superslurp 0.0.2

A parser for SuperU receipts

superslurp : Super, Sublime, Light, and Unprecedented Receipt Parser

Parser for SuperU receipts. Take the PDF receipt sent by mail as input and return a json.

Useful when you want to display the instantaneous cheese consumption intensity of your home in € inside grafana.

1. Parse a receipt

from superslurp import parse_superu_receipt

result = parse_superu_receipt("Ticket de caisse_01032022-165652.pdf")

The receipt line QUENELLE NATURE U X6 240G / 3 x 0,85 € 2,55 € 11 is parsed as:

{
  "name": "QUENELLE NATURE U",
  "price": 0.85,
  "quantity": 3,
  "units": 6,
  "grams": 240.0,
  "tr": false,
  "way_of_paying": "11",
  "discount": null
}

Pass debug=True to include the original receipt line ("raw" field):

from superslurp import parse_superu_receipt

result = parse_superu_receipt("receipt.pdf", debug=True)

Pass a synonyms dict to expand receipt abbreviations in item names:

from superslurp import parse_superu_receipt

synonyms = {"TABS": "TABLETTES", "VAISS": "VAISSELLE"}
result = parse_superu_receipt("receipt.pdf", synonyms=synonyms)
# "TABS LAVE VAISS.STANDARD U" → "TABLETTES LAVE VAISSELLE STANDARD U"

CLI:

superu-receipt-parser receipt.pdf --synonyms synonyms.json

2. Aggregate receipts

Compare items across multiple parsed receipts. Products are grouped under a canonical name using fuzzy matching (via difflib).

from pathlib import Path

from superslurp.compare.aggregate import compare_receipt_files

synonyms = {"TABS": "TABLETTES", "VAISS": "VAISSELLE"}

result = compare_receipt_files(
    paths=[Path("receipt1.json"), Path("receipt2.json")],
    threshold=0.90,       # difflib threshold (default: 0.90)
    synonyms=synonyms,    # optional, same format as parse
)

The result contains stores, sessions, per-session totals, a rolling weekly average, and products with their observations:

{
  "stores": [{ "id": "123_456", "store_name": "...", "location": "..." }],
  "sessions": [{ "id": 1, "date": "2025-01-15 10:00:00", "store_id": "123_456" }],
  "session_totals": [{ "session_id": 1, "date": "2025-01-15", "total": 42.5 }],
  "rolling_average": [{ "date": "2025-01-13", "value": 85.3 }, "..."],
  "products": [
    {
      "canonical_name": "OEUFS",
      "observations": [
        {
          "original_name": "OEUFS PLEIN AIR MOYEN",
          "session_id": 1,
          "price": 3.15,
          "quantity": 1,
          "grams": null,
          "discount": null,
          "price_per_kg": null,
          "unit_count": 12,
          "price_per_unit": 0.2625
        }
      ]
    }
  ]
}

CLI:

superu-aggregate-parsed-receipt receipts/ --synonyms synonyms.json --output aggregate.json

3. Generate an HTML report

From PDFs directly

Parse receipt PDFs and generate a self-contained HTML dashboard in one step:

from superslurp import generate_report

html = generate_report(
    ["receipt1.pdf", "receipt2.pdf", "receipt3.pdf"],
    synonyms=synonyms,    # optional
    threshold=0.90,       # fuzzy matching threshold (default: 0.90)
)
Path("report.html").write_text(html)

CLI:

superu-report receipts/*.pdf --synonyms synonyms.json --output report.html

From an existing aggregate JSON

If you already have an aggregate JSON (from step 2):

from superslurp.compare.html_report import generate_html

html = generate_html(aggregate_result)
Path("report.html").write_text(html)

superu-report-from-aggregate aggregate.json --output report.html

Or pipe directly from aggregate:

superu-aggregate-parsed-receipt receipts/ --synonyms synonyms.json \
  | superu-report-from-aggregate - --output report.html

Synonyms

Synonyms is an ordered dict[str, str]. Entries are applied sequentially with word-boundary matching — insertion order matters. Earlier entries are replaced first, so later entries won't match words already consumed.

Dots in both names and keys are normalized to spaces before matching, so "FROM.BLC" matches FROM.BLC on the receipt.

synonyms = {
    "FROM.BLC": "FROMAGE BLANC",          # applied 1st: consumes FROM and BLC
    "CHOCO PATIS": "CHOCOLAT PATISSIER",  # applied 2nd: consumes CHOCO and PATIS
    "CHOCO": "CHOCOLAT",                  # applied 3rd: only if CHOCO still present
    "FROM": "FROMAGE",                    # applied 4th: only if FROM still present
    "PATIS": "PATISSERIE",               # applied 5th: only if PATIS still present
}
# "FROM.BLC NAT"         → "FROMAGE BLANC NAT"            (FROM.BLC consumed by 1st)
# "FROM.RAPE"            → "FROMAGE RAPE"                 (FROM consumed by 4th)
# "CHOCO.PATIS.NOIR 52%" → "CHOCOLAT PATISSIER NOIR 52%"  (CHOCO PATIS consumed by 2nd)
# "CHOCO.NOIR"           → "CHOCOLAT NOIR"                (CHOCO consumed by 3rd)

The JSON file is a standard object (key order is preserved since Python 3.7):

{
  "FROM.BLC": "FROMAGE BLANC",
  "CHOCO PATIS": "CHOCOLAT PATISSIER",
  "CHOCO": "CHOCOLAT",
  "FROM": "FROMAGE",
  "PATIS": "PATISSERIE"
}