import pandas as pd
import warnings
import re
warnings.filterwarnings("ignore")


from fuzzywuzzy import process

def standardize_author_names(df):
    # Create a list of unique author names
    unique_authors = df['Author'].unique()
    
    # Create a dictionary to map original names to standard names
    author_map = {}
    
    for author in unique_authors:
        match = process.extractOne(author, author_map.keys())
        if match and match[1] > 90:  # Adjust threshold as needed
            author_map[author] = match[0]
        else:
            author_map[author] = author
    
    # Apply the mapping to the dataframe
    df['Author'] = df['Author'].map(author_map)
    
    return df

def is_contained(subtitle, fulltitle):
    return subtitle in fulltitle

def consolidate_titles_based_on_containment(df):
    # Get unique titles
    titles = df['Title'].unique()
    
    # Create a dictionary to map original titles to consolidated titles
    title_map = {}
    
    for title in titles:
        # Check if the title is already in the map
        if title not in title_map:
            # Find all titles that this title is contained in
            for candidate in titles:
                if "All Stars Bonus" not in candidate and title != candidate and is_contained(title, candidate):
                    title_map[title] = candidate
                    break
            else:
                title_map[title] = title
    
    # Apply the mapping to the dataframe
    df['Title'] = df['Title'].map(title_map)
    
    return df


def generate_report(royalties_path, payouts_path):
    sales_df = pd.read_excel(royalties_path, sheet_name="Total Earnings", header=1)
    payments_df = pd.read_excel(payouts_path, sheet_name="Payments")

    sales_df_trim = sales_df[["Title", "Author", "Payout Plan", "Currency", "Earnings"]]
    sales_df_trim['Title'] = sales_df_trim.apply(
    lambda row: f"{row['Title']} ({row['Payout Plan']})" if row['Payout Plan'] == "All Stars Bonus" else row['Title'],
    axis=1)

    royalties_norm_text = consolidate_titles_based_on_containment(standardize_author_names(sales_df_trim))
    royalties_currencies = royalties_norm_text.groupby('Currency', as_index=False).agg({'Earnings':'sum'}) 


    payouts_df= payments_df[["Currency","Net Earnings","FX Rate","Payout Amount"]].dropna(subset=['Payout Amount'])
    payouts_df = payouts_df.groupby('Currency', as_index=False).agg({'Net Earnings':'sum', 'FX Rate':'mean', 'Payout Amount':'sum'})
    payouts_df['FX Rate'] = payouts_df['FX Rate'].fillna(1)


    merged_df = pd.merge(royalties_currencies, payouts_df, on='Currency', how='left')

    merged_df['Earnings_conversion'] = merged_df['Earnings'] * merged_df['FX Rate']

    merged_df['Division_Quotient'] = merged_df['Earnings_conversion']/merged_df['Payout Amount']    

    merged_df['Adjusted_FX_rate'] = (merged_df['FX Rate']/merged_df['Division_Quotient'])

    final_royalties = pd.merge(royalties_norm_text,(merged_df[['Currency', 'Adjusted_FX_rate']]),  on='Currency', how='left')

    final_royalties['Adjusted_Earnings'] = final_royalties['Earnings'] * final_royalties['Adjusted_FX_rate']

    return final_royalties

def print_report(royalties_path, payments_path):

    final_royalties = generate_report(royalties_path, payments_path)

    grouped = final_royalties.groupby(['Author', 'Title'], as_index=False)['Adjusted_Earnings'].sum()

    # Step 5: Generate the report
    print("Total: ", final_royalties["Adjusted_Earnings"].sum())
    print("-----------------------\n")

    for author, group in grouped.groupby('Author'):
        total_earnings = group['Adjusted_Earnings'].sum()
        print(f"Author: {author}")
        print(f"Total Earnings: ${total_earnings:.2f}")
        
        # Loop through the books and print each title with earnings
        for _, row in group.iterrows():
            print(f"  {row['Title']}: ${row['Adjusted_Earnings']:.2f}")
        
        print("\n")

def generate_report_for_insertion(royalties_path, payouts_path):
    """Generates a DataFrame of individual royalty entries for database insertion."""

    # Extract month and year from the royalties_path filename
    match = re.search(r"(\d{4})-(\d{2})", royalties_path)
    if match:
        year = int(match.group(1))
        month = int(match.group(2))
    else:
        # Handle cases where the pattern doesn't match (e.g., raise an error or use defaults)
        raise ValueError("Could not extract year and month from royalties_path filename.")

    sales_df = pd.read_excel(royalties_path, sheet_name="Total Earnings", header=1)
    payments_df = pd.read_excel(payouts_path, sheet_name="Payments")

    sales_df_trim = sales_df[["Title", "Author", "Payout Plan", "Currency", "Earnings"]]
    sales_df_trim['Title'] = sales_df_trim.apply(
        lambda row: f"{row['Title']} ({row['Payout Plan']})" if row['Payout Plan'] == "All Stars Bonus" else row['Title'],
        axis=1
    )

    royalties_norm_text = consolidate_titles_based_on_containment(standardize_author_names(sales_df_trim))
    royalties_currencies = royalties_norm_text.groupby('Currency', as_index=False).agg({'Earnings': 'sum'})

    payouts_df = payments_df[["Currency", "Net Earnings", "FX Rate", "Payout Amount"]].dropna(subset=['Payout Amount'])
    payouts_df = payouts_df.groupby('Currency', as_index=False).agg({'Net Earnings': 'sum', 'FX Rate': 'mean', 'Payout Amount': 'sum'})
    payouts_df['FX Rate'] = payouts_df['FX Rate'].fillna(1)

    merged_df = pd.merge(royalties_currencies, payouts_df, on='Currency', how='left')

    merged_df['Earnings_conversion'] = merged_df['Earnings'] * merged_df['FX Rate']

    merged_df['Division_Quotient'] = merged_df['Earnings_conversion'] / merged_df['Payout Amount']

    merged_df['Adjusted_FX_rate'] = (merged_df['FX Rate'] / merged_df['Division_Quotient'])

    final_royalties = pd.merge(royalties_norm_text, (merged_df[['Currency', 'Adjusted_FX_rate']]), on='Currency', how='left')

    final_royalties['Adjusted_Earnings'] = final_royalties['Earnings'] * final_royalties['Adjusted_FX_rate']

    # Add month and year columns
    final_royalties['month'] = month
    final_royalties['year'] = year

    # Return the DataFrame
    return final_royalties