Bulk work-order PDF extraction

The parser uses table positions to preserve site IDs when adjacent PDF cells are blank, with regex fallbacks for inconsistent layouts. Combined equipment configurations are split into separate columns before the batch becomes a Pandas DataFrame. A background thread keeps the desktop interface responsive during extraction.

Excerpt from the extractor’s normalization function:

def strip_all_spaces(val):
    if val is None:
        return ""
    return re.sub(r'\s+', '', str(val))

def split_config(val, expected_len):
    if not val:
        return [""] * expected_len
    parts = [strip_all_spaces(p) for p in str(val).split('/')]
    if len(parts) < expected_len:
        parts.extend([""] * (expected_len - len(parts)))
    return parts[:expected_len]

This excerpt contains no customer data. It shows how a variable-length PDF field becomes a consistent set of columns.