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>
Add shared hide_streamlit_chrome() helper that removes header bar,
hamburger menu, footer, and deploy button via CSS injection. Called
on every page. Add .streamlit/config.toml with minimal toolbar mode.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Convert single-page deduplicator into a multi-page suite. Home page shows
tool card grid. Deduplicator extracted to its own page (fully working).
8 stub pages added for Text Cleaner, Format Standardizer, Missing Values,
Column Mapper, Outlier Detector, Multi-File Merger, Validator & Reporter,
and Pipeline Runner — each with functional file upload and coming-soon UI.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Update README, CLI reference, and developer guide to cover delimiter
selector, inline checkboxes/dropdowns, live surviving rows preview,
multi-row survivors, and apply_review_decisions(). Remove dead link.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Delimiter dropdown now includes "Other" option with a text input for
custom delimiter characters. Subtitle updated to mention delimited text.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Auto-detects delimiter on upload and shows a selectbox with comma, tab,
semicolon, and pipe options. Changing re-reads the file immediately.
Line terminators (Windows/Unix/Mac) already handled by universal newlines.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Shows a read-only preview of the output rows below the editor,
updating as checkboxes and column dropdowns are changed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Only the row chosen by the survivor rule (first, last, most-recent, etc.)
is checked by default. Other rows start unchecked.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace separate checkbox row and "Customize columns" toggle with a
unified st.data_editor grid — Keep checkboxes at the start of each row,
differing columns render as inline selectbox dropdowns.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace radio + Merge/Keep Both buttons with per-row checkboxes
and a single Confirm button. Users can now:
- Keep all rows (not duplicates) — check all, confirm
- Merge to one row — uncheck all but one, optionally customize columns
- Split a group — keep some rows, remove others (new capability)
Decision format changed from {action, survivor_idx, overrides} to
{keep_indices, overrides}. apply_review_decisions() updated to handle
all three modes. Batch actions updated accordingly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Each match group card now has:
- Radio button to pick which row to keep as the base survivor
- "Customize columns" toggle showing only columns that differ
- Per-column selectbox to pick values from any row in the group
- Decisions stored as {action, survivor_idx, overrides} dicts
Added apply_review_decisions() that builds the final DataFrame by
applying survivor selection + column overrides without re-running
the dedup engine. Batch actions also use the new dict format.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace st.rerun() with on_click callbacks so decisions write to
session state before the natural rerun. Decided groups auto-collapse
with status in the label; undecided groups stay expanded. Added undo
button on decided groups.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Rewrite README.md with project overview, quick-start, and CLI summary
- Add docs/CLI-REFERENCE.md with full flag reference and 8 recipe sections
- Add docs/DEVELOPER.md with architecture, data flow, and extension guides
- Rewrite src/core/__init__.py with public API exports and module docstring
- Add Streamlit GUI (src/gui/) with file upload, advanced options, interactive
match group review with side-by-side diff, and download buttons
- Add .gitignore, requirements.txt, all source code, tests, and sample data
- Add streamlit to requirements.txt
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
