A R R R Associates
391 lines
13 KiB
Python
391 lines
13 KiB
Python
from __future__ import annotations
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import re
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from pathlib import Path
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import pandas as pd
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from .base import *
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from .common import find
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def _signed_amount(value: str | None, suffix: str | None) -> float | None:
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"""Return CR as positive and DR as negative."""
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parsed = amount(value)
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if parsed is None:
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return None
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return -parsed if (suffix or "").upper() == "DR" else parsed
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def _parse_modern_date(value: str) -> str:
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"""Normalize both '01 Apr 2025' and 'Apr 01 2025' to ISO date."""
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return pd.to_datetime(value, dayfirst=True, errors="raise").strftime("%Y-%m-%d")
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class IndianBankModernParser(BaseParser):
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bank_name = "Indian Bank"
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parser_name = "IndianBankModernParser"
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@classmethod
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def detect(cls, text):
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# pdfplumber's layout mode can insert multiple spaces inside headings
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# (for example, 'ACCOUNT STATEMENT'). Normalize whitespace before
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# matching so the same bank PDF is detected whether pdftotext is
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# installed in the runtime image or the pdfplumber fallback is used.
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u = norm(text).upper()
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return (
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0.98
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if "ACCOUNT STATEMENT" in u
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and "TRANSACTION DETAILS" in u
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and "TOTAL CREDITS" in u
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else 0
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)
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def parse(self, path, text=None):
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text = text or extract_text(path)
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meta = StatementMeta(
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bank_name=self.bank_name,
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source_file=Path(path).name,
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parser_name=self.parser_name,
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confidence="High",
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)
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# Some Indian Bank statements leave these values blank. Do not let a
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# value from the adjacent ACCOUNT SUMMARY column become the customer
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# name merely because PDF text extraction merges the two columns.
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customer = find(r"Account Holder Name[ \t]*([^\n]*)", text)
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customer = norm(customer)
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if customer and not re.search(
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r"^(Opening Balance|Account Type|Account Number|Customer)",
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customer,
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re.I,
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):
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meta.customer_name = customer
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meta.account_number = find(
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r"Account Number[ \t]*([0-9X*]{4,})",
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text,
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)
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period_match = re.search(
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r"For period:\s*(\d{2}\s+[A-Za-z]{3}\s+\d{4})\s*-\s*"
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r"(\d{2}\s+[A-Za-z]{3}\s+\d{4})",
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text,
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re.I,
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)
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if period_match:
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meta.period_from = _parse_modern_date(period_match.group(1))
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meta.period_to = _parse_modern_date(period_match.group(2))
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opening_match = re.search(
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r"Opening Balance\s+INR\s*([\d,]+\.\d{2})\s*(CR|DR)?",
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text,
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re.I,
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)
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if opening_match:
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meta.opening_balance = _signed_amount(
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opening_match.group(1),
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opening_match.group(2),
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)
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meta.total_credit = amount(
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find(r"Total Credits\s+\+\s*INR\s*([\d,]+\.\d{2})", text)
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)
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meta.total_debit = amount(
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find(r"Total Debits\s+-\s*INR\s*([\d,]+\.\d{2})", text)
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)
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closing_match = re.search(
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r"Ending Balance\s+INR\s*([\d,]+\.\d{2})\s*(CR|DR)?",
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text,
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re.I,
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)
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if closing_match:
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meta.closing_balance = _signed_amount(
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closing_match.group(1),
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closing_match.group(2),
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)
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# Indian Bank's current PDF layout is seen in both date orders,
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# depending on the text extraction engine:
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# 01 Apr 2025 ...
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# Apr 01 2025 ...
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# Accept both without changing the parser selected for older samples.
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date_re = re.compile(
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r"^\s*((?:\d{2}\s+[A-Za-z]{3}|[A-Za-z]{3}\s+\d{2})\s+\d{4})\s+(.*)$"
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)
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rows = []
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current = None
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page = 1
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for line in text.splitlines():
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if "\f" in line:
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page += line.count("\f")
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match = date_re.match(line)
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if match:
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if current:
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rows.append(current)
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transaction_date = _parse_modern_date(match.group(1))
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current = {
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"transaction_date": transaction_date,
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"value_date": transaction_date,
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"lines": [match.group(2)],
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"source_page": page,
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}
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elif (
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current
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and line.strip()
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and not re.match(
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r"^(Date\s+Transaction|ACCOUNT STATEMENT|Page)",
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line.strip(),
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re.I,
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)
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):
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current["lines"].append(line)
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if current:
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rows.append(current)
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out = []
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previous_balance = meta.opening_balance
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for row in rows:
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first_line = row["lines"][0]
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all_text = norm(" ".join(row["lines"]))
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# Balance is always the last INR amount and may be CR or DR.
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balance_match = re.search(
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r"INR\s*([\d,]+\.\d{2})\s*(CR|DR)\s*$",
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first_line,
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re.I,
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)
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if balance_match:
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balance = _signed_amount(
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balance_match.group(1),
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balance_match.group(2),
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)
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prefix = first_line[: balance_match.start()]
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else:
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balance = None
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prefix = first_line
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amount_matches = list(
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re.finditer(r"INR\s*([\d,]+\.\d{2})", prefix, re.I)
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)
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transaction_amount = (
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amount(amount_matches[-1].group(1)) if amount_matches else None
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)
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debit = None
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credit = None
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if (
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transaction_amount is not None
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and previous_balance is not None
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and balance is not None
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):
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if abs((previous_balance - transaction_amount) - balance) < 0.05:
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debit = transaction_amount
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elif abs((previous_balance + transaction_amount) - balance) < 0.05:
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credit = transaction_amount
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# Fallback for the first row or when running-balance inference is
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# unavailable. In the rendered Indian Bank table, a dash occupies
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# the empty debit/credit column. Determine the side from text
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# preceding the transaction amount.
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if transaction_amount is not None and debit is None and credit is None:
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before_amount = prefix[: amount_matches[-1].start()]
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after_amount = prefix[amount_matches[-1].end() :]
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# "- INR 1,000.00" means debit is empty, therefore credit.
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if re.search(r"-\s*$", before_amount):
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credit = transaction_amount
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# "INR 1,000.00 -" means credit is empty, therefore debit.
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elif re.match(r"^\s*-", after_amount):
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debit = transaction_amount
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else:
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# Position fallback retained for extraction engines that
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# preserve table spacing but omit the dash placeholder.
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if amount_matches[-1].start() < 52:
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debit = transaction_amount
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else:
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credit = transaction_amount
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narration = re.split(
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r"\s+INR\s*[\d,]+\.\d{2}",
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all_text,
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1,
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flags=re.I,
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)[0]
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reference_no = ""
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reference_match = re.search(
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r"(?:NEFT|IMPS|UPI|RTGS)[/A-Z0-9-]{6,}",
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all_text,
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re.I,
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)
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if reference_match:
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reference_no = reference_match.group(0)
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if debit is None and credit is None:
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continue
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out.append(
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{
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**row,
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"narration": narration,
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"reference_no": reference_no,
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"debit": debit,
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"credit": credit,
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"balance": balance,
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}
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)
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if balance is not None:
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previous_balance = balance
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return meta, finalize(pd.DataFrame(out), meta)
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class IndianBankLegacyParser(BaseParser):
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bank_name = "Indian Bank"
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parser_name = "IndianBankLegacyParser"
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@classmethod
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def detect(cls, text):
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u = text.upper()
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return (
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0.97
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if "STATEMENT OF ACCOUNT FROM" in u
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and "REMITTER" in u
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and "CHEQUE NO" in u
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else 0
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)
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def parse(self, path, text=None):
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text = text or extract_text(path)
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meta = StatementMeta(
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bank_name=self.bank_name,
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source_file=Path(path).name,
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parser_name=self.parser_name,
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confidence="Medium",
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)
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meta.account_number = find(r"for Account Number\s*\.?\s*([0-9X*]+)", text)
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m = re.search(
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r"STATEMENT OF ACCOUNT from\s*(\d{2}/\d{2}/\d{4})\s*to\s*"
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r"(\d{2}/\d{2}/\d{4})",
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text,
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re.I,
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)
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if m:
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meta.period_from = pd.to_datetime(
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m.group(1), dayfirst=True
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).strftime("%Y-%m-%d")
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meta.period_to = pd.to_datetime(
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m.group(2), dayfirst=True
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).strftime("%Y-%m-%d")
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date_re = re.compile(
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r"^\s*(\d{2}/\d{2})(?:/\d{4})?\s+"
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r"(\d{2}/\d{2})(?:/\d{4})?\s+(.*)$"
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)
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rows = []
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current = None
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page = 1
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for line in text.splitlines():
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if "\f" in line:
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page += line.count("\f")
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match = date_re.match(line)
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if match:
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if current:
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rows.append(current)
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year = meta.period_from[:4] if meta.period_from else "2024"
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transaction_date = match.group(1).replace(" ", "") + "/" + year
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value_date = match.group(2).replace(" ", "") + "/" + year
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current = {
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"transaction_date": transaction_date,
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"value_date": value_date,
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"lines": [match.group(3)],
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"source_page": page,
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}
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elif (
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current
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and line.strip()
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and not re.match(
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r"^(Value Post|Date Date|STATEMENT OF ACCOUNT|Page No)",
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line.strip(),
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re.I,
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)
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):
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current["lines"].append(line)
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if current:
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rows.append(current)
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out = []
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previous_balance = None
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for row in rows:
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first_line = row["lines"][0]
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all_text = norm(" ".join(row["lines"]))
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balance_match = re.search(
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r"([\d,]+\.\d{2})(CR|DR)\s*$",
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first_line,
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re.I,
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)
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if not balance_match:
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continue
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balance = _signed_amount(
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balance_match.group(1),
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balance_match.group(2),
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)
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prefix = first_line[: balance_match.start()]
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numbers = list(
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re.finditer(r"(?<!\d)([\d,]+\.\d{2})(?!\d)", prefix)
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)
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transaction_amount = (
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amount(numbers[-1].group(1)) if numbers else None
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)
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debit = None
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credit = None
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if transaction_amount is not None and previous_balance is not None:
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if abs((previous_balance - transaction_amount) - balance) < 0.05:
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debit = transaction_amount
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elif abs((previous_balance + transaction_amount) - balance) < 0.05:
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credit = transaction_amount
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if transaction_amount is not None and debit is None and credit is None:
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credit = transaction_amount if numbers[-1].start() > 70 else None
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debit = transaction_amount if numbers[-1].start() <= 70 else None
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reference_no = ""
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reference_match = re.search(
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r"(?:UPI|NEFT|IMPS|RTGS)[/A-Z0-9-]{6,}",
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all_text,
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re.I,
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)
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if reference_match:
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reference_no = reference_match.group(0)
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out.append(
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{
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**row,
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"narration": all_text,
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"reference_no": reference_no,
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"debit": debit,
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"credit": credit,
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"balance": balance,
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}
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)
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previous_balance = balance
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if out and meta.opening_balance is None:
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first = out[0]
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transaction_amount = (first.get("debit") or 0) - (
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first.get("credit") or 0
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)
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meta.opening_balance = round(
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(first["balance"] or 0) + transaction_amount,
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2,
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)
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return meta, finalize(pd.DataFrame(out), meta)
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