v0.1.19 - Throughput overview & responsive header
- Throughput: new "Throughput Overview" header with Gauge icon and brand-green badge icons on each card (Today, This week, 4-week average, Horse Mix, Grain Mix) - Throughput: inline rolling-range selector (7d / 4w / 6w / 12w, default 4 weeks) driving the customer-mix cards; stats window widened to 12 weeks so switching range is a pure client-side re-filter - Throughput: cards collapse to a single even 5-across row on laptop and up, with container-query value text that scales to each card's width - Throughput: date logic pinned to Australian Eastern time (fixes the day-early date); This week subtitle shows the Mon-Sun date range - Throughput: subtler tinted add-form; removed the inline-entry kicker and the "Open full form" link - Topbar: fix cramped laptop header - action toggles no longer wrap above the user button; search drops to its own row earlier Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.8
@@ -1,5 +1,7 @@
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from __future__ import annotations
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import csv
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import io
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import logging
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import os
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from datetime import date, datetime
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@@ -369,3 +371,310 @@ def resolve_workbook_path() -> Path | None:
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if candidate.exists():
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return candidate
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return None
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# ── Ad-hoc CSV / spreadsheet upload import ──────────────────────────────────
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# Lets an operator upload their own CSV or .xlsx of packing runs (from Settings
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# → Import) and have every row saved as a throughput entry. Unlike the bundled
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# workbook seed above, this is column-header driven so the file can be a simple
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# hand-built sheet rather than the exact "Operations Throughput.xlsx" layout.
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# Maps the column headers we accept (normalised: lower-cased, spaces/dashes →
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# single spaces) onto the canonical field used internally. Several aliases per
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# field so a human-built sheet "just works".
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_HEADER_ALIASES: dict[str, str] = {
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"date": "date",
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"production date": "date",
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"production_date": "date",
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"product": "product",
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"product name": "product",
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"product_name": "product",
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"product name snapshot": "product",
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"name": "product",
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"item id": "item_id",
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"item_id": "item_id",
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"itemid": "item_id",
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"sku": "item_id",
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"quantity": "quantity",
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"qty": "quantity",
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"packed": "quantity",
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"quantity packed": "quantity",
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"amount": "quantity",
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"quantity type": "quantity_type",
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"type": "quantity_type",
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"unit": "quantity_type",
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"packed as": "quantity_type",
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"bag size": "bag_size",
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"bag_size": "bag_size",
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"kg per bag": "bag_size",
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"kg/bag": "bag_size",
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"bagsize": "bag_size",
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"staff": "staff_name",
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"staff name": "staff_name",
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"packed by": "staff_name",
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"operator": "staff_name",
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"for order": "for_order",
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"order": "for_order",
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"for stock": "for_stock",
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"stock": "for_stock",
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"job number": "job_number",
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"job": "job_number",
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"job no": "job_number",
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"order number": "job_number",
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"stock quantity": "stock_quantity",
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"stock qty": "stock_quantity",
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"sample box no": "sample_box_no",
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"sample box": "sample_box_no",
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"scales checked": "scales_checked",
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"scales": "scales_checked",
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"label correct": "label_correct",
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"label": "label_correct",
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"bag sealed": "bag_sealed",
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"sealed": "bag_sealed",
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"pallet good condition": "pallet_good_condition",
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"pallet": "pallet_good_condition",
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"notes": "notes",
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"note": "notes",
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"comment": "notes",
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"comments": "notes",
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}
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# How many row-level errors we collect before truncating, to keep the response
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# (and the toast) sane on a badly-formed file.
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_MAX_REPORTED_ERRORS = 50
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def _normalise_header(raw: object) -> str | None:
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if raw is None:
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return None
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key = " ".join(str(raw).strip().lower().replace("-", " ").replace("_", " ").split())
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if not key:
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return None
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if key in _HEADER_ALIASES:
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return _HEADER_ALIASES[key]
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# Test weights: "test weight 1".."test weight 5" (and "tw1" style).
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for n in range(1, 6):
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if key in {f"test weight {n}", f"tw{n}", f"test {n}"}:
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return f"test_weight_{n}"
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return None
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def _coerce_quantity_type(value: object) -> str | None:
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if value is None:
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return None
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text = str(value).strip().lower()
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if not text:
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return None
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if text in {"bag", "bags", "b"}:
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return "bags"
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if text in {"kg", "kgs", "kilogram", "kilograms", "bulka", "bulk"}:
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return "kg"
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return None
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def _read_tabular_file(filename: str, content: bytes) -> tuple[list[str | None], list[tuple]]:
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"""Return (headers, data_rows). Detects CSV vs .xlsx by extension/content."""
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lowered = (filename or "").lower()
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is_excel = lowered.endswith((".xlsx", ".xlsm", ".xls"))
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if is_excel:
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workbook = load_workbook(io.BytesIO(content), data_only=True, read_only=True)
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ws = workbook.active
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rows = [tuple(r) for r in ws.iter_rows(values_only=True)]
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workbook.close()
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else:
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text = None
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for encoding in ("utf-8-sig", "utf-8", "latin-1"):
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try:
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text = content.decode(encoding)
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break
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except UnicodeDecodeError:
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continue
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if text is None:
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raise ValueError("Could not decode the file as text. Save it as UTF-8 CSV or .xlsx.")
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# Sniff the delimiter (comma/semicolon/tab) but fall back to comma.
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sample = text[:4096]
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try:
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dialect = csv.Sniffer().sniff(sample, delimiters=",;\t")
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except csv.Error:
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dialect = csv.excel
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rows = [tuple(r) for r in csv.reader(io.StringIO(text), dialect)]
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# Find the first row that has at least one recognised header; treat it as
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# the header row and everything after as data.
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for index, row in enumerate(rows):
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if any(_normalise_header(cell) is not None for cell in row):
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return list(row), rows[index + 1 :]
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return [], []
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def import_entries_from_file(
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db: Session,
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*,
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filename: str,
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content: bytes,
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tenant_id: str,
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created_by: str | None,
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) -> dict:
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"""Parse an uploaded CSV/spreadsheet and persist each row as a throughput
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entry. Products are matched by item_id then name, and auto-created when not
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found so every entry stays linked. Returns a summary with row-level errors.
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"""
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headers, data_rows = _read_tabular_file(filename, content)
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if not headers:
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raise ValueError(
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"No recognised columns found. The file needs a header row with at "
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"least Date, Product and Quantity columns."
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)
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# Map canonical field name → column index. First occurrence wins.
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field_index: dict[str, int] = {}
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for col, raw in enumerate(headers):
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field = _normalise_header(raw)
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if field and field not in field_index:
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field_index[field] = col
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for required in ("date", "product", "quantity"):
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if required not in field_index:
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raise ValueError(
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f"Missing required '{required}' column. Required columns are "
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"Date, Product and Quantity."
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)
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def cell(row: tuple, field: str) -> object:
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idx = field_index.get(field)
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if idx is None or idx >= len(row):
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return None
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return row[idx]
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# Index existing products for matching (by item_id and by lower-cased name).
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by_item: dict[str, ThroughputProduct] = {}
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by_name: dict[str, ThroughputProduct] = {}
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for product in db.scalars(
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select(ThroughputProduct).where(ThroughputProduct.tenant_id == tenant_id)
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).all():
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if product.item_id:
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by_item[str(product.item_id)] = product
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by_name[product.name.lower()] = product
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imported = 0
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skipped = 0
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products_created = 0
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errors: list[str] = []
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def note_error(message: str) -> None:
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if len(errors) < _MAX_REPORTED_ERRORS:
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errors.append(message)
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for offset, row in enumerate(data_rows):
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# Sheet/file row number for human-friendly error messages (header = 1).
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line_no = offset + 2
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if not row or all(value is None or str(value).strip() == "" for value in row):
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continue
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production_date = _coerce_date(cell(row, "date"))
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product_name = _coerce_text(cell(row, "product"))
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quantity = _coerce_float(cell(row, "quantity"))
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if production_date is None:
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skipped += 1
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note_error(f"Row {line_no}: missing or invalid date.")
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continue
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if not product_name:
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skipped += 1
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note_error(f"Row {line_no}: missing product name.")
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continue
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if quantity is None or quantity < 0:
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skipped += 1
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note_error(f"Row {line_no}: missing or invalid quantity.")
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continue
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bag_size = _coerce_float(cell(row, "bag_size"))
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quantity_type = _coerce_quantity_type(cell(row, "quantity_type"))
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if quantity_type is None:
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# Infer: bulka-style rows have a blank or very large bag size.
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if bag_size is None or bag_size >= _BULKA_BAG_SIZE_THRESHOLD or "bulka" in product_name.lower():
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quantity_type = "kg"
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else:
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quantity_type = "bags"
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if quantity_type == "bags" and (bag_size is None or bag_size <= 0):
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skipped += 1
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note_error(f"Row {line_no}: bag size is required when packed as bags.")
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continue
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item_id_raw = cell(row, "item_id")
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item_id = None
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if item_id_raw is not None:
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if isinstance(item_id_raw, float) and item_id_raw.is_integer():
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item_id = str(int(item_id_raw))
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else:
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item_id = _coerce_text(item_id_raw)
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product = (by_item.get(item_id) if item_id else None) or by_name.get(product_name.lower())
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if product is None:
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product = ThroughputProduct(
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tenant_id=tenant_id,
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item_id=item_id,
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name=product_name,
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default_bag_size=bag_size,
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is_bulka_default=_infer_bulka_default(product_name, bag_size),
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active=True,
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notes="Auto-created during throughput import",
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)
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db.add(product)
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db.flush()
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products_created += 1
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if item_id:
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by_item[item_id] = product
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by_name[product_name.lower()] = product
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for_order = _coerce_bool(cell(row, "for_order")) if field_index.get("for_order") is not None else False
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for_stock = _coerce_bool(cell(row, "for_stock")) if field_index.get("for_stock") is not None else False
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stock_quantity = _coerce_float(cell(row, "stock_quantity")) if for_stock else None
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calculated = calculate_kg(quantity, quantity_type, bag_size)
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entry = ProductionThroughput(
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tenant_id=tenant_id,
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production_date=production_date,
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product_id=product.id,
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product_name_snapshot=product_name,
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bag_size=bag_size,
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scales_checked=_coerce_bool(cell(row, "scales_checked")),
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label_correct=_coerce_bool(cell(row, "label_correct")),
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bag_sealed=_coerce_bool(cell(row, "bag_sealed")),
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pallet_good_condition=_coerce_bool(cell(row, "pallet_good_condition")),
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for_order=for_order,
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for_stock=for_stock,
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job_number=_coerce_text(cell(row, "job_number")) if for_order else None,
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stock_quantity=stock_quantity,
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sample_box_no=_coerce_text(cell(row, "sample_box_no")),
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test_weight_1=_coerce_float(cell(row, "test_weight_1")),
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test_weight_2=_coerce_float(cell(row, "test_weight_2")),
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test_weight_3=_coerce_float(cell(row, "test_weight_3")),
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test_weight_4=_coerce_float(cell(row, "test_weight_4")),
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test_weight_5=_coerce_float(cell(row, "test_weight_5")),
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quantity=quantity,
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quantity_type=quantity_type,
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calculated_kg=calculated,
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staff_name=normalise_staff_name(cell(row, "staff_name")),
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notes=_coerce_text(cell(row, "notes")),
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created_by=created_by or "csv-import",
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)
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db.add(entry)
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imported += 1
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if imported == 0 and products_created == 0:
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# Nothing landed — don't leave a half-open transaction.
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db.rollback()
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else:
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db.commit()
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return {
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"entries_imported": imported,
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"entries_skipped": skipped,
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"products_created": products_created,
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"errors": errors,
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}
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