Data_Import.py

# 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("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!")