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When the Flask server starts, it automatically reads two Excel files and populates the MySQL database with responsible persons, locations, and inventory items. This removes the need for manual data entry and makes the initial setup reproducible from source files.

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How it works

app/controllers/excel_loader.py exports a single public function: 
load_excel_data(area_excel, articulos_excel)
It is called once during create_app(), inside the active Flask application context, immediately after db.create_all(). The function receives the paths to both Excel files and processes them in order — areas first, then articles — so that location records exist before items are linked to them.

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areas.xls

This file defines the physical locations and their responsible persons. Expected format:
  • Column headers are on row 5 (header=4 in pandas, zero-indexed).
  • Required columns: Responsable(s), Ubicación, Descripción.
  • Any row with a NaN value in one of those columns is silently skipped.
  • For each valid row the loader searches for an existing Responsible record by name. If none is found, a new record is created. It then creates or updates the corresponding Location.
excel_loader.py
df_area = pd.read_excel(area_excel, header=4)
df_area.columns = df_area.columns.str.strip()
df_area = df_area.dropna(subset=['Responsable(s)', 'Ubicación', 'Descripción'])

for _, row in df_area.iterrows():
    responsable_name = str(row['Responsable(s)']).strip().replace("*", " ")
    ubicacion_num = int(row['Ubicación'])
    descripcion = str(row['Descripción']).strip()
    # find-or-create Responsible, then Location

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articulos.xls

This file lists the individual inventory items. Expected format:
  • Column headers are on row 7 (header=6 in pandas, zero-indexed).
  • Required columns: NO.CUENTA, UBICA, DESCRIPCION.
  • Any row with a NaN value in one of those columns is silently skipped.
  • Items whose UBICA value does not match a known Location record are silently skipped — the location data in areas.xls must be loaded first.
  • NO.CUENTA is stored as a string containing only the integer portion of the value (the decimal part, if any, is discarded by splitting on .).
excel_loader.py
df_articulos = pd.read_excel(articulos_excel, header=6)
df_articulos.columns = df_articulos.columns.str.strip()
df_articulos = df_articulos.dropna(subset=['NO.CUENTA', 'UBICA', 'DESCRIPCION'])

for _, row in df_articulos.iterrows():
    no_cuenta = str(row['NO.CUENTA']).split('.')[0]
    ubicacion = int(row['UBICA'])
    descripcion = str(row['DESCRIPCION']).strip()
    location = Location.query.filter_by(number_location=ubicacion).first()
    if not location:
        continue  # skip items with no matching location

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Idempotency

load_excel_data is safe to call on every server startup. Throughout both import loops, the loader uses find-or-create logic: it queries the database for an existing record before inserting a new one, and updates fields on existing records to match the current file contents. No duplicate rows are created across restarts.
To replace the inventory data, update areas.xls and articulos.xls in the app/data/ directory and restart the server. Existing records in the database will be updated to reflect the new file contents.

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