import camelot
import pandas as pd
import re

def extract_bank_data(file_path):
    print(f"Reading {file_path}... processing all pages.")
    
    # We use 'stream' flavor for the NLB PDF layout
    tables = camelot.read_pdf(file_path, pages='1-end', flavor='stream')
    
    final_transactions = []
    current_tx = None
    
    # Precise Regex Patterns
    iban_pattern = r'SI56\s?\d{4}\s?\d{4}\s?\d{4}\s?\d{3}'
    amount_pattern = r'=(\d+[\.,]\d{2})'
    date_pattern = r'(\d{2}\.\d{2}\.\d{2})'
    
    for table in tables:
        df = table.df
        for _, row in df.iterrows():
            cells = [str(c).strip() for c in row]
            row_text = " ".join(cells)
            
            # 1. TRIGGER: A new transaction starts with an amount (=xx,xx)
            amount_match = re.search(amount_pattern, row_text)
            date_match = re.search(date_pattern, row_text)
            
            if amount_match and date_match:
                # Save previous transaction
                if current_tx:
                    final_transactions.append(current_tx)
                
                # Assign Debit/Credit based on the column index of the '=' sign
                amt_val = amount_match.group(1)
                is_credit = any('=' in cells[i] for i in range(len(cells)) if i >= 2)
                
                current_tx = {
                    "value date": date_match.group(1),
                    "Creditor / Debtor": cells[0].split('\n')[0], # Only take the first line of text
                    "account": "",
                    "payment details": "",
                    "debit": "" if is_credit else amt_val,
                    "credit": amt_val if is_credit else "",
                    "is_collecting_details": False
                }
                
                # Check if IBAN is in this row
                iban_search = re.search(iban_pattern, cells[0])
                if iban_search:
                    current_tx["account"] = iban_search.group(0)
            
            elif current_tx:
                # 2. CONTINUATION: Extract info from rows following the amount row
                text_line = cells[0]
                if not text_line:
                    continue
                
                # Check for IBAN
                iban_search = re.search(iban_pattern, text_line)
                if iban_search:
                    current_tx["account"] = iban_search.group(0)
                    # Once we hit the IBAN, everything following it is usually 'Payment Details'
                    current_tx["is_collecting_details"] = True
                    remaining = re.sub(iban_pattern, '', text_line).strip()
                    # Filter out BIC/Internal Routing codes
                    remaining = re.sub(r'[A-Z]{8,11}', '', remaining).strip()
                    if remaining:
                        current_tx["payment details"] += " " + remaining
                    continue
                
                # Identify if this is a Payment Detail row (Reference numbers or purpose text)
                # We filter out common address keywords (Cesta, Ulica, Trg, Postcodes)
                is_address = any(kw in text_line.upper() for kw in ["CESTA", "ULICA", "TRG", " LJUBLJANA", " LOGATEC"])
                
                if current_tx["is_collecting_details"] or ("SI00" in text_line or "SI12" in text_line):
                    # Filter out routing codes like BACXSI...
                    clean_text = re.sub(r'[A-Z]{8,11}', '', text_line).strip()
                    if clean_text:
                        current_tx["payment details"] = (current_tx["payment details"] + " " + clean_text).strip()
                elif not is_address and not current_tx["account"]:
                    # If it's not an address and we don't have an IBAN yet, it might be Name continuation
                    if len(current_tx["Creditor / Debtor"]) < 20: # Only append if the name is very short
                        current_tx["Creditor / Debtor"] += " " + text_line

    # Append the last transaction
    if current_tx:
        final_transactions.append(current_tx)

    # 3. Clean up and Export
    df_final = pd.DataFrame(final_transactions)
    if "is_collecting_details" in df_final.columns:
        df_final = df_final.drop(columns=["is_collecting_details"])

    # Final text cleaning (remove double spaces, routing codes)
    df_final["payment details"] = df_final["payment details"].str.replace(r'[A-Z]{8,11}', '', regex=True)
    df_final["payment details"] = df_final["payment details"].str.replace(r'\s+', ' ', regex=True).str.strip()
    
    # Export
    output_file = "bank_export_fixed_v3.csv"
    df_final.to_csv(output_file, sep=';', index=False, encoding='utf-8-sig')
    print(f"Success! Exported {len(df_final)} transactions to {output_file}")

if __name__ == "__main__":
    extract_bank_data("MSDataReport (1).pdf")