(size = 10)
-        , marker=dict(colors=colors)
-    )])
+#     '#ffabab',  # Pastel Purple
+#     '#29b09d',  # Pastel Green
+#     '#7defa1',  # Pastel Yellow
+#     '#ff8700',  # Pastel Gray
+#     '#ffd16a',  # Pastel Red
+#     '#6d3fc0',  # Pastel Rose
+#     '#d5dae5',  # Pastel Lavender
+#     '#309bff',  # Pastel Mauve
+#     '#e9f5ff',  # Pastel Beige
+#     '#BEBADA'   # Pastel Lilac
+#     ]
+#     start_date = pd.to_datetime(start_date)
+#     end_date = pd.to_datetime(end_date)
+#     cur_data = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
+#     data = pd.DataFrame(cur_data[contribution_cols].sum().transpose())
+#     data.index = channels
+#     data.columns = ["p"]
+#     # Create a pie chart with custom options
+#     fig = go.Figure(data=[go.Pie(
+#         labels=channels, 
+#         values=data["p"],#ype(str)+'<br>'+data.index,
+#         hoverinfo='label+percent',
+#         textinfo= 'label+percent',
+#         textposition='auto',
+#         showlegend= False,
+#         textfont=dict(size = 10)
+#         , marker=dict(colors=colors)
+#     )])
 
-    # fig.add_annotation(showarrow=False)
-    # Customize the layout
-    fig.update_layout(
-        # title="Distribution Of Contributions",
-        title={
-                    'text': "Distribution of Revenue",
-                    'font': {
-                    'size': 24,
-                    'family': 'Arial',
-                    'color': 'black',
-                    # 'bold': True
-                }
-            }
-        # margin=dict(t=0, b=0, l=0, r=0)
-    )
+#     # fig.add_annotation(showarrow=False)
+#     # Customize the layout
+#     fig.update_layout(
+#         # title="Distribution Of Contributions",
+#         title={
+#                     'text': "Distribution of Revenue",
+#                     'font': {
+#                     'size': 24,
+#                     'family': 'Arial',
+#                     'color': 'black',
+#                     # 'bold': True
+#                 }
+#             }
+#         # margin=dict(t=0, b=0, l=0, r=0)
+#     )
     
-    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'
-    )
+#     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'
+#     )
+
+#     # Show the figure
+#     return fig
+
+
 
-    # Show the figure
-    return fig
 def waterfall2(start_date1,end_date1,start_date2,end_date2):
     btn_chart = "Month on Month" 
     # if pd.isnull(start_date) == True :
@@ -356,21 +362,21 @@ def waterfall2(start_date1,end_date1,start_date2,end_date2):
 
     # Example data for the waterfall chart
     data = [
-        {'label': 'Previous Period', 'value': round(prev_data[contribution_cols].values.sum())},
-        {'label': 'Broadcast TV', 'value': round(cur_data['Broadcast TV_Prospects'].sum()-prev_data['Broadcast TV_Prospects'].sum())},
+        {'label': 'Previous Period', 'value': round(prev_data[contribution_cols].values.sum(), 1)},
+        {'label': 'Broadcast TV', 'value': round(cur_data['Broadcast TV_Prospects'].sum()-prev_data['Broadcast TV_Prospects'].sum(), 1)},
         {'label': 'Cable TV', 'value': round(cur_data['Cable TV_Prospects'].sum()-prev_data['Cable TV_Prospects'].sum())},
-        {'label': 'Connected & OTT TV', 'value': round(cur_data['Connected & OTT TV_Prospects'].sum()-prev_data['Connected & OTT TV_Prospects'].sum())}, 
-        {'label': 'Video', 'value': round(cur_data['Video_Prospects'].sum()-prev_data['Video_Prospects'].sum())}, 
-        {'label': 'Display Prospecting', 'value': round(cur_data['Display Prospecting_Prospects'].sum()-prev_data['Display Prospecting_Prospects'].sum())},
-        {'label': 'Display Retargeting', 'value': round(cur_data['Display Retargeting_Prospects'].sum()-prev_data['Display Retargeting_Prospects'].sum())},
-        {'label': 'Social Prospecting', 'value': round(cur_data['Social Prospecting_Prospects'].sum()-prev_data['Social Prospecting_Prospects'].sum())},
-        {'label': 'Social Retargeting', 'value': round(cur_data['Social Retargeting_Prospects'].sum()-prev_data['Social Retargeting_Prospects'].sum())},
-        {'label': 'Search Brand', 'value': round(cur_data['Search Brand_Prospects'].sum()-prev_data['Search Brand_Prospects'].sum())}, 
-        {'label': 'Search Non-brand', 'value': round(cur_data['Search Non-brand_Prospects'].sum()-prev_data['Search Non-brand_Prospects'].sum())}, 
-        {'label': 'Digital Partners', 'value': round(cur_data['Digital Partners_Prospects'].sum()-prev_data['Digital Partners_Prospects'].sum())}, 
-        {'label': 'Audio', 'value': round(cur_data['Audio_Prospects'].sum()-prev_data['Audio_Prospects'].sum())}, 
-        {'label': 'Email', 'value': round(cur_data['Email_Prospects'].sum()-prev_data['Email_Prospects'].sum())},
-        {'label': 'Current Period', 'value': round(cur_data[contribution_cols].values.sum())}
+        {'label': 'Connected & OTT TV', 'value': round(cur_data['Connected & OTT TV_Prospects'].sum()-prev_data['Connected & OTT TV_Prospects'].sum(), 1)}, 
+        {'label': 'Video', 'value': round(cur_data['Video_Prospects'].sum()-prev_data['Video_Prospects'].sum(), 1)}, 
+        {'label': 'Display Prospecting', 'value': round(cur_data['Display Prospecting_Prospects'].sum()-prev_data['Display Prospecting_Prospects'].sum(), 1)},
+        {'label': 'Display Retargeting', 'value': round(cur_data['Display Retargeting_Prospects'].sum()-prev_data['Display Retargeting_Prospects'].sum(), 1)},
+        {'label': 'Social Prospecting', 'value': round(cur_data['Social Prospecting_Prospects'].sum()-prev_data['Social Prospecting_Prospects'].sum(), 1)},
+        {'label': 'Social Retargeting', 'value': round(cur_data['Social Retargeting_Prospects'].sum()-prev_data['Social Retargeting_Prospects'].sum(), 1)},
+        {'label': 'Search Brand', 'value': round(cur_data['Search Brand_Prospects'].sum()-prev_data['Search Brand_Prospects'].sum(), 1)}, 
+        {'label': 'Search Non-brand', 'value': round(cur_data['Search Non-brand_Prospects'].sum()-prev_data['Search Non-brand_Prospects'].sum(), 1)}, 
+        {'label': 'Digital Partners', 'value': round(cur_data['Digital Partners_Prospects'].sum()-prev_data['Digital Partners_Prospects'].sum(), 1)}, 
+        {'label': 'Audio', 'value': round(cur_data['Audio_Prospects'].sum()-prev_data['Audio_Prospects'].sum(), 1)}, 
+        {'label': 'Email', 'value': round(cur_data['Email_Prospects'].sum()-prev_data['Email_Prospects'].sum(), 1)},
+        {'label': 'Current Period', 'value': round(cur_data[contribution_cols].values.sum(), 1)}
     ]
 
     # Calculate cumulative values for the waterfall chart
@@ -953,10 +959,10 @@ def shares_table_func(shares_df):
     shares_table_df = shares_df[["channels","cur_spend_share","cur_support_share","cur_contributions_share"]].copy()
     
     # Calculating ROI as Revenue Contribution / Spends
-    shares_table_df["ROI"] = (shares_df["cur_contributions_share"] / shares_df["cur_spend_share"]).round(2)
+    shares_table_df["ROI"] = (shares_df["cur_total_contributions"] / shares_df["cur_total_spend"]).round(1)
     
     # Calculating Effectiveness as Revenue Contribution * 1,000,000 / Support
-    shares_table_df["Effectiveness"] = (shares_df["cur_contributions_share"] * 1_000_000 / shares_df["cur_support_share"]).round(2)
+    shares_table_df["Effectiveness"] = (shares_df["cur_contributions_share"] * 1000 / shares_df["cur_support_share"]).round(1)
 
     shares_table_df = shares_table_df.rename(columns = {"channels":"METRIC",
                                                         "cur_spend_share":"Spend Share",

classes.py

@@ -123,6 +123,7 @@ class Channel:
 
     def hill_equation(x, Kd, n):
         return x**n / (Kd**n + x**n)
+    
     def response_curve(self, x):
         # # # # print(x)
         # if self.penalty:
@@ -132,6 +133,7 @@ class Channel:
         #         x,
         #         self.upper_limit + (x - self.upper_limit) * self.upper_limit / x,
         #     )
+
         if self.response_curve_type == "hill-eq":
             # dividing_parameter = check_dividing_parameter()
             # # # # print("lalala")
@@ -139,12 +141,14 @@ class Channel:
             # # # # print(len(x))
             # # # # print("in response curve function")
             # # # # print(x)
+
             if len(x) == 1:
-                dividing_rate = self.response_curve_params["num_pos_obsv"]
+                dividing_rate = 1
                 # # # # print(dividing_rate)
                 # x = np.sum(x)
             else:
-                dividing_rate = 1
+                dividing_rate = self.response_curve_params["num_pos_obsv"]
+
             # dividing_rate = self.response_curve_params["num_pos_obsv"]
                 # x = np.sum(x)
             # dividing_rate = 104
@@ -158,13 +162,12 @@ class Channel:
             # # # # # print(x_min)
             # # # # # print(Kd,n,x_min,x_max,y_min,y_max)
             # # # # # print(np.sum(x)/104)
-            x_inp = ( x/dividing_rate- x_min) / (x_max - x_min)
+            x_inp = ( (x/dividing_rate) - x_min) / (x_max - x_min)
             # # # # # print("x",x)
             # # # # # print("x_inp",x_inp)
             x_out = x_inp**n / (Kd**n + x_inp**n) #self.hill_equation(x_inp,Kd, n)
             # # # # # print("x_out",x_out)
 
-
             x_val_inv = (x_out*x_max + (1 - x_out) * x_min)
             sales = (x_val_inv*y_min/y_max)*dividing_rate
             # sales = ((x_max - x_min)*x_out + x_min)*dividing_rate
@@ -364,9 +367,10 @@ class Scenario:
 
     
     
-    
+
 
     def optimize_spends(self, sales_percent, channels_list, algo="trust-constr"):
+        import streamlit as st
         num_channels = len(channels_list)
         # # # # # print("%"*100)
         desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
@@ -404,14 +408,14 @@ class Scenario:
         constraints = [NonlinearConstraint(constraint, -1.0, 1.0),
                     #    LinearConstraint(np.ones((num_channels,)), lb = -50*calc_overall_bounds(channels_list), ub = 50*calc_overall_bounds(channels_list))
                        ]
-
+        
         res = minimize(
             lambda x: sum(x) / 10 ** (power),
             bounds=bounds,
             x0=initial_point,
             constraints=constraints,
             method=algo,
-            options={"maxiter": int(2e7), "xtol": 10},
+            options={"maxiter": int(1e5), "xtol": 1000},
         )
 
         for channel_name, modified_spends in zip(channels_list, res.x):
@@ -452,8 +456,27 @@ class Scenario:
             # )
             old_spends.append(channel_actual_total_spends)
             # bounds.append((1+ channel_bounds / 100) * channel_actual_total_spends)
-            lb = (1- int(_channel_class.channel_bounds_min) / 100) * _channel_class.actual_total_spends
-            ub = (1+  int(_channel_class.channel_bounds_max) / 100)  * _channel_class.actual_total_spends
+
+            # lb = (1- int(_channel_class.channel_bounds_min) / 100) * _channel_class.actual_total_spends
+            # ub = (1+  int(_channel_class.channel_bounds_max) / 100)  * _channel_class.actual_total_spends
+
+            import streamlit as st
+
+            if "overall_lower_bound" in st.session_state:
+                try:
+                    lower_val = float(st.session_state["overall_lower_bound"])
+                except (ValueError, TypeError):
+                    lower_val = 30.0
+
+            if "overall_upper_bound" in st.session_state:
+                try:
+                    upper_val = float(st.session_state["overall_upper_bound"])
+                except (ValueError, TypeError):
+                    upper_val = 30.0
+
+            lb = (1 - float(lower_val) / 100) * _channel_class.actual_total_spends
+            ub = (1 +  float(upper_val) / 100)  * _channel_class.actual_total_spends
+
             bounds.append((lb,ub))
             # # # # print("aaaaaa")
             # # # print((_channel_class.channel_bounds_max,_channel_class.channel_bounds_min))
@@ -500,7 +523,7 @@ class Scenario:
             x0=old_spends,
             constraints=constraint,
             bounds=bounds,
-            options={"maxiter": int(1e7), "xtol": 0.1},
+            options={"maxiter": int(1e7), "xtol": 100},
         )
 
         for channel_name, modified_spends in zip(channels_list, res.x):

pages/2_Scenario_Planner.py

@@ -359,15 +359,33 @@ def update_all_spends_abs():
 
     actual_spends = _scenario.actual_total_spends
     if (
-        validate_input(st.session_state["total_spends_change_abs"])
+        "total_spends_change_abs" in st.session_state and validate_input(st.session_state["total_spends_change_abs"])
         and st.session_state["allow_spends_update"]
     ):
         modified_spends = extract_number_for_string(
             st.session_state["total_spends_change_abs"]
         )
-        st.session_state["total_spends_change"] = (
+        total_spends_change_val = (
             (modified_spends / actual_spends) - 1
         ) * 100
+
+        if "overall_lower_bound" in st.session_state:
+            try:
+                lower_val = float(st.session_state["overall_lower_bound"])
+            except (ValueError, TypeError):
+                lower_val = 30.0
+
+        if "overall_upper_bound" in st.session_state:
+            try:
+                upper_val = float(st.session_state["overall_upper_bound"])
+            except (ValueError, TypeError):
+                upper_val = 30.0
+
+        if total_spends_change_val > upper_val or total_spends_change_val < -lower_val:
+            del st.session_state.total_spends_change_abs
+            return 
+            
+        st.session_state["total_spends_change"] = total_spends_change_val
         st.session_state["total_spends_change_abs_slider"] = st.session_state[
             "total_spends_change_abs"
         ]
@@ -499,9 +517,29 @@ def update_data(channel_name):
             st.session_state["scenario"].channels[channel_name].actual_total_spends
             * st.session_state["scenario"].channels[channel_name].conversion_rate
         )
-        st.session_state[f"{channel_name}_change"] = round(
+
+
+        channel_spends_change = round(
             100 * (modified_spends - prev_spends) / prev_spends, 2
         )
+
+        if "overall_lower_bound" in st.session_state:
+            try:
+                lower_val = float(st.session_state["overall_lower_bound"])
+            except (ValueError, TypeError):
+                lower_val = 30.0
+
+        if "overall_upper_bound" in st.session_state:
+            try:
+                upper_val = float(st.session_state["overall_upper_bound"])
+            except (ValueError, TypeError):
+                upper_val = 30.0
+
+        if channel_spends_change > upper_val or channel_spends_change < -lower_val:
+            del st.session_state[channel_name]
+            return 
+
+        st.session_state[f"{channel_name}_change"] = channel_spends_change
         st.session_state["scenario"].update(
             channel_name,
             modified_spends
@@ -584,6 +622,13 @@ def reset_scenario(panel_selected, file_selected, updated_rcs):
 
     st.session_state["total_sales_change"] = 0
 
+    if "overall_lower_bound" in st.session_state and "overall_upper_bound" in st.session_state:
+        del st.session_state["overall_lower_bound"]
+        del st.session_state["overall_upper_bound"]
+
+        st.session_state["overall_lower_bound"] = 30.0
+        st.session_state["overall_upper_bound"] = 30.0
+
     update_spends()
     update_sales()
 
@@ -739,9 +784,10 @@ def plot_response_curves(summary_df_sorted):
             summary_df_sorted["Optimized_spend"][channels_list[i]],
             summary_df_sorted["New_sales"][channels_list[i]],
         )
-        for i in range(13)
+        for i in range(13) if channels_list[i] != "Email"
     ]
 
+
     # for i in range()
 
     # Display figures in a grid layout
@@ -1596,7 +1642,7 @@ if auth_status == True:
         _columns = st.columns((1, 1, 1, 1, 1))
         with _columns[0]:
             # st.header("Prospects")
-            st.markdown("""<h4> Revenue</h4>""", unsafe_allow_html=True)
+            st.markdown("""<h4>Revenue</h4>""", unsafe_allow_html=True)
         with _columns[1]:
             st.markdown(
                 f"""<h4>$ {header_df["Prospects"]["Actual"]}</h4>""",
@@ -1656,13 +1702,13 @@ if auth_status == True:
             st.markdown("""<h4>ROI</h4>""", unsafe_allow_html=True)
             # st.header("Cost Per Prospect")
         with _columns[1]:
-            st.markdown(f"""<h4>{numerize(ef1,0)}</h4>""", unsafe_allow_html=True)
+            st.markdown(f"""<h4>{numerize(ef1, 2)}</h4>""", unsafe_allow_html=True)
             # st.metric(label="", value='$ '+numerize(ef1,0))
         with _columns[2]:
-            st.markdown(f"""<h4>{numerize(ef2,0)}</h4>""", unsafe_allow_html=True)
+            st.markdown(f"""<h4>{numerize(ef2, 2)}</h4>""", unsafe_allow_html=True)
             # st.metric(label="", value='$ '+numerize(ef2,0))
 
-        if ef2 <= ef1:
+        if ef2 >= ef1:
             st.markdown(
                 """
                 <style>
@@ -1691,7 +1737,7 @@ if auth_status == True:
 
             # Apply custom styles to text
             st.markdown(
-                f'<h4 class="custom-text1">{numerize(ef2-ef1,0)}</h4>',
+                f'<h4 class="custom-text1">{numerize(ef2-ef1, 2)}</h4>',
                 unsafe_allow_html=True,
             )
             # st.markdown(f'<p style="color: red;">{st.metric(label="", value=header_df["Prospects"]["Change"])}</p>', unsafe_allow_html=True)
@@ -1701,7 +1747,7 @@ if auth_status == True:
             # st.markdown(f'<p></hr></p>', unsafe_allow_html=True)
             # Apply custom styles to text
             st.markdown(
-                f'<h4 class="custom-text1">{round((ef2-ef1)/ef1*100,2)}%</h4>',
+                f'<h4 class="custom-text1">{round((ef2-ef1)/ef1*100, 2)}%</h4>',
                 unsafe_allow_html=True,
             )
         st.markdown("""<hr class="spends-child-seperator">""", unsafe_allow_html=True)
@@ -1804,7 +1850,7 @@ if auth_status == True:
             _columns1 = st.columns((1, 1, 1, 1))
             with _columns1[0]:
                 optimization_selection = st.selectbox(
-                    "Optimize", options=["Media Spends", target], key="optimization_key"
+                    "Optimize", options=["Media Spends"], key="optimization_key"
                 )
 
             with _columns1[1]:
@@ -1845,17 +1891,22 @@ if auth_status == True:
                 with _columns2[2]:
                     overall_lower_bound = st.number_input(
                         "Overall Lower Bound for Spends",
-                        value=50.0,
+                        value=30.0,
+                        min_value=0.0,
+                        max_value=30.0,
                         key="overall_lower_bound",
                         # on_change=partial(update_data_bound_min_overall)
                     )
                 with _columns2[3]:
                     overall_upper_bound = st.number_input(
                         "Overall Upper Bound for Spends",
-                        value=50.0,
+                        value=30.0,
+                        min_value=0.0,
+                        max_value=30.0,
                         key="overall_upper_bound",
                         # on_change=partial(update_data_bound_max_overall)
                     )
+
                 min_value = round(
                     _scenario.actual_total_spends * (1 - overall_lower_bound / 100)
                 )
@@ -1876,7 +1927,9 @@ if auth_status == True:
                     st.number_input(
                         "Percent Change",
                         key="total_spends_change",
-                        min_value=-1 * overall_lower_bound,
+                        # min_value=-1 * overall_lower_bound,
+                        # max_value=overall_upper_bound,
+                        min_value=-overall_lower_bound,
                         max_value=overall_upper_bound,
                         step=0.01,
                         value=0.00,
@@ -1906,7 +1959,7 @@ if auth_status == True:
 
                 with _columns2[1]:
                     st.number_input(
-                        "Percent Change",
+                        "Percent aaChange",
                         key="total_sales_change",
                         min_value=-50.00,
                         max_value=50.00,
@@ -2001,6 +2054,10 @@ if auth_status == True:
                     "Old_sales": [],
                 }
             for i, channel_name in enumerate(channels_list):
+
+                if channel_name == "Email":
+                    continue
+                
                 # st.write(channel_name)
                 _channel_class = st.session_state["scenario"].channels[channel_name]
                 # st.write(st.session_state["scenario"].channels[channel_name])
@@ -2060,12 +2117,14 @@ if auth_status == True:
                                     / param_dicts["current_spends"][channel_name]
                                 )
                             )
-                        # st.write(st.session_state[channel_name_lower_bound])
-                        channel_bounds_min = st.text_input(
-                            "Lower Bound Percentage",
-                            key=channel_name_lower_bound,
-                            on_change=partial(update_data_bound_min, channel_name),
-                        )
+                        # # st.write(st.session_state[channel_name_lower_bound])
+                        # channel_bounds_min = st.text_input(
+                        #     "Lower Bound Percentage",
+                        #     key=channel_name_lower_bound,
+                        #     on_change=partial(update_data_bound_min, channel_name),
+                        # )
+
+                        channel_bounds_min = 30.0
                         # st.write(st.session_state[channel_name_lower_bound])
                         # if not validate_input_lb(str(channel_bounds_min)):
                         #     st.error("Invalid input")
@@ -2087,30 +2146,32 @@ if auth_status == True:
                             step=1.00,
                             value=0.00,
                             on_change=partial(update_data_by_percent, channel_name),
-                            max_value=calc_min_value(),
-                            min_value=-1
-                            * float(st.session_state[channel_name_lower_bound]),
+                            max_value=st.session_state["overall_upper_bound"],
+                            min_value=-st.session_state["overall_lower_bound"],
                         )
 
-                        channel_bounds_max = st.text_input(
-                            "Upper Bound Percentage",
-                            key=channel_name_upper_bound,
-                            on_change=partial(update_data_bound_max, channel_name),
-                        )
+                        # channel_bounds_max = st.text_input(
+                        #     "Upper Bound Percentage",
+                        #     key=channel_name_upper_bound,
+                        #     on_change=partial(update_data_bound_max, channel_name),
+                        # )
                         # if not validate_input(channel_bounds_max):
                         #     st.error("Invalid input")
+                        channel_bounds_max = 30.0
+
                 with _columns[2]:
                     # spends
                     current_channel_spends = float(
                         _channel_class.modified_total_spends
-                        * _channel_class.conversion_rate
+                        # * _channel_class.conversion_rate
                     )
                     actual_channel_spends = float(
                         _channel_class.actual_total_spends
-                        * _channel_class.conversion_rate
+                        # * _channel_class.conversion_rate
                     )
                     spends_delta = float(
-                        _channel_class.delta_spends * _channel_class.conversion_rate
+                        # _channel_class.delta_spends * _channel_class.conversion_rate
+                        _channel_class.delta_spends
                     )
                     st.session_state["acutual_predicted"]["Channel_name"].append(
                         channel_name
@@ -2183,6 +2244,7 @@ if auth_status == True:
                     st.session_state["acutual_predicted"]["New_sales"].append(
                         current_channel_sales
                     )
+
                     # st.write(actual_channel_sales)
 
                     _prospect_cols = st.columns(2)
@@ -2404,9 +2466,9 @@ if auth_status == True:
                 # # print(summary_df_sorted['Old_sales'])
                 # # print(col, "old efficiency ", a)
                 with bin_placeholder:
-                    if a > 1.2:
+                    if a > 1:
                         fill_color_box = "#6bbf6b"
-                    elif a <= 1.2:
+                    elif a <= 1:
                         fill_color_box = "#ff6868"
                     else:
                         fill_color_box = "#ff6868"

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4279102a6e3c64422e780dad800d1de417ff74112d57e124b790a1f6af5376ea
-size 1820
+oid sha256:35f08123f49d9966c361ad8e23d429995e4eac8ac44b70b337e3c2764129ddfc
+size 1756