Michael
2f349e8191
Phase 3/6. Wires the PDF Extractor into the GUI as a new
"transformations" tool with three modes selected by a horizontal
radio at the top of the page:
**Extract** — pick a saved template, upload one or more
statement PDFs (single + batch shipping together to keep the
common case one-step), get a previewed DataFrame + CSV download.
Per-file row counts and warnings are surfaced; failures on one
file don't kill the whole batch. The combined CSV gets a
``source_file`` first column so the accountant can sort/filter
by statement.
**Build template** — load an existing template or start fresh,
upload a sample PDF, edit every schema field across four tabs
(Pages & table / Columns / Parsing / Save). A live preview below
re-runs ``apply_template`` against the sample on each re-render
so the user sees their changes hit rows immediately. The column-
boundary editor is text-input ("comma-separated x-positions") for
now — replaced by the drawable-canvas visual picker in commit 5.
**Manage templates** — list with rename / delete / export
(downloads the canonical JSON) / import (uploads someone else's
JSON, validated through ``template_from_json``).
Heavy work (``extract_pages_auto``) only runs on explicit user
action (Extract / a new sample upload), and the parsed Page list
is cached in ``st.session_state`` so widget-edit reruns don't
re-parse the PDF.
Logging: tool runs and template saves both hit the audit log via
``log_event("tool_run", …)``, matching every other tool's
instrumentation pattern.
Registered in ``tools_registry.py`` under ``transformations``
with status ``Ready`` and the picture-as-pdf Material icon. i18n
keys added for en + es ("PDF to CSV" / "PDF a CSV").
OCR is wired in this commit — ``extract_pages_auto`` already
falls back through ``pytesseract`` when the binary is available,
and the warning strings it returns surface as ``st.info`` /
``st.warning`` per-file. Commit 6 will polish the OCR UX with a
status row.
Next commits build on this page:
4 — batch progress + cancellation + per-file error grouping
5 — drawable-canvas visual picker replaces text x-positions
6 — OCR availability banner + scanned-page indicators
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.