Michael
bece2b4030
User feedback: the template / visual-picker / mode-dispatch
implementation was too complex for the actual workflow.
Statements drift between months, the canvas state didn't survive
multi-page navigation, and accountants don't want to maintain
per-bank configuration just to convert PDFs to CSV.
Start-over design — one public function, one page, no
persistence:
``scan_pdf_for_transactions(pdf_bytes) → (rows, warnings)``
A row is "any text line with a date pattern AND at least one
amount pattern." Each detected row is a dict shaped::
{
"date": "2026-01-15",
"description": "Coffee Shop",
"amount_1": -4.50,
"amount_2": 1000.00, # if a second amount was found
"page": 1,
"raw": "01/15/2026 Coffee Shop (4.50) 1,000.00",
"source_file": "chase-jan-2026.pdf",
}
Multi-line descriptions still merge (no-date no-amount lines
attach to the previous transaction). Multi-PDF batches share a
single combined table with a ``source_file`` column.
**Page UX:**
- Upload PDF(s) → optional Options expander (parens-negative,
use-OCR) → click Scan → see all detected rows in an
``st.data_editor``.
- The editor has an ``Include`` checkbox column (default on),
plus user-editable date / description / amount cells and a
read-only ``raw`` column showing the original PDF text for
verification.
- A ``Columns to include in CSV`` multiselect hides
``page`` / ``raw`` from the download by default; user can
re-add either.
- Download CSV gets only the checked rows.
No template save/load. No visual picker. No mode dispatch. No
column boundaries. No schema migration. No per-bank
configuration files.
**Deletions:**
- ``src/pdf_templates.py`` — template storage layer
- ``src/gui/_drawable_canvas_compat.py`` — Streamlit compat shim
for the canvas (no canvas now)
- ``tests/test_pdf_templates.py``, ``test_pdf_row_heuristic.py``,
``test_drawable_canvas_compat.py`` — covered the removed APIs
- ``build/hooks/hook-streamlit_drawable_canvas.py`` — hook for
the removed dep
- ``streamlit-drawable-canvas==0.9.3`` from ``requirements.txt``
- The drawable-canvas references in ``build/datatools.spec``
**``src/pdf_extract.py``** shrinks from ~30 helper functions to
~10. Keeps: value parsers, row clusterer, date/amount token
finders, OCR pipeline, dependency guards. The one new public
function ``scan_pdf_for_transactions`` glues them together.
**Tests** (59 passing): the unit layer keeps full coverage of
the building blocks; the smoke layer pins the end-to-end PDF
roundtrip, OCR discovery, dependency-import behavior, and the
multi-line-description merge. The fpdf2-generated fixture PDF
still drives the real-PDF test.
Rollback: ``git revert HEAD`` brings back the template system if
needed — but the simpler model should make that unlikely.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
🌐 Language: English · Español
DataTools
Local CSV / Excel cleaning. CLI + browser GUI, no cloud, no install ceremony. GUI ships with English and Spanish language packs.
Tools
| # | Tool | Status |
|---|---|---|
| 01 | Find Duplicates — exact + fuzzy match, 5 normalizers, survivor rules, audit | Ready |
| 02 | Clean Text — whitespace, smart chars, BOM, line endings, case ops | Ready |
| 03 | Standardize Formats — dates, phones, emails, addresses, names, currencies, booleans | Ready |
| 04 | Fix Missing Values — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies | Ready |
| 05 | Map Columns — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder | Ready |
| 06 | Find Unusual Values | Coming Soon |
| 07 | Combine Files | Coming Soon |
| 08 | Quality Check | Coming Soon |
| 09 | Automated Workflows — chain tools with recommended (not forced) order, save/load JSON, automate weekly cleanups | Ready |
Download (non-technical users)
Pre-built installers — no Python required:
| Platform | Download | First-launch note |
|---|---|---|
| macOS | DataTools-X.Y.Z-mac.dmg |
Drag DataTools.app into /Applications, then double-click. |
| Windows | DataTools-X.Y.Z-win-setup.exe |
Run the installer; launches from Start Menu. |
| Linux | DataTools-X.Y.Z-linux-x86_64.AppImage |
chmod +x the file, then double-click. |
Latest release: see GitHub Releases (or the Gumroad listing). The installers are ~150–200 MB; the launcher boots a local server at http://127.0.0.1:8501 and opens your browser. Nothing is sent to the cloud.
Install from source (developers)
pip install -r requirements.txt
Python 3.10+ required.
Run
GUI (recommended):
streamlit run src/gui/app.py
CLI — seven entry points:
python -m src.cli customers.csv [--apply] # dedup
python -m src.cli_text_clean messy.csv [--apply] # text clean
python -m src.cli_format intl.csv [--apply] # format standardize (auto-streams >100 MB)
python -m src.cli_missing holes.csv [--apply] # missing values
python -m src.cli_column_map vendor.csv [--apply] # column mapper
python -m src.cli_pipeline any_file.csv [--apply] # chain tools end-to-end
python -m src.cli_analyze any_file.csv [--json] # scan only
Every CLI runs preview-only by default; add --apply to write output.
Language
The GUI sidebar has a language picker. Packs ship for English and Español (src/i18n/packs/); the choice persists for the session. Adding a language: drop a <code>.json next to en.json mirroring its key tree, then list it in LANGUAGES. See Developer Guide §i18n.
Review & Normalize gate
Every uploaded file passes through a CSV-normalization gate before any tool sees it. The analyzer flags ~15 issue types (whitespace, NBSP / zero-width chars, BOM, encoding, smart punct, dirty headers, null sentinels, mojibake, …) tagged by confidence (high / medium / low) and fix action. The GUI shows each finding with Auto-fix / Skip / Customize, a live before/after preview, and an encoding-override picker. Tool pages refuse to load until the gate passes.
Output
Every run writes:
{input}_<tool>.csv— the cleaned data{input}_changes.csv(text cleaner) or{input}_match_groups.csv(dedup) — audit traillogs/<tool>_YYYYMMDD_HHMMSS.log— debug-level run log
Original input file is never modified.
Docs
- User Guide — install, GUI workflow, gate
- CLI Reference — every flag with recipes
- Requirements — file sizes, encodings, detectors, perf targets
- Technical — architecture, gate internals, fix registry
- Developer Guide — adding fixes / detectors / standardizers
Dependencies
pandas, openpyxl, rapidfuzz, phonenumbers, typer, loguru, charset-normalizer, streamlit. Optional: ftfy for mojibake repair.
License
Proprietary.