Developer Documentation

Architecture

Single Python server using Flask. No external AI APIs — all classification is done locally with rule-based keyword matching.

File Structure

Classification Engine

classifier.py uses pdfplumber for PDF text extraction and keyword matching for classification. No LLM, no external API calls.

How it works

1. extract_pdf_text() — extracts text from PDF using pdfplumber
2. parse_invoice_text() — identifies invoice metadata (supplier, number, date, totals) and table rows
3. _classify_item() — checks description against PAINT_KEYWORDS and MATERIALS_KEYWORDS
4. _confidence_check() — flags items with Review status if low confidence

Currently handles Wattyl/Hempel invoice format. To add support for new suppliers, adjust _parse_table_rows() in classifier.py.

API Endpoints

Email Monitoring

email_monitor.py supports two modes:

• IMAP — connects via IMAP4_SSL (port 993). Works with basic auth. Fails on Exchange Online if Basic Auth is disabled.
• Graph API — uses Microsoft Graph API with OAuth2 (client credentials). Reuses the SharePoint app registration. Requires Mail.Read application permission.

Polling runs in a background daemon thread. Configurable interval (default 60s). Found emails are processed then marked as read.

SharePoint Integration

sharepoint_client.py uses Microsoft Graph API with MSAL client credentials flow.

Required Graph permissions:

• Sites.ReadWrite.All — read/write files in SharePoint
• Mail.Read — (for email monitoring) read mailbox messages

The Excel register is downloaded, updated with openpyxl, and uploaded back. Uses PUT /drives/{id}/items/{id}/content for updates.

Running Locally

pip install flask pdfplumber openpyxl msal
export PORT=5000
python3 server.py