latest

Home.py

@@ -14,6 +14,7 @@ import pandas as pd
 import pickle
 from pathlib import Path
 import re
+
 st.set_page_config(layout="wide")
 load_local_css("styles.css")
 set_header()
@@ -30,17 +31,26 @@ conn = sqlite3.connect(database_file, check_same_thread=False)
 c = conn.cursor()
 
 
+def get_excel_files(directory="API_data"):
+    excel_files = {}
+    for filename in os.listdir(directory):
+        if filename.endswith(".xlsx"):
+            file_path = os.path.join(directory, filename)
+            file_key = os.path.splitext(filename)[0]  # Remove the .xlsx extension
+            excel_files[file_key] = file_path
+    return excel_files
+
 
 def update_summary_df():
-    #print("[DEBUG]: Running update_summary_df")
-    
-    """ Function to fetch the project details everytime user 
-    changes the slection box this function is being called on change in 
-    username select box """
+    # print("[DEBUG]: Running update_summary_df")
+
+    """Function to fetch the project details everytime user
+    changes the slection box this function is being called on change in
+    username select box"""
 
     # Execute a SQL query to select distinct project names, the last edited page,
     # and the last updated time from the 'sessions' table where the owner matches the user's name
-    
+
     c.execute(
         """
         SELECT project_name, last_edited_page, updated_time as last_updated 
@@ -52,12 +62,11 @@ def update_summary_df():
         ) sub
         GROUP BY project_name
         """,
-        (st.session_state["username"],)
+        (st.session_state["username"],),
     )
 
     # Fetch all the results of the query
     project_summary = c.fetchall()
-    
 
     # This will hold the user's owned sessions
 
@@ -68,9 +77,9 @@ def update_summary_df():
     )
 
     # Convert the 'Modified Date' column to datetime format
-    project_summary_df["Modified Date"] = project_summary_df[
-        "Modified Date"
-    ].map(lambda x: pd.to_datetime(x).date())
+    project_summary_df["Modified Date"] = project_summary_df["Modified Date"].map(
+        lambda x: pd.to_datetime(x).date()
+    )
 
     # Sort the DataFrame by 'Modified Date' in descending order
     session_summary_df = project_summary_df.sort_values(
@@ -79,12 +88,11 @@ def update_summary_df():
 
     session_summary_df["Last Page Modified"] = session_summary_df[
         "Last Page Edited"
-    ].map(lambda x: re.sub(r'[_1-9]', ' ', x).replace(".py", ""))
+    ].map(lambda x: re.sub(r"[_1-9]", " ", x).replace(".py", ""))
 
     # Save the resulting DataFrame to the session state
     st.session_state["session_summary_df"] = session_summary_df
 
-
     # Add a 'selected' column to the DataFrame and initialize it with False for all rows
 
     if "selected" not in st.session_state.session_summary_df.columns:
@@ -99,9 +107,7 @@ def update_summary_df():
 
 
 st.header("Manage Projects")
-# c.execute("PRAGMA table_info(sessions);")
 
-# st.write(c.fetchall())
 users = {
     "ioannis": "Ioannis Papadopoulos",
     "sharon": "Sharon Sheng",
@@ -110,12 +116,17 @@ users = {
     "geetha": "Geetha Krishna",
     "srishti": "Srishti Verma",
     "samkeet": "Samkeet Sangai",
-    "manoj": "Manoj P"
+    "manoj": "Manoj P",
+    "loveesh": "Loveesh Bhatt",
+    "bhavya": "Bhavya Jayantilal Kanzariya",
+    "pritisha": "Pritisha Punukollu",
+    "ashish": "Ashish Sharma",
+    "swarupa": "Swarupa Parepalli",
 }
 
 
-if 'username' not in st.session_state:
-    st.session_state['username']=''
+if "username" not in st.session_state:
+    st.session_state["username"] = ""
 
 # first_name_value = [key for key, value in users.items() if value == st.session_state['username']]
 
@@ -123,34 +134,55 @@ if 'username' not in st.session_state:
 # first_name_value = first_name_value[0] if first_name_value else ''
 
 
-first_name=st.text_input('Enter Name').lower()
+first_name = st.text_input("Enter Name").lower()
 
 
-if st.button('Login'):
+if st.button("Login"):
 
     if first_name not in users.keys():
-        st.warning('Please enter a valid name')
+        st.warning("Please enter a valid name")
         st.stop()
 
-    name=users[first_name]
+    name = users[first_name]
 
-    st.session_state.name=name # storing in session state
+    st.session_state.name = name  # storing in session state
 
-    st.session_state["username"]= name 
+    st.session_state["username"] = name
 
-    update_summary_df() #function call to fetch user saved projects
+    update_summary_df()  # function call to fetch user saved projects
 
-    #st.success('Projects sucessfully loaded')
+    # st.success('Projects sucessfully loaded')
 
-if len(first_name)==0 or first_name not in users.keys():
+if len(first_name) == 0 or first_name not in users.keys():
     st.stop()
 
 
-
 # name=st.session_state['Username']
 
 # c.execute('Delete from sessions')
 # conn.commit()
+
+# c.execute("PRAGMA table_info(users);")
+# c.execute("SELECT * FROM users;")
+# st.write(c.fetchall())
+
+# Delete all existing data in the users table
+# c.execute("DELETE FROM users")
+
+# # Insert new data into the users table
+# for idx, (username, full_name) in enumerate(users.items(), start=1):
+#     user_id = idx
+#     email = f"{username}@mastercard.com"
+#     user_type = "technical"
+
+#     c.execute(
+#         "INSERT INTO users (user_id, username, email, user_type) VALUES (?, ?, ?, ?)",
+#         (user_id, full_name, email, user_type)
+#     )
+
+# # Commit the changes and close the connection
+# conn.commit()
+
 if st.session_state["username"] in users.values():
 
     if "session_summary_df" not in st.session_state:
@@ -166,19 +198,22 @@ if st.session_state["username"] in users.values():
     with cols1[1]:
         st.markdown(f"**Current Project: {st.session_state['project_name']}**")
 
-
     # Execute a SQL query to select all project names from the 'sessions' table
     # where the owner matches the user's name
-    c.execute("SELECT project_name FROM sessions WHERE owner=?", (st.session_state['username'],))
+    c.execute(
+        "SELECT project_name FROM sessions WHERE owner=?",
+        (st.session_state["username"],),
+    )
 
     # Fetch all the results and create a list of project names
     user_projects = [project[0] for project in c.fetchall()]
 
-
     c.execute("SELECT DISTINCT username FROM users")
 
     # Fetch all the results and create a list of usernames excluding the current user's name
-    allowed_users_db = [user[0] for user in c.fetchall() if user[0] != st.session_state['username']]
+    allowed_users_db = [
+        user[0] for user in c.fetchall() if user[0] != st.session_state["username"]
+    ]
 
     page_name = "Home Page"
 
@@ -186,7 +221,7 @@ if st.session_state["username"] in users.values():
     # where the username matches the current user's name
     c.execute(
         "SELECT email, user_id, user_type FROM users WHERE username = ?",
-        (st.session_state['username'],),
+        (st.session_state["username"],),
     )
 
     # Fetch the result of the query (assume there is only one matching row)
@@ -195,20 +230,19 @@ if st.session_state["username"] in users.values():
     # Unpack the fetched data into corresponding variables
     email, user_id, user_type = user_data
 
-
-
     folder_path = r"Users"
     user_folder_path = os.path.join(folder_path, email)
 
     if not os.path.exists(user_folder_path):
         os.makedirs(user_folder_path)
 
-
     def dump_session_details_db(allowed_users, project_name):
 
-        'Function to dump details of project in db when a project is created/modified/cloned '
+        "Function to dump details of project in db when a project is created/modified/cloned"
 
-        created_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")  # Get the current time
+        created_time = datetime.datetime.now().strftime(
+            "%Y-%m-%d %H:%M"
+        )  # Get the current time
         session_id = str(uuid.uuid4())  # Generate a unique session ID
 
         if len(allowed_users) == 0:
@@ -217,7 +251,7 @@ if st.session_state["username"] in users.values():
                 "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
                 (
                     user_id,
-                    st.session_state['username'],
+                    st.session_state["username"],
                     session_id,
                     project_name,
                     page_name,
@@ -234,7 +268,7 @@ if st.session_state["username"] in users.values():
                     "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
                     (
                         user_id,
-                        st.session_state['username'],
+                        st.session_state["username"],
                         session_id,
                         project_name,
                         "1_Home.py",
@@ -245,8 +279,6 @@ if st.session_state["username"] in users.values():
                 )
             conn.commit()  # Commit the transaction
 
-
-
     st.markdown(
         """
     * **Delete Project:** If you wish to delete a project, select it and click 'Delete Project'.
@@ -261,13 +293,12 @@ push
     if "selected_row_index" not in st.session_state:
         st.session_state["selected_row_index"] = None
 
-
     def selection_change():
         # Get the edited rows from the session state
-        #print(st.session_state['session_summary_df'])
-        edited_rows: dict = st.session_state['project_selection']["edited_rows"]
-        #print(edited_rows)
-        
+        # print(st.session_state['session_summary_df'])
+        edited_rows: dict = st.session_state["project_selection"]["edited_rows"]
+        # print(edited_rows)
+
         # Set the selected row index in the session state
         st.session_state["selected_row_index"] = next(iter(edited_rows))
 
@@ -285,57 +316,64 @@ push
         )
 
         # Reset the DataFrame index
-        st.session_state['session_summary_df'] = st.session_state['session_summary_df'].reset_index(drop=True)
-        
-
-
+        st.session_state["session_summary_df"] = st.session_state[
+            "session_summary_df"
+        ].reset_index(drop=True)
 
     st.markdown("Select Project")
 
-    if len(st.session_state["session_summary_df"])!=0:
-        
+    if len(st.session_state["session_summary_df"]) != 0:
+
         with st.container():
             # Display an editable data table using Streamlit's data editor component
-            
+
             table = st.data_editor(
-                st.session_state["session_summary_df"].drop(['Last Page Edited'],axis=1).reindex(
-                    columns=['selected','Project Name','Last Page Modified','Modified Date'
-                            ]),  
-                hide_index=True,  
+                st.session_state["session_summary_df"]
+                .drop(["Last Page Edited"], axis=1)
+                .reindex(
+                    columns=[
+                        "selected",
+                        "Project Name",
+                        "Last Page Modified",
+                        "Modified Date",
+                    ]
+                ),
+                hide_index=True,
                 on_change=selection_change,  # Function to call when data is edited
                 key="project_selection",  # Key for the data editor component in the session state
-                use_container_width=True,  
+                use_container_width=False,
             )
 
-        if len(st.session_state["session_summary_df"]) > 0 and st.session_state["selected_row_index"] is not None :
+        if (
+            len(st.session_state["session_summary_df"]) > 0
+            and st.session_state["selected_row_index"] is not None
+        ):
 
             selected_row_index = st.session_state["session_summary_df"]["selected"]
-        
 
             # st.write(st.session_state['selected_row_index'])
 
             if len(selected_row_index) != 0:
-                
-                try :
+
+                try:
                     project_name = st.session_state["session_summary_df"].at[
                         st.session_state["selected_row_index"], "Project Name"
                     ]
                 except Exception as e:
-                    st.session_state["selected_row_index"]=None
-                    st.rerun() 
+                    st.session_state["selected_row_index"] = None
+                    st.rerun()
 
                 last_edited_page = st.session_state["session_summary_df"].at[
-                        st.session_state["selected_row_index"], "Last Page Edited"
-                    ]
+                    st.session_state["selected_row_index"], "Last Page Edited"
+                ]
 
                 st.session_state["project_name"] = project_name
 
-
                 project_col = st.columns(2)
 
                 with project_col[0]:
-                    
-                    if st.button('Load Project',use_container_width=True):
+
+                    if st.button("Load Project", use_container_width=True):
                         st.session_state["project_name"] = project_name
                         st.rerun()
 
@@ -348,16 +386,19 @@ push
                     with open(project_dct_path, "rb") as f:
                         try:
                             st.session_state["project_dct"] = pickle.load(f)
-                            st.success('Project Loded')
+                            st.success("Project Loded")
 
                         except Exception as e:
-                            st.warning('Something went wrong unable to load saved details / data is lost due to app refresh. Please uncheck the check box and create a new project.')
+                            st.warning(
+                                "Something went wrong unable to load saved details / data is lost due to app refresh. Please uncheck the check box and create a new project."
+                            )
                             st.stop()
 
-
                 with project_col[1]:
 
-                    if st.button(f"Delete Project - **{project_name}**",use_container_width=True):
+                    if st.button(
+                        f"Delete Project - **{project_name}**", use_container_width=True
+                    ):
 
                         project_name_to_delete = project_name
                         st.warning(
@@ -380,7 +421,9 @@ push
                             st.rerun()
 
                         except:
-                            st.warning('Failed to Delete project try refreshing the page or try after some time')
+                            st.warning(
+                                "Failed to Delete project try refreshing the page or try after some time"
+                            )
                             st.stop()
 
                 with st.expander("Add users with access to the selected project"):
@@ -395,7 +438,8 @@ push
                     present_users = [
                         user[0]
                         for user in present_users
-                        if user[0] != st.session_state['username'] and user[0] is not None
+                        if user[0] != st.session_state["username"]
+                        and user[0] is not None
                     ]
 
                     present_users = None if len(present_users) == 0 else present_users
@@ -407,12 +451,12 @@ push
                             list(set(allowed_users_db) - set(present_users)),
                         )
                     else:
-                        
+
                         allowed_users = st.multiselect(
                             "",
                             list(set(allowed_users_db)),
                         )
-                        
+
                     if st.button("Save Changes", use_container_width=True):
                         dump_session_details_db(allowed_users, project_name)
                         c.execute("SELECT * from sessions")
@@ -423,11 +467,9 @@ push
             "To create a new project, Enter Project name below, select user who you want to give access of this project and click **Create New Project**"
         )
 
-        project_col1 = st.columns(2)
+        project_col1 = st.columns(3)
         with project_col1[0]:
-            project_name = st.text_input(
-                "Enter Project Name", key="project_name_box"
-            )
+            project_name = st.text_input("Enter Project Name", key="project_name_box")
         if project_name in user_projects:
             st.warning("Project already exists please enter new name")
 
@@ -438,7 +480,14 @@ push
             )
             allowed_users = list(allowed_users)
 
-        Create = st.button("Create New Project", use_container_width=True )
+        with project_col1[2]:
+            API_path_dict = get_excel_files()
+
+            api_name = st.selectbox("Select API data", API_path_dict.keys(), index=0)
+            api_path = API_path_dict[api_name]
+
+
+        Create = st.button("Create New Project", use_container_width=True)
 
         if Create:
 
@@ -451,7 +500,7 @@ push
                 st.stop()
 
             project_path = os.path.join(user_folder_path, project_name)
-            
+
             if not os.path.exists(project_path):
                 os.makedirs(project_path)
 
@@ -476,6 +525,8 @@ push
                     "default_df": None,
                     "final_df": None,
                     "edited_df": None,
+                    "api_path": api_path,
+                    "api_name": api_name,
                 },
                 "data_validation": {
                     "target_column": 0,
@@ -551,10 +602,9 @@ push
 
             st.success("Project Created")
 
-            update_summary_df() 
-            
-            st.rerun()
+            update_summary_df()
 
+            st.rerun()
 
     # st.header('Clone Project')
 
@@ -562,11 +612,13 @@ push
 
         c.execute(
             "SELECT DISTINCT owner FROM sessions WHERE allowed_users=?",
-            (st.session_state['username'],),
+            (st.session_state["username"],),
         )  # owner
         owners = c.fetchall()
 
-        owners = [owner[0] for owner in owners if owner[0]!=st.session_state["username"]]
+        owners = [
+            owner[0] for owner in owners if owner[0] != st.session_state["username"]
+        ]
 
         if len(owners) == 0:
 
@@ -590,7 +642,7 @@ push
 
             c.execute(
                 "SELECT project_name FROM sessions WHERE  owner=? AND allowed_users = ?",
-                (owner, st.session_state['username']),
+                (owner, st.session_state["username"]),
             )  # available sessions for user
             project_names = c.fetchall()
 
@@ -600,7 +652,6 @@ push
             )
             owner_project_path = os.path.join(owner_folder_path, project_name)
 
-    
         project_name_user = st.text_input(
             "Enter Project Name", value=project_name_owner
         )
@@ -613,12 +664,9 @@ push
 
         project_path = os.path.join(user_folder_path, project_name_user)
 
-        owner_project_path = os.path.join(
-            owner_folder_path, project_name_owner
-        )
-
+        owner_project_path = os.path.join(owner_folder_path, project_name_owner)
 
-        if st.button("Load Project", use_container_width=True):
+        if st.button("Load Project", use_container_width=True, key="load_project_button_key"):
 
             if os.path.exists(project_path):
 
@@ -637,8 +685,8 @@ push
 
             st.session_state["project_path"] = project_path
 
-
-            dump_session_details_db([], project_name_user) #passing empty list for allowed users
+            dump_session_details_db(
+                [], project_name_user
+            )  # passing empty list for allowed users
             st.success("Project Cloned")
             st.rerun()
-

Model_Results_Pretrained.py

@@ -1,349 +0,0 @@
-import streamlit as st
-import plotly.express as px
-import numpy as np
-import plotly.graph_objects as go
-from sklearn.metrics import r2_score
-from collections import OrderedDict
-import pickle
-import json
-import streamlit as st
-import plotly.express as px
-import numpy as np
-import plotly.graph_objects as go
-from sklearn.metrics import r2_score
-import pickle
-import json
-import pandas as pd
-import statsmodels.api as sm
-from sklearn.metrics import mean_absolute_percentage_error
-import sys
-import os
-from utilities import (set_header, 
-                       initialize_data,
-                       load_local_css,
-                       create_channel_summary,
-                       create_contribution_pie,
-                       create_contribuion_stacked_plot,
-                       create_channel_spends_sales_plot,
-                       format_numbers,
-                       channel_name_formating,
-                       load_authenticator)
-import seaborn as sns
-import matplotlib.pyplot as plt
-import sweetviz as sv
-import tempfile
-
-original_stdout = sys.stdout
-sys.stdout = open('temp_stdout.txt', 'w')
-sys.stdout.close()
-sys.stdout = original_stdout
-
-st.set_page_config(layout='wide')
-load_local_css('styles.css')
-set_header()
-
-for k, v in st.session_state.items():
-    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
-        st.session_state[k] = v
-
-authenticator = st.session_state.get('authenticator')
-if authenticator is None:
-    authenticator = load_authenticator()
-
-name, authentication_status, username = authenticator.login('Login', 'main')
-auth_status = st.session_state.get('authentication_status')
-
-if auth_status == True:
-    is_state_initiaized = st.session_state.get('initialized',False)
-    if not is_state_initiaized:
-        a=1
-      
-
-    def plot_residual_predicted(actual, predicted, df_):
-            df_['Residuals'] = actual - pd.Series(predicted)
-            df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
-            
-            # Create a Plotly scatter plot
-            fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
-            
-            # Add horizontal lines
-            fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
-            fig.add_hline(y=2, line_color="red")
-            fig.add_hline(y=-2, line_color="red")
-            
-            fig.update_xaxes(title='Predicted')
-            fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
-            
-            # Set the same width and height for both figures
-            fig.update_layout(title='Residuals over Predicted Values', autosize=False, width=600, height=400)
-            
-            return fig
-
-    def residual_distribution(actual, predicted):
-            Residuals = actual - pd.Series(predicted)
-            
-            # Create a Seaborn distribution plot
-            sns.set(style="whitegrid")
-            plt.figure(figsize=(6, 4))
-            sns.histplot(Residuals, kde=True, color="#11B6BD")
-            
-            plt.title(' Distribution of Residuals')
-            plt.xlabel('Residuals')
-            plt.ylabel('Probability Density')
-            
-            return plt
-
-    
-    def qqplot(actual, predicted):
-            Residuals = actual - pd.Series(predicted)
-            Residuals = pd.Series(Residuals)
-            Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
-            
-            # Create a QQ plot using Plotly with custom colors
-            fig = go.Figure()
-            fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
-                                    y=sm.ProbPlot(Resud_std).sample_quantiles,
-                                    mode='markers',
-                                    marker=dict(size=5, color="#11B6BD"),
-                                    name='QQ Plot'))
-            
-            # Add the 45-degree reference line
-            diagonal_line = go.Scatter(
-                x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
-                y=[-2, 2],  # Adjust the y values accordingly
-                mode='lines',
-                line=dict(color='red'),  # Customize the line color and style
-                name=' '
-            )
-            fig.add_trace(diagonal_line)
-            
-            # Customize the layout
-            fig.update_layout(title='QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
-                            xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
-            
-            return fig
-
-
-    def plot_actual_vs_predicted(date, y, predicted_values, model):
-        fig = go.Figure()
-
-        fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='blue')))
-        fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='orange')))
-        
-        # Calculate MAPE
-        mape = mean_absolute_percentage_error(y, predicted_values)*100
-        
-        # Calculate R-squared
-        rss = np.sum((y - predicted_values) ** 2)
-        tss = np.sum((y - np.mean(y)) ** 2)
-        r_squared = 1 - (rss / tss)
-        
-        # Get the number of predictors
-        num_predictors = model.df_model
-        
-        # Get the number of samples
-        num_samples = len(y)
-        
-        # Calculate Adjusted R-squared
-        adj_r_squared = 1 - ((1 - r_squared) * ((num_samples - 1) / (num_samples - num_predictors - 1)))
-        metrics_table = pd.DataFrame({
-        'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
-        'Value': [mape, r_squared, adj_r_squared]})
-        fig.update_layout(
-            xaxis=dict(title='Date'),
-            yaxis=dict(title='Value'),
-            title=f'MAPE : {mape:.2f}%, AdjR2: {adj_r_squared:.2f}',
-            xaxis_tickangle=-30
-        )
-
-        return metrics_table,fig
-
-
-
-
-    # # Perform linear regression
-    # model = sm.OLS(y, X).fit()
-    eda_columns=st.columns(3)
-    with eda_columns[0]:
-        tactic=st.checkbox('Tactic Level Model')
-    if tactic:
-        with open('mastercard_mmm_model.pkl', 'rb') as file:
-            model = pickle.load(file)
-        train=pd.read_csv('train_mastercard.csv')
-        test=pd.read_csv('test_mastercard.csv')
-        train['Date']=pd.to_datetime(train['Date'])
-        test['Date']=pd.to_datetime(test['Date'])
-        train.set_index('Date',inplace=True)
-        test.set_index('Date',inplace=True)
-        test.dropna(inplace=True)
-        X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
-        y_train=train['total_approved_accounts_revenue']
-        X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
-        X_train=sm.add_constant(X_train)
-        X_test=sm.add_constant(X_test)
-        y_test=test['total_approved_accounts_revenue']
-
-        # sys.stdout.close()
-        # sys.stdout = original_stdout
-
-        # st.set_page_config(layout='wide')
-        # load_local_css('styles.css')
-        # set_header()
-
-        channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
-        target_column='Total Approved Accounts - Revenue'
-
-  
-        with eda_columns[1]:
-          if st.button('Generate EDA Report'):
-              def generate_report_with_target(channel_data, target_feature):
-                  report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature,verbose=False)
-                  temp_dir = tempfile.mkdtemp()
-                  report_path = os.path.join(temp_dir, "report.html")
-                  report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
-                  return report_path
-          
-              report_file = generate_report_with_target(channel_data, target_column)
-          
-              if os.path.exists(report_file):
-                  with open(report_file, 'rb') as f:
-                      st.download_button(
-                          label="Download EDA Report",
-                          data=f.read(),
-                          file_name="report.html",
-                          mime="text/html"
-                      )
-              else:
-                  st.warning("Report generation failed. Unable to find the report file.")
-
-
-        st.title('Analysis of Result')
-
-        st.write(model.summary(yname='Revenue'))
-
-        metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
-        metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
-
-        metrics_table_train=metrics_table_train.set_index('Metric').transpose()
-        metrics_table_train.index=['Train']
-        metrics_table_test=metrics_table_test.set_index('Metric').transpose()
-        metrics_table_test.index=['test']
-        metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
-
-        st.markdown('Result Overview')
-        st.dataframe(np.round(metrics_table,2),use_container_width=True)
-
-        st.subheader('Actual vs Predicted Plot Train')
-
-        st.plotly_chart(fig_train,use_container_width=True)
-        st.subheader('Actual vs Predicted Plot Test')
-        st.plotly_chart(fig_test,use_container_width=True)
-
-        st.markdown('## Residual Analysis')
-        columns=st.columns(2)
-        Xtrain1=X_train.copy()
-        with columns[0]:
-            fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
-            st.plotly_chart(fig)
-
-        with columns[1]:
-            st.empty()
-            fig = qqplot(y_train,model.predict(X_train))
-            st.plotly_chart(fig)
-
-        with columns[0]:
-            fig=residual_distribution(y_train,model.predict(X_train))
-            st.pyplot(fig)
-    else:
-        with open('mastercard_mmm_model_channel.pkl', 'rb') as file:
-            model = pickle.load(file)
-        train=pd.read_csv('train_mastercard_channel.csv')
-        test=pd.read_csv('test_mastercard_channel.csv')
-        # train['Date']=pd.to_datetime(train['Date'])
-        # test['Date']=pd.to_datetime(test['Date'])
-        # train.set_index('Date',inplace=True)
-        # test.set_index('Date',inplace=True)
-        test.dropna(inplace=True)
-        X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
-        y_train=train['total_approved_accounts_revenue']
-        X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
-        X_train=sm.add_constant(X_train)
-        X_test=sm.add_constant(X_test)
-        y_test=test['total_approved_accounts_revenue']
-
-    
-
-        channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
-        target_column='Total Approved Accounts - Revenue'
-        with eda_columns[1]:
-          if st.button('Generate EDA Report'):
-              def generate_report_with_target(channel_data, target_feature):
-                  report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature)
-                  temp_dir = tempfile.mkdtemp()
-                  report_path = os.path.join(temp_dir, "report.html")
-                  report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
-                  return report_path
-          
-              report_file = generate_report_with_target(channel_data, target_column)
-          
-              # Provide a link to download the generated report
-              with open(report_file, 'rb') as f:
-                  st.download_button(
-                      label="Download EDA Report",
-                      data=f.read(),
-                      file_name="report.html",
-                      mime="text/html"
-                  )
-
-
-        st.title('Analysis of Result')
-
-        st.write(model.summary(yname='Revenue'))
-
-        metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
-        metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
-
-        metrics_table_train=metrics_table_train.set_index('Metric').transpose()
-        metrics_table_train.index=['Train']
-        metrics_table_test=metrics_table_test.set_index('Metric').transpose()
-        metrics_table_test.index=['test']
-        metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
-
-        st.markdown('Result Overview')
-        st.dataframe(np.round(metrics_table,2),use_container_width=True)
-
-        st.subheader('Actual vs Predicted Plot Train')
-
-        st.plotly_chart(fig_train,use_container_width=True)
-        st.subheader('Actual vs Predicted Plot Test')
-        st.plotly_chart(fig_test,use_container_width=True)
-
-        st.markdown('## Residual Analysis')
-        columns=st.columns(2)
-        Xtrain1=X_train.copy()
-        with columns[0]:
-            fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
-            st.plotly_chart(fig)
-
-        with columns[1]:
-            st.empty()
-            fig = qqplot(y_train,model.predict(X_train))
-            st.plotly_chart(fig)
-
-        with columns[0]:
-            fig=residual_distribution(y_train,model.predict(X_train))
-            st.pyplot(fig)
-
-elif auth_status == False:
-    st.error('Username/Password is incorrect')
-    
-if auth_status != True:
-    try:
-        username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
-        if username_forgot_pw:
-            st.success('New password sent securely')
-            # Random password to be transferred to user securely
-        elif username_forgot_pw == False:
-            st.error('Username not found')
-    except Exception as e:
-        st.error(e)

README.md

@@ -1,6 +1,6 @@
 ---
-title: Limev5
-emoji: 🏆
+title: dev-demo
+emoji: ⚖️
 colorFrom: indigo
 colorTo: pink
 sdk: streamlit

Test/overall_contributions.csv

@@ -1,143 +1,143 @@
-bing_pmax_impressions,bing_searchlink_clicks,facebook_clicks_lag_2_adstock_0_7,google_pmax_clicks_lag_1_adstock_0_7,google_demand_generation_link_clicks_lag_2,google_search_link_clicks,Trend,date,all_form_completion,pred,google_search_link_clicks_contr,facebook_clicks_lag_2_adstock_0_7_contr,google_demand_generation_link_clicks_lag_2_contr,google_pmax_clicks_lag_1_adstock_0_7_contr,bing_searchlink_clicks_contr,bing_pmax_impressions_contr,base_contr
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Test/smr_x_train_contribution.csv

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Test/test_contr.csv

@@ -1,31 +1,31 @@
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Transformation_functions.py

@@ -1,133 +0,0 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-from Eda_functions import format_numbers,line_plot,summary
-import numpy as np
-import re
-
-def sanitize_key(key, prefix=""):
-    # Use regular expressions to remove non-alphanumeric characters and spaces
-    key = re.sub(r'[^a-zA-Z0-9]', '', key)
-    return f"{prefix}{key}"
-
-
-def check_box(options, ad_stock_value,lag_value,num_columns=4, prefix=""):
-    num_rows = -(-len(options) // num_columns)  # Ceiling division to calculate rows
-
-    selected_options = []
-    adstock_info = {}  # Store adstock and lag info for each selected option
-    if ad_stock_value!=0:
-        for row in range(num_rows):
-            cols = st.columns(num_columns)
-            for col in cols:
-                if options:
-                    option = options.pop(0)
-                    key = sanitize_key(f"{option}_{row}", prefix=prefix)
-                    selected = col.checkbox(option, key=key)
-                    if selected:
-                        selected_options.append(option)
-                        
-                        # Input minimum and maximum adstock values
-                        adstock = col.slider('Select Adstock Range', 0.0, 1.0, ad_stock_value, step=0.05, format="%.2f",key= f"adstock_{key}" )
-
-                        # Input minimum and maximum lag values
-                        lag = col.slider('Select Lag Range', 0, 7, lag_value, step=1,key=f"lag_{key}" )
-
-                        # Create a dictionary to store adstock and lag info for the option
-                        option_info = {
-                            'adstock': adstock,
-                            'lag': lag}
-                        # Append the dictionary to the adstock_info list
-                        adstock_info[option]=option_info
-
-                    else:adstock_info[option]={
-                            'adstock': ad_stock_value,
-                            'lag': lag_value}
-
-        return selected_options, adstock_info   
-    else:
-        for row in range(num_rows):
-            cols = st.columns(num_columns)
-            for col in cols:
-                if options:
-                    option = options.pop(0)
-                    key = sanitize_key(f"{option}_{row}", prefix=prefix)
-                    selected = col.checkbox(option, key=key)
-                    if selected:
-                        selected_options.append(option)
-                        
-                        # Input minimum and maximum lag values
-                        lag = col.slider('Select Lag Range', 0, 7, lag_value, step=1,key=f"lag_{key}" )
-
-                        # dictionary to store adstock and lag info for the option
-                        option_info = {
-                            'lag': lag}
-                        # Append the dictionary to the adstock_info list
-                        adstock_info[option]=option_info
-
-                    else:adstock_info[option]={
-                            'lag': lag_value}
-                        
-        return selected_options, adstock_info 
-
-def apply_lag(X, features,lag_dict):
-    #lag_data=pd.DataFrame()
-    for col in features:
-        for lag in range(lag_dict[col]['lag'][0], lag_dict[col]['lag'][1] + 1):
-            if lag>0:
-                X[f'{col}_lag{lag}'] = X[col].shift(periods=lag, fill_value=0)
-    return X
-
-def apply_adstock(X, variable_name, decay):
-    values = X[variable_name].values
-    adstock = np.zeros(len(values))
-    
-    for row in range(len(values)):
-        if row == 0:
-            adstock[row] = values[row]
-        else:
-            adstock[row] = values[row] + adstock[row - 1] * decay
-    
-    return adstock
-
-def top_correlated_features(df,target,media_data):
-    corr_df=df.drop(target,axis=1)
-    #corr_df[target]=df[target]
-    #st.dataframe(corr_df)
-    for i in media_data:
-        #st.write(media_data[2])
-        #st.dataframe(corr_df.filter(like=media_data[2]))
-        d=(pd.concat([corr_df.filter(like=i),df[target]],axis=1)).corr()[target]
-        d=d.sort_values(ascending=False)
-        d=d.drop(target,axis=0)
-        corr=pd.DataFrame({'Feature_name':d.index,"Correlation":d.values})
-        corr.columns = pd.MultiIndex.from_product([[i], ['Feature_name', 'Correlation']])
-
-    return corr
-
-def top_correlated_features(df,variables,target):
-    correlation_df=pd.DataFrame()
-    for col in variables:
-        d=pd.concat([df.filter(like=col),df[target]],axis=1).corr()[target]
-        #st.dataframe(d)
-        d=d.sort_values(ascending=False).iloc[1:]
-        corr_df=pd.DataFrame({'Media_channel':d.index,'Correlation':d.values})
-        corr_df.columns=pd.MultiIndex.from_tuples([(col, 'Variable'), (col, 'Correlation')])
-        correlation_df=pd.concat([corr_df,correlation_df],axis=1)
-    return correlation_df
-
-def top_correlated_feature(df,variable,target):
-    d=pd.concat([df.filter(like=variable),df[target]],axis=1).corr()[target]
-    # st.dataframe(d)
-    d=d.sort_values(ascending=False).iloc[1:]
-    # st.dataframe(d)
-    corr_df=pd.DataFrame({'Media_channel':d.index,'Correlation':d.values})
-    corr_df['Adstock']=corr_df['Media_channel'].map(lambda x:x.split('_adst')[1] if len(x.split('_adst'))>1 else '-')
-    corr_df['Lag']=corr_df['Media_channel'].map(lambda x:x.split('_lag')[1][0] if len(x.split('_lag'))>1 else '-' )
-    corr_df.drop(['Correlation'],axis=1,inplace=True)
-    corr_df['Correlation']=np.round(d.values,2)
-    sorted_corr_df= corr_df.loc[corr_df['Correlation'].abs().sort_values(ascending=False).index]
-    #corr_df.columns=pd.MultiIndex.from_tuples([(variable, 'Variable'), (variable, 'Correlation')])
-    #correlation_df=pd.concat([corr_df,correlation_df],axis=1)
-    return sorted_corr_df

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