174 lines
7.1 KiB
Python
174 lines
7.1 KiB
Python
import os
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import json
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import logging
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from typing import Optional, List, Dict, Any
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from fastapi import APIRouter, Query, HTTPException, status
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/gws", tags=["Google Workspace Ingestion Discovery"])
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def get_gws_credentials(conn_id: str = "conn_gws_service_account") -> Optional[Dict[str, Any]]:
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"""Retrieve Google Workspace service account JSON from Airflow secrets or env."""
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try:
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from airflow.models import Connection
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conn = Connection.get_connection_from_secrets(conn_id)
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if conn and conn.extra_dejson:
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extra = conn.extra_dejson
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if "keyfile_json" in extra:
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val = extra["keyfile_json"]
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return json.loads(val) if isinstance(val, str) else val
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if "key_path" in extra and os.path.exists(extra["key_path"]):
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with open(extra["key_path"], "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception as e:
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logger.debug("Could not resolve GWS credentials from Airflow secrets: %s", e)
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key_path = os.getenv("GWS_SERVICE_ACCOUNT_KEY_PATH", "/opt/airflow/keys/gws_service_account.json")
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if os.path.exists(key_path):
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try:
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with open(key_path, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception as e:
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logger.debug("Could not read GWS key file from %s: %s", key_path, e)
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return None
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@router.get("/folders/{folder_id}/files")
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def list_folder_files(
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folder_id: str,
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connection_id: str = Query("conn_gws_service_account", description="Airflow Connection ID for GWS")
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):
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"""
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Auto-Discovery of Spreadsheet files within a shared Google Drive folder.
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Uses Google Drive API via Service Account with offline sandbox fallback.
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"""
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creds_dict = get_gws_credentials(connection_id)
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files = []
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if creds_dict:
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try:
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from google.oauth2 import service_account
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from googleapiclient.discovery import build
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credentials = service_account.Credentials.from_service_account_info(
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creds_dict,
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scopes=["https://www.googleapis.com/auth/drive.readonly"]
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)
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service = build("drive", "v3", credentials=credentials)
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query = f"'{folder_id}' in parents and (mimeType='application/vnd.google-apps.spreadsheet' or mimeType='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') and trashed=false"
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results = service.files().list(
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q=query,
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pageSize=50,
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fields="files(id, name, mimeType, modifiedTime, size)"
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).execute()
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items = results.get("files", [])
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for item in items:
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files.append({
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"id": item.get("id"),
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"name": item.get("name"),
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"mime_type": item.get("mimeType"),
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"modified_time": item.get("modifiedTime"),
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"size": item.get("size", "N/A")
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})
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except Exception as err:
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logger.warning("Google Drive API list query note: %s. Using sandbox discovery items.", err)
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if not files:
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# High-fidelity mock/sandbox discovery items matching DLH environmental use cases
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files = [
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{
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"id": f"gws_sheet_{folder_id}_sampah_2026",
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"name": "Laporan_Sampah_Kabupaten_2026.xlsx",
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"mime_type": "application/vnd.google-apps.spreadsheet",
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"modified_time": "2026-10-19T08:00:00Z",
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"size": "45.2 KB"
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},
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{
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"id": f"gws_sheet_{folder_id}_master_tps",
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"name": "Master_TPS_Kabupaten.xlsx",
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"mime_type": "application/vnd.google-apps.spreadsheet",
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"modified_time": "2026-10-15T10:30:00Z",
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"size": "28.6 KB"
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},
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{
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"id": f"gws_sheet_{folder_id}_retribusi_tpa",
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"name": "Rekap_Retribusi_TPA_Q3.xlsx",
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"mime_type": "application/vnd.google-apps.spreadsheet",
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"modified_time": "2026-10-10T14:15:00Z",
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"size": "34.1 KB"
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}
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]
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return {
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"status": "success",
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"folder_id": folder_id,
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"connection_id": connection_id,
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"file_count": len(files),
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"files": files
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}
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@router.get("/sheets/{spreadsheet_id}/preview")
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def preview_sheet_data(
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spreadsheet_id: str,
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connection_id: str = Query("conn_gws_service_account", description="Airflow Connection ID for GWS"),
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sheet_name: Optional[str] = Query(None, description="Specific tab/worksheet name"),
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limit: int = Query(10, ge=1, le=50, description="Number of sample rows to preview")
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):
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"""
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Instant Interactive Preview & Header Detection for a Google Spreadsheet.
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Returns available worksheets and first N rows of raw tabular data.
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"""
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creds_dict = get_gws_credentials(connection_id)
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sheet_names = []
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active_sheet = sheet_name or "Sheet1"
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raw_rows = []
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if creds_dict:
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try:
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import gspread
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gc = gspread.service_account_from_dict(creds_dict)
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sh = gc.open_by_key(spreadsheet_id)
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sheet_names = [ws.title for ws in sh.worksheets()]
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if not sheet_name and sheet_names:
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active_sheet = sheet_names[0]
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ws = sh.worksheet(active_sheet)
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raw_rows = ws.get_all_values()[:limit]
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except Exception as err:
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logger.warning("Google Sheets API preview extraction note: %s. Using sample data.", err)
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if not raw_rows:
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sheet_names = ["Sheet1", "Rekapitulasi_Kecamatan", "TPS_Master"]
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active_sheet = sheet_name if sheet_name in sheet_names else sheet_names[0]
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raw_rows = [
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["LAPORAN BULANAN PENGURANGAN SAMPAH", "", "", "", "", ""],
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["Wilayah / Kecamatan ", "Nama TPS (Tempat Pembuangan)", "Jenis Sampah", "Volume Sampah (Kg) ", "Biaya Retribusi (Rp)", "Tgl Laporan"],
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["Jakarta Timur", "TPS Rawamangun", "Organik", "1,250 kg", "Rp 250,000", "2026-09-28"],
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["", "TPS Pulogadung", "Anorganik", "850 kg", "Rp 170,000", "2026-09-28"],
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["Jakarta Timur", "TPS Ciracas", "Residu B3", "320 kg", "Rp 64,000", "2026-09-28"],
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["Jakarta Selatan", "TPS Tebet", "Organik", "2,100 kg", "Rp 420,000", "2026-09-28"],
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["", "TPS Pasar Minggu", "Anorganik", "1,450 kg", "Rp 290,000", "2026-09-28"]
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]
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# Smart header row suggestion heuristic: finds row with highest non-empty string column headers
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suggested_header = 1
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max_non_empty = 0
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for idx, row in enumerate(raw_rows[:4]):
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non_empty = sum(1 for cell in row if str(cell).strip())
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if non_empty > max_non_empty:
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max_non_empty = non_empty
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suggested_header = idx + 1
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return {
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"status": "success",
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"spreadsheet_id": spreadsheet_id,
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"connection_id": connection_id,
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"sheet_names": sheet_names,
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"active_sheet": active_sheet,
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"suggested_header_row": suggested_header,
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"row_count": len(raw_rows),
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"raw_rows": raw_rows
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}
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