m1

Streamlit_functions.py

@@ -281,7 +281,7 @@ def waterfall(start_date,end_date,btn_chart):
     # Updating layout for black background and gray gridlines
     if btn_chart == "Month on Month":
         fig.update_layout(
-            title=f"Change In MMM Estimated Prospect Contribution <br>{start_date_prev.strftime('%m-%d-%Y')} To {end_date_prev.strftime('%m-%d-%Y')} Vs. {start_date.strftime('%m-%d-%Y')} To {end_date.strftime('%m-%d-%Y')}"
+            title=f"Change In MMM Estimated Prospect Contribution"
             ,showlegend=False,
             # plot_bgcolor='black',
             # paper_bgcolor='black',
@@ -293,11 +293,22 @@ def waterfall(start_date,end_date,btn_chart):
             yaxis=dict(
                 title="Prospects",
                 showgrid=True,
-                gridcolor='gray',  # Setting y-axis gridline color to gray
+                gridcolor='lightgray',
+                griddash='dot',  # Setting y-axis gridline color to gray
                 zeroline=False,  # Hiding the y-axis zero line
                 # range=[18000, max(max(cumulative), max(values)) + 1000] # Setting the y-axis range from 19k to slightly above the maximum value
             )
             )
+        fig.add_annotation(
+        text=f"{start_date_prev.strftime('%m-%d-%Y')} to {end_date_prev.strftime('%m-%d-%Y')} vs. {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=16),
+        # align='left'
+    )
         # fig.update_xaxes(
         #         tickmode="array",
         #         # categoryorder="total ascending",
@@ -308,7 +319,7 @@ def waterfall(start_date,end_date,btn_chart):
         # )
     else :
         fig.update_layout(
-            title=f"Change In MMM Estimated Prospect Contribution <br>{start_date_prev.strftime('%m-%d-%Y')} To {end_date_prev.strftime('%m-%d-%Y')} Vs. {start_date.strftime('%m-%d-%Y')} To {end_date.strftime('%m-%d-%Y')}"
+            title="Change In MMM Estimated Prospect Contribution "
             ,showlegend=False,
             # plot_bgcolor='black',
             # paper_bgcolor='black',
@@ -320,12 +331,23 @@ def waterfall(start_date,end_date,btn_chart):
             yaxis=dict(
                 title="Prospects",
                 showgrid=True,
-                gridcolor='gray',  # Setting y-axis gridline color to gray
+                gridcolor='lightgray',
+                griddash='dot',  # Setting y-axis gridline color to gray
                 zeroline=False,  # Hiding the y-axis zero line
                 # range=[10000, max(cumulative)+1000]  # Setting the y-axis range from 19k to slightly above the maximum value
             )
         
         )
+        fig.add_annotation(
+        text=f"{start_date_prev.strftime('%m-%d-%Y')} to {end_date_prev.strftime('%m-%d-%Y')} vs. {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=16),
+        # align='left'
+    )
     # print(cur_data)
     # print(prev_data)
     # fig.show()
@@ -431,7 +453,7 @@ def channel_contribution(start_date,end_date):
 
     # Updating layout for better visualization
     fig.update_layout(
-        title=f"Media Contribution <br> {cur_data['Date'].min().strftime('%m-%d-%Y')} To {cur_data['Date'].max().strftime('%m-%d-%Y')}",
+        title=f"Media Contribution",
         # plot_bgcolor='black',
         # paper_bgcolor='black',
         # font=dict(color='white'),  # Changing font color to white for better contrast
@@ -443,10 +465,21 @@ def channel_contribution(start_date,end_date):
         yaxis=dict(
             title="Prospect",
             showgrid=True,
-            gridcolor='gray',  # Setting y-axis gridline color to gray
+            gridcolor='lightgray',
+            griddash='dot',   # Setting y-axis gridline color to gray
             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,
+        y=1.15,
+        xref="x domain",
+        yref="y domain",
+        showarrow=False,
+        font=dict(size=16),
+        # align='left'
+    )
 
     return fig
 
@@ -478,7 +511,7 @@ def chanel_spends(start_date,end_date):
 
     # Updating layout for better visualization
     fig.update_layout(
-        title=f"Media Spends <br> {cur_data['Date'].min().strftime('%m-%d-%Y')} To {cur_data['Date'].max().strftime('%m-%d-%Y')}",
+        title=f"Media Spends",
         # plot_bgcolor='black',
         # paper_bgcolor='black',
         # font=dict(color='white'),  # Changing font color to white for better contrast
@@ -490,10 +523,21 @@ def chanel_spends(start_date,end_date):
         yaxis=dict(
             title="Spends ($)",
             showgrid=True,
-            gridcolor='gray',  # Setting y-axis gridline color to gray
+            gridcolor='lightgray',
+            griddash='dot',   # Setting y-axis gridline color to gray
             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,
+        y=1.15,
+        xref="x domain",
+        yref="y domain",
+        showarrow=False,
+        font=dict(size=16),
+        # align='left'
+    )
 
     return fig
 
@@ -590,24 +634,36 @@ def cpp(start_date,end_date):
 
     # Update layout for better visualization
     fig.update_layout(
-        title=f"CPP Distribution  <br>{cur_data['Date'].min().strftime('%m-%d-%Y')} To {cur_data['Date'].max().strftime('%m-%d-%Y')}"
+        title=f"CPP Distribution"
         ,
         # plot_bgcolor='black',
         # paper_bgcolor='black',
         # font=dict(color='white'),  # Changing font color to white for better contrast
         xaxis=dict(
-            showgrid=True,
-            gridcolor='gray',  # Setting x-axis gridline color to gray
+            showgrid=False,
+            gridcolor='lightgray',
+            griddash='dot',   # Setting x-axis gridline color to gray
             zeroline=False,  # Hiding the x-axis zero line
         ),
         yaxis=dict(
             title="CPP",
             showgrid=True,
-            gridcolor='gray',  # Setting y-axis gridline color to gray
+            gridcolor='lightgray',
+                griddash='dot',   # Setting y-axis gridline color to gray
             zeroline=False,  # Hiding the y-axis zero line
         ),
         hovermode='x'  # Show hover info for all lines at a single point
     )
+    fig.add_annotation(
+        text=f"{cur_data['Date'].min().strftime('%m-%d-%Y')} to {cur_data['Date'].max().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 base_decomp():
@@ -641,7 +697,8 @@ def base_decomp():
         yaxis=dict(
             title="Prospect",
             showgrid=True,
-            gridcolor='gray',  # Setting y-axis gridline color to gray
+            gridcolor='lightgray',
+            griddash='dot',  # Setting y-axis gridline color to gray
             zeroline=False,  # Hiding the y-axis zero line
         ),
         hovermode='x'  # Show hover info for all lines at a single point
@@ -731,7 +788,8 @@ def media_decomp():
         yaxis=dict(
         title="Prospect",
             showgrid=True,
-            gridcolor='gray',  # Setting y-axis gridline color to gray
+            gridcolor='lightgray',
+            griddash='dot',   # Setting y-axis gridline color to gray
             zeroline=False,  # Hiding the y-axis zero line
         )
     )
@@ -749,7 +807,7 @@ def mmm_model_quality():
 
     # Update layout for better visualization
     fig.update_layout(
-        title=f"Model Predicted V/S Actual Prospects"
+        title=f"Model Predicted v/s Actual Prospects"
         ,
         # plot_bgcolor='black',
         # paper_bgcolor='black',
@@ -762,7 +820,8 @@ def mmm_model_quality():
         yaxis=dict(
             title="Prospects",
             showgrid=True,
-            gridcolor='gray',  # Setting y-axis gridline color to gray
+            gridcolor='lightgray',
+            griddash='dot',  # Setting y-axis gridline color to gray
             zeroline=False,  # Hiding the y-axis zero line
         ),
         hovermode='x'  # Show hover info for all lines at a single point
@@ -819,7 +878,8 @@ def elasticity(media_df):
         xaxis=dict(
             title="Elasticity (coefficient)",
             showgrid=True,
-            gridcolor='gray',  # Setting x-axis gridline color to gray
+            gridcolor='lightgray',
+            griddash='dot',  # Setting x-axis gridline color to gray
             zeroline=False,  # Hiding the x-axis zero line
         ),
         yaxis=dict(
@@ -858,7 +918,8 @@ def half_life(media_df):
         xaxis=dict(
             title="Weeks",
             showgrid=True,
-            gridcolor='gray',  # Setting x-axis gridline color to gray
+            gridcolor='lightgray',
+            griddash='dot',  # Setting x-axis gridline color to gray
             zeroline=False,  # Hiding the x-axis zero line
         ),
         yaxis=dict(
@@ -1024,7 +1085,7 @@ def scenario_spend_forecasting2(delta_df,start_date,end_date):
     cur_data = cur_data[spend_cols2]
     cur_data.columns = channels2
     
-    cur_data["Date2"] = cur_data["Date"]+ pd.Timedelta(days=5)
+    cur_data["Date2"] = cur_data["Date"]+ pd.Timedelta(days=6)
     # cur_data["Date"] = delta_df["Date"]
     cur_data["Date_diff"] = (cur_data["Date"]-start_date).dt.days
     cur_data["Date_diff_months"] =(np.ceil(cur_data["Date_diff"] / 30))

classes.py

@@ -3,7 +3,7 @@ from scipy.optimize import minimize, LinearConstraint, NonlinearConstraint
 from collections import OrderedDict
 import pandas as pd
 from numerize.numerize import numerize
-
+from gekko import GEKKO
 
 def class_to_dict(class_instance):
     attr_dict = {}
@@ -23,8 +23,8 @@ def class_to_dict(class_instance):
         attr_dict["actual_total_sales"] = class_instance.actual_total_sales
         attr_dict["modified_total_spends"] = class_instance.modified_total_spends
         attr_dict["modified_total_sales"] = class_instance.modified_total_sales
-        attr_dict["actual_mroi"] = class_instance.get_marginal_roi("actual")
-        attr_dict["modified_mroi"] = class_instance.get_marginal_roi("modified")
+        # attr_dict["actual_mroi"] = class_instance.get_marginal_roi("actual")
+        # attr_dict["modified_mroi"] = class_instance.get_marginal_roi("modified")
 
     elif isinstance(class_instance, Scenario):
         attr_dict["type"] = "Scenario"
@@ -56,6 +56,7 @@ class Channel:
         name,
         dates,
         spends,
+        sales,
         response_curve_type,
         response_curve_params,
         bounds,channel_bounds_min,channel_bounds_max,
@@ -67,6 +68,7 @@ class Channel:
         self.dates = dates
         self.conversion_rate = conversion_rate
         self.actual_spends = spends.copy()
+        # self.actual_sales = sales.copy()
 
         if modified_spends is None:
             self.modified_spends = self.actual_spends.copy()
@@ -83,7 +85,7 @@ class Channel:
         self.upper_limit = self.actual_spends.max() + self.actual_spends.std()
         self.power = np.ceil(np.log(self.actual_spends.max()) / np.log(10)) - 3
         self.actual_sales = None
-        self.actual_sales = self.response_curve(self.actual_spends)
+        self.actual_sales = self.response_curve(self.actual_spends)#sales.copy()#
         self.actual_total_spends = self.actual_spends.sum()
         self.actual_total_sales = self.actual_sales.sum()
         self.modified_sales = self.calculate_sales()
@@ -104,8 +106,11 @@ class Channel:
         )
 
     def calculate_sales(self):
+        print("in calc_sales")
         return self.response_curve(self.modified_spends)
 
+    def hill_equation(x, Kd, n):
+        return x**n / (Kd**n + x**n)
     def response_curve(self, x):
         if self.penalty:
             x = np.where(
@@ -113,11 +118,33 @@ class Channel:
                 x,
                 self.upper_limit + (x - self.upper_limit) * self.upper_limit / x,
             )
+        if self.response_curve_type == "hill-eq":
+            print("lalala")
+            print(self.name)
+            Kd= self.response_curve_params["Kd"]
+            n= self.response_curve_params["n"]
+            x_min= self.response_curve_params["x_min"]
+            x_max= self.response_curve_params["x_max"]
+            y_min= self.response_curve_params["y_min"]
+            y_max= self.response_curve_params['y_max']  
+            # print(x_min)
+            # print(Kd,n,x_min,x_max,y_min,y_max)
+            print(np.sum(x)/104)
+            x_inp = ( x- x_min) / (x_max - x_min)
+            # print(x_inp)
+            x_out = x_inp**n / (Kd**n + x_inp**n) #self.hill_equation(x_inp,Kd, n)
+            print(x_out)
+            
+            sales = (y_max - y_min)*x_out + y_min
+            sales[np.isnan(sales)] = 0
+            print(sales) 
+            print(np.sum(sales))
+            # print(sales)
         if self.response_curve_type == "s-curve":
             if self.power >= 0:
                 x = x / 10**self.power
             x = x.astype("float64")
-            K = self.response_curve_params["K"]
+            K = self.response_curve_params["Kd"]
             b = self.response_curve_params["b"]
             a = self.response_curve_params["a"]
             x0 = self.response_curve_params["x0"]
@@ -247,7 +274,7 @@ class Scenario:
 
     def update_bounds_min(self, channel_name,modified_bound):
         # self.modify_spends(total_spends)
-        self.channels[channel_name].update_bounds_min(modified_bound)
+        self.channels[channel_name].update_bounds_min(modified_bound)  
 
     def update_bounds_max(self, channel_name,modified_bound):
         # self.modify_spends(total_spends)
@@ -290,6 +317,20 @@ class Scenario:
 
     #     return zip(channels_list, res.x)
 
+    def hill_equation(x, Kd, n):
+        return x**n / (Kd**n + x**n)
+    
+    def cost_func(channel,x):
+        x_inp = (x/104 - param_dicts["x_min"][channel]) / (param_dicts["x_max"][channel] - param_dicts["x_min"][channel])
+    #     print(x_inp)
+        x_out = hill_equation(x_inp, param_dicts["Kd"][channel], param_dicts["n"][channel])
+    #     print(x_out)
+    #    
+        return (param_dicts["y_max"][channel] - param_dicts["y_min"][channel])*(x_out + param_dicts["y_min"][channel])*104
+    
+    
+    
+
     def optimize_spends(self, sales_percent, channels_list, algo="trust-constr"):
         print("%"*100)
         desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
@@ -327,6 +368,8 @@ class Scenario:
             self.update(channel_name, modified_spends)
 
         return zip(channels_list, res.x)
+    
+
 
     def optimize(self, spends_percent, channels_list):
         # channels_list = self.channels.keys()
@@ -386,6 +429,69 @@ class Scenario:
             self.update(channel_name, modified_spends)
 
         return zip(channels_list, res.x)
+    
+
+    def hill_equation(self,x, Kd, n):
+        return x**n / (Kd**n + x**n)
+    
+    def cost_func(self ,channel,x):
+
+        response_curve_params = pd.read_excel(r"C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulator uploaded - Copy\Simulator-UOPX\response_curves_parameters.xlsx",index_col = "channel")
+        param_dicts = {col: response_curve_params[col].to_dict() for col in response_curve_params.columns}
+        
+        x_inp = (x/104 - param_dicts["x_min"][channel]) / (param_dicts["x_max"][channel] - param_dicts["x_min"][channel])
+    #     print(x_inp)
+        x_out = self.hill_equation(x_inp, param_dicts["Kd"][channel], param_dicts["n"][channel])
+    #     print(x_out)
+    #    
+        return (param_dicts["y_max"][channel] - param_dicts["y_min"][channel])*(x_out + param_dicts["y_min"][channel])*104
+        
+
+    def spends_optimisation(self, spends_percent,channels_list):
+        m = GEKKO(remote=False)
+        # Define variables        
+        # Initialize 13 variables with specific bounds
+        response_curve_params = pd.read_excel(r"C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulator uploaded - Copy\Simulator-UOPX\response_curves_parameters.xlsx",index_col = "channel")
+        param_dicts = {col: response_curve_params[col].to_dict() for col in response_curve_params.columns}
+        
+        initial_values = list(param_dicts["x_min"].values())
+        current_spends = list(param_dicts["current_spends"].values()) 
+        lower_bounds = list(param_dicts["x_min"].values()) 
+        
+        num_channels = len(channels_list)
+
+        x_vars=[]
+        x_vars = [m.Var(value=param_dicts["current_spends"][_], lb=param_dicts["x_min"][_]*104, ub=5*param_dicts["current_spends"][_]) for _ in channels_list]
+        print(x_vars)
+    #     x_vars,lower_bounds
+        
+        # Define the objective function to minimize
+        cost = 0
+        spends = 0 
+        i = 0
+        for i,c in enumerate(channels_list):
+            # print(c)
+            # print(x_vars[i])
+            cost = cost + (self.cost_func(c, x_vars[i]))
+            spends = spends +x_vars[i]
+
+           
+        m.Maximize(cost)
+        
+        # Define constraints
+        m.Equation(spends == sum(current_spends)*(1 + spends_percent / 100))
+        m.Equation(spends <= sum(current_spends)*0.5)
+        m.Equation(spends >= sum(current_spends)*1.5)
+
+        m.solve(disp=True)
+        
+        for i, var in enumerate(x_vars):
+            print(f"x{i+1} = {var.value[0]}")
+        
+        for channel_name, modified_spends in zip(channels_list, x_vars):
+            self.update(channel_name, modified_spends.value[0])
+
+        return zip(channels_list, x_vars)
 
     def save(self):
         details = {}

pages/2_Scenario_Planner.py

@@ -62,9 +62,15 @@ def optimize(key, status_placeholder):
         if key.lower() == "media spends":
             with status_placeholder:
                 with st.spinner("Optimizing"):
+                    print(channel_list)
+                    print(st.session_state["total_spends_change"])
                     result = st.session_state["scenario"].optimize(
                         st.session_state["total_spends_change"], channel_list
+                        # result = st.session_state["scenario"].spends_optimisation(
+                        # st.session_state["total_spends_change"], channel_list
                     )
+    
+
         # elif key.lower() == "revenue":
         else:
             with status_placeholder:
@@ -565,117 +571,6 @@ def plot_response_curves(summary_df_sorted):
         with col:
             st.plotly_chart(fig, use_container_width=True)
 
-    # cols = st.columns(3)
-    # for i in range(0, len(channels_list)):
-
-    #     col = channels_list[i]
-    #     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():
-#     cols = 4
-#     rcs = st.session_state["rcs"]
-#     shapes = []
-#     fig = make_subplots(rows=6, cols=cols, subplot_titles=channels_list)
-#     for i in range(0, len(channels_list)):
-#         col = channels_list[i]
-#         x = st.session_state["actual_df"][col].values
-#         spends = x.sum()
-#         power = np.ceil(np.log(x.max()) / np.log(10)) - 3
-#         x = np.linspace(0, 3 * x.max(), 200)
-
-#         K = rcs[col]["K"]
-#         b = rcs[col]["b"]
-#         a = rcs[col]["a"]
-#         x0 = rcs[col]["x0"]
-
-#         y = s_curve(x / 10**power, K, b, a, x0)
-#         roi = y / x
-#         marginal_roi = a * (y) * (1 - y / K)
-#         fig.add_trace(
-#             go.Scatter(
-#                 x=52
-#                 * x
-#                 * st.session_state["scenario"].channels[col].conversion_rate,
-#                 y=52 * 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}",
-#             ),
-#             row=1 + (i) // cols,
-#             col=i % cols + 1,
-#         )
-
-#         fig.add_trace(
-#             go.Scatter(
-#                 x=[
-#                     spends
-#                     * st.session_state["scenario"]
-#                     .channels[col]
-#                     .conversion_rate
-#                 ],
-#                 y=[52 * s_curve(spends / (10**power * 52), K, b, a, x0)],
-#                 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=52 * s_curve(spends / (10**power * 52), K, b, a, x0),
-#                 x1=spends
-#                 * st.session_state["scenario"].channels[col].conversion_rate,
-#                 y1=52 * s_curve(spends / (10**power * 52), K, b, a, x0),
-#                 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=spends
-#                 * st.session_state["scenario"].channels[col].conversion_rate,
-#                 y0=0,
-#                 x1=spends
-#                 * st.session_state["scenario"].channels[col].conversion_rate,
-#                 y1=52 * s_curve(spends / (10**power * 52), K, b, a, x0),
-#                 line_width=1,
-#                 line_dash="dash",
-#                 line_color="black",
-#                 xref=f"x{i+1}",
-#                 yref=f"y{i+1}",
-#             )
-#         )
-
-#     fig.update_layout(
-#         height=1500,
-#         width=1000,
-#         title_text="Response Curves",
-#         showlegend=False,
-#         shapes=shapes,
-#     )
-#     fig.update_annotations(font_size=10)
-#     fig.update_xaxes(title="Spends")
-#     fig.update_yaxes(title=target)
-#     return fig
-
-
 # ======================================================== #
 # ==================== HTML Components =================== #
 # ======================================================== #
@@ -683,7 +578,7 @@ def plot_response_curves(summary_df_sorted):
 
 def generate_spending_header(heading):
     return st.markdown(
-        f"""<h2 class="spends-header">{heading}</h2>""", unsafe_allow_html=True
+        f"""<h4 class="spends-header"><bold>{heading}<bold></h4>""", unsafe_allow_html=True
     )
 
 
@@ -776,6 +671,7 @@ def reset_inputs():
         del st.session_state.total_sales_change_abs_slider
 
     st.session_state["initialized"] = False
+
 def scenario_planner_plots():
     with st.expander('Optimized Spends Overview'):
     # if st.button('Refresh'):
@@ -818,59 +714,6 @@ def scenario_planner_plots():
         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
 
@@ -993,17 +836,7 @@ if auth_status == True:
     # Check if state is initiaized
     is_state_initiaized = st.session_state.get("initialized", False)
     if not is_state_initiaized or st.session_state["first_time"]:
-        # initialize_data()
-        if panel_selected == "Total Market":
-            initialize_data(
-                panel=panel_selected,
-                target_file=file_selected,
-                updated_rcs=updated_rcs,
-                metrics=metrics_selected,
-            )
-            panel = None
-        else:
-            initialize_data(
+        initialize_data(
                 panel=panel_selected,
                 target_file=file_selected,
                 updated_rcs=updated_rcs,
@@ -1044,6 +877,7 @@ if auth_status == True:
             _scenario.actual_total_spends, 1
         )
 
+    st.write(_scenario.actual_total_sales)
     if "total_sales_change_abs" not in st.session_state:
         st.session_state["total_sales_change_abs"] = numerize(
             _scenario.actual_total_sales, 1
@@ -1065,7 +899,7 @@ if auth_status == True:
     if "upper_bound_key" not in st.session_state:
         st.session_state["upper_bound_key"] = 10
 
-    
+    st.write(_scenario.modified_total_sales)
     header_df = pd.DataFrame(index=["Actual","Simulated","Change","Percent Change"],columns=["Spends","Prospects"])
     header_df["Spends"]["Actual"] = format_numbers(_scenario.actual_total_spends)
     header_df["Spends"]["Simulated"] = format_numbers(_scenario.modified_total_spends)
@@ -1083,7 +917,7 @@ if auth_status == True:
             """
             <style>
             .custom-text_head {
-                font-size: 36px; /* Adjust font size */
+                font-size: 24px; /* Adjust font size */
                 color: 'blue' ; /* Adjust text color */
             
                 font-weight: bold;
@@ -1111,93 +945,85 @@ if auth_status == True:
 
     _columns = st.columns((1, 1, 1, 1, 1))
     with _columns[0]:
-        st.header("Spends")
+        st.markdown("""<h4> Spends</h4>""",unsafe_allow_html=True)
+        # st.write("Spends")
     with _columns[1]:
-        st.metric(label="", value=header_df["Spends"]["Actual"])
+        st.markdown(f"""<h4>{header_df["Spends"]["Actual"]}</h4>""",unsafe_allow_html=True)
+        # st.metric(label="", value=header_df["Spends"]["Actual"])
     with _columns[2]:
-        st.metric(label="", value=header_df["Spends"]["Simulated"])
-    with _columns[3]:
-        if _scenario.delta_spends >= 0:
+        st.markdown(f"""<h4>{header_df["Spends"]["Simulated"]}</h4>""",unsafe_allow_html=True)
+    if _scenario.delta_spends < 0:
             st.markdown(
             """
             <style>
             .custom-text {
-                font-size: 36px; /* Adjust font size */
+                 font-size: 24px; /* Adjust font size */
                 color: #6bbf6b ; /* Adjust text color */
             }
             </style>
             """,
             unsafe_allow_html=True
         )
-        else:
+    else:
             st.markdown(
             """
             <style>
             .custom-text {
-                font-size: 36px; /* Adjust font size */
+                 font-size: 24px; /* Adjust font size */
                 color: #ff6868; /* Adjust text color */
             }
             </style>
             """,
             unsafe_allow_html=True
         )
+        # st.metric(label="", value=header_df["Spends"]["Simulated"])
+    with _columns[3]:
         
-
         # Apply custom styles to text
-        st.markdown(f'<p class="custom-text">{header_df["Spends"]["Change"]}</p>', unsafe_allow_html=True)
+        st.markdown(f'<h4 class="custom-text">{header_df["Spends"]["Change"]}</h4>', unsafe_allow_html=True)
     with _columns[4]:
         # Apply custom styles to text
-        st.markdown(f'<p></hr></p>', unsafe_allow_html=True)
-        st.markdown(f'<p class="custom-text">{header_df["Spends"]["Percent Change"]}</p>', unsafe_allow_html=True)
-        
-    
+        # st.markdown(f'<p></hr></p>', unsafe_allow_html=True)
+        st.markdown(f'<h4 class="custom-text">{header_df["Spends"]["Percent Change"]}</h4>', unsafe_allow_html=True)
     st.markdown(
                 """<hr class="spends-child-seperator">""",
                 unsafe_allow_html=True,
             )
     _columns = st.columns((1, 1, 1, 1, 1))
     with _columns[0]:
-        st.header("Prospects")
+        # st.header("Prospects")
+        st.markdown("""<h4> Prospects</h4>""",unsafe_allow_html=True)
     with _columns[1]:
-        st.metric(label="", value=header_df["Prospects"]["Actual"])
+        st.markdown(f"""<h4>{header_df["Prospects"]["Actual"]}</h4>""",unsafe_allow_html=True)
+        # st.metric(label="", value=header_df["Prospects"]["Actual"])
     with _columns[2]:
-        st.metric(label="", value=header_df["Prospects"]["Simulated"])
-    with _columns[3]:
-        
-        if _scenario.delta_sales >= 0:
+        st.markdown(f"""<h4>{header_df["Prospects"]["Simulated"]}</h4>""",unsafe_allow_html=True)
+        # st.metric(label="", value=header_df["Prospects"]["Simulated"])
+    
+    if _scenario.delta_sales >= 0:
             st.markdown(
             """
             <style>
             .custom-text {
-                font-size: 36px; /* Adjust font size */
+                font-size: 24px; /* Adjust font size */
                 color:#6bbf6b ; /* Adjust text color */
             }
             </style>
             """,
             unsafe_allow_html=True
         )
-        else:
-            st.markdown(
-            """
-            <style>
-            .custom-text {
-                font-size: 36px; /* Adjust font size */
-                color: #ff6868; /* Adjust text color */
-            }
-            </style>
-            """,
-            unsafe_allow_html=True
-        )
-
+    else:
+            st.markdown("""<style>.custom-text {font-size: 24px; /* Adjust font size */color: #ff6868; /* Adjust text color */}</style>""",unsafe_allow_html=True)
+    with _columns[3]:
         # Apply custom styles to text
-        st.markdown(f'<p class="custom-text">{header_df["Prospects"]["Change"]}</p>', unsafe_allow_html=True)
+        st.markdown(f'<h4 class="custom-text">{header_df["Prospects"]["Change"]}</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) 
         # st.markdown(f'<p style="color: red;">{header_df["Prospects"]["Change"]}</p>', unsafe_allow_html=True) 
 
     with _columns[4]:
-        st.markdown(f'<p></hr></p>', unsafe_allow_html=True)
+        # st.markdown(f'<p></hr></p>', unsafe_allow_html=True)
         # Apply custom styles to text
-        st.markdown(f'<p class="custom-text">{header_df["Prospects"]["Percent Change"]}</p>', unsafe_allow_html=True)
+        st.markdown(f'<h4 class="custom-text">{header_df["Prospects"]["Percent Change"]}</h4>', unsafe_allow_html=True)
     st.markdown(
                 """<hr class="spends-child-seperator">""",
                 unsafe_allow_html=True,
@@ -1207,31 +1033,33 @@ if auth_status == True:
     ef1 = (_scenario.actual_total_spends/_scenario.actual_total_sales)
     ef2 = (_scenario.modified_total_spends/_scenario.modified_total_sales)
     with _columns[0]:
-        st.header("Cost Per Prospect")
+        st.markdown("""<h4>Cost Per Prospect</h4>""",unsafe_allow_html=True)
+        # st.header("Cost Per Prospect")
     with _columns[1]:
-        st.metric(label="", value='$ '+numerize(ef1,0))
+        st.markdown(f"""<h4>{'$ '+numerize(ef1,0)}</h4>""",unsafe_allow_html=True)
+        # st.metric(label="", value='$ '+numerize(ef1,0))
     with _columns[2]:
-        st.metric(label="", value='$ '+numerize(ef2,0))
-    with _columns[3]:
-        
-        if ef2 >= ef1:
+        st.markdown(f"""<h4>{'$ '+numerize(ef2,0)}</h4>""",unsafe_allow_html=True)
+        # st.metric(label="", value='$ '+numerize(ef2,0))
+
+    if ef2 <= ef1:
             st.markdown(
             """
             <style>
             .custom-text1 {
-                font-size: 36px; /* Adjust font size */
+                font-size: 24px; /* Adjust font size */
                 color:#6bbf6b ; /* Adjust text color */
             }
             </style>
             """,
             unsafe_allow_html=True
         )
-        else:
+    else:
             st.markdown(
             """
             <style>
             .custom-text1 {
-                font-size: 36px; /* Adjust font size */
+                font-size: 24px; /* Adjust font size */
                 color: #ff6868; /* Adjust text color */
             }
             </style>
@@ -1239,19 +1067,19 @@ if auth_status == True:
             unsafe_allow_html=True
         )
 
+    with _columns[3]:
+        
+        
         # Apply custom styles to text
-        st.markdown(f'<p class="custom-text1">{"$ "+numerize(ef2-ef1,0)}</p>', unsafe_allow_html=True)
+        st.markdown(f'<h4 class="custom-text1">{"$ "+numerize(ef2-ef1,0)}</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) 
         # st.markdown(f'<p style="color: red;">{header_df["Prospects"]["Change"]}</p>', unsafe_allow_html=True) 
 
     with _columns[4]:
-        st.markdown(f'<p></hr></p>', unsafe_allow_html=True)
+        # st.markdown(f'<p></hr></p>', unsafe_allow_html=True)
         # Apply custom styles to text
-        st.markdown(f'<p class="custom-text1">{round((ef2-ef1)/ef1*100,2)}%</p>', unsafe_allow_html=True)
-    st.markdown(
-                """<hr class="spends-child-seperator">""",
-                unsafe_allow_html=True,
-            )
+        st.markdown(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)
 
     # st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)
 
@@ -1431,11 +1259,8 @@ if auth_status == True:
                         # on_change=partial(update_data_bound_max_overall)
                     )
 
-
-
-
-                min_value = round(_scenario.actual_total_spends * 0.5)
-                max_value = round(_scenario.actual_total_spends * 1.5)
+                min_value = round(_scenario.actual_total_spends *  (1-overall_lower_bound/100))
+                max_value = round(_scenario.actual_total_spends  * (1-overall_upper_bound/100))
                 st.session_state["total_spends_change_abs_slider_options"] = [
                     numerize(value, 1)
                     for value in range(min_value, max_value + 1, int(1e4))
@@ -1486,10 +1311,10 @@ if auth_status == True:
                 # 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(100))
-                ]
+                # st.session_state["total_sales_change_abs_slider_options"] = [
+                #     numerize(value, 1)
+                #     for value in range(min_value, max_value + 1, int(100))
+                # ]
 
                 # st.select_slider(
                 #     "Absolute Slider",
@@ -1642,33 +1467,41 @@ if auth_status == True:
                 st.session_state["acutual_predicted"]["Delta"].append(spends_delta)
                 _spend_cols = st.columns(2)
                 with _spend_cols[0]:
-                    st.metric(
-                        label="Actual Spends",
-                        value=format_numbers(actual_channel_spends),
-                        # delta=numerize(spends_delta, 1),
-                        # label_visibility="collapsed",
-                    )
-
-                    st.metric(
-                        label="Change",
-                        value= format_numbers_f(spends_delta),
-                        delta=numerize(spends_delta, 1),
-                        # label_visibility="collapsed",
-                    )
+                    # st.write("Actual")
+                    st.markdown(f'<p> Actual <h5>{format_numbers(actual_channel_spends)}</h5> </p>', unsafe_allow_html=True)
+                    # st.metric(
+                    #     label="Actual Spends",
+                    #     value=format_numbers(actual_channel_spends),
+                    #     # delta=numerize(spends_delta, 1),
+                    #     # label_visibility="collapsed",
+                    # )
+
+                    # st.write("Actual")
+                    st.markdown(f'<p> Change <h5>{format_numbers(spends_delta)}</h5> </p>', unsafe_allow_html=True)
+                    # st.markdown(f'<h4 class="custom-text1">{format_numbers(spends_delta)}%</h4>', unsafe_allow_html=True)
+
+                    # st.metric(
+                    #     label="Change",
+                    #     value= format_numbers_f(spends_delta),
+                    #     delta=numerize(spends_delta, 1),
+                    #     # label_visibility="collapsed",
+                    # )
                 with _spend_cols[1]:
-                    st.metric(
-                        label="Simulated Spends",
-                        value=format_numbers(current_channel_spends),
-                        # delta=numerize(spends_delta, 1),
-                        # label_visibility="collapsed",
-                    )
-
-                    st.metric(
-                        label="Percent Change",
-                        value=  numerize(( spends_delta/actual_channel_spends)*100,0) +"%",
-                        delta=numerize(spends_delta, 1),
-                        # label_visibility="collapsed",
-                    )
+                    st.markdown(f'<p> Simulated <h5>{format_numbers(current_channel_spends)}</h5> </p>', unsafe_allow_html=True)
+                    st.markdown(f'<p>Percent<h5>{numerize(( spends_delta/actual_channel_spends)*100,0) +"%"}</h5> </p>', unsafe_allow_html=True)
+                    # st.metric(
+                    #     label="Simulated Spends",
+                    #     value=format_numbers(current_channel_spends),
+                    #     # delta=numerize(spends_delta, 1),
+                    #     # label_visibility="collapsed",
+                    # )
+
+                    # st.metric(
+                    #     label="Percent Change",
+                    #     value=  numerize(( spends_delta/actual_channel_spends)*100,0) +"%",
+                    #     delta=numerize(spends_delta, 1),
+                    #     # label_visibility="collapsed",
+                    # )
 
                     
             with _columns[3]:
@@ -1682,33 +1515,40 @@ if auth_status == True:
                 
                 _prospect_cols = st.columns(2)
                 with _prospect_cols[0]:
-                    st.metric(
-                        # target,
-                        label="Actual Prospects",
-                        value= format_numbers_f(actual_channel_sales),
-                        # delta=numerize(sales_delta, 1),
-                        # label_visibility="collapsed",
-                    )
-                    st.metric(
-                        label="Change",
-                        value= format_numbers_f(_channel_class.delta_sales),
-                        delta=numerize(sales_delta, 1),
-                        # label_visibility="collapsed",
-                    )
+                    # st.write("Actual")
+                    st.markdown(f'<p> Actual <h5>{format_numbers(actual_channel_sales)}</h5> </p>', unsafe_allow_html=True)
+                    st.markdown(f'<p> Change <h5>{format_numbers(sales_delta)}</h5> </p>', unsafe_allow_html=True)
+
+                #     st.metric(
+                #         # target,
+                #         label="Actual Prospects",
+                #         value= format_numbers_f(actual_channel_sales),
+                #         # delta=numerize(sales_delta, 1),
+                #         # label_visibility="collapsed",
+                #     )
+                #     st.metric(
+                #         label="Change",
+                #         value= format_numbers_f(_channel_class.delta_sales),
+                #         delta=numerize(sales_delta, 1),
+                #         # label_visibility="collapsed",
+                #     )
                 with _prospect_cols[1]:
-                    st.metric(
-                    label="Simulated Prospects",
-                    value= format_numbers_f(current_channel_sales),
-                    # delta=numerize(sales_delta, 1),
-                    # label_visibility="collapsed",
-                    )
-
-                    st.metric(
-                        label="Percent Change",
-                        value=  numerize((_channel_class.delta_sales/actual_channel_sales)*100,0) +"%",
-                        delta=numerize(sales_delta, 1),
-                        # label_visibility="collapsed",
-                    )
+                    st.markdown(f'<p> Simulated <h5>{format_numbers(current_channel_sales)}</h5> </p>', unsafe_allow_html=True)
+                    st.markdown(f'<p>Percent<h5>{numerize(( _channel_class.delta_sales/actual_channel_sales)*100,0) +"%"}</h5> </p>', unsafe_allow_html=True)
+
+                #     st.metric(
+                #     label="Simulated Prospects",
+                #     value= format_numbers_f(current_channel_sales),
+                #     # delta=numerize(sales_delta, 1),
+                #     # label_visibility="collapsed",
+                #     )
+
+                #     st.metric(
+                #         label="Percent Change",
+                #         value=  numerize((_channel_class.delta_sales/actual_channel_sales)*100,0) +"%",
+                #         delta=numerize(sales_delta, 1),
+                #         # label_visibility="collapsed",
+                #     )
 
                     
 
@@ -1752,77 +1592,77 @@ if auth_status == True:
 
             updated_rcs_key = f"{metrics_selected}#@{panel_selected}#@{channel_name}"
 
-            if updated_rcs and updated_rcs_key in list(updated_rcs.keys()):
-                K = updated_rcs[updated_rcs_key]["K"]
-                b = updated_rcs[updated_rcs_key]["b"]
-                a = updated_rcs[updated_rcs_key]["a"]
-                x0 = updated_rcs[updated_rcs_key]["x0"]
-            else:
-                K = st.session_state["rcs"][col]["K"]
-                b = st.session_state["rcs"][col]["b"]
-                a = st.session_state["rcs"][col]["a"]
-                x0 = st.session_state["rcs"][col]["x0"]
-
-            x_plot = np.linspace(0, 5 * x_actual.sum(), 200)
-
-            # Append current_channel_spends to the end of x_plot
-            x_plot = np.append(x_plot, current_channel_spends)
-
-            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
-            )
+            # if updated_rcs and updated_rcs_key in list(updated_rcs.keys()):
+            #     K = updated_rcs[updated_rcs_key]["K"]
+            #     b = updated_rcs[updated_rcs_key]["b"]
+            #     a = updated_rcs[updated_rcs_key]["a"]
+            #     x0 = updated_rcs[updated_rcs_key]["x0"]
+            # else:
+            #     K = st.session_state["rcs"][col]["K"]
+            #     b = st.session_state["rcs"][col]["b"]
+            #     a = st.session_state["rcs"][col]["a"]
+            #     x0 = st.session_state["rcs"][col]["x0"]
+
+            # x_plot = np.linspace(0, 5 * x_actual.sum(), 200)
+
+            # # Append current_channel_spends to the end of x_plot
+            # x_plot = np.append(x_plot, current_channel_spends)
+
+            # 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)
-            # roi = (y/np.sum(y))/(x/np.sum(x))
-            # st.write(x)
-            # st.write(y)
-            # st.write(roi)
+            # 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],
-                y[:-1],
-                roi[:-1],
-                marginal_roi[:-1],
-            )  # Drop data for current spends
-
-            # roi_current = 
-
-            start_value, end_value, left_value, right_value = find_segment_value(
-                x,
-                roi,
-                marginal_roi,
-            )
+            # roi_current, marginal_roi_current = roi[-1], marginal_roi[-1]
+            # x, y, roi, marginal_roi = (
+            #     x[:-1],
+            #     y[:-1],
+            #     roi[:-1],
+            #     marginal_roi[:-1],
+            # )  # Drop data for current spends
+
+            # # roi_current = 
+
+            # start_value, end_value, left_value, right_value = find_segment_value(
+            #     x,
+            #     roi,
+            #     marginal_roi,
+            # )
 
             #st.write(roi_current)
 
-            rgba = calculate_rgba(
-                start_value,
-                end_value,
-                left_value,
-                right_value,
-                current_channel_spends,
-            )
+            # rgba = calculate_rgba(
+            #     start_value,
+            #     end_value,
+            #     left_value,
+            #     right_value,
+            #     current_channel_spends,
+            # )
 
             summary_df = pd.DataFrame(st.session_state["acutual_predicted"])
             # st.dataframe(summary_df)
@@ -1860,12 +1700,14 @@ if auth_status == True:
                         text-align: center;
                         color: {'black'};
                         ">
-                        <p style="margin: 0; font-size: 20px;">Simulated Efficiency: {round(a,2)} </br> Actual Efficiency: {round(b,2)} </p>
-                        <!--<p style="margin: 0; font-size: 20px;">Marginal ROI: {round(marginal_roi_current,1)}</p>-->
+                        <p style="margin: 0; font-size: 16px;">Simulated Efficiency: {round(a,2)} </br> Actual Efficiency: {round(b,2)} </p>
+                        <!--<p style="margin: 0; font-size: 16px;">Marginal ROI: {round(b,1)}</p>-->
                     </div>
                     """,
                     unsafe_allow_html=True,
                 )
+                # <p style="margin: 0; font-size: 16px;">Simulated Efficiency: {round(a,2)} </br> Actual Efficiency: {round(b,2)} </p>
+                #         <!--<p style="margin: 0; font-size: 16px;">Marginal ROI: {round(b,1)}</p>-->
 
     with st.expander("See Response Curves", expanded=True):
         fig = plot_response_curves(summary_df_sorted)

pages/3_Saved_Scenarios.py

@@ -405,6 +405,7 @@ if auth_status == True:
                 #     'Pick a scenario to view details',
                 #     list(saved_scenarios.keys())
                 # )
+                st.button('Delete scenario', on_click=delete_scenario)
             with st.expander('Download Scenario'):
                 st.markdown("""<hr>""", unsafe_allow_html=True)
                 scenarios_to_download = st.multiselect('Select scenarios to download',
@@ -429,8 +430,9 @@ if auth_status == True:
         column_1, column_2,column_3 = st.columns((6,1,1))
         with column_1:
             st.header(selected_scenario)
-        with column_3:
-            st.button('Delete scenarios', on_click=delete_scenario)
+        # with column_3:
+        #     st.write("")
+            # st.button('Delete scenario', on_click=delete_scenario)
         # with column_3:
             # st.button('Load Scenario', on_click=load_scenario)
         

response_curves_model_quality.py

@@ -125,7 +125,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
 
     ext_df['MAT'] = ["ext","ext","ext"]
     
-    print(ext_df)
+    # print(ext_df)
     plot_df= plot_df.append(ext_df)
     return plot_df
 
@@ -148,7 +148,7 @@ def input_data(df,spend_col,prospect_col):
     return X,y,x_data,y_data,x_minmax,y_minmax
 
 def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
-    print(x_max)
+    # print(x_max)
     x_ext_data = [x_max*1.2,x_max*1.3,x_max*1.5]
 #     x_ext_data = [1500000,2000000,2500000]
 #     x_ext_data = [x_max+100,x_max+200,x_max+5000]
@@ -157,7 +157,7 @@ def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
     for i in range(len(x_scaled)):
         x_data.append(x_scaled[i][0])
         
-    print(x_data)
+    # print(x_data)
     y_fit = hill_equation(x_data, Kd_fit, n_fit)
     y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
     
@@ -170,8 +170,8 @@ def fit_data(spend_col,prospect_col,channel):
 
     X,y,x_data,y_data,x_minmax,y_minmax = input_data(temp_df,spend_col,prospect_col)
     y_fit, y_fit_inv, Kd_fit, n_fit = hill_func(x_data,y_data,x_minmax,y_minmax)
-    print('k: ',Kd_fit)
-    print('n: ', n_fit)
+    # print('k: ',Kd_fit)
+    # print('n: ', n_fit)
     
     ##### extend_s_curve
     x_ext_data,y_fit_inv_ext=  extend_s_curve(temp_df[spend_col].max(),x_minmax,y_minmax, Kd_fit, n_fit)
@@ -183,7 +183,7 @@ plotly_data = fit_data(spend_cols[0],prospect_cols[0],channel_cols[0])
 plotly_data.tail()
 
 for i in range(1,13):
-    print(i)
+    # print(i)
     pdf =  fit_data(spend_cols[i],prospect_cols[i],channel_cols[i])
     plotly_data = plotly_data.merge(pdf,on = ["Date","MAT"],how = "left")
     
@@ -223,8 +223,8 @@ def response_curves(channel,x_modified,y_modified):
     ))
 
     fig.add_trace(go.Scatter(
-        x=[x_modified],
-        y=[y_modified],
+        x=[x_modified/104],
+        y=[y_modified/104],
         mode='markers',
         marker=dict(
         size=13  # Adjust the size value to make the markers larger or smaller
@@ -369,7 +369,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
 
     ext_df['MAT'] = ["ext","ext","ext"]
     
-    print(ext_df)
+    # print(ext_df)
     plot_df= plot_df.append(ext_df)
     return plot_df
 
@@ -392,7 +392,7 @@ def input_data(df,spend_col,prospect_col):
     return X,y,x_data,y_data,x_minmax,y_minmax
 
 def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
-    print(x_max)
+    # print(x_max)
     x_ext_data = [x_max*1.2,x_max*1.3,x_max*1.5]
 #     x_ext_data = [1500000,2000000,2500000]
 #     x_ext_data = [x_max+100,x_max+200,x_max+5000]
@@ -401,7 +401,7 @@ def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
     for i in range(len(x_scaled)):
         x_data.append(x_scaled[i][0])
         
-    print(x_data)
+    # print(x_data)
     y_fit = hill_equation(x_data, Kd_fit, n_fit)
     y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
     
@@ -414,8 +414,8 @@ def fit_data(spend_col,prospect_col,channel):
 
     X,y,x_data,y_data,x_minmax,y_minmax = input_data(temp_df,spend_col,prospect_col)
     y_fit, y_fit_inv, Kd_fit, n_fit = hill_func(x_data,y_data,x_minmax,y_minmax)
-    print('k: ',Kd_fit)
-    print('n: ', n_fit)
+    # print('k: ',Kd_fit)
+    # print('n: ', n_fit)
     
     ##### extend_s_curve
     x_ext_data,y_fit_inv_ext=  extend_s_curve(temp_df[spend_col].max(),x_minmax,y_minmax, Kd_fit, n_fit)
@@ -427,7 +427,7 @@ plotly_data = fit_data(spend_cols[0],prospect_cols[0],channel_cols[0])
 plotly_data.tail()
 
 for i in range(1,13):
-    print(i)
+    # print(i)
     pdf =  fit_data(spend_cols[i],prospect_cols[i],channel_cols[i])
     plotly_data = plotly_data.merge(pdf,on = ["Date","MAT"],how = "left")
     

response_curves_model_quality_base.py

@@ -125,7 +125,7 @@ def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext):
 
     ext_df['MAT'] = ["ext","ext","ext"]
     
-    print(ext_df.columns)
+    # print(ext_df.columns)
     plot_df= plot_df.append(ext_df)
     return plot_df
 
@@ -148,7 +148,7 @@ def input_data(df,spend_col,prospect_col):
     return X,y,x_data,y_data,x_minmax,y_minmax
 
 def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
-    print(x_max)
+    # print(x_max)
     x_ext_data = [x_max*1.2,x_max*1.3,x_max*1.5]
 #     x_ext_data = [1500000,2000000,2500000]
 #     x_ext_data = [x_max+100,x_max+200,x_max+5000]
@@ -157,7 +157,7 @@ def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
     for i in range(len(x_scaled)):
         x_data.append(x_scaled[i][0])
         
-    print(x_data)
+    # print(x_data)
     y_fit = hill_equation(x_data, Kd_fit, n_fit)
     y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
     
@@ -170,8 +170,8 @@ def fit_data(spend_col,prospect_col,channel):
 
     X,y,x_data,y_data,x_minmax,y_minmax = input_data(temp_df,spend_col,prospect_col)
     y_fit, y_fit_inv, Kd_fit, n_fit = hill_func(x_data,y_data,x_minmax,y_minmax)
-    print('k: ',Kd_fit)
-    print('n: ', n_fit)
+    # print('k: ',Kd_fit)
+    # print('n: ', n_fit)
     
     ##### extend_s_curve
     x_ext_data,y_fit_inv_ext=  extend_s_curve(temp_df[spend_col].max(),x_minmax,y_minmax, Kd_fit, n_fit)
@@ -190,23 +190,59 @@ for i in range(1,13):
 def response_curves(channel,chart_typ):
     if chart_typ == 'View Scattered Plot':
         mode_f1 = "markers"
+        # Initialize the Plotly figure
+        fig = go.Figure()
+    
+        x_col = channel+"_Spends"
+        y_col = channel+"_Prospects"
+        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],
+            mode=mode_f1,
+            name=x_col.replace('_Spends', '')
+        ))
     elif chart_typ == 'View Line Plot':
         mode_f1 = "lines"
+        # Initialize the Plotly figure
+        fig = go.Figure()
+    
+        x_col = channel+"_Spends"
+        y_col = 'Fit_Data_'+channel
+        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],
+            mode=mode_f1,
+            name=x_col.replace('_Spends', '')
+        ))
     else:
-        mode_f1 = "lines+markers"
-
+        mode_f1 = "markers"
+        # Initialize the Plotly figure
+        fig = go.Figure()
+    
+        x_col = channel+"_Spends"
+        y_col = channel+"_Prospects"
+        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],
+            mode=mode_f1,
+            name=x_col.replace('_Spends', '')
+        ))
 
-    # Initialize the Plotly figure
-    fig = go.Figure()
+        # mode_f1 = "lines+markers"
+        mode_f1 = "lines"
+        # Initialize the Plotly figure
+        # fig = go.Figure()
+    
+        x_col = channel+"_Spends"
+        y_col = 'Fit_Data_'+channel
+        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],
+            mode=mode_f1,
+            name=x_col.replace('_Spends', '')
+        ))
 
-    x_col = channel+"_Spends"
-    y_col = "Fit_Data_"+channel
-    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],
-        mode=mode_f1,
-        name=x_col.replace('_Spends', '')
-    ))
+    
     plotly_data2 = plotly_data[plotly_data[x_col].isnull()==False]
     # import steamlit as st
     # st.dataframe()    

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ef1d30472601a41557559d3fe1b39fe0a08bfa581a66af23a210e9dc459b750c
+oid sha256:bbfe530ac30a045842611782bde814043862ff721a910f19cfbb8525f1dfb82a
 size 1822

utilities.py

@@ -221,14 +221,10 @@ def initialize_data(
     contri_df = excel["CONTRIBUTION MMM"]
 
     # Check if the panel is not None
-    if panel is not None and panel != "Total Market":
-        raw_df = raw_df[raw_df["Panel"] == panel].drop(columns=["Panel"])
-        spend_df = spend_df[spend_df["Panel"] == panel].drop(columns=["Panel"])
-        contri_df = contri_df[contri_df["Panel"] == panel].drop(columns=["Panel"])
-    elif panel == "Total Market":
-        raw_df = panel_level(raw_df, date_column="Date")
-        spend_df = panel_level(spend_df, date_column="Week")
-        contri_df = panel_level(contri_df, date_column="Date")
+    
+    raw_df = panel_level(raw_df, date_column="Date")
+    spend_df = panel_level(spend_df, date_column="Week")
+    contri_df = panel_level(contri_df, date_column="Date")
 
     # Revenue_df = excel['Revenue']
 
@@ -294,48 +290,21 @@ def initialize_data(
         ##output cols aggregation
         output_cols.append(out_col)
 
-        ## scale the input
-        power = np.ceil(np.log(x.max()) / np.log(10)) - 3
-        if power >= 0:
-            x = x / 10**power
-
-        x = x.astype("float64")
-        y = y.astype("float64")
-        # print('#printing yyyyyyyyy')
-        # print(inp_col)
-        # print(x.max())
-        # print(y.max())
-        bounds = ((0, 0, 0, 0), (3 * y.max(), 1000, 1, x.max()))
-
-        # bounds = ((y.max(), 3*y.max()),(0,1000),(0,1),(0,x.max()))
-        params, _ = curve_fit(
-            s_curve,
-            x,
-            y,
-            p0=(2 * y.max(), 0.01, 1e-5, x.max()),
-            bounds=bounds,
-            maxfev=int(1e5),
-        )
-        mape = (100 * abs(1 - s_curve(x, *params) / y.clip(min=1))).mean()
-        rmse = np.sqrt(((y - s_curve(x, *params)) ** 2).mean())
-        r2_ = r2_score(y, s_curve(x, *params))
-
+        params = pd.read_excel(r"C:\Users\PragyaJatav\Downloads\Untitled Folder 2\simulator uploaded - Copy\Simulator-UOPX\response_curves_parameters.xlsx",index_col = "channel")
+        param_dicts = {col: params[col].to_dict() for col in params.columns}
         response_curves[inp_col] = {
-            "K": params[0],
-            "b": params[1],
-            "a": params[2],
-            "x0": params[3],
+            "Kd": param_dicts["Kd"][inp_col],
+            "n": param_dicts["n"][inp_col],
+            "x_min": param_dicts["x_min"][inp_col],
+            "x_max": param_dicts["x_max"][inp_col],
+            "y_min": param_dicts["y_min"][inp_col],
+            "y_max": param_dicts["y_max"][inp_col]
         }
 
         updated_rcs_key = f"{metrics}#@{panel}#@{inp_col}"
         if updated_rcs is not None and updated_rcs_key in list(updated_rcs.keys()):
             response_curves[inp_col] = updated_rcs[updated_rcs_key]
 
-        mapes[inp_col] = mape
-        rmses[inp_col] = rmse
-        r2[inp_col] = r2_
-        powers[inp_col] = power
-
         ## conversion rates
         spend_col = [
             _col
@@ -362,14 +331,17 @@ def initialize_data(
             name=inp_col,
             dates=dates,
             spends=spends,
+            sales= y.copy(),
             # conversion_rate = np.mean(list(conv_rates[inp_col].values())),
             conversion_rate=conv_rates[inp_col],
-            response_curve_type="s-curve",
+            response_curve_type="hill-eq",
             response_curve_params={
-                "K": params[0],
-                "b": params[1],
-                "a": params[2],
-                "x0": params[3],
+                "Kd": param_dicts["Kd"][inp_col],
+                "n": param_dicts["n"][inp_col],
+                "x_min": param_dicts["x_min"][inp_col],
+                "x_max": param_dicts["x_max"][inp_col],
+                "y_min": param_dicts["y_min"][inp_col],
+                "y_max": param_dicts["y_max"][inp_col]
             },
             bounds=np.array([-10, 10]),
             channel_bounds_min = 10,
@@ -430,94 +402,6 @@ def initialize_data(
     st.session_state["disable_download_button"] = True
 
 
-# def initialize_data():
-#     # fetch data from excel
-#     output = pd.read_excel('data.xlsx',sheet_name=None)
-#     raw_df = output['RAW DATA MMM']
-#     contribution_df = output['CONTRIBUTION MMM']
-#     Revenue_df = output['Revenue']
-
-#     ## channels to be shows
-#     channel_list = []
-#     for col in raw_df.columns:
-#         if 'click' in col.lower() or 'spend' in col.lower() or 'imp' in col.lower():
-#             ##print(col)
-#             channel_list.append(col)
-#         else:
-#             pass
-
-#     ## NOTE : Considered only Desktop spends for all calculations
-#     acutal_df = raw_df[raw_df.Region == 'Desktop'].copy()
-#     ## NOTE : Considered one year of data
-#     acutal_df = acutal_df[acutal_df.Date>'2020-12-31']
-#     actual_df = acutal_df.drop('Region',axis=1).sort_values(by='Date')[[*channel_list,'Date']]
-
-#     ##load response curves
-#     with open('./grammarly_response_curves.json','r') as f:
-#         response_curves = json.load(f)
-
-#     ## create channel dict for scenario creation
-#     dates = actual_df.Date.values
-#     channels = {}
-#     rcs = {}
-#     constant = 0.
-#     for i,info_dict in enumerate(response_curves):
-#         name = info_dict.get('name')
-#         response_curve_type = info_dict.get('response_curve')
-#         response_curve_params = info_dict.get('params')
-#         rcs[name] = response_curve_params
-#         if name != 'constant':
-#             spends = actual_df[name].values
-#             channel = Channel(name=name,dates=dates,
-#                             spends=spends,
-#                             response_curve_type=response_curve_type,
-#                             response_curve_params=response_curve_params,
-#                             bounds=np.array([-30,30]))
-
-#             channels[name] = channel
-#         else:
-#             constant = info_dict.get('value',0.) * len(dates)
-
-#     ## create scenario
-#     scenario = Scenario(name='default', channels=channels, constant=constant)
-#     default_scenario_dict = class_to_dict(scenario)
-
-
-#     ## setting session variables
-#     st.session_state['initialized'] = True
-#     st.session_state['actual_df'] = actual_df
-#     st.session_state['raw_df'] = raw_df
-#     st.session_state['default_scenario_dict'] = default_scenario_dict
-#     st.session_state['scenario'] = scenario
-#     st.session_state['channels_list'] = channel_list
-#     st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
-#     st.session_state['rcs'] = rcs
-#     for channel in channels.values():
-#         if channel.name not in st.session_state:
-#             st.session_state[channel.name] = float(channel.actual_total_spends)
-
-#     if 'xlsx_buffer' not in st.session_state:
-#         st.session_state['xlsx_buffer'] = io.BytesIO()
-
-#     ## for saving scenarios
-#     if 'saved_scenarios' not in st.session_state:
-#         if Path('../saved_scenarios.pkl').exists():
-#             with open('../saved_scenarios.pkl','rb') as f:
-#                 st.session_state['saved_scenarios'] = pickle.load(f)
-
-#         else:
-#             st.session_state['saved_scenarios'] = OrderedDict()
-
-#     if 'total_spends_change' not in st.session_state:
-#         st.session_state['total_spends_change'] = 0
-
-#     if 'optimization_channels' not in st.session_state:
-#         st.session_state['optimization_channels'] = {channel_name : False for channel_name in channel_list}
-
-#     if 'disable_download_button' not in st.session_state:
-#         st.session_state['disable_download_button'] = True
-
-
 def create_channel_summary(scenario):
 
     # Provided data
@@ -618,90 +502,6 @@ def create_channel_summary(scenario):
     return df
 
 
-# @st.cache(allow_output_mutation=True)
-# def create_contribution_pie(scenario):
-#     #c1f7dc
-#     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=['Spends','Revenue'],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],
-#                     marker=dict(colors = [plotly.colors.label_rgb(colors_map[channel_name]) for channel_name in st.session_state['channels_list']] + ['#F0F0F0']),
-#                         hole=0.3),
-#                 row=1, col=1)
-
-#     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= [scenario.channels[channel_name].actual_total_sales for channel_name in st.session_state['channels_list']] + [scenario.correction.sum() + scenario.constant.sum()],
-#                         hole=0.3),
-#                 row=1, col=2)
-
-#     total_contribution_fig.update_traces(textposition='inside',texttemplate='%{percent:.1%}')
-#     total_contribution_fig.update_layout(uniformtext_minsize=12,title='Channel contribution', uniformtext_mode='hide')
-#     return total_contribution_fig
-
-# @st.cache(allow_output_mutation=True)
-
-# def create_contribuion_stacked_plot(scenario):
-#     weekly_contribution_fig = make_subplots(rows=1, cols=2,subplot_titles=['Spends','Revenue'],specs=[[{"type": "bar"}, {"type": "bar"}]])
-#     raw_df = st.session_state['raw_df']
-#     df = raw_df.sort_values(by='Date')
-#     x = df.Date
-#     weekly_spends_data = []
-#     weekly_sales_data = []
-#     for channel_name in st.session_state['channels_list']:
-#         weekly_spends_data.append((go.Bar(x=x,
-#                                           y=scenario.channels[channel_name].actual_spends * scenario.channels[channel_name].conversion_rate,
-#                                           name=channel_name_formating(channel_name),
-#                                           hovertemplate="Date:%{x}<br>Spend:%{y:$.2s}",
-#                                           legendgroup=channel_name)))
-#         weekly_sales_data.append((go.Bar(x=x,
-#                                          y=scenario.channels[channel_name].actual_sales,
-#                                          name=channel_name_formating(channel_name),
-#                                          hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}",
-#                                          legendgroup=channel_name, showlegend=False)))
-#     for _d in weekly_spends_data:
-#         weekly_contribution_fig.add_trace(_d, row=1, col=1)
-#     for _d in weekly_sales_data:
-#         weekly_contribution_fig.add_trace(_d, row=1, col=2)
-#     weekly_contribution_fig.add_trace(go.Bar(x=x,
-#                                          y=scenario.constant + scenario.correction,
-#                                          name='Non Media',
-#                                          hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}"), row=1, col=2)
-#     weekly_contribution_fig.update_layout(barmode='stack', title='Channel contribuion by week', xaxis_title='Date')
-#     weekly_contribution_fig.update_xaxes(showgrid=False)
-#     weekly_contribution_fig.update_yaxes(showgrid=False)
-#     return weekly_contribution_fig
-
-# @st.cache(allow_output_mutation=True)
-# def create_channel_spends_sales_plot(channel):
-#     if channel is not None:
-#         x = channel.dates
-#         _spends = channel.actual_spends * channel.conversion_rate
-#         _sales = channel.actual_sales
-#         channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
-#         channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}"), secondary_y = False)
-#         channel_sales_spends_fig.add_trace(go.Scatter(x=x, y=_spends,line=dict(color='#005b96'),name='Spends',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_xaxes(showgrid=False)
-#         channel_sales_spends_fig.update_yaxes(showgrid=False)
-#     else:
-#         raw_df = st.session_state['raw_df']
-#         df = raw_df.sort_values(by='Date')
-#         x = df.Date
-#         scenario = class_from_dict(st.session_state['default_scenario_dict'])
-#         _sales = scenario.constant + scenario.correction
-#         channel_sales_spends_fig = make_subplots(specs=[[{"secondary_y": True}]])
-#         channel_sales_spends_fig.add_trace(go.Bar(x=x, y=_sales,marker_color='#c1f7dc',name='Revenue', hovertemplate="Date:%{x}<br>Revenue:%{y:$.2s}"), secondary_y = False)
-#         # channel_sales_spends_fig.add_trace(go.Scatter(x=x, y=_spends,line=dict(color='#15C39A'),name='Spends',hovertemplate="Date:%{x}<br>Spend:%{y:$.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_xaxes(showgrid=False)
-#         channel_sales_spends_fig.update_yaxes(showgrid=False)
-#     return channel_sales_spends_fig
-
-
-# Define a shared color palette
-
 
 def create_contribution_pie():
     color_palette = [