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data_loader.py — loads current grant snapshots (JSON) and optional past winners
This module keeps IO concerns simple and robust:
- Recursively loads current-grant JSON files under a snapshots directory
- Optionally loads past winners from either an Excel file or a JSON directory
- Returns Python lists of dictionaries; no model code here
Public API
---------
load_current_grants(snapshots_dir: Path | str, limit: int | None = None) -> list[dict]
load_past_winners(history_xlsx: Path | str | None = None,
history_json_dir: Path | str | None = None) -> list[dict]
"""
from __future__ import annotations
from pathlib import Path
from typing import Any, Dict, List, Optional
import json
import logging
import pandas as pd
logger = logging.getLogger(__name__)
# ----------------------------- Current grants ---------------------------------
def load_current_grants(snapshots_dir: Path | str, limit: Optional[int] = None) -> List[Dict[str, Any]]:
"""Load current grant JSON snapshots from a directory tree.
Each file is expected to be one JSON object. The function tolerates
missing keys and will attach an `id` from the filename if not present.
"""
snapshots_dir = Path(snapshots_dir)
if not snapshots_dir.exists():
logger.warning("snapshots directory not found: %s", snapshots_dir)
return []
records: List[Dict[str, Any]] = []
for p in sorted(snapshots_dir.rglob("*.json")):
try:
with open(p, "r", encoding="utf-8") as f:
rec = json.load(f)
if not isinstance(rec, dict):
logger.debug("Skipping non-object JSON: %s", p)
continue
rec.setdefault("id", p.stem)
rec.setdefault("_path", str(p))
records.append(rec)
if limit and len(records) >= limit:
break
except Exception as e: # pragma: no cover
logger.warning("Failed to load %s: %s", p, e)
continue
logger.info("Loaded %d current grants from %s", len(records), snapshots_dir)
return records
# ------------------------------ Past winners ----------------------------------
_CANON_COLS = {
# canonical : candidate column names (lower/underscore)
"project_title": ["project_title", "title", "name"],
"abstract": ["abstract", "description", "summary", "public_description"],
"competition": ["competition", "programme", "program"],
"award_amount": ["award_amount", "amount", "grant", "project_cost", "award"],
"lead_org": ["lead_org", "lead_organisation", "lead_organization", "organisation_name", "organization_name"],
"year": ["year", "fy", "start_year"],
"project_url": ["project_url", "url", "link"],
}
def _norm_cols(df: pd.DataFrame) -> pd.DataFrame:
df = df.copy()
df.columns = [
(c if isinstance(c, str) else str(c))
.lower()
.replace(" ", "_")
.replace("-", "_")
for c in df.columns
]
return df
def _ensure_canonical(df: pd.DataFrame) -> pd.DataFrame:
for canon, candidates in _CANON_COLS.items():
if canon in df.columns:
continue
for c in candidates:
if c in df.columns:
df[canon] = df[c]
break
if canon not in df.columns:
df[canon] = None
return df
def _load_past_winners_from_excel(xlsx_path: Path) -> List[Dict[str, Any]]:
df = pd.read_excel(xlsx_path)
df = _norm_cols(df)
df = _ensure_canonical(df)
return [row._asdict() if hasattr(row, "_asdict") else row.to_dict() for _, row in df.iterrows()]
def _load_past_winners_from_json_dir(json_dir: Path) -> List[Dict[str, Any]]:
records: List[Dict[str, Any]] = []
for p in sorted(json_dir.rglob("*.json")):
try:
with open(p, "r", encoding="utf-8") as f:
rec = json.load(f)
if not isinstance(rec, dict):
continue
rec.setdefault("_path", str(p))
records.append(rec)
except Exception:
continue
return records
def _load_past_winners_from_jsonl(jsonl_path: Path) -> List[Dict[str, Any]]:
"""Load past winners from JSONL file (optionally gzipped)."""
import gzip
records: List[Dict[str, Any]] = []
# Check if file is gzipped
open_func = gzip.open if str(jsonl_path).endswith('.gz') else open
try:
with open_func(jsonl_path, 'rt', encoding='utf-8') as f:
for line in f:
line = line.strip()
if not line:
continue
try:
rec = json.loads(line)
if isinstance(rec, dict):
records.append(rec)
except json.JSONDecodeError:
continue
return records
except Exception as e:
logger.warning(f"Failed to load JSONL from {jsonl_path}: {e}")
return []
def load_past_winners(
history_xlsx: Path | str | None = None,
history_json_dir: Path | str | None = None,
history_jsonl: Path | str | None = None
) -> List[Dict[str, Any]]:
"""Load past winners from Excel, JSONL, or JSON directory.
Priority order: JSONL > JSON dir > Excel.
If neither exists, returns an empty list.
"""
# Try JSONL first (most efficient for large datasets)
if history_jsonl is not None:
jsonl = Path(history_jsonl)
if jsonl.exists():
recs = _load_past_winners_from_jsonl(jsonl)
if recs:
logger.info("Loaded %d past winners from JSONL: %s", len(recs), jsonl)
return recs
# Also check for past_winners.jsonl.gz in default location (for HF deployment)
default_jsonl = Path("data/past_winners.jsonl.gz")
if default_jsonl.exists() and history_jsonl is None:
recs = _load_past_winners_from_jsonl(default_jsonl)
if recs:
logger.info("Loaded %d past winners from default JSONL: %s", len(recs), default_jsonl)
return recs
# JSON dir next
if history_json_dir is not None:
jdir = Path(history_json_dir)
if jdir.exists():
recs = _load_past_winners_from_json_dir(jdir)
if recs:
logger.info("Loaded %d past winners from JSON dir: %s", len(recs), jdir)
return recs
else:
logger.info("No JSON past winners found under %s", jdir)
# Excel last
if history_xlsx is not None:
xlsx = Path(history_xlsx)
if xlsx.exists():
recs = _load_past_winners_from_excel(xlsx)
logger.info("Loaded %d past winners from Excel: %s", len(recs), xlsx)
return recs
else:
logger.info("History Excel not found: %s", xlsx)
return []
# Self-test
if __name__ == "__main__":
import argparse
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument("--snapshots-dir", type=Path, default=Path("data/snapshots"))
parser.add_argument("--history-xlsx", type=Path, default=Path("data/past_winners.xlsx"))
parser.add_argument("--limit", type=int, default=3)
args = parser.parse_args()
current = load_current_grants(args.snapshots_dir, limit=args.limit)
history = load_past_winners(args.history_xlsx)
print(f"current: {len(current)} | history: {len(history)}")
if current:
print("example current keys:", sorted(current[0].keys())[:12])
if history:
print("example history keys:", sorted(history[0].keys())[:12])
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