Delete Data_Import .py

Data_Import .py

@@ -1,1019 +0,0 @@
-# Importing necessary libraries
-import streamlit as st
-
-st.set_page_config(
-    page_title="Data Import",
-    page_icon=":shark:",
-    layout="wide",
-    initial_sidebar_state="collapsed",
-)
-
-import pickle
-import pandas as pd
-from utilities import set_header, load_local_css
-import streamlit_authenticator as stauth
-import yaml
-from yaml import SafeLoader
-
-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
-with open("config.yaml") as file:
-    config = yaml.load(file, Loader=SafeLoader)
-    st.session_state["config"] = config
-authenticator = stauth.Authenticate(
-    config["credentials"],
-    config["cookie"]["name"],
-    config["cookie"]["key"],
-    config["cookie"]["expiry_days"],
-    config["preauthorized"],
-)
-st.session_state["authenticator"] = authenticator
-name, authentication_status, username = authenticator.login("Login", "main")
-auth_status = st.session_state.get("authentication_status")
-
-if auth_status == True:
-    authenticator.logout("Logout", "main")
-    is_state_initiaized = st.session_state.get("initialized", False)
-
-    if not is_state_initiaized:
-        
-        if 'session_name' not in st.session_state:
-            st.session_state['session_name']=None
-
-
-# Function to validate date column in dataframe
-    def validate_date_column(df):
-        try:
-            # Attempt to convert the 'Date' column to datetime
-            df["date"] = pd.to_datetime(df["date"], format="%d-%m-%Y")
-            return True
-        except:
-            return False
-
-
-    # Function to determine data interval
-    def determine_data_interval(common_freq):
-        if common_freq == 1:
-            return "daily"
-        elif common_freq == 7:
-            return "weekly"
-        elif 28 <= common_freq <= 31:
-            return "monthly"
-        else:
-            return "irregular"
-
-
-    # Function to read each uploaded Excel file into a pandas DataFrame and stores them in a dictionary
-    st.cache_resource(show_spinner=False)
-
-
-    def files_to_dataframes(uploaded_files):
-        df_dict = {}
-        for uploaded_file in uploaded_files:
-            # Extract file name without extension
-            file_name = uploaded_file.name.rsplit(".", 1)[0]
-
-            # Check for duplicate file names
-            if file_name in df_dict:
-                st.warning(
-                    f"Duplicate File: {file_name}. This file will be skipped.",
-                    icon="⚠️",
-                )
-                continue
-
-            # Read the file into a DataFrame
-            df = pd.read_excel(uploaded_file)
-
-            # Convert all column names to lowercase
-            df.columns = df.columns.str.lower().str.strip()
-
-            # Separate numeric and non-numeric columns
-            numeric_cols = list(df.select_dtypes(include=["number"]).columns)
-            non_numeric_cols = [
-                col
-                for col in df.select_dtypes(exclude=["number"]).columns
-                if col.lower() != "date"
-            ]
-
-            # Check for 'Date' column
-            if not (validate_date_column(df) and len(numeric_cols) > 0):
-                st.warning(
-                    f"File Name: {file_name} ➜ Please upload data with Date column in 'DD-MM-YYYY' format and at least one media/exogenous column. This file will be skipped.",
-                    icon="⚠️",
-                )
-                continue
-
-            # Check for interval
-            common_freq = common_freq = (
-                pd.Series(df["date"].unique()).diff().dt.days.dropna().mode()[0]
-            )
-            # Calculate the data interval (daily, weekly, monthly or irregular)
-            interval = determine_data_interval(common_freq)
-            if interval == "irregular":
-                st.warning(
-                    f"File Name: {file_name} ➜ Please upload data in daily, weekly or monthly interval. This file will be skipped.",
-                    icon="⚠️",
-                )
-                continue
-
-            # Store both DataFrames in the dictionary under their respective keys
-            df_dict[file_name] = {
-                "numeric": numeric_cols,
-                "non_numeric": non_numeric_cols,
-                "interval": interval,
-                "df": df,
-            }
-
-        return df_dict
-
-
-    # Function to adjust dataframe granularity
-    def adjust_dataframe_granularity(df, current_granularity, target_granularity):
-        # Set index
-        df.set_index("date", inplace=True)
-
-        # Define aggregation rules for resampling
-        aggregation_rules = {
-            col: "sum" if pd.api.types.is_numeric_dtype(df[col]) else "first"
-            for col in df.columns
-        }
-
-        # Initialize resampled_df
-        resampled_df = df
-        if current_granularity == "daily" and target_granularity == "weekly":
-            resampled_df = df.resample("W-MON", closed="left", label="left").agg(
-                aggregation_rules
-            )
-
-        elif current_granularity == "daily" and target_granularity == "monthly":
-            resampled_df = df.resample("MS", closed="left", label="left").agg(
-                aggregation_rules
-            )
-
-        elif current_granularity == "daily" and target_granularity == "daily":
-            resampled_df = df.resample("D").agg(aggregation_rules)
-
-        elif current_granularity in ["weekly", "monthly"] and target_granularity == "daily":
-            # For higher to lower granularity, distribute numeric and replicate non-numeric values equally across the new period
-            expanded_data = []
-            for _, row in df.iterrows():
-                if current_granularity == "weekly":
-                    period_range = pd.date_range(start=row.name, periods=7)
-                elif current_granularity == "monthly":
-                    period_range = pd.date_range(
-                        start=row.name, periods=row.name.days_in_month
-                    )
-
-                for date in period_range:
-                    new_row = {}
-                    for col in df.columns:
-                        if pd.api.types.is_numeric_dtype(df[col]):
-                            if current_granularity == "weekly":
-                                new_row[col] = row[col] / 7
-                            elif current_granularity == "monthly":
-                                new_row[col] = row[col] / row.name.days_in_month
-                        else:
-                            new_row[col] = row[col]
-                    expanded_data.append((date, new_row))
-
-            resampled_df = pd.DataFrame(
-                [data for _, data in expanded_data],
-                index=[date for date, _ in expanded_data],
-            )
-
-        # Reset index
-        resampled_df = resampled_df.reset_index().rename(columns={"index": "date"})
-
-        return resampled_df
-
-
-    # Function to clean and extract unique values of Panel_1 and Panel_2
-    st.cache_resource(show_spinner=False)
-
-
-    def clean_and_extract_unique_values(files_dict, selections):
-        all_panel1_values = set()
-        all_panel2_values = set()
-
-        for file_name, file_data in files_dict.items():
-            df = file_data["df"]
-
-            # 'Panel_1' and 'Panel_2' selections
-            selected_panel1 = selections[file_name].get("Panel_1")
-            selected_panel2 = selections[file_name].get("Panel_2")
-
-            # Clean and standardize Panel_1 column if it exists and is selected
-            if (
-                selected_panel1
-                and selected_panel1 != "N/A"
-                and selected_panel1 in df.columns
-            ):
-                df[selected_panel1] = (
-                    df[selected_panel1].str.lower().str.strip().str.replace("_", " ")
-                )
-                all_panel1_values.update(df[selected_panel1].dropna().unique())
-
-            # Clean and standardize Panel_2 column if it exists and is selected
-            if (
-                selected_panel2
-                and selected_panel2 != "N/A"
-                and selected_panel2 in df.columns
-            ):
-                df[selected_panel2] = (
-                    df[selected_panel2].str.lower().str.strip().str.replace("_", " ")
-                )
-                all_panel2_values.update(df[selected_panel2].dropna().unique())
-
-            # Update the processed DataFrame back in the dictionary
-            files_dict[file_name]["df"] = df
-
-        return all_panel1_values, all_panel2_values
-
-
-    # Function to format values for display
-    st.cache_resource(show_spinner=False)
-
-
-    def format_values_for_display(values_list):
-        # Capitalize the first letter of each word and replace underscores with spaces
-        formatted_list = [value.replace("_", " ").title() for value in values_list]
-        # Join values with commas and 'and' before the last value
-        if len(formatted_list) > 1:
-            return ", ".join(formatted_list[:-1]) + ", and " + formatted_list[-1]
-        elif formatted_list:
-            return formatted_list[0]
-        return "No values available"
-
-
-    # Function to normalizes all data within files_dict to a daily granularity
-    st.cache(show_spinner=False, allow_output_mutation=True)
-
-
-    def standardize_data_to_daily(files_dict, selections):
-        # Normalize all data to a daily granularity using a provided function
-        files_dict = apply_granularity_to_all(files_dict, "daily", selections)
-
-        # Update the "interval" attribute for each dataset to indicate the new granularity
-        for files_name, files_data in files_dict.items():
-            files_data["interval"] = "daily"
-
-        return files_dict
-
-
-    # Function to apply granularity transformation to all DataFrames in files_dict
-    st.cache_resource(show_spinner=False)
-
-
-    def apply_granularity_to_all(files_dict, granularity_selection, selections):
-        for file_name, file_data in files_dict.items():
-            df = file_data["df"].copy()
-
-            # Handling when Panel_1 or Panel_2 might be 'N/A'
-            selected_panel1 = selections[file_name].get("Panel_1")
-            selected_panel2 = selections[file_name].get("Panel_2")
-
-            # Correcting the segment selection logic & handling 'N/A'
-            if selected_panel1 != "N/A" and selected_panel2 != "N/A":
-                unique_combinations = df[
-                    [selected_panel1, selected_panel2]
-                ].drop_duplicates()
-            elif selected_panel1 != "N/A":
-                unique_combinations = df[[selected_panel1]].drop_duplicates()
-                selected_panel2 = None  # Ensure Panel_2 is ignored if N/A
-            elif selected_panel2 != "N/A":
-                unique_combinations = df[[selected_panel2]].drop_duplicates()
-                selected_panel1 = None  # Ensure Panel_1 is ignored if N/A
-            else:
-                # If both are 'N/A', process the entire dataframe as is
-                df = adjust_dataframe_granularity(
-                    df, file_data["interval"], granularity_selection
-                )
-                files_dict[file_name]["df"] = df
-                continue  # Skip to the next file
-
-            transformed_segments = []
-            for _, combo in unique_combinations.iterrows():
-                if selected_panel1 and selected_panel2:
-                    segment = df[
-                        (df[selected_panel1] == combo[selected_panel1])
-                        & (df[selected_panel2] == combo[selected_panel2])
-                    ]
-                elif selected_panel1:
-                    segment = df[df[selected_panel1] == combo[selected_panel1]]
-                elif selected_panel2:
-                    segment = df[df[selected_panel2] == combo[selected_panel2]]
-
-                # Adjust granularity of the segment
-                transformed_segment = adjust_dataframe_granularity(
-                    segment, file_data["interval"], granularity_selection
-                )
-                transformed_segments.append(transformed_segment)
-
-            # Combine all transformed segments into a single DataFrame for this file
-            transformed_df = pd.concat(transformed_segments, ignore_index=True)
-            files_dict[file_name]["df"] = transformed_df
-
-        return files_dict
-
-
-    # Function to create main dataframe structure
-    st.cache_resource(show_spinner=False)
-
-
-    def create_main_dataframe(
-        files_dict, all_panel1_values, all_panel2_values, granularity_selection
-    ):
-        # Determine the global start and end dates across all DataFrames
-        global_start = min(df["df"]["date"].min() for df in files_dict.values())
-        global_end = max(df["df"]["date"].max() for df in files_dict.values())
-
-        # Adjust the date_range generation based on the granularity_selection
-        if granularity_selection == "weekly":
-            # Generate a weekly range, with weeks starting on Monday
-            date_range = pd.date_range(start=global_start, end=global_end, freq="W-MON")
-        elif granularity_selection == "monthly":
-            # Generate a monthly range, starting from the first day of each month
-            date_range = pd.date_range(start=global_start, end=global_end, freq="MS")
-        else:  # Default to daily if not weekly or monthly
-            date_range = pd.date_range(start=global_start, end=global_end, freq="D")
-
-        # Collect all unique Panel_1 and Panel_2 values, excluding 'N/A'
-        all_panel1s = all_panel1_values
-        all_panel2s = all_panel2_values
-
-        # Dynamically build the list of dimensions (Panel_1, Panel_2) to include in the main DataFrame based on availability
-        dimensions, merge_keys = [], []
-        if all_panel1s:
-            dimensions.append(all_panel1s)
-            merge_keys.append("Panel_1")
-        if all_panel2s:
-            dimensions.append(all_panel2s)
-            merge_keys.append("Panel_2")
-
-        dimensions.append(date_range)  # Date range is always included
-        merge_keys.append("date")  # Date range is always included
-
-        # Create a main DataFrame template with the dimensions
-        main_df = pd.MultiIndex.from_product(
-            dimensions,
-            names=[name for name, _ in zip(merge_keys, dimensions)],
-        ).to_frame(index=False)
-
-        return main_df.reset_index(drop=True)
-
-
-    # Function to prepare and merge dataFrames
-    st.cache_resource(show_spinner=False)
-
-
-    def merge_into_main_df(main_df, files_dict, selections):
-        for file_name, file_data in files_dict.items():
-            df = file_data["df"].copy()
-
-            # Rename selected Panel_1 and Panel_2 columns if not 'N/A'
-            selected_panel1 = selections[file_name].get("Panel_1", "N/A")
-            selected_panel2 = selections[file_name].get("Panel_2", "N/A")
-            if selected_panel1 != "N/A":
-                df.rename(columns={selected_panel1: "Panel_1"}, inplace=True)
-            if selected_panel2 != "N/A":
-                df.rename(columns={selected_panel2: "Panel_2"}, inplace=True)
-
-            # Merge current DataFrame into main_df based on 'date', and where applicable, 'Panel_1' and 'Panel_2'
-            merge_keys = ["date"]
-            if "Panel_1" in df.columns:
-                merge_keys.append("Panel_1")
-            if "Panel_2" in df.columns:
-                merge_keys.append("Panel_2")
-            main_df = pd.merge(main_df, df, on=merge_keys, how="left")
-
-        # After all merges, sort by 'date' and reset index for cleanliness
-        sort_by = ["date"]
-        if "Panel_1" in main_df.columns:
-            sort_by.append("Panel_1")
-        if "Panel_2" in main_df.columns:
-            sort_by.append("Panel_2")
-        main_df.sort_values(by=sort_by, inplace=True)
-        main_df.reset_index(drop=True, inplace=True)
-
-        return main_df
-
-
-    # Function to categorize column
-    def categorize_column(column_name):
-        # Define keywords for each category
-        internal_keywords = [
-            "Price",
-            "Discount",
-            "product_price",
-            "cost",
-            "margin",
-            "inventory",
-            "sales",
-            "revenue",
-            "turnover",
-            "expense",
-        ]
-        exogenous_keywords = [
-            "GDP",
-            "Tax",
-            "Inflation",
-            "interest_rate",
-            "employment_rate",
-            "exchange_rate",
-            "consumer_spending",
-            "retail_sales",
-            "oil_prices",
-            "weather",
-        ]
-
-        # Check if the column name matches any of the keywords for Internal or Exogenous categories
-        for keyword in internal_keywords:
-            if keyword.lower() in column_name.lower():
-                return "Internal"
-        for keyword in exogenous_keywords:
-            if keyword.lower() in column_name.lower():
-                return "Exogenous"
-
-        # Default to Media if no match found
-        return "Media"
-
-
-    # Function to calculate missing stats and prepare for editable DataFrame
-    st.cache_resource(show_spinner=False)
-
-
-    def prepare_missing_stats_df(df):
-        missing_stats = []
-        for column in df.columns:
-            if (
-                column == "date" or column == "Panel_2" or column == "Panel_1"
-            ):  # Skip Date, Panel_1 and Panel_2 column
-                continue
-
-            missing = df[column].isnull().sum()
-            pct_missing = round((missing / len(df)) * 100, 2)
-
-            # Dynamically assign category based on column name
-            category = categorize_column(column)
-            # category = "Media"  # Keep default bin as Media
-
-            missing_stats.append(
-                {
-                    "Column": column,
-                    "Missing Values": missing,
-                    "Missing Percentage": pct_missing,
-                    "Impute Method": "Fill with 0",  # Default value
-                    "Category": category,
-                }
-            )
-        stats_df = pd.DataFrame(missing_stats)
-
-        return stats_df
-
-
-    # Function to add API DataFrame details to the files dictionary
-    st.cache_resource(show_spinner=False)
-
-
-    def add_api_dataframe_to_dict(main_df, files_dict):
-        files_dict["API"] = {
-            "numeric": list(main_df.select_dtypes(include=["number"]).columns),
-            "non_numeric": [
-                col
-                for col in main_df.select_dtypes(exclude=["number"]).columns
-                if col.lower() != "date"
-            ],
-            "interval": determine_data_interval(
-                pd.Series(main_df["date"].unique()).diff().dt.days.dropna().mode()[0]
-            ),
-            "df": main_df,
-        }
-
-        return files_dict
-
-
-    # Function to reads an API into a DataFrame, parsing specified columns as datetime
-    @st.cache_resource(show_spinner=False)
-    def read_API_data():
-        return pd.read_excel(r".\upf_data_converted_randomized_resp_metrics.xlsx", parse_dates=["Date"])
-
-
-    # Function to set the 'Panel_1_Panel_2_Selected' session state variable to False
-    def set_Panel_1_Panel_2_Selected_false():
-        st.session_state["Panel_1_Panel_2_Selected"] = False
-
-
-    # Function to serialize and save the objects into a pickle file
-    @st.cache_resource(show_spinner=False)
-    def save_to_pickle(file_path, final_df, bin_dict):
-        # Open the file in write-binary mode and dump the objects
-        with open(file_path, "wb") as f:
-            pickle.dump({"final_df": final_df, "bin_dict": bin_dict}, f)
-            # Data is now saved to file
-
-
-    # Function to processes the merged_df DataFrame based on operations defined in edited_df
-    @st.cache_resource(show_spinner=False)
-    def process_dataframes(merged_df, edited_df, edited_stats_df):
-        # Ensure there are operations defined by the user
-        if edited_df.empty:
-            return merged_df, edited_stats_df  # No operations to apply
-
-        # Perform operations as defined by the user
-        for index, row in edited_df.iterrows():
-            result_column_name = f"{row['Column 1']}{row['Operator']}{row['Column 2']}"
-            col1 = row["Column 1"]
-            col2 = row["Column 2"]
-            op = row["Operator"]
-
-            # Apply the specified operation
-            if op == "+":
-                merged_df[result_column_name] = merged_df[col1] + merged_df[col2]
-            elif op == "-":
-                merged_df[result_column_name] = merged_df[col1] - merged_df[col2]
-            elif op == "*":
-                merged_df[result_column_name] = merged_df[col1] * merged_df[col2]
-            elif op == "/":
-                merged_df[result_column_name] = merged_df[col1] / merged_df[col2].replace(
-                    0, 1e-9
-                )
-
-            # Add summary of operation to edited_stats_df
-            new_row = {
-                "Column": result_column_name,
-                "Missing Values": None,
-                "Missing Percentage": None,
-                "Impute Method": None,
-                "Category": row["Category"],
-            }
-            new_row_df = pd.DataFrame([new_row])
-
-            # Use pd.concat to add the new_row_df to edited_stats_df
-            edited_stats_df = pd.concat(
-                [edited_stats_df, new_row_df], ignore_index=True, axis=0
-            )
-
-        # Combine column names from edited_df for cleanup
-        combined_columns = set(edited_df["Column 1"]).union(set(edited_df["Column 2"]))
-
-        # Filter out rows in edited_stats_df and drop columns from merged_df
-        edited_stats_df = edited_stats_df[~edited_stats_df["Column"].isin(combined_columns)]
-        merged_df.drop(columns=list(combined_columns), errors="ignore", inplace=True)
-
-        return merged_df, edited_stats_df
-
-
-    # Function to prepare a list of numeric column names and initialize an empty DataFrame with predefined structure
-    st.cache_resource(show_spinner=False)
-
-
-    def prepare_numeric_columns_and_default_df(merged_df, edited_stats_df):
-        # Get columns categorized as 'Response Metrics'
-        columns_response_metrics = edited_stats_df[
-            edited_stats_df["Category"] == "Response Metrics"
-        ]["Column"].tolist()
-
-        # Filter numeric columns, excluding those categorized as 'Response Metrics'
-        numeric_columns = [
-            col
-            for col in merged_df.select_dtypes(include=["number"]).columns
-            if col not in columns_response_metrics
-        ]
-
-        # Define the structure of the empty DataFrame
-        data = {
-            "Column 1": pd.Series([], dtype="str"),
-            "Operator": pd.Series([], dtype="str"),
-            "Column 2": pd.Series([], dtype="str"),
-            "Category": pd.Series([], dtype="str"),
-        }
-        default_df = pd.DataFrame(data)
-
-        return numeric_columns, default_df
-
-
-    # Initialize 'final_df' in session state
-    if "final_df" not in st.session_state:
-        st.session_state["final_df"] = pd.DataFrame()
-
-    # Initialize 'bin_dict' in session state
-    if "bin_dict" not in st.session_state:
-        st.session_state["bin_dict"] = {}
-
-    # Initialize 'Panel_1_Panel_2_Selected' in session state
-    if "Panel_1_Panel_2_Selected" not in st.session_state:
-        st.session_state["Panel_1_Panel_2_Selected"] = False
-
-
-    # Page Title
-    st.write("")  # Top padding
-    st.title("Data Import")
-
-
-    #########################################################################################################################################################
-    # Create a dictionary to hold all DataFrames and collect user input to specify "Panel_2" and "Panel_1" columns for each file
-    #########################################################################################################################################################
-
-
-    # Read the Excel file, parsing 'Date' column as datetime
-    main_df = read_API_data()
-
-    # Convert all column names to lowercase
-    main_df.columns = main_df.columns.str.lower().str.strip()
-
-    # File uploader
-    uploaded_files = st.file_uploader(
-        "Upload additional data",
-        type=["xlsx"],
-        accept_multiple_files=True,
-        on_change=set_Panel_1_Panel_2_Selected_false,
-    )
-
-    # Custom HTML for upload instructions
-    recommendation_html = f"""
-    <div style="text-align: justify;">
-    <strong>Recommendation:</strong> For optimal processing, please ensure that all uploaded datasets including panel, media, internal, and exogenous data adhere to the following guidelines: Each dataset must include a <code>Date</code> column formatted as <code>DD-MM-YYYY</code>, be free of missing values.
-    </div>
-    """
-    st.markdown(recommendation_html, unsafe_allow_html=True)
-
-    # Choose Desired Granularity
-    st.markdown("#### Choose Desired Granularity")
-    # Granularity Selection
-    granularity_selection = st.selectbox(
-        "Choose Date Granularity",
-        ["Daily", "Weekly", "Monthly"],
-        label_visibility="collapsed",
-        on_change=set_Panel_1_Panel_2_Selected_false,
-    )
-    granularity_selection = str(granularity_selection).lower()
-
-    # Convert files to dataframes
-    files_dict = files_to_dataframes(uploaded_files)
-
-    # Add API Dataframe
-    if main_df is not None:
-        files_dict = add_api_dataframe_to_dict(main_df, files_dict)
-
-    # Display a warning message if no files have been uploaded and halt further execution
-    if not files_dict:
-        st.warning(
-            "Please upload at least one file to proceed.",
-            icon="⚠️",
-        )
-        st.stop()  # Halts further execution until file is uploaded
-
-
-    # Select Panel_1 and Panel_2 columns
-    st.markdown("#### Select Panel columns")
-    selections = {}
-    with st.expander("Select Panel columns", expanded=False):
-        count = 0  # Initialize counter to manage the visibility of labels and keys
-        for file_name, file_data in files_dict.items():
-            # Determine visibility of the label based on the count
-            if count == 0:
-                label_visibility = "visible"
-            else:
-                label_visibility = "collapsed"
-
-            # Extract non-numeric columns
-            non_numeric_cols = file_data["non_numeric"]
-
-            # Prepare Panel_1 and Panel_2 values for dropdown, adding "N/A" as an option
-            panel1_values = non_numeric_cols + ["N/A"]
-            panel2_values = non_numeric_cols + ["N/A"]
-
-            # Skip if only one option is available
-            if len(panel1_values) == 1 and len(panel2_values) == 1:
-                selected_panel1, selected_panel2 = "N/A", "N/A"
-                # Update the selections for Panel_1 and Panel_2 for the current file
-                selections[file_name] = {
-                    "Panel_1": selected_panel1,
-                    "Panel_2": selected_panel2,
-                }
-                continue
-
-            # Create layout columns for File Name, Panel_2, and Panel_1 selections
-            file_name_col, Panel_1_col, Panel_2_col = st.columns([2, 4, 4])
-
-            with file_name_col:
-                # Display "File Name" label only for the first file
-                if count == 0:
-                    st.write("File Name")
-                else:
-                    st.write("")
-                st.write(file_name)  # Display the file name
-
-            with Panel_1_col:
-                # Display a selectbox for Panel_1 values
-                selected_panel1 = st.selectbox(
-                    "Select Panel Level 1",
-                    panel2_values,
-                    on_change=set_Panel_1_Panel_2_Selected_false,
-                    label_visibility=label_visibility,  # Control visibility of the label
-                    key=f"Panel_1_selectbox{count}",  # Ensure unique key for each selectbox
-                )
-
-            with Panel_2_col:
-                # Display a selectbox for Panel_2 values
-                selected_panel2 = st.selectbox(
-                    "Select Panel Level 2",
-                    panel1_values,
-                    on_change=set_Panel_1_Panel_2_Selected_false,
-                    label_visibility=label_visibility,  # Control visibility of the label
-                    key=f"Panel_2_selectbox{count}",  # Ensure unique key for each selectbox
-                )
-
-            # Skip processing if the same column is selected for both Panel_1 and Panel_2 due to potential data integrity issues
-            if selected_panel2 == selected_panel1 and not (
-                selected_panel2 == "N/A" and selected_panel1 == "N/A"
-            ):
-                st.warning(
-                    f"File: {file_name} → The same column cannot serve as both Panel_1 and Panel_2. Please adjust your selections.",
-                )
-                selected_panel1, selected_panel2 = "N/A", "N/A"
-                st.stop()
-
-            # Update the selections for Panel_1 and Panel_2 for the current file
-            selections[file_name] = {
-                "Panel_1": selected_panel1,
-                "Panel_2": selected_panel2,
-            }
-
-            count += 1  # Increment the counter after processing each file
-
-        # Accept Panel_1 and Panel_2 selection
-        if st.button("Accept and Process", use_container_width=True):
-
-            # Normalize all data to a daily granularity. This initial standardization simplifies subsequent conversions to other levels of granularity
-            with st.spinner("Processing..."):
-                files_dict = standardize_data_to_daily(files_dict, selections)
-
-                # Convert all data to daily level granularity
-                files_dict = apply_granularity_to_all(
-                    files_dict, granularity_selection, selections
-                )
-
-            # Update the 'files_dict' in the session state
-            st.session_state["files_dict"] = files_dict
-
-            # Set a flag in the session state to indicate that selection has been made
-            st.session_state["Panel_1_Panel_2_Selected"] = True
-
-
-    #########################################################################################################################################################
-    # Display unique Panel_1 and Panel_2 values
-    #########################################################################################################################################################
-
-
-    # Halts further execution until Panel_1 and Panel_2 columns are selected
-    if "files_dict" in st.session_state and st.session_state["Panel_1_Panel_2_Selected"]:
-        files_dict = st.session_state["files_dict"]
-    else:
-        st.stop()
-
-    # Set to store unique values of Panel_1 and Panel_2
-    with st.spinner("Fetching Panel values..."):
-        all_panel1_values, all_panel2_values = clean_and_extract_unique_values(
-            files_dict, selections
-        )
-
-        # List of Panel_1 and Panel_2 columns unique values
-        list_of_all_panel1_values = list(all_panel1_values)
-        list_of_all_panel2_values = list(all_panel2_values)
-
-        # Format Panel_1 and Panel_2 values for display
-        formatted_panel1_values = format_values_for_display(list_of_all_panel1_values)
-        formatted_panel2_values = format_values_for_display(list_of_all_panel2_values)
-
-    # Unique Panel_1 and Panel_2 values
-    st.markdown("#### Unique Panel values")
-    # Display Panel_1 and Panel_2 values
-    with st.expander("Unique Panel values"):
-        st.write("")
-        st.markdown(
-            f"""
-        <style>
-        .justify-text {{
-        text-align: justify;
-        }}
-        </style>
-        <div class="justify-text">
-        <strong>Panel Level 1 Values:</strong> {formatted_panel1_values}<br>
-        <strong>Panel Level 2 Values:</strong> {formatted_panel2_values}
-        </div>
-        """,
-            unsafe_allow_html=True,
-        )
-
-        # Display total Panel_1 and Panel_2
-        st.write("")
-        st.markdown(
-            f"""
-        <div style="text-align: justify;">
-            <strong>Number of Level 1 Panels detected:</strong> {len(list_of_all_panel1_values)}<br>
-            <strong>Number of Level 2 Panels detected:</strong> {len(list_of_all_panel2_values)}
-        </div>
-        """,
-            unsafe_allow_html=True,
-        )
-        st.write("")
-
-
-    #########################################################################################################################################################
-    # Merge all DataFrames
-    #########################################################################################################################################################
-
-
-    # Merge all DataFrames selected
-    main_df = create_main_dataframe(
-        files_dict, all_panel1_values, all_panel2_values, granularity_selection
-    )
-    merged_df = merge_into_main_df(main_df, files_dict, selections)
-
-
-    #########################################################################################################################################################
-    # Categorize Variables and Impute Missing Values
-    #########################################################################################################################################################
-
-
-    # Create an editable DataFrame in Streamlit
-    st.markdown("#### Select Variables Category & Impute Missing Values")
-
-    # Prepare missing stats DataFrame for editing
-    missing_stats_df = prepare_missing_stats_df(merged_df)
-
-    edited_stats_df = st.data_editor(
-        missing_stats_df,
-        column_config={
-            "Impute Method": st.column_config.SelectboxColumn(
-                options=[
-                    "Drop Column",
-                    "Fill with Mean",
-                    "Fill with Median",
-                    "Fill with 0",
-                ],
-                required=True,
-                default="Fill with 0",
-            ),
-            "Category": st.column_config.SelectboxColumn(
-                options=[
-                    "Media",
-                    "Exogenous",
-                    "Internal",
-                    "Response Metrics",
-                ],
-                required=True,
-                default="Media",
-            ),
-        },
-        disabled=["Column", "Missing Values", "Missing Percentage"],
-        hide_index=True,
-        use_container_width=True,
-    )
-
-    # Apply changes based on edited DataFrame
-    for i, row in edited_stats_df.iterrows():
-        column = row["Column"]
-        if row["Impute Method"] == "Drop Column":
-            merged_df.drop(columns=[column], inplace=True)
-
-        elif row["Impute Method"] == "Fill with Mean":
-            merged_df[column].fillna(merged_df[column].mean(), inplace=True)
-
-        elif row["Impute Method"] == "Fill with Median":
-            merged_df[column].fillna(merged_df[column].median(), inplace=True)
-
-        elif row["Impute Method"] == "Fill with 0":
-            merged_df[column].fillna(0, inplace=True)
-
-
-    #########################################################################################################################################################
-    # Group columns
-    #########################################################################################################################################################
-
-
-    # Display Group columns header
-    st.markdown("#### Feature engineering")
-
-    # Prepare the numeric columns and an empty DataFrame for user input
-    numeric_columns, default_df = prepare_numeric_columns_and_default_df(
-        merged_df, edited_stats_df
-    )
-
-    # Display editable Dataframe
-    edited_df = st.data_editor(
-        default_df,
-        column_config={
-            "Column 1": st.column_config.SelectboxColumn(
-                options=numeric_columns,
-                required=True,
-                default=numeric_columns[0],
-                width=400,
-            ),
-            "Operator": st.column_config.SelectboxColumn(
-                options=["+", "-", "*", "/"],
-                required=True,
-                default="+",
-                width=100,
-            ),
-            "Column 2": st.column_config.SelectboxColumn(
-                options=numeric_columns,
-                required=True,
-                default=numeric_columns[0],
-                width=400,
-            ),
-            "Category": st.column_config.SelectboxColumn(
-                options=[
-                    "Media",
-                    "Exogenous",
-                    "Internal",
-                    "Response Metrics",
-                ],
-                required=True,
-                default="Media",
-                width=200,
-            ),
-        },
-        num_rows="dynamic",
-    )
-
-    # Process the DataFrame based on user inputs and operations specified in edited_df
-    final_df, edited_stats_df = process_dataframes(merged_df, edited_df, edited_stats_df)
-
-
-    #########################################################################################################################################################
-    # Display the Final DataFrame and variables
-    #########################################################################################################################################################
-
-
-    # Display the Final DataFrame and variables
-    st.markdown("#### Final DataFrame")
-    st.dataframe(final_df, hide_index=True)
-
-    # Initialize an empty dictionary to hold categories and their variables
-    category_dict = {}
-
-    # Iterate over each row in the edited DataFrame to populate the dictionary
-    for i, row in edited_stats_df.iterrows():
-        column = row["Column"]
-        category = row["Category"]  # The category chosen by the user for this variable
-
-        # Check if the category already exists in the dictionary
-        if category not in category_dict:
-            # If not, initialize it with the current column as its first element
-            category_dict[category] = [column]
-        else:
-            # If it exists, append the current column to the list of variables under this category
-            category_dict[category].append(column)
-
-    # Add Date, Panel_1 and Panel_12 in category dictionary
-    category_dict.update({"Date": ["date"]})
-    if "Panel_1" in final_df.columns:
-        category_dict["Panel Level 1"] = ["Panel_1"]
-    if "Panel_2" in final_df.columns:
-        category_dict["Panel Level 2"] = ["Panel_2"]
-
-    # Display the dictionary
-    st.markdown("#### Variable Category")
-    for category, variables in category_dict.items():
-        # Check if there are multiple variables to handle "and" insertion correctly
-        if len(variables) > 1:
-            # Join all but the last variable with ", ", then add " and " before the last variable
-            variables_str = ", ".join(variables[:-1]) + " and " + variables[-1]
-        else:
-            # If there's only one variable, no need for "and"
-            variables_str = variables[0]
-
-        # Display the category and its variables in the desired format
-        st.markdown(
-            f"<div style='text-align: justify;'><strong>{category}:</strong> {variables_str}</div>",
-            unsafe_allow_html=True,
-        )
-
-    # Function to check if Response Metrics is selected
-    st.write("")
-    response_metrics_col = category_dict.get("Response Metrics", [])
-    if len(response_metrics_col) == 0:
-        st.warning("Please select Response Metrics column", icon="⚠️")
-        st.stop()
-    # elif len(response_metrics_col) > 1:
-    #     st.warning("Please select only one Response Metrics column", icon="⚠️")
-    #     st.stop()
-
-    # Store final dataframe and bin dictionary into session state
-    st.session_state["final_df"], st.session_state["bin_dict"] = final_df, category_dict
-
-    # Save the DataFrame and dictionary from the session state to the pickle file
-    if st.button("Accept and Save", use_container_width=True):
-        save_to_pickle(
-            "data_import.pkl", st.session_state["final_df"], st.session_state["bin_dict"]
-        )
-        st.toast("💾 Saved Successfully!")