Until now every test ran against core or the CLI; the Streamlit GUI
was verified by hand. This commit adds tests/gui/ — 139 AppTest-
driven tests behind a 'gui' marker so the quick loop
(``pytest -m 'not gui'``) stays at 1777 tests / ~10s while
``pytest`` runs everything (1916 / ~14s).
Coverage:
- test_smoke.py (59): every page renders in EN and ES, expected
substring present, sidebar selector mounted.
- test_chrome.py (18): language selector flips session state and
re-renders; quit button + farewell strings localize; tool-card
names use the active language.
- test_gate.py (9): require_normalization_gate no-op / warning /
short-circuit / hash-mismatch invariants; warning + button
localized.
- test_workflows.py (14): happy path per Ready tool — stash
upload, render, find primary action, verify result lands in
session state.
- test_dedup_review.py (8): Accept All / Reject All / Clear
Decisions wire through to review_decisions; apply_review_decisions
semantics (keep-all, merge, column override).
- test_advanced_panels.py (15): config_panel widget defaults and
options (algorithm, threshold, survivor rule, merge, multiselects,
config save/load).
- test_errors.py (4): garbage / empty / single-column uploads don't
crash; duplicate-target mapping raises InputValidationError.
- test_findings_panel.py (12): driven via a small standalone harness
page so we test the component without faking a file_uploader. EN
+ ES strings, per-tool grouping, open-tool button label, untargeted
expander, severity summary.
Shared infrastructure in tests/gui/conftest.py:
- ``stash_upload`` / ``stash_upload_without_gate`` — populate
session_state to pre-pass or block the gate.
- ``with_language`` — set ``ui_lang`` before run().
- ``collected_text`` — flatten title/caption/markdown/etc. into
one string for substring assertions.
- Auto-marking: every test in tests/gui/ gets ``@pytest.mark.gui``
via ``pytest_collection_modifyitems``, so the marker isn't
per-test boilerplate.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
REQUIREMENTS §10 carries the new measured numbers and the dedup
blocking trade-off note. DEVELOPER known-limitations is rewritten to
reflect that exact-only dedup is now O(n), fuzzy-blocking is opt-in,
and column-parallelism is scaffolding for free-threaded Python.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three follow-on wins from the audit, each with shape-pinning tests.
1. Dedup blocking
- Exact-only strategies (every column EXACT @ 100 — covers strong-
key dedup like email/phone, the drop-duplicates fallback, and
explicit "match on this exact column" calls) now route through
an O(n) groupby fast path. Lossless; no API change required.
Measured: 10k-row email-exact dedup → 73 ms (was ~30 minutes
via the O(n²) pair compare).
- Fuzzy strategies still pair-compare, with opt-in prefix blocking
via deduplicate(..., blocking_columns=[...], blocking_prefix_len=1).
Measured: 5k-row fuzzy-name → 25.6s with blocking vs 179s
without (7x). Trade-off: cross-block matches missed.
2. Column-parallel standardize
- StandardizeOptions.parallel_columns (default 1) lands a
ThreadPoolExecutor over the column loop. Output order and
audit-record order are preserved deterministically via a merge
step keyed off column_types order. Honest doc: under CPython
3.12's GIL the win is roughly neutral (phonenumbers/dateutil
hold the GIL); the API is ready for free-threaded Python 3.13+.
3. Lazy-copy in missing / column_mapper
- _standardize_sentinels now builds per-column changes in a dict
and only materialises the output frame when at least one column
actually changed. On a clean 1 GB file this skips a 1 GB
allocation.
- handle_missing carries an out_is_owned flag, copying on demand
before any mutating step. No-op runs return the input frame.
- map_columns drops the unconditional upfront df.copy(); rename
and drop both return fresh frames already, and schema-add /
coerce trigger _ensure_owned() lazily.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
REQUIREMENTS §10 reflects the post-optimisation numbers and the
known O(n²) dedup match step (flagged for a future blocking pass).
en/es upload-limit copy and uploader help now say 1.5 GB.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Five targeted wins driven by an end-to-end audit, with shape-pinning
regression tests so reverts are loud:
- format_standardize: fuse the dispatcher loop into one pass — was
calling Series.tolist() three times per typed column and materialising
an intermediate triples list; now one tolist, one walk. On a
synthetic 1M-row phone+email frame this measures ~2.7M rows/sec
(vs. the previous 150k/sec doc target).
- dedup: wrap normalizers in a per-call lru_cache so repeat phones /
emails / addresses skip re-parsing. phonenumbers.parse is the
expensive call; ~2–5x faster on the normalisation step for realistic
workloads.
- analyze: _detect_near_duplicates no longer copies the full input
frame; builds only the normalised string columns via a dict and
references non-string columns by view. Skips the redundant
astype(str) when a column is already pandas string dtype.
- text_clean: hoist _build_pipeline out of the per-cell loop and add a
per-call string cache so 100k repeats of "Active" only run the
pipeline once. ~1M rows/sec on repetition-heavy columns.
- io.repair_bytes: the non-UTF-8 smart-quote fold path used a
Python-level zip walk over the entire decoded string to count
replacements — replaced with sum(text.count(c) ...) which runs in
C at ~GB/s. Was a latent ~100s on a 1 GB cp1252 file; now <1s.
Updates REQUIREMENTS §10 with measured numbers and bumps the buyer-
facing upload limit from 1 GB to 1.5 GB across the i18n packs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds README.es.md, docs/README.es.md, docs/USER-GUIDE.es.md, and
docs/CLI-REFERENCE.es.md mirroring the English client-facing set.
Each English doc gains a one-line language-switch banner pointing at
its Spanish counterpart; the docs index advertises both language sets
in the buyer-facing section. Internal docs (TECHNICAL, DECISIONS,
REQUIREMENTS, BUSINESS, RECOVERY) stay English-only by design — they
don't ship with the product.
The CLI itself emits English only, so CLI-REFERENCE.es.md notes that
flags and values are language-invariant while translating the prose.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
README + USER-GUIDE describe the sidebar picker and current coverage
(home + shared chrome, per-tool bodies pending). DEVELOPER gains a
how-to for adding packs and keys with the parity-test guarantee.
TECHNICAL §10b records the in-house-JSON architecture and locks in the
no-gettext decision (also logged in DECISIONS). REQUIREMENTS reflects
the new interface surface and updated test count. COPY.md adds a
"Language claim" slot so landing/email work can pick it up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Introduces ``src/i18n`` with a tiny JSON-backed t() lookup, an in-session
language preference, and a sidebar selector wired through
``hide_streamlit_chrome`` so every page picks up the same picker. Covers
home, tool cards, findings panel, gate, shutdown, and pickup banner
strings. Tests pin pack parity and the farewell-overlay JS escape so
future packs can't silently regress.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stand up the seamless-download path for non-technical buyers:
* .github/workflows/build.yml — matrix CI (mac/win/linux) that builds
PyInstaller bundles and packages them per platform on tag push,
attaching the resulting installers to a GitHub Release.
* build/installer.iss — Inno Setup script for the Windows installer
(per-user install, optional desktop shortcut, runs on finish).
* build/macos/build_dmg.sh — wraps DataTools.app into a .dmg with a
drag-to-/Applications layout.
* build/appimage/{AppRun,datatools.desktop,build.sh} — AppImage recipe.
* src/__init__.py — single source of truth for __version__; the spec
reads it (was hardcoded), CI passes it through to all packagers.
Buyer download path now lives in the top-level README. Per-build
README documents the Phase 2 step (signing/notarization) that needs
the owner's Apple Developer + Windows code-signing credentials —
those are intentionally not in CI yet because they require setup
outside this repo.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Update the Close page intro, the shutdown overlay, and the toast so
they all read "you can close this window" — clearer for users running
the app in a dedicated browser window rather than a tab.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replaces the data:-URL navigation (blocked by Chrome since v60 for
top-frame navigation) with a direct DOM-append of a full-screen
overlay onto the parent document. Uses z-index 2147483647 so it sits
above Streamlit's connection-error banner when the websocket drops.
Note: still doesn't fully suppress the connection-error banner in
testing — the next iteration will render the overlay through
Streamlit's own page rather than via a component iframe.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Move the shutdown control out of the inline sidebar widget and into
its own page (pages/99_Close.py), so it appears in the sidebar nav
alongside the tool pages. An explicit confirm button on the page
prevents accidental nav clicks from killing a live session.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Signalling the process with SIGTERM/SIGINT didn't reliably shut Streamlit
down — its tornado/asyncio loop swallowed or deferred the signal, so the
browser saw the websocket drop ("Connection error") while the python
process kept running. Replace the signal with a daemon-thread
``os._exit(0)`` after a short delay so the current rerun can paint the
"shutting down" message before the process is hard-killed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The footer placement was easy to miss (below all tool cards) and only
rendered on the home page. Hook the button into hide_streamlit_chrome()
so every page that hides default chrome — home + all 9 tool pages — gets
the Quit button at the bottom of the sidebar without per-page edits.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The chrome-hiding CSS was removing the Streamlit header wholesale,
which also took the sidebar's expand chevron with it — a collapsed
sidebar became unreopenable. Make the header transparent instead and
explicitly preserve the sidebar collapsed-control.
Also add a Quit button in the app footer that signals the Streamlit
server (SIGTERM, falling back to SIGINT) so closing the GUI returns
the shell prompt cleanly instead of leaving Python hung.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes ~17 high-value international gaps surfaced by parallel review.
Adds 93 regression tests; full project suite now 1323 / 0 / 17 (passed
/ failed / xfailed).
DATES
- Adds Portuguese, Italian, Dutch, Russian month dictionaries to the
opt-in ``month_locales`` set (now: en, fr, de, es, pt, it, nl, ru).
- Adds localized weekday recognition for those locales — "Lundi",
"Montag", "lunedì", "понедельник", etc. all strip cleanly before
format matching.
- New CJK separator normalization: Japanese ``2024年01月15日`` and
fullwidth digits ``２０２４/０１/１５`` fold to ASCII before parsing.
- New named-timezone resolution: EST/PST/JST/CET/IST/GMT/etc. map to
fixed UTC offsets via ``_NAMED_TZ_OFFSETS`` so the trailing TZ
doesn't block format matching.
- New ISO 8601 extended formats: week date (``2024-W03-1``) and
ordinal date (``2024-015``), plus RFC 2822 mail-header form
(``Mon, 15 Jan 2024 10:30:00``).
- New ``two_digit_year_cutoff`` parameter on ``standardize_date()`` —
defaults to Python's stdlib 69; lower it for birth-year columns
where most subjects were born ≤ 1999.
NAMES
- Particles set extended with Arabic patronymic markers (bin, ibn,
bint, abu, abd, al, al-, el-) and Hebrew (ben, bat, ha, ha-).
- Title set extended with German (Herr, Frau), French (M., Mme,
Mlle), Spanish (Sr., Sra., Srta., Don, Doña), Italian (Sig., Sig.ra,
Dott.), Portuguese.
- Acronym map extended with international academic credentials
(Dipl, Ing, Mag, Habil, MSc, BSc, LLB, LLM).
- New East Asian honorific suffix handler: ``Tanaka-san``,
``Lee-ssi``, ``Park-nim`` keep the suffix lowercase after the
hyphen instead of being title-cased into ``Tanaka-San``.
- Hyphenated-segment handler now keeps Arabic prefixes ``al-`` /
``el-`` lowercase per Arabic transliteration convention.
- New ``family_first`` parameter on ``standardize_name()`` and matching
``name_family_first`` field on ``StandardizeOptions`` — set
per-column for East Asian data to skip Western comma-format reversal
(``Kim, Min-jae`` stays ``Kim, …`` instead of becoming ``Min-jae Kim``).
CURRENCY
- Symbol map extended: ฿(THB), ₫(VND), ₮(MNT), ₴(UAH), ₦(NGN),
₱(PHP), ₲(PYG), ﷼(SAR), ₨(PKR), ₵(GHS) — covers SE Asia, Africa,
Eastern Europe, Latin America gaps.
- ISO 4217 code list extended from 23 to ~50: SAR, AED, QAR, KWD,
BHD, OMR, ARS, CLP, COP, EGP, IDR, MYR, PHP, THB, VND, NGN, GHS,
KES, HUF, CZK, RON, UAH, KZT, etc.
EMAIL
- New BIDI / RTL override stripping (``standardize_email``):
U+202A-U+202E and U+2066-U+2069 stripped from every email. These
are a known phishing vector — ``alice@example.com`` displays as
``alice@elpmaxe.com`` to RTL-aware renderers.
ADDRESS
- Canadian provinces: 13 codes + names → 2-letter (Ontario → ON).
- UK postcode pattern recognition (``SW1A 2AA`` shape).
- Australian states: 8 codes + names (NSW, VIC, QLD, … + full names).
- German Bundesland: 16 codes + names (Bayern → BY, etc.).
- International PO Box variants: ``Postfach`` (DE), ``Boîte postale``
(FR), ``Apartado`` (ES), ``Casella postale`` (IT), ``Caixa postal``
(PT) — all fold to canonical ``PO Box``.
- ``_INTL_STATE_CODES`` now combines US/CA/AU/DE codes; the position
check that preserves state codes regardless of input case applies
to all four jurisdictions.
- ``_is_state_code_position`` postal pattern broadened to recognize
US ZIP, AU 4-digit, CA first half, and UK outward code.
CONSTANTS
- ``src/core/_constants.py`` gains: ``CA_PROVINCE_CODES`` /
``CA_PROVINCE_NAMES``, ``AU_STATE_CODES`` / ``AU_STATE_NAMES``,
``DE_STATE_CODES`` / ``DE_STATE_NAMES``, ``POSTAL_PATTERNS``
(us/ca/uk/de/au/fr), ``INTL_PO_BOX_PATTERNS`` (per-language regex),
``INTL_STREET_SUFFIXES`` (de/fr/es/it/uk dictionaries — ready for
use when address takes a `country_hint` parameter in a future pass).
DOCS
- TECHNICAL.md §11.3 domain table updated with the new handling per
domain plus a new "International coverage" sub-section listing the
supported locales / symbols / jurisdictions.
DEFERRED (out of scope or rare)
- Alternative calendars (Japanese era, Hijri, Hebrew, Buddhist) —
corpus § 3.5 marks out of scope.
- Persian/Arabic-Indic digit conversion — rare in tabular data.
- Trailing-minus RTL currency convention.
- Punycode ↔ Unicode IDN normalization.
- Mixed-country phone column auto-detection (user can override
``default_region`` per column).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Introduces src/core/errors.py with a small structured error hierarchy
that every public entry point now uses. Each error carries the
context a user needs to fix it and the context a maintainer needs to
trace it.
The hierarchy:
DataToolsError (base — formats path, column, operation, suggestion)
InputValidationError (extends ValueError — bad arg / wrong type)
ConfigError (extends ValueError — bad config / options)
FileFormatError (extends ValueError — file is not what we expected)
FileAccessError (extends OSError — file I/O failure)
Subclassing the stdlib bases means existing `except OSError` /
`except ValueError` handlers still catch them — no breaking change.
Helpers:
- ensure_dataframe(value, function=...) — uniform DataFrame guard
- ensure_choice(value, name=, choices=) — uniform enum/literal guard
- wrap_file_read(path, op, exc) — tag OSError with hint + path
- wrap_file_write(path, op, exc) — same, with Windows-aware tip
- format_for_user(exc, context=) — user-facing string for st.error / stderr
Library hardening:
- io.read_file: missing files surface FileAccessError listing whether
the parent directory exists, and the suggestion to check the path.
- io.read_file: chunk_size <= 0 now raises InputValidationError with
a positive-integer suggestion.
- io._read_excel: openpyxl BadZipFile / InvalidFileException / pandas
ValueError ("sheet not found") wrapped as FileFormatError listing
the path and a "list sheets with list_sheets()" hint.
- io._detect_excel_header_row: bare except narrowed to specific
openpyxl exceptions; falls back gracefully and logs at debug so
the real error surfaces from pd.read_excel.
- io.write_file: OSError / PermissionError on to_csv/to_excel wrapped
with file path and Windows-aware "file may be open in another
program" hint.
- dedup._parse_date: bare `except Exception` narrowed to
(TypeError, ValueError, OutOfBoundsDatetime); failed values
logged at debug for survivor-selection forensics.
- dedup._select_survivor: KEEP_MOST_RECENT now raises
InputValidationError instead of silently falling back to keep_first.
- dedup.deduplicate: input validation errors are InputValidationError
with operation/column/suggestion fields.
- format_standardize.from_dict: invalid FieldType for a column raises
ConfigError naming the column AND the bad value AND listing valid
values; same for date_order / phone_format / etc.
- format_standardize.from_file: OSError / JSON decode wrapped with
path AND line/column where parsing failed.
- format_standardize.to_file: TypeError on json.dumps wrapped as
ConfigError with the suspected source (extra_abbreviations).
- format_standardize._apply_field_type: dispatcher's "unknown field
type" branch now raises AssertionError (it's an internal invariant,
not user error — a new enum value was added without a branch).
- format_standardize._resolve_column_types: missing-column error now
InputValidationError with a "check for typos / unparsed header"
suggestion.
- format_standardize.standardize_dataframe: ensure_dataframe at entry.
- text_clean.clean_dataframe: ensure_dataframe at entry.
- config.to_strategies: invalid Algorithm/NormalizerType wrapped as
ConfigError naming the strategy index AND the column.
- config.to_survivor_rule: invalid SurvivorRule wrapped as ConfigError
listing valid values.
- config.from_file: OSError / JSON decode wrapped (mirror of
StandardizeOptions.from_file).
- fixes.repair_mojibake: ImportError on ftfy now logged at info level
with the underlying ImportError so a corrupt-package vs not-installed
distinction is visible in the logs.
- normalizers.normalize_phone: phonenumbers.NumberParseException now
logged at debug when the digits-only fallback drops extension /
country-code information — gives a trail when matching results
look wrong.
GUI / CLI surfaces:
- All 9 page handlers (`except Exception as e: st.error(...)`) now
use format_for_user(), which renders DataToolsError fields nicely
and falls back to "ClassName: message" for unrecognized errors.
- 2_Text_Cleaner and 3_Format_Standardizer additionally distinguish
UnicodeDecodeError with an "re-save as UTF-8" suggestion before
the generic handler.
- cli.py's "Error reading file" handler now uses format_for_user()
and includes the input path in the prefix.
Tests:
- tests/test_errors.py — 22 new tests covering: base class formatting,
stdlib inheritance, ensure_dataframe / ensure_choice helpers,
wrap_file_read / wrap_file_write, format_for_user behavior, and
end-to-end integration (missing file, missing dir, bad JSON, bad
algorithm, bad enum, missing column).
- tests/test_audit_fixes.py + tests/test_io.py — updated 4 tests for
the new exception types (InputValidationError replaces TypeError,
FileAccessError extends OSError).
Full project suite: 1230 passed, 4 skipped, 17 xfailed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes 16 high-value findings from a parallel cross-module review.
Refactors:
- New src/core/_constants.py centralizes USPS street-suffix
abbreviations, US state names, and 2-letter postal codes — one source
of truth for both normalize_address (matching keys) and
standardize_address (display formatting). Eliminates ~80 lines of
duplicated dicts across normalizers.py and format_standardize.py.
- format_standardize.py: collapse 4 identical nested _err() helpers
into one shared _err_or_passthrough() module function; drop a dead
duplicate `return _err("not a phone number")` branch in
standardize_phone.
- format_standardize.py: precompile per-locale month-name regexes
(_MONTH_LOCALE_PATTERNS) and per-state-name regexes
(_STATE_NAME_PATTERNS) at import time — they were rebuilt on every
cell, a measurable hot path on million-row inputs.
- dedup.py: extract _is_missing(value) helper; one definition of
"this cell is None / NaN / pd.NA" instead of two.
- fixes.py: extract _is_string_column(ser) helper; one dtype check
instead of three duplicates across _apply_to_strings,
_vectorized_translate, _vectorized_regex_sub.
Production-readiness:
- format_standardize.standardize_dataframe now logs a warning when
more than 10% of typed cells are unparseable — surfaces the
silently-broken-pipeline failure mode.
- StandardizeOptions.from_dict validates date_order / phone_format /
currency_decimal / name_case / boolean_style / *_error_policy
enum values up front, with a clear error message instead of a deep
crash inside the per-cell function.
- StandardizeOptions.from_file and DeduplicationConfig.from_file wrap
read + json.loads with descriptive OSError / ValueError messages
including the file path.
- standardize_date(month_locales=...) validates locale codes against
the available set instead of silently passing through unknown ones.
- io.read_file rejects chunk_size <= 0 (was silently failing inside
pandas) and logs the resolved suffix + chunk_size at info level so
data-pipeline runs are debuggable.
- io.read_file's FileNotFoundError gains explanatory context.
- io.write_file, text_clean.clean_dataframe, and dedup.deduplicate
now reject non-DataFrame inputs with clear TypeError instead of
cryptic pandas tracebacks downstream.
- dedup.deduplicate validates that survivor_rule=KEEP_MOST_RECENT has
a usable date_column up front; the helper _select_survivor now
raises (instead of silently falling back to keep_first) when called
directly with bad arguments.
- dedup.deduplicate gains a structured no-op return when strategies
is empty after auto-detection — preserves schema instead of crashing.
- analyze._detect_inconsistent_date_format narrows its bare except to
(TypeError, ValueError) and logs a debug line so genuine bugs don't
hide behind silent skip.
Tests:
- tests/test_audit_fixes.py grows by 11 cases covering the new
validation paths (chunk_size, DataFrame guards, KEEP_MOST_RECENT
date_column, enum validation, locale validation, JSON error wrapping).
Full project suite: 1208 passed, 4 skipped, 17 xfailed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes 12 bugs and 8 gaps surfaced by parallel audits across all core
modules, plus aligns the dedup-side normalizers with the new
format_standardize behavior where they had silently diverged.
Bugs (data integrity / correctness):
- dedup: NaN/None values matched as duplicates because str(None)='None'.
Two rows with missing email silently merged.
- dedup: removed_df had 0 columns when nothing was removed; downstream
code expecting matching schema broke. Now preserves column shape.
- dedup: ColumnMatchStrategy threshold accepted any value; out-of-range
silently broke matching. Validated to [0, 100] in __post_init__.
- dedup: strategy referencing a missing column was silently skipped.
Now raises ValueError listing available columns.
- fixes: replace_null_sentinels crashed on non-string sentinels (int/None
from JSON payload). Coerced to str.
- fixes: _vectorized_regex_sub raised raw re.error on bad patterns. Now
wraps as ValueError with clear message.
- io: detect_header_row mis-identified all-empty and metadata-only rows
as headers (all([]) is True). Now requires ≥2 non-empty cells.
- config: from_dict crashed when JSON had unknown fields, breaking
forward compat. Now filters to known fields.
- analyze: mixed-case email detector flagged all-None columns because
str(None)='None' contains both N and one. Now drops NaN before stringify.
New features and gap closures:
- io: _detect_excel_header_row mirrors detect_header_row for Excel via
openpyxl read-only; _read_excel uses it when header_row=None.
- io: write_file gains delimiter + encoding params; .tsv extension
defaults to tab.
- normalizers: normalize_phone preserves extensions as ;ext=N suffix.
- normalizers: normalize_address folds spelled-out US state names to
2-letter codes (California ≡ CA).
- normalizers: normalize_name drops surname particles (van, de, von)
so "Charles de Gaulle" ≡ "Charles Gaulle" for matching.
- analyze: new _detect_inconsistent_date_format detector flags columns
with mixed ISO/US/EU date shapes; routes to format standardizer.
- analyze: _NULL_LIKE recognizes "<na>" (pd.NA repr).
- analyze: duplicate-row finding renamed count → n_extra (rows that
would actually be removed) with clarified description.
- dedup: group_confidence no longer falsely 100.0 when transitive group
members lack a recorded direct pair; falls back to 100.0 only when
truly no pairs were observed.
- dedup: MatchResult / DeduplicationResult docstrings clarify that
row_indices refer to the input frame's positional index (output index
is reset).
- text_clean: visualize_hidden_html(None) now returns None (matches
visualize_hidden_text); strip_bom strips at most one BOM per call;
sentence_case dead elif branch removed.
Tests:
- tests/test_audit_fixes.py — 28 regression tests, one or more per
numbered finding, named after BUG/GAP/NIT tags so future readers
can trace each test back to its audit.
- tests/test_fixes_unit.py — 26 isolated unit tests for previously
integration-only fix functions (trim_whitespace, strip_nbsp,
strip_zero_width, normalize_line_endings, clean_headers,
repair_mojibake — last skipped if ftfy unavailable).
- tests/test_io.py — adds CSV / TSV / semicolon / UTF-8-BOM round-trip
tests + Excel auto-header-detection tests.
- tests/test_normalizers.py — adds 8 tests for the alignment work
above (phone extension, state names, particles).
Adds .claude/ to .gitignore (agent worktrees + local settings).
Full project suite: 1197 passed, 4 skipped, 17 xfailed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds src/core/format_standardize.py — a per-cell standardizer for dates,
phones, emails, addresses, names, currencies, booleans — wired through
StandardizeOptions / standardize_dataframe with FieldType registry.
Includes:
- Date parser handles ISO/US/EU/longform/excel-serial/unix-timestamp/
partial-precision/quarter notation; opt-in French/German/Spanish month
dictionaries via month_locales.
- Phone via libphonenumber with extension preservation (;ext=N), 001
international prefix handling, error sentinels for placeholders /
multi-number cells.
- Email lowercase/trim/mailto/angle-bracket strip with optional
--gmail-canonical mode.
- Address USPS abbreviation expansion or compression (expand=False per
corpus § 6.3), state-name → 2-letter conversion, multi-line collapse,
PO Box normalization, state-code preservation regardless of input case.
- Name handler: Mc/Mac/O'/D' inner caps, hyphen segments, particle
lowercasing (von/van/de/da), comma-format reversal, period stripping
for titles/suffixes/initials, PhD/MD acronym preservation, conservative
mode for mixed-case input.
- Currency: auto-detect EU vs US separators, space-thousands, Swiss
apostrophe, accounting parens, optional ISO code preservation, error
sentinels for percentages/ranges/word-values/ambiguous separators.
- Per-domain error_policy ("passthrough" | "sentinel") for surfacing
malformed values as <error: reason> per corpus § 0.3.
Test corpus from Business/DataTools/test-cases-format-cleaner copied to
test-cases/format-cleaner-corpus/ — 7 fixtures plus FORMATS-CASES.md.
tests/test_format_standardize_corpus.py drives all 199 rows through the
per-cell standardizers; 0 xfailed.
Wires the GUI page (3_Format_Standardizer.py) to "Ready" status.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Streamlit's default file_uploader footer reads "Limit 200MB per file —
CSV, TSV, XLSX, XLS" which contradicts the 1 GB efficiency target shipped
in 438bc0f and codified in docs/REQUIREMENTS.md §1.1.
Three changes:
1. .streamlit/config.toml — set [server] maxUploadSize = 1024. Footer
now reads "Limit 1024MB per file".
2. upload_and_analyze_section (home page) — adds an explicit caption
above the uploader stating size limit, supported formats, the four
auto-detected delimiters, and the 13 auto-detected encodings (with
the Review-page override as the safety net).
3. pickup_or_upload (every tool page that falls back to its own
uploader when no home-page upload is present) — same caption,
only rendered when the upload accepts CSV/TSV/XLSX/XLS so JSON
schema / config uploaders aren't decorated.
Test suite: 765 passed, 17 xfailed (no regressions). Home + Review +
Deduplicator pages all serve HTTP 200 under the new config.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
New docs/REQUIREMENTS.md catalogs every shipped capability in 17 numbered
categories — file handling, input/output encodings, delimiters, line
endings, detectors, finding schema, confidence tiers, decisions,
performance targets (1 GB), tools, gate behavior, interfaces, platforms,
deps, test coverage, privacy. Linked from README and USER-GUIDE so a
buyer / integrator can scan compliance in under a minute.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The stress benchmark served its purpose — perf findings shipped in
438bc0f (1 GB-class file efficiency for the analyzer + gate pipeline).
Removing the script and the (already auto-deleted) test fixture so the
repo doesn't carry one-time scaffolding. Future ad-hoc benchmarks can
resurrect this from git history.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Six targeted changes that drop the user-visible analyzer scan time from
"go for coffee" to sub-second on 1 GB inputs and reduce peak RSS by ~10×.
src/core/io.py
- detect_encoding: open + read sample bytes instead of read_bytes()[:N].
Was allocating the full file in memory just to slice the head; on a
1 GB input this saves a 1 GB intermediate allocation.
- repair_bytes: byte-level smart-quote fold via bytes.replace when the
input is UTF-8. The probe (b"\\xe2\\x80" / b"\\xc2\\xab" / b"\\xc2\\xbb")
is a single C-implemented contains check that skips the entire fold
stage on files with no smart quotes — most of them.
- repair_bytes: skip the per-row csv.reader walk unless a cheap byte
scan finds a currency sigil ($/€/£), the delimiter is non-comma, the
decoder substituted U+FFFD, or _has_field_count_mismatch detects an
unquoted-delimiter row. csv.reader was the dominant cost in
repair_bytes on big files (materializes a list of every row).
- _has_field_count_mismatch: hand-rolled quote-state walker; one pass,
no allocation, returns True at first mismatch. False positives just
fall through to the slower _repair_rows pass.
src/core/analyze.py
- _load_for_analysis: read only ~max(4KB, sample_rows × 256B × 2) head
bytes for the analyzer's sample-mode scan. Drops analyze(sample_rows
=1000) from "read + repair full file" to "read + repair 500KB" —
150× faster on a 1.25 GB file. Falls back to a single full-file
retry if pandas reports fewer rows than the cap.
- Compiled regex character classes for hot-path detectors and a
_vec_match_count helper that runs Series.str.contains in C instead
of Python per-cell loops. Detectors converted: smart_punctuation,
invisible_chars (NBSP + zero-width), whitespace_padding,
null_like_sentinels, mojibake, encoding_uncertainty,
mixed_case_email, leading_zero_ids.
src/core/fixes.py
- _vectorized_translate / _vectorized_regex_sub: pandas-native string
transforms for the fixes that are pure character maps (strip_nbsp,
fold_smart_punctuation, strip_zero_width). Series.str.translate
runs in C — 10-50× faster than per-cell Python.
- _apply_to_strings: replaced inner per-cell loops with Series.map +
boolean-mask diff for the count.
- All fix entry points read an "inplace" flag from payload and thread
it through the helpers.
src/core/normalize.py
- apply_decisions: takes a single working copy at the top, then sets
payload["inplace"] = True so each chained fix mutates that copy.
Previously every fix did df.copy(); N fixes × 6 GB DataFrame =
30+ GB peak. Now: one 6 GB allocation.
Validation: 765 passed, 17 xfailed (no regressions). 100 MB benchmark:
stage before after
------------------------------ ------- --------
detect_encoding 0.97s+1.3GB ~0s + 0 MB
analyze (sample_rows=1000) 235.76s 0.08s
_load_for_analysis (1000 rows) 148.17s 0.01s
repair_bytes (full file) 150s/1.25GB 2.91s/100MB
The user-visible analyzer scan dropped from minutes to sub-second on
1 GB-class files. Full-DataFrame analyze + auto_fix improvements are
more modest (~25%) because trim_whitespace and replace_null_sentinels
still need per-cell Python for the structural-shape checks, but the
hot path through these is now bounded by pandas' .map rather than a
manual for loop.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two low-risk seam moves to enable selling per-tool subsets without
breaking the existing all-in-one bundle. Behaviour identical; every
existing import still resolves; full pytest suite + every page returns
HTTP 200.
1. **Tool registry** (src/gui/tools_registry.py) — replaces the
inline dict-of-dicts in app.py with a Tool dataclass and a TOOLS
list. Adds a tier field ("core" today, "pro" / "enterprise" later)
and tools_for_tier() / tool_by_id() / display_name() helpers. A
per-tool build slices TOOLS at import time without code changes.
2. **components package** (src/gui/components/) — converts the former
single components.py into a package with:
_legacy.py — original file, unchanged.
__init__.py — re-exports the legacy surface; existing
"from src.gui.components import …" calls
continue to work.
shared.py — hide_streamlit_chrome, pickup_or_upload
(every build needs these).
gate.py — require_normalization_gate (Pro / Suite SKUs).
findings.py — analyzer-finding widgets (drops out of a
standalone-Dedup build).
dedup_review.py — match-group cards + apply pipeline (drops out
of a non-dedup build).
The seam modules are narrow re-exports today. As code migrates out
of _legacy.py into the focused modules, the public import path
stays stable via the shim.
E2E: 765 passed, 17 xfailed (unchanged); home page + all 9 tool pages
+ Review page render HTTP 200; full pipeline (analyze → auto_fix →
apply_decisions → output bytes) round-trips on the kitchen-sink
fixture with zero high-confidence findings remaining post-fix.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Generates a synthetic messy CSV at the target size, then runs every
pipeline stage end-to-end (detect_encoding, repair_bytes, analyze,
auto_fix on sample + full file) capturing wall-clock and peak RSS at
each stage. Not part of the automated suite — invoke directly via
``python scripts/stress_1_25gb.py``. ``--keep`` to preserve the file
between runs, ``--target-gb`` to tune the size.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a Review & Normalize page that sits between upload and every tool
page. The analyzer now tags each finding with confidence (high/medium/low)
and a fix_action; the gate auto-applies high-confidence fixes, surfaces
medium/low ones for user review, and blocks tool pages on error-level
findings until resolved or waived.
Core (src/core/):
- analyze.py: Finding gains confidence, fix_action, pre_applied; new
detectors for encoding_uncertain, encoding_decode_failed; new top-
level encoding_override parameter.
- fixes.py: registry of fix algorithms keyed by fix_action id.
- normalize.py: auto_fix(), apply_decisions(), is_normalized(), and
the NormalizationResult / Decision dataclasses the gate consumes.
- io.py: detect_encoding tries strict UTF-8 first; repair_bytes now
transcodes UTF-16/32 to UTF-8 before NUL-strip (fixes UTF-16 corruption)
and normalizes line endings (fixes bare-CR parser crash); empty file
handled gracefully instead of EmptyDataError traceback.
GUI (src/gui/):
- pages/0_Review.py: gate page with per-finding decision controls,
encoding override picker (16 codepages + custom), and Advanced output
options (encoding, delimiter, line terminator) on the download.
- components.py: require_normalization_gate() helper.
- pages/1-9: gate guard wired on every tool page.
Test corpora:
- test-cases/encodings-corpus/: 31 encoded CSV fixtures + 9 reference
UTF-8 files + manifest, synced from Business/DataTools.
- test-cases/text-cleaner-corpus/test_data/17: synced malformed input
(unquoted $1,500.00) for the unquoted-delimiter detector.
Tests (94 new):
- test_normalize.py (48): finding fields, fix registry, auto_fix scope,
decision paths, gate idempotency, output-options helper.
- test_encodings_corpus.py (90, 16 xfailed): parametric detection +
decode + analyzer-no-crash sweep against the manifest.
- test_analyze.py: encoding override + encoding_uncertain detectors.
- test_corpus.py: pre-parse repair in the strict reader.
run_tests.py: new aliases --tool normalize, --tool encodings, --tool gate;
encodings corpus added to --fixtures category.
Docs: USER-GUIDE §3.3 covers the gate workflow, encoding override, and
output options; TECHNICAL §10.2.1-10.2.4 documents the analyzer schema,
gate API, Review page, and pre-parse repair pipeline; CLI-REFERENCE adds
the analyzer JSON schema with the new fields; README links to all of it.
Suite: 765 passed, 17 xfailed (was 458 passed).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The Text Cleaner had two st.dataframe previews — the initial upload
preview ("Preview: filename") and the post-clean "Cleaned preview"
table — that both rendered cells with the same browser-collapses-
whitespace, hides-invisibles problem the analyzer findings panel had
before commit 1049c03.
components.render_hidden_aware_preview(df, n_rows, caption) renders a
DataFrame as an HTML table where:
- every cell uses visualize_hidden_html(mark_outer_whitespace=True),
so leading/trailing ASCII spaces appear as per-character "·" badges
- white-space: pre-wrap on every cell preserves internal multi-space
runs and embedded newlines visually
- headers route through the same visualizer so dirty column names
(NBSP padding, ZWSP, smart quotes) show their badges too
- NaN cells render as a faint "NaN" placeholder
- rows are sticky-headed and scrollable inside a 26rem capped
container so a 10-row preview doesn't push the rest of the UI off
screen
2_Text_Cleaner.py wires it into both previews:
- The upload preview gains its own "Show hidden characters in preview"
toggle (default on).
- The cleaned preview reuses the existing show_hidden toggle that
already governs the Examples changes table, so one switch controls
the whole results section.
Either toggle off falls back to the original st.dataframe view.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The analyzer's "Run Analysis" panel rendered sample cells via st.dataframe,
which (a) silently collapses leading/trailing ASCII whitespace and (b)
displays NBSP/ZWSP/control chars as nothing. The user couldn't see the
exact pollution they were being told about.
visualize_hidden_html gains a mark_outer_whitespace=True option that
wraps each leading and trailing ASCII space/tab in its own badge with a
"SP LEAD" / "SP TRAIL" tooltip. The badges are per-character so the
user can count exactly how much padding the cleaner will strip.
components.render_findings_panel now:
- injects hidden_char_css() once at the top of the panel
- replaces st.dataframe(samples) with a custom HTML table
- renders the value column with mark_outer_whitespace=True
- applies white-space: pre-wrap on value cells so any internal ASCII
whitespace also stays visible (browsers collapse runs by default)
Four new tests cover: leading+trailing badge counts, default-off
behaviour, leading tab badge, all-whitespace string treated entirely
as leading.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
st.page_link resolves paths from the directory of the entrypoint file
(src/gui/app.py), so the existing "src/gui/{page_slug}" prefix doubled
up and produced StreamlitPageNotFoundError on first upload + analysis
(reproducible on Windows; the stack trace from a Windows install
surfaced the bug).
The _TOOL_PAGE_PATHS map already stores the correct relative form
("pages/2_Text_Cleaner.py"); just pass the slug straight to
st.page_link.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The whole point of the cleaner is to remove characters the user can't
see — which makes the "before / after" preview nearly useless by default.
A cell with NBSP padding looks identical to a cell with regular spaces.
Two new helpers in src.core.text_clean:
visualize_hidden_text(s)
Plain-text rendering: each invisible/control/smart character is
replaced by a glyph + [LABEL] (e.g. "·[NBSP]", "→[TAB]", "∅[ZWSP]",
"""[L DQUOTE]"). Suitable for terminal output, CSV exports, anywhere
HTML is wrong. Unmapped C0 controls render as [U+XXXX].
visualize_hidden_html(s) + hidden_char_css()
HTML rendering: every flagged character is wrapped in a <span> with
a CSS class and a tooltip showing the codepoint and label. Pair with
hidden_char_css() to inject the matching styles. Three colour bands
(whitespace, special, control) so the user can scan an audit table
and spot what's being changed at a glance.
Mapping covers: ASCII tab/LF/CR, every NBSP variant (U+00A0, U+202F,
U+2009, …), zero-width family (ZWSP/ZWNJ/ZWJ/WJ/BOM/SHY), bidi marks
(LRM/RLM), all smart quotes, en/em dashes, ellipsis, prime/double-prime,
and guillemets. ASCII printable text passes through; HTML output also
escapes &/</> .
GUI wiring (src/gui/pages/2_Text_Cleaner.py)
The "Examples" changes table now defaults to a hidden-char-rendered
HTML view: every NBSP/ZWSP/smart-quote/control char is shown with its
badge and codepoint tooltip. A "Show hidden characters" toggle lets
the user fall back to the raw st.dataframe view if they prefer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the last UX gap from the analyzer review: each tool page had its
own st.file_uploader, so users had to upload the same file twice (once
on the home page for analysis, once on each tool page).
components.pickup_or_upload(label, key, types) returns either:
- a _StashedUpload shim wrapping the home-page bytes (when present and
the user hasn't asked for a different file on this page), or
- the standard st.file_uploader (when nothing is stashed or the user
clicked "Use a different file").
_StashedUpload duck-types Streamlit's UploadedFile (.name, .size,
.getvalue(), .read()) so existing tool-page code consumes it without
changes. A "Use a different file" button per page sets a session-state
override flag; a "Switch back to upload-screen file" button clears it.
Wired into 2_Text_Cleaner.py and 1_Deduplicator.py — the two pages with
working uploaders today. The remaining stub pages adopt it when they're
implemented; the helper is the public surface they'll use.
Verified by smoke-launching streamlit headless and curling the home,
text-cleaner, and deduplicator routes — all return 200 with no errors
in the server log.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two more detectors close the analyzer gap list:
mixed_line_endings (warn, tool=02): scans raw bytes for combinations of
CRLF / LF / bare CR. Disaster pattern after multi-source concat
(Windows + macOS + Linux exports stitched together). Operates on raw
bytes only — DataFrame-mode analyze() skips it because raw bytes
aren't available. _load_for_analysis now returns the raw bytes
alongside the DataFrame and repair result so the detector has them.
near_duplicate_rows (info, tool=01): cheap dedup signal — strip and
lowercase every string column, then count df.duplicated(). Catches the
most common case (same customer entered twice with subtle formatting
differences) without paying for fuzzy matching. Anything more
sophisticated stays in tool 01.
Six new tests cover both detectors plus the dataframe-mode skip path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Previously only analyze() and direct read_csv_repaired() callers got the
byte-level repair pass (BOM strip, NUL strip, smart-double-quote fold,
unquoted-delimiter merge). The dedup CLI and any other read_file consumer
silently missed it.
read_file gains a repair=True default. CSV/TSV inputs run through
repair_bytes before pandas sees them; Excel inputs still pass through
unchanged. Chunked reads (chunk_size set) bypass repair because the pre-
parse pass loads the whole file — preserving streaming behavior on huge
files. Repair actions and unrepairable lines are logged at INFO/WARNING.
cli_text_clean opts out (repair=False): the cleaner offers fine-grained
control via --preset and per-op flags, and a byte-level smart-quote fold
under the user's "minimal" preset would violate that contract. The
cell-level cleaner does the equivalent work itself when its options ask
for it.
Tests: read_file default strips BOM and folds curly double quotes;
repair=False preserves smart quotes; chunked reads still work and skip
repair as documented.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the §4.17 spec gap that test_gap_coverage.py was tracking via xfail:
collapse_whitespace must NOT touch cells whose shape carries meaningful
internal whitespace.
Adds _looks_structured(s) — returns True when s matches:
- numeric (currency optional, thousand-grouping by , . or single space)
- date (ISO/slash/dot separator, or 'Mon DD YYYY' / 'DD Mon YYYY')
- phone (digits + parens/dots/dashes/+/spaces, >= 7 digits, no letters)
The pipeline uses a new _smart_collapse_whitespace wrapper that defers to
collapse_whitespace only when _looks_structured returns False. The raw
collapse_whitespace function is unchanged so direct callers and existing
unit tests remain valid.
Five new positive tests replace the xfail:
- "(555) 123-4567" preserved (phone, double space inside)
- "1 234" preserved (European thousands)
- "2024-01-15" preserved (ISO date)
- "Jan 15 2024" preserved (textual date)
- "hello world" still collapsed to "hello world" (free-text negative case)
Conservative on purpose: a false negative just collapses (existing
behavior); a false positive leaves intentional double spaces in prose.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a top-level test infrastructure layer addressing four needs at once:
a single command to run anything, cross-platform automation, install/e2e
sanity, and zero-config pickup of new fixtures dropped into test-cases/.
Top-level runner — run_tests.py
python run_tests.py # everything (default)
python run_tests.py --tool dedup # one tool's tests
python run_tests.py --unit # category scopes
python run_tests.py --e2e # end-to-end CLI
python run_tests.py --install # import / dependency sanity
python run_tests.py --fixtures # corpus + dropped-file sweep
python run_tests.py --coverage # term-missing report
python run_tests.py --quick # skip @pytest.mark.slow
Tools: analyze, cli, config, dedup, io, normalizers, text_clean.
Cross-platform — tox.ini
Envs for py310-py313 plus install / e2e / fixtures / coverage / lint.
Forces UTF-8 (PYTHONUTF8=1, PYTHONIOENCODING=utf-8) so identical fixture
bytes parse the same on Linux/macOS/Windows.
Shared config — pytest.ini
testpaths, python_files conventions, custom markers (slow, e2e, install,
fixture_sweep), warning filters that fail on our own DeprecationWarnings
while tolerating third-party ones.
New test layers
tests/test_install.py — required deps import; project modules import;
src.core public API surface; CLI --help exits 0; streamlit app.py
parses as valid Python; run_tests.py --help works.
tests/test_e2e.py — CLI roundtrips: cli_analyze table + JSON, cli_text_clean
--apply writes a real file with NBSP/smart-quote folded, dedup CLI
removes duplicates, run_tests.py self-tests.
tests/test_fixtures_sweep.py — parametrizes over every CSV/TSV/XLSX
inside test-cases/ (excluding text-cleaner-corpus/, which has its own
suite). Each fixture must: load through repair_bytes, run analyze()
cleanly, and survive clean_dataframe() with row/col counts unchanged
plus idempotency. Drop a CSV in, re-run — no test code changes needed.
tests/test_gap_coverage.py — closes audit gaps: clean_headers=False
toggle, repair_bytes with tab/semicolon delimiters, BOM+NUL+smart-
quote combined-fix scenario, analyze() over an XLSX path, sample_rows
larger than the DataFrame, mid-cell BOM, findings_by_tool edges, plus
a strict xfail documenting the known §4.17 numeric/phone whitespace
heuristic gap.
Test count
Before: 288 passed + 1 xfailed
After: 475 passed + 2 xfailed (the second xfail is the documented
collapse_whitespace gap on phone-shaped cells; spec §4.17 calls
for a heuristic that hasn't been implemented yet).
Functional gaps surfaced (not fixed in this commit):
- Text cleaner: collapse_whitespace runs unconditionally on every string
cell, including phone/numeric/date-shaped ones. Spec §4.17 requires a
skip heuristic. Captured as strict xfail so the gap stays visible.
- io.read_file does not run pre-parse repair; only analyze() and direct
callers of read_csv_repaired() get it. CLI tool pages and the dedup
CLI miss the safety net.
- Analyzer has no mixed_line_endings detector or near_duplicate_rows
detector; both planned but require additional plumbing.
- GUI tool pages each have their own uploader instead of picking up the
home-page upload through session_state.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Home page (src/gui/app.py) gains an upload + analyze section above the tool
grid: file uploader, "Run analysis" / "Skip" buttons, and a findings panel
grouped by destination tool. Tool cards now carry a "N findings" badge
when the active session's findings reference that tool, so the user sees
at a glance which tools their just-uploaded file would benefit from.
src/gui/components.py adds the shared GUI surface:
- TOOL_DISPLAY_NAMES + tool_display_name() — single source of truth for
GUI labels, keeping detector tool ids decoupled from the UI.
- render_findings_panel(findings) — severity icons, expander per tool,
open-tool page link, sample-cells dataframe.
- upload_and_analyze_section() — the home-page widget; stashes file
bytes and findings in session_state so future tool pages can pick up
the existing upload instead of re-prompting.
- findings_count_for_tool(tool_id) — used by app.py to badge cards.
CSV/TSV uploads run through repair_bytes() before analysis, so the user
also sees csv_bom_stripped / csv_smart_quotes_folded findings synthesized
from the pre-parse repair pass. Excel uploads skip that step.
The Text Cleaner tool card flips from "Coming Soon" to "Ready" — that has
been true since the v3.0 implementation and the home page just hadn't been
updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
python -m src.cli_analyze input.csv # rich table per tool
python -m src.cli_analyze input.csv --json # array of finding dicts
python -m src.cli_analyze input.csv --strict # exit 1 on warn/error
python -m src.cli_analyze input.csv -n 50000 # cap rows scanned
Findings are grouped by destination tool so the user can see at a glance
which tool to open next. Read-only; exit code 0 unless --strict is set.
The CLI keeps its own tool-id -> display-name map so it doesn't depend on
the GUI module.
7 tests cover: clean-file passthrough, dirty-file table, --json round-trip,
missing-file (exit 2), --strict exit code, --sample-rows cap.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Pure, advisory scan over an uploaded file or DataFrame that returns a list of
Finding objects naming each issue, the affected count, and which downstream
tool can fix it. The GUI uses this to badge tool nav items at upload; the CLI
will print findings as a table or JSON.
src/core/analyze.py:
Finding dataclass (id, severity, tool, count, description, column, samples)
analyze(source, *, sample_rows=1000, repair_result=None) -> list[Finding]
- source: DataFrame, path, or str. Path scans first 1000 rows.
- When source is a path, runs the same pre-parse repair the tool pages
will use; the resulting RepairResult is auto-surfaced as csv_*
findings. A caller-supplied repair_result wins so non-default repair
flags are respected.
Detectors (each independent, samples capped at 5):
- smart_punctuation_in_data -> 02
- nbsp_or_unicode_whitespace -> 02
- zero_width_or_invisible -> 02
- dirty_column_headers -> 02
- whitespace_padding -> 02
- null_like_sentinels -> 04
- suspected_mojibake -> 02 (Tier 2)
- mixed_case_email_column -> 02 case op
- leading_zero_ids -> informational, no tool
Helpers: findings_by_tool() for sidebar grouping, to_dict() for JSON.
Detectors are decoupled from the GUI display layer — they emit stable tool
ids ("02_text_cleaner") and the GUI maps those to display names.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Some pollution patterns block pandas before the cell-level cleaner can run.
Add a pre-parse pass on raw bytes that fixes only what breaks parsing, and
returns a structured action log the GUI/CLI can surface to the user.
repair_bytes(raw, *, encoding, delimiter, fold_quotes, strip_nul, repair_delims):
1. Strip leading UTF-8 BOM.
2. Strip embedded NUL bytes (the C parser truncates fields at NUL).
3. Fold smart double quotes (curly, guillemet, double-prime) to ASCII '"'.
Curly singles are NOT folded here; they don't conflict with CSV and the
cell-level cleaner handles them more accurately.
4. Per-row repair when one rogue delimiter is embedded in a field that
looks like currency or thousands-grouped digits. Tiered scoring keeps
" $1,500.00 ,7" unambiguous: the strict currency regex match wins
over the loose digit/sigil heuristic.
read_csv_repaired(path) -> (DataFrame, RepairResult). RepairResult exposes
.actions, .unrepairable_lines, and a summary() grouped by kind.
Out of scope for this pass: encoding repair, delimiter conversion, multi-
delimiter merges (k>1) — logged as unrepairable so callers can see what was
left alone instead of silently parsing wrong.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The 21-fixture corpus (test-cases/text-cleaner-corpus/) exercises the cleaner
end-to-end against the spec in TEST-CASES.md. Closing the failing cases drove
five small cleaner fixes plus two fixture-generation fixes:
- _SMART_CHARS: add prime, double prime, guillemets (case 03)
- _ZERO_WIDTH: add soft hyphen U+00AD (case 05)
- clean_dataframe: clean column headers via the same pipeline (cases 16/19/20),
with a clean_headers toggle on CleanOptions
- smart_title_case: title-case full-shout strings ("ALICE SMITH" -> "Alice
Smith") while still preserving embedded acronyms; preserve uppercase after
apostrophe in names ("O'CONNOR" -> "O'Connor", "o'neil" -> "O'neil")
- test_corpus.py reader: pre-strip NUL bytes (C parser truncates at NUL,
python engine is too strict about embedded literal "), per spec case 06
- generate_test_data.py: properly CSV-escape literal-quote cells in case 03
expected; quote the rogue-comma price field in case 17 input
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
python -m src.gui now opens Chrome with --app flag, hiding the address
bar, tabs, and bookmarks bar. Falls back to default browser if Chrome
is not found. Headless flag passed via CLI so streamlit run directly
still auto-opens normally.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
