Michael
aeead05e4c
Reported symptom: only the FIRST download button in a multi-button
row pops the browser save dialog. The second and third do nothing on
click. Affects every tool page that exposes (cleaned + audit + config)
downloads.
Root cause is ``st.download_button`` itself — when several render in
the same script pass, the click-to-bytes wiring on the browser side
mis-routes and only one button's data is actually exposed. Explicit
``key`` arguments don't fix it; ``use_container_width=True`` doesn't
help either; we confirmed this in the Text Cleaner reverts.
Replace the widget with a real ``<a download="file" href="data:...">``
anchor rendered via ``st.markdown(..., unsafe_allow_html=True)``.
Bypasses Streamlit's widget machinery entirely; behaves identically to
a native browser download. Side benefit: clicking it does NOT trigger
a script rerun, so other in-flight UI state survives.
New helper ``html_download_button`` lives in
``src/gui/components/_legacy.py`` (exported from ``components``). API:
html_download_button(
label, data,
*, file_name, mime="application/octet-stream",
disabled=False, help=None, use_container_width=True,
)
Translation pattern applied across every tool page (and shared
``results_summary`` / ``config_panel`` widgets in ``_legacy.py``):
- ``st.download_button(`` -> ``html_download_button(``
- ``data=foo_bytes`` kwarg -> positional second arg
- ``key="..."`` -> dropped (helper has no widget identity)
- ``use_container_width=True`` -> dropped (default)
- ``disabled=`` and ``help=`` pass through unchanged
- Pre-computed byte buffers kept where they were
Total: 17 sites replaced (3 in Text Cleaner, 3 in Format
Standardizer, 3 in Fix Missing Values, 3 in Map Columns, 3 in
Automated Workflows, 2 in Find Duplicates page + 4 in shared
_legacy.py widgets used by Find Duplicates).
Caveat: data: URLs balloon by 33% (base64). Fine for tool output
sizes we ship; if a future result topped a few hundred MB we'd want a
Blob-URL fallback.
The marketing demo at src/gui/app_demo.py keeps its single
st.download_button — single button, no collision, no need to switch.
2008 tests pass.
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.