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uprn

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Jun-te Kim 2026-01-29 12:16:46 +00:00
parent 36af9de912
commit 0819b08e5a
1 changed files with 3 additions and 3 deletions
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6
backend/address2UPRN/main.py
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@ -269,7 +269,7 @@ def get_uprn(user_inputed_address: str, postcode: str):
df = get_epc_data_with_postcode(postcode=postcode) df = get_epc_data_with_postcode(postcode=postcode)
if df.empty: if df.empty:
return False return None
scored_df = get_uprn_candidates( scored_df = get_uprn_candidates(
df, df,
@ -280,14 +280,14 @@ def get_uprn(user_inputed_address: str, postcode: str):
best_score = scored_df.iloc[0]["lexiscore"] best_score = scored_df.iloc[0]["lexiscore"]
if best_score <= 0: if best_score <= 0:
return False return None
# All rank-1 rows (possible draw) # All rank-1 rows (possible draw)
top_rank_df = scored_df[scored_df["lexirank"] == 1] top_rank_df = scored_df[scored_df["lexirank"] == 1]
# If rank-1 rows do not agree on a single UPRN → ambiguous # If rank-1 rows do not agree on a single UPRN → ambiguous
if not df_has_single_uprn(top_rank_df, uprn=top_rank_df.iloc[0]["uprn"]): if not df_has_single_uprn(top_rank_df, uprn=top_rank_df.iloc[0]["uprn"]):
return False return None
address = top_rank_df["address"].values[0] address = top_rank_df["address"].values[0]
lexiscore = float(top_rank_df["lexiscore"].values[0]) lexiscore = float(top_rank_df["lexiscore"].values[0])