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import gradio as gr
import zipfile
from tqdm import tqdm
from sklearn.metrics.pairwise import cosine_similarity
from pathlib import Path
import spacy
from normalize_japanese_addresses import normalize
from enum import Enum
import time
import requests
import pandas as pd
import os
from fastapi import FastAPI, Request
from pymilvus import MilvusClient
from dotenv import load_dotenv
import time
from contextlib import contextmanager
import numpy as np
import re
import os
from openai import AzureOpenAI
# .envファイルを読み込む
load_dotenv()
# =========================
# Global variables
# =========================
TARGET_DIR = Path(os.environ.get('TARGET_DIR'))
HUGGING_FACE_TOKEN = os.environ.get('HUGGING_FACE_TOKEN')
EMBEDDING_MODEL_ENDPOINT = os.environ.get('EMBEDDING_MODEL_ENDPOINT')
ABRG_ENDPOINT = os.environ.get('ABRG_ENDPOINT')
VECTOR_SEARCH_ENDPOINT = os.environ.get('VECTOR_SEARCH_ENDPOINT')
VECTOR_SEARCH_TOKEN = os.environ.get('VECTOR_SEARCH_TOKEN')
VECTOR_SEARCH_COLLECTION_NAME = os.environ.get('VECTOR_SEARCH_COLLECTION_NAME')
VECTOR_SEARCH_COLLECTION_NAME_V2 = os.environ.get('VECTOR_SEARCH_COLLECTION_NAME_V2')
GOOGLE_SEARCH_API_KEY = os.environ.get('GOOGLE_SEARCH_API_KEY')
GOOGLE_SEARCH_ENGINE_ID = os.environ.get('GOOGLE_SEARCH_ENGINE_ID')
MILVUS_CLIENT = MilvusClient(uri=VECTOR_SEARCH_ENDPOINT, token=VECTOR_SEARCH_TOKEN)
print(f"Connected to DB: {VECTOR_SEARCH_ENDPOINT} successfully")
# 47都道府県のリスト
prefs = [
'北海道', '青森県', '岩手県', '宮城県', '秋田県', '山形県', '福島県',
'茨城県', '栃木県', '群馬県', '埼玉県', '千葉県', '東京都', '神奈川県',
'新潟県', '富山県', '石川県', '福井県', '山梨県', '長野県', '岐阜県',
'静岡県', '愛知県', '三重県', '滋賀県', '京都府', '大阪府', '兵庫県',
'奈良県', '和歌山県', '鳥取県', '島根県', '岡山県', '広島県', '山口県',
'徳島県', '香川県', '愛媛県', '高知県', '福岡県', '佐賀県', '長崎県',
'熊本県', '大分県', '宮崎県', '鹿児島県', '沖縄県'
]
# ----------------------------
# Azure OpenAI API
# ----------------------------
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2025-03-01-preview",
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
)
# ----------------------------
# Download mt_city_all.csv
# ----------------------------
temp_dir = Path('temp')
temp_dir.mkdir(exist_ok=True)
city_all_url = 'https://catalog.registries.digital.go.jp/rsc/address/mt_city_all.csv.zip'
zip_file_path = temp_dir / 'mt_city_all.csv.zip'
# すでにファイルが存在する場合はダウンロードをスキップ
if not os.path.exists(zip_file_path):
# ZIPファイルをダウンロード
response = requests.get(city_all_url)
with open(zip_file_path, 'wb') as f:
f.write(response.content)
# target_dir直下に解凍
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(TARGET_DIR)
# ------------------------------------
# Download mt_parcel_cityXXXXXX.csv
# ------------------------------------
city_all_file = TARGET_DIR / 'mt_city_all.csv'
city_all_df = pd.read_csv(city_all_file)
lg_codes = city_all_df['lg_code'].tolist()
print('lg_codes', len(lg_codes))
for lg_code in tqdm(lg_codes):
parcel_url = f'https://catalog.registries.digital.go.jp/rsc/address/mt_parcel_city{lg_code:06d}.csv.zip'
zip_file_path = temp_dir / f'mt_parcel_city{lg_code:06d}.csv.zip'
if not os.path.exists(TARGET_DIR / 'parcel' / f'mt_parcel_city{lg_code:06d}.csv'):
response = requests.get(parcel_url)
if response.status_code == 200: # URLが存在する場合のみ処理を続ける
with open(zip_file_path, 'wb') as f:
f.write(response.content)
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(TARGET_DIR / 'parcel')
time.sleep(0.2) # ダウンロードごとに200msのスリープを入れる
# ------------------------------------
# Download mt_rsdtdsp_rsdt_prefXX.csv
# ------------------------------------
pref_codes = list(set([('%06d' % lg_code)[0:2] for lg_code in lg_codes]))
for pref_code in tqdm(pref_codes):
rsdt_url = f'https://catalog.registries.digital.go.jp/rsc/address/mt_rsdtdsp_rsdt_pref{pref_code}.csv.zip'
zip_file_path = temp_dir / f'mt_rsdtdsp_rsdt_pref{pref_code}.csv.zip'
if not os.path.exists(TARGET_DIR / 'rsdt' / f'mt_rsdtdsp_rsdt_pref{pref_code}.csv'):
response = requests.get(rsdt_url)
if response.status_code == 200: # URLが存在する場合のみ処理を続ける
with open(zip_file_path, 'wb') as f:
f.write(response.content)
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(TARGET_DIR / 'rsdt')
time.sleep(0.2) # ダウンロードごとに200msのスリープを入れる
# 一時ディレクトリを削除
for file in temp_dir.iterdir():
file.unlink()
temp_dir.rmdir()
# =========================
# Utilitiy functions
# =========================
@contextmanager
def measure(label="処理"):
start = time.time()
yield
end = time.time()
print(f"{label} 実行時間: {end - start:.6f} 秒")
def get_spelling(query_address):
# APIリクエストを作成
url = f'https://www.googleapis.com/customsearch/v1?key={GOOGLE_SEARCH_API_KEY}&cx={GOOGLE_SEARCH_ENGINE_ID}&q={query_address}'
# リクエストを送信
response = requests.get(url)
results = response.json()
return results.get('spelling', {}).get('correctedQuery', '')
def convert_zenkaku_to_hankaku(text):
zenkaku_numbers = '0123456789'
hankaku_numbers = '0123456789'
zenkaku_hyphens = '-'
hankaku_hyphens = '-'
translation_table = str.maketrans(zenkaku_numbers + zenkaku_hyphens, hankaku_numbers + hankaku_hyphens)
return text.translate(translation_table)
ADDRESS_REGEX = re.compile(
r'^'
r'(?P<address>'
r'.+?[都道府県]' # 都道府県
r'.+?[市区町村]' # 市区町村
r'.*?' # 町名など(最小マッチ)
r'[0-90-9]+' # 番地の先頭数字
r'(?:[-ー−–][0-90-9]+)*' # 「-数字」の繰返し
r'(?:(?:丁目|番地|番|号)' # 「丁目」「番地」「番」「号」
r'(?:[0-90-9]+' # のあとに続く数字
r'(?:[-ー−–][0-90-9]+)*' # 「-数字」の繰返し
r')?'
r')*' # 上記ユニットを何度でも繰返し
r')'
r'(?P<building>.*)' # 残りを建物名としてキャプチャ
r'$'
)
def split_address_building(address: str) -> dict:
m = ADDRESS_REGEX.match(address)
if not m:
return {'address': address, 'building': ''}
return {
'address': m.group('address').strip(),
'building': m.group('building').strip()
}
def split_address_building_with_gpt(query_address: str) -> dict:
class SplittedAddress(BaseModel):
address: str
building: str
response = client.responses.parse(
model="gpt-4o-mini",
input=[
{"role": "system", "content": "Extract the event information."},
{
"role": "user",
"content": f"与えられた住所をaddressとbuildingに分けろ:{query_address}",
},
],
text_format=SplittedAddress,
)
response = response.output_parsed
return {
'address': response.address,
'building': response.building,
}
def split_address(normalized_address):
splits = normalize(normalized_address)
return splits
def compare(normalized_address1, normalized_address2):
split1 = split_address(normalized_address1)
split2 = split_address(normalized_address2)
result = {
'pref': False,
'city': False,
'town': False,
'addr': False,
}
for key in result.keys():
if split1[key] == split2[key]:
result[key] = True
return all(result.values())
def vector_search(query_address, top_k):
wait_time = 30
max_retries = 5
for attempt in range(max_retries):
try:
with measure('vector_search - embed_via_multilingual_e5_large'):
query_embeds = embed_via_multilingual_e5_large([query_address])
break # 成功した場合はループを抜ける
except InferenceEndpointError as e:
if e.code == InferenceEndpointErrorCode.SERVICE_UNAVAILABLE:
if attempt < max_retries - 1:
gr.Warning(f"{InferenceEndpointErrorCode.SERVICE_UNAVAILABLE}: 埋め込みモデルの推論エンドポイントが起動中です。{wait_time}秒後にリトライします。", duration=wait_time)
time.sleep(wait_time) # 30秒待機
else:
raise gr.Error(f"{InferenceEndpointErrorCode.SERVICE_UNAVAILABLE}: 最大リトライ回数に達しました。しばらくしてから再度実行してみてください。")
elif e.code == InferenceEndpointErrorCode.INVALID_STATE:
raise gr.Error(f"{InferenceEndpointErrorCode.INVALID_STATE}: 埋め込みモデルの推論エンドポイントが停止中です。再起動するよう管理者に問い合わせてください。")
elif e.code == InferenceEndpointErrorCode.UNKNOWN_ERROR:
raise gr.Error(f"{InferenceEndpointErrorCode.UNKNOWN_ERROR}: {e.message}")
with measure('vector_search - search_via_milvus'):
hits = search_via_milvus(query_embeds[0], top_k, VECTOR_SEARCH_COLLECTION_NAME)
return hits
def replace_circle(input_text):
output_text = input_text.replace('◯', '0')
return output_text
def remove_filler(input_text: str) -> str:
"""
GiNZAを用いて日本語テキストからフィラーを除去する関数。
Parameters:
text (str): 入力テキスト。
Returns:
str: フィラーを除去したテキスト。
"""
# GiNZAモデルの読み込み
nlp = spacy.load("ja_ginza")
# テキストの解析
doc = nlp(input_text)
# フィラーを除去したテキストの生成
cleaned_text = ''.join([token.text for token in doc if token.tag_ != "感動詞-フィラー"])
return cleaned_text
def remove_left_of_pref(text):
for pref in prefs:
pref_index = text.find(pref)
if pref_index != -1:
return text[pref_index:] # 都道府県名の位置から右側の文字列を返す
return text # 都道府県名が見つからない場合は元のテキストを返す
def preprocess(input_text):
output_text = remove_left_of_pref(input_text)
output_text = replace_circle(output_text)
output_text = remove_filler(output_text)
return output_text
class InferenceEndpointErrorCode(Enum):
INVALID_STATE = 400
SERVICE_UNAVAILABLE = 503
UNKNOWN_ERROR = 520
class InferenceEndpointError(Exception):
def __init__(self, code: InferenceEndpointErrorCode, message="エラー"):
self.code = code
self.message = message
super().__init__(self.message)
def embed_via_multilingual_e5_large(query_addresses):
headers = {
"Accept": "application/json",
"Authorization": f"Bearer {HUGGING_FACE_TOKEN}",
"Content-Type": "application/json"
}
all_responses = []
for i in range(0, len(query_addresses), 2048):
chunk = query_addresses[i:i + 2048]
response = requests.post(EMBEDDING_MODEL_ENDPOINT, headers=headers, json={"inputs": chunk})
response_json = response.json()
if 'error' in response_json:
if response_json['error'] == 'Bad Request: Invalid state':
raise InferenceEndpointError(InferenceEndpointErrorCode.INVALID_STATE, "Bad Request: Invalid state")
elif response_json['error'] == '503 Service Unavailable':
raise InferenceEndpointError(InferenceEndpointErrorCode.SERVICE_UNAVAILABLE, "Service Unavailable")
else:
raise InferenceEndpointError(InferenceEndpointErrorCode.UNKNOWN_ERROR, response_json['error'])
all_responses.extend(response_json)
return all_responses
def search_via_milvus(query_vector, top_k, collection_name, thresh=0.0):
search_params = {"metric_type": "COSINE", "params": {"nprobe": 10}} # MiniLM系はCOSINE推奨
results = MILVUS_CLIENT.search(
collection_name=collection_name,
data=[query_vector],
search_params=search_params,
limit=top_k,
anns_field='embedding',
output_fields=['address', 'pref', 'county', 'city', 'ward', 'oaza_cho', 'chome', 'koaza'],
)[0]
hits = []
for i, result in enumerate(results, start=1):
distance = result['distance']
address = result['entity'].get('address')
pref = result['entity'].get('pref')
county = result['entity'].get('county')
city = result['entity'].get('city')
ward = result['entity'].get('ward')
oaza_cho = result['entity'].get('oaza_cho')
chome = result['entity'].get('chome')
koaza = result['entity'].get('koaza')
if distance >= thresh:
hits.append([i, distance, address, pref, county, city, ward, oaza_cho, chome, koaza])
return hits
def get_lg_code(pref, county, city, ward):
city_all_file = TARGET_DIR / 'mt_city_all.csv'
city_all_df = pd.read_csv(city_all_file)
city_all_df_temp = city_all_df[city_all_df['pref'] == pref]
city_name1 = city_all_df_temp['county'].fillna('') + city_all_df_temp['city'].fillna('') + city_all_df_temp['ward'].fillna('')
city_name2 = county + city + ward
lg_codes = city_all_df_temp[city_name1 == city_name2]['lg_code'].values
if len(lg_codes) > 1:
raise Exception('Too many lg_code')
return lg_codes[0]
def get_addresses_with_parcel(pref, county, city, ward, oaza_cho, chome, koaza):
lg_code = get_lg_code(pref, county, city, ward)
parcel_city_file = TARGET_DIR / 'parcel' / f'mt_parcel_city{lg_code:06d}.csv'
if not os.path.exists(parcel_city_file):
raise gr.Error('Not found: ', parcel_city_file)
parcel_city_df = pd.read_csv(parcel_city_file)
cities = parcel_city_df['city'].fillna('')
wards = parcel_city_df['ward'].fillna('')
oaza_chos = parcel_city_df['oaza_cho'].fillna('')
chomes = parcel_city_df['chome'].fillna('')
koazas = parcel_city_df['koaza'].fillna('')
city_name1 = cities + wards
city_name2 = county + city + ward
city_mask = city_name1 == city_name2
town_name1 = oaza_chos + chomes
town_name2 = oaza_cho + chome
town_mask = town_name1 == town_name2
koaza_mask = koazas == koaza
parcel_city_df_filtered = parcel_city_df[city_mask & town_mask & koaza_mask]
if len(parcel_city_df_filtered) == 0:
return [pref + county + city + ward + oaza_cho + chome + koaza]
cities = parcel_city_df_filtered['city'].fillna('')
wards = parcel_city_df_filtered['ward'].fillna('')
oaza_chos = parcel_city_df_filtered['oaza_cho'].fillna('')
chomes = parcel_city_df_filtered['chome'].fillna('')
koazas = parcel_city_df_filtered['koaza'].fillna('')
prc_num1s = parcel_city_df_filtered['prc_num1'].fillna(9999).astype(int).astype(str).replace('9999', '')
prc_num2s = parcel_city_df_filtered['prc_num2'].fillna(9999).astype(int).astype(str).replace('9999', '')
prc_num3s = parcel_city_df_filtered['prc_num3'].fillna(9999).astype(int).astype(str).replace('9999', '')
# アドレスを生成
return [
f"{pref}{_city}{_ward}{_oaza_cho}{_chome}{_koaza}{_prc_num1}" +
(f"-{_prc_num2}" if _prc_num2 else '') +
(f"-{_prc_num3}" if _prc_num3 else '')
for _city, _ward, _oaza_cho, _chome, _koaza, _prc_num1, _prc_num2, _prc_num3 in zip(
cities, wards, oaza_chos, chomes, koazas, prc_num1s, prc_num2s, prc_num3s
)
]
def get_pref_code(pref):
return prefs.index(pref) + 1
def get_addresses_with_rsdtdsp(pref, county, city, ward, oaza_cho, chome, koaza):
pref_code = get_pref_code(pref)
rsdtdsp_file = TARGET_DIR / 'rsdt' / f'mt_rsdtdsp_rsdt_pref{pref_code:02d}.csv'
if not os.path.exists(rsdtdsp_file):
raise gr.Error(f'Not found: {rsdtdsp_file}')
rsdtdsp_df = pd.read_csv(rsdtdsp_file)
city_name1 = rsdtdsp_df['city'].fillna('') + rsdtdsp_df['ward'].fillna('')
city_name2 = county + city + ward
city_mask = city_name1 == city_name2
town_name1 = rsdtdsp_df['oaza_cho'].fillna('') + rsdtdsp_df['chome'].fillna('')
town_name2 = oaza_cho + chome
town_mask = town_name1 == town_name2
koaza_mask = rsdtdsp_df['koaza'].fillna('') == koaza
rsdtdsp_df_filtered = rsdtdsp_df[city_mask & town_mask & koaza_mask]
if len(rsdtdsp_df_filtered) == 0:
return [pref + county + city + ward + oaza_cho + chome + koaza]
cities = rsdtdsp_df_filtered['city'].fillna('')
wards = rsdtdsp_df_filtered['ward'].fillna('')
oaza_chos = rsdtdsp_df_filtered['oaza_cho'].fillna('')
chomes = rsdtdsp_df_filtered['chome'].fillna('')
koazas = rsdtdsp_df_filtered['koaza'].fillna('')
blk_nums = rsdtdsp_df_filtered['blk_num'].fillna(9999).astype(int).astype(str).replace('9999', '')
rsdt_nums = rsdtdsp_df_filtered['rsdt_num'].fillna(9999).astype(int).astype(str).replace('9999', '')
rsdt_num2s = rsdtdsp_df_filtered['rsdt_num2'].fillna(9999).astype(int).astype(str).replace('9999', '')
# アドレスを生成
return [
f"{pref}{_city}{_ward}{_oaza_cho}{_chome}{_koaza}{_blk_num}" +
(f"-{_rsdt_num}" if _rsdt_num else '') +
(f"-{_rsdt_num2}" if _rsdt_num2 else '')
for _city, _ward, _oaza_cho, _chome, _koaza, _blk_num, _rsdt_num, _rsdt_num2 in zip(
cities, wards, oaza_chos, chomes, koazas, blk_nums, rsdt_nums, rsdt_num2s)
]
def compare_two_addresses(address1, address2):
preprocessed1 = preprocess(address1)
preprocessed2 = preprocess(address2)
hits1 = vector_search(preprocessed1, top_k=1)
hits2 = vector_search(preprocessed2, top_k=1)
normalized1 = hits1[0][-1]
normalized2 = hits2[0][-1]
result = compare(normalized1, normalized2)
return result
def normalize_address(query_address):
with measure('convert_zenkaku_to_hankaku'):
query_address = convert_zenkaku_to_hankaku(query_address)
with measure('split_address_building_with_gpt'):
splitted = split_address_building_with_gpt(query_address)
with measure('preprocess'):
preprocessed = preprocess(splitted['address'])
with measure('vector_search'):
hits = vector_search(preprocessed, 1)
with measure('split_address'):
splits = {
'pref': hits[0][3],
'county': hits[0][4],
'city': hits[0][5],
'ward': hits[0][6],
'oaza_cho': hits[0][7],
'chome': hits[0][8],
'koaza': hits[0][9],
}
with measure('get_addresses_with_parcel'):
addresses = get_addresses_with_parcel(
splits['pref'], splits['county'], splits['city'], splits['ward'],
splits['oaza_cho'], splits['chome'], splits['koaza'])
with measure('get_addresses_with_rsdtdsp'):
addresses += get_addresses_with_rsdtdsp(
splits['pref'], splits['county'], splits['city'], splits['ward'],
splits['oaza_cho'], splits['chome'], splits['koaza'])
addresses = list(set(addresses)) # 重複を除去
with measure('embed_via_multilingual_e5_large'):
embeds = embed_via_multilingual_e5_large([splitted['address']] + addresses)
query_embed = [embeds[0]]
address_embeds = embeds[1:]
with measure('cosine'):
# コサイン類似度を計算
similarities = cosine_similarity(query_embed, address_embeds)
best_match_indices = np.argsort(similarities[0])[-1:][::-1] # 上位Kのインデックスを取得
best_addresses = [addresses[i] for i in best_match_indices]
best_address = best_addresses[0]
return best_address + splitted['building']
def convert_no_to_hyphen(query_address):
return re.sub(r'(?<=\d)の(?=\d)', '-', query_address)
def normalize_address_v2(query_address, top_k=1):
with measure('convert_zenkaku_to_hankaku'):
query_address = convert_zenkaku_to_hankaku(query_address)
with measure('split_address_building_with_gpt'):
splitted = split_address_building_with_gpt(query_address)
with measure('get_spelling'):
spelling = get_spelling(splitted['address'])
if spelling:
splitted['address'] = spelling
with measure(''):
splitted['address'] = convert_no_to_hyphen(splitted['address'])
with measure('preprocess'):
preprocessed = preprocess(splitted['address'])
with measure('vector_search'):
hits = vector_search(preprocessed, 1)
with measure('split_address'):
splits = {
'pref': hits[0][3],
'county': hits[0][4],
'city': hits[0][5],
'ward': hits[0][6],
'oaza_cho': hits[0][7],
'chome': hits[0][8],
'koaza': hits[0][9],
}
with measure('get_addresses_with_parcel'):
addresses = get_addresses_with_parcel(
splits['pref'], splits['county'], splits['city'], splits['ward'],
splits['oaza_cho'], splits['chome'], splits['koaza'])
with measure('get_addresses_with_rsdtdsp'):
addresses += get_addresses_with_rsdtdsp(
splits['pref'], splits['county'], splits['city'], splits['ward'],
splits['oaza_cho'], splits['chome'], splits['koaza'])
addresses = list(set(addresses)) # 重複を除去
with measure('embed_via_multilingual_e5_large'):
embeds = embed_via_multilingual_e5_large([splitted['address']] + addresses)
query_embed = [embeds[0]]
address_embeds = embeds[1:]
with measure('cosine'):
# コサイン類似度を計算
similarities = cosine_similarity(query_embed, address_embeds)
best_match_indices = np.argsort(similarities[0])[-top_k:][::-1] # 上位Kのインデックスを取得
best_addresses = [addresses[i] for i in best_match_indices]
best_similarities = similarities[0][best_match_indices]
return splitted, hits, splits, best_addresses, best_similarities
# =========================
# FastAPI definition
# =========================
from fastapi import FastAPI
from pydantic import BaseModel, Field
from typing import Literal
app = FastAPI(
title="住所処理API",
description="住所の正規化・比較を行うAPIです。",
version="1.0.0"
)
# ---------------------------
# リクエスト・レスポンス定義
# ---------------------------
class CompareAddressesRequest(BaseModel):
address1: str = Field(..., description="比較する最初の住所", example="東京 墨田区 押上 1丁目1-1")
address2: str = Field(..., description="比較する2番目の住所", example="東京 墨田区 押上 1-1-1")
class CompareAddressesResponse(BaseModel):
result: Literal[True, False] = Field(..., description="比較結果", example=True)
class NormalizeAddressRequest(BaseModel):
query_address: str = Field(..., description="正規化する住所", example="東京 墨田区 押上 1丁目1-1")
class NormalizeAddressResponse(BaseModel):
normalized: str = Field(..., description="正規化された住所", example="東京都墨田区押上一丁目1-1")
# ---------------------------
# エンドポイント定義
# ---------------------------
@app.post(
"/compare-two-addresses",
response_model=CompareAddressesResponse,
summary="2つの住所を比較する",
description="2つの住所を比較し、一致するかどうかを返します。",
responses={
200: {
"description": "比較結果を Bool 値 (true/false) として返す",
"content": {
"application/json": {
"example": {
"result": True
}
}
}
}
}
)
async def compare_two_addresses_api(request: CompareAddressesRequest):
"""
- **address1**: 比較する最初の住所
- **address2**: 比較する2番目の住所
"""
result = compare_two_addresses(request.address1, request.address2)
return {"result": result}
@app.post(
"/normalize-address",
response_model=NormalizeAddressResponse,
summary="住所を正規化する",
description="指定された住所を正規化し、正規化後の住所を返します。",
responses={
200: {
"description": "正規化結果の返却",
"content": {
"application/json": {
"example": {
"normalized": "東京都千代田区一丁目1番"
}
}
}
}
}
)
async def normalize_address_api(request: NormalizeAddressRequest):
"""
- **query_address**: 正規化する住所
"""
normalized = normalize_address(request.query_address)
return {"normalized": normalized}
@app.post(
"/normalize-address-v2",
response_model=NormalizeAddressResponse,
summary="住所を正規化する",
description="指定された住所を正規化し、正規化後の住所を返します。",
responses={
200: {
"description": "正規化結果の返却",
"content": {
"application/json": {
"example": {
"normalized": "東京都千代田区一丁目1番"
}
}
}
}
}
)
async def normalize_address_v2_api(request: NormalizeAddressRequest):
"""
- **query_address**: 正規化する住所
"""
_, __, ___, bests, _____ = normalize_address_v2(request.query_address)
return {"normalized": bests[0]}
# =========================
# Gradio tabs definition
# =========================
examples = [
'東京都中央区みなと3の12の10、プレサンスロゼ東京港301。',
'東京都荒川区1−5−6荒川マンション102',
'福岡市中央区天神1の11の2',
'私の住所は京都府京都市右京区太秦青木元町4-10です。',
'京都府京都市右京区太秦青木元町4-10',
'京都府京都市右京区太秦青木元町4-10ダックス101号室',
'京都府宇治市伊勢田町名木1-1-4ダックス101号室',
'東京都渋谷区道玄坂1-12-1',
'私の住所は東京都渋谷区道玄坂1-12-1です。',
'私の住所は東京都しぶや道玄坂1の12の1です。',
'東京都渋谷区道玄坂1の12の1で契約しています。',
'秋田県秋田市山王四丁目1番1号です。',
'東京 墨田区 押上 1丁目1',
'三重県伊勢市宇治館町',
'住所は 030-0803 青森県青森市安方1丁目1−40になります。',
'東京都大島町差木地 字クダッチ',
'前橋市大手町1丁目1番地1',
'東京都渋谷区表参道の3の5の6。',
'琉球圏尾張町3の5の6に住んでます。',
'3254987の場所です。',
'大阪府でした。',
'1940923の東京都渋谷区道玄坂一丁目。渋谷マークシティウェスト23階です。',
'名前は山田太郎です。',
'はい。名古屋、あ、愛知県名古屋市南里2の3の4だと思います。',
'ー',
'少し待ってください。',
]
def create_function_test_tab():
def create_remove_left_of_pref_tab():
with gr.Tab("remove_left_of_pref"):
in_tb = gr.Textbox(label='インプット', placeholder='テキストを入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=remove_left_of_pref,
inputs=[in_tb],
outputs=[out_tb],
)
def create_replace_circle_tab():
with gr.Tab("replace_circle"):
in_tb = gr.Textbox(label='インプット', placeholder='テキストを入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=replace_circle,
inputs=[in_tb],
outputs=[out_tb],
)
def create_remove_filler_tab():
with gr.Tab("remove_filler"):
in_tb = gr.Textbox(label='インプット', placeholder='テキストを入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=remove_filler,
inputs=[in_tb],
outputs=[out_tb],
)
def create_preprocess_tab():
with gr.Tab("preprocess"):
in_tb = gr.Textbox(label='インプット', placeholder='テキストを入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=preprocess,
inputs=[in_tb],
outputs=[out_tb],
)
def create_compare_two_addresses_tab():
with gr.Tab("compare_two_addresses"):
in_tb1 = gr.Textbox(label='住所1 (顧客が発言した住所)', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb1])
in_tb2 = gr.Textbox(label='住所2 (CRM 内に格納されている住所)', placeholder='住所を入力してください')
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=compare_two_addresses,
inputs=[in_tb1, in_tb2],
outputs=[out_tb],
)
def create_normalize_address_tab():
with gr.Tab("normalize_address"):
in_tb = gr.Textbox(label='住所', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=normalize_address,
inputs=[in_tb],
outputs=[out_tb],
)
def create_normalize_address__v2_tab():
with gr.Tab("normalize_address_v2"):
in_tb = gr.Textbox(label='住所', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
def f(query_address):
splitted, __, ___, bests, _____ = normalize_address_v2(query_address)
return bests[0] + splitted['building']
exe_button.click(
fn=f,
inputs=[in_tb],
outputs=[out_tb],
)
def create_split_address_tab():
with gr.Tab("split_address"):
in_tb = gr.Textbox(label='住所', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=split_address,
inputs=[in_tb],
outputs=[out_tb],
)
def create_split_address_building_tab():
with gr.Tab("split_address_building_with_gpt"):
in_tb = gr.Textbox(label='住所', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=split_address_building_with_gpt,
inputs=[in_tb],
outputs=[out_tb],
)
def create_convert_zenkaku_to_hankaku_tab():
with gr.Tab("convert_zenkaku_to_hankaku"):
in_tb = gr.Textbox(label='住所', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=convert_zenkaku_to_hankaku,
inputs=[in_tb],
outputs=[out_tb],
)
def create_vector_search():
def f(query_address, top_k):
with measure('preprocess'):
preprocessed = preprocess(query_address)
with measure('vector_search'):
hits = vector_search(preprocessed, top_k=top_k)
return pd.DataFrame(hits, columns=['Top-k', '類似度', '住所', '都道府県', '郡', '市区町村', '政令市区', '大字・町', '丁目', '小字'])
with gr.Tab("vector_search"):
in_tb = gr.Textbox(label='住所', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
top_k_input = gr.Slider(minimum=1, maximum=100, step=1, value=5, label='検索数top-k')
out_df = gr.Dataframe(label="アウトプット", wrap=True)
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=f,
inputs=[in_tb, top_k_input],
outputs=[out_df],
)
def create_get_addresses_with_parcel_tab():
def f(query_address):
with measure('preprocess'):
preprocessed = preprocess(query_address)
with measure('vector_search'):
hits = vector_search(preprocessed, top_k=1)
with measure('split_address'):
splits = {
'pref': hits[0][3],
'county': hits[0][4],
'city': hits[0][5],
'ward': hits[0][6],
'oaza_cho': hits[0][7],
'chome': hits[0][8],
'koaza': hits[0][9],
}
with measure('get_addresses_with_parcel'):
addresses = get_addresses_with_parcel(
splits['pref'], splits['county'], splits['city'], splits['ward'],
splits['oaza_cho'], splits['chome'], splits['koaza'])
return pd.DataFrame(addresses, columns=['住所'])
with gr.Tab("get_addresses_with_parcel"):
in_tb = gr.Textbox(label='住所', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_df = gr.Dataframe(label="アウトプット", wrap=True)
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=f,
inputs=[in_tb],
outputs=[out_df],
)
def create_get_spelling_tab():
with gr.Tab("create_get_spelling_tab"):
in_tb = gr.Textbox(label='住所', placeholder='住所を入力してください')
gr.Examples(examples=examples, inputs=[in_tb])
out_tb = gr.Textbox(label='アウトプット')
exe_button = gr.Button(value='実行', variant='primary')
exe_button.click(
fn=get_spelling,
inputs=[in_tb],
outputs=[out_tb],
)
with gr.Tab("関数テスト"):
create_normalize_address_tab()
create_normalize_address__v2_tab()
create_compare_two_addresses_tab()
create_get_spelling_tab()
create_get_addresses_with_parcel_tab()
create_vector_search()
create_remove_left_of_pref_tab()
create_replace_circle_tab()
create_remove_filler_tab()
create_preprocess_tab()
create_split_address_tab()
create_split_address_building_tab()
create_convert_zenkaku_to_hankaku_tab()
def create_digital_agency_tab():
with gr.Tab("デジ庁API"):
with gr.Row():
with gr.Column():
address_input_tab2 = gr.Textbox(label='住所', placeholder='検索したい住所を入力してください')
gr.Examples(examples=examples, inputs=[address_input_tab2])
search_button_tab2 = gr.Button(value='検索', variant='primary')
result_tb = gr.Textbox(label='正規化後')
result_df = gr.Dataframe(label="正規化後(分割)", wrap=True)
def normalize_address_via_abrg_geocode(query_address):
query_address = preprocess(query_address)
url = f'{ABRG_ENDPOINT}/geocode?address={query_address}'
response = requests.get(url)
result = response.json()[0]['result']
normalized = result['output']
data = {
'pref': result['pref'],
'county': result['county'],
'city': result['city'],
'ward': result['ward'],
'oaza_cho': result['oaza_cho'],
'chome': result['chome'],
'koaza': result['koaza'],
'blk_num': result['blk_num'],
'rsdt_num': result['rsdt_num'],
'rsdt_num2': result['rsdt_num2'],
'prc_num1': result['prc_num1'],
'prc_num2': result['prc_num2'],
'prc_num3': result['prc_num3'],
'others': ''.join(result['others'])
}
df = pd.DataFrame([data])
return normalized, df
search_button_tab2.click(
fn=normalize_address_via_abrg_geocode,
inputs=[address_input_tab2],
outputs=[result_tb, result_df],
)
def create_vector_search_tab():
with gr.Tab("ベクトル検索"):
with gr.Row():
with gr.Column():
address_input = gr.Textbox(label='住所', placeholder='検索したい住所を入力してください')
gr.Examples(examples=examples, inputs=[address_input])
top_k_input = gr.Slider(minimum=1, maximum=100, step=1, value=5, label='検索数top-k')
search_button = gr.Button(value='検索', variant='primary')
result_tb = gr.Textbox(label='正規化後')
result_df = gr.Dataframe(label="正規化後(分割)", wrap=True)
search_result_df = gr.Dataframe(label="町丁目まで検索結果")
chiban_result_df = gr.Dataframe(label="地番・住居表示検索結果")
def search_address(query_address, top_k):
with measure('convert_zenkaku_to_hankaku'):
query_address = convert_zenkaku_to_hankaku(query_address)
with measure('split_address_building_with_gpt'):
splitted = split_address_building_with_gpt(query_address)
with measure('preprocess'):
preprocessed = preprocess(splitted['address'])
with measure('vector_search'):
hits = vector_search(preprocessed, top_k)
search_result_df = pd.DataFrame(hits, columns=['Top-k', '類似度', '住所', '都道府県', '郡', '市区町村', '政令市区', '大字・町', '丁目', '小字'])
with measure('split_address'):
splits = {
'pref': hits[0][3],
'county': hits[0][4],
'city': hits[0][5],
'ward': hits[0][6],
'oaza_cho': hits[0][7],
'chome': hits[0][8],
'koaza': hits[0][9],
}
result_df = pd.DataFrame([splits.values()], columns=splits.keys())
with measure('get_addresses_with_parcel'):
addresses = get_addresses_with_parcel(
splits['pref'], splits['county'], splits['city'], splits['ward'],
splits['oaza_cho'], splits['chome'], splits['koaza'])
with measure('get_addresses_with_rsdtdsp'):
addresses += get_addresses_with_rsdtdsp(
splits['pref'], splits['county'], splits['city'], splits['ward'],
splits['oaza_cho'], splits['chome'], splits['koaza'])
addresses = list(set(addresses)) # 重複を除去
with measure('embed_via_multilingual_e5_large'):
embeds = embed_via_multilingual_e5_large([splitted['address']] + addresses)
query_embed = [embeds[0]]
address_embeds = embeds[1:]
with measure('cosine'):
# コサイン類似度を計算
similarities = cosine_similarity(query_embed, address_embeds)
best_match_indices = np.argsort(similarities[0])[-top_k:][::-1] # 上位Kのインデックスを取得
best_addresses = [addresses[i] for i in best_match_indices]
best_similarities = similarities[0][best_match_indices]
print(top_k)
print('len(best_similarities)', len(best_similarities))
print('len(best_addresses)', len(best_addresses))
chiban_result_df = pd.DataFrame({
'Top-k': range(1, len(best_similarities) + 1),
'類似度': best_similarities,
'住所': [best_address + splitted['building'] for best_address in best_addresses]
})
best_address = best_addresses[0] + splitted['building']
return search_result_df, chiban_result_df, best_address, result_df
search_button.click(
fn=search_address,
inputs=[address_input, top_k_input],
outputs=[search_result_df, chiban_result_df, result_tb, result_df],
)
def create_vector_search_v2_tab():
with gr.Tab("ベクトル検索V2"):
with gr.Row():
with gr.Column():
address_input = gr.Textbox(label='住所', placeholder='検索したい住所を入力してください')
gr.Examples(examples=examples, inputs=[address_input])
top_k_input = gr.Slider(minimum=1, maximum=100, step=1, value=5, label='検索数top-k')
search_button = gr.Button(value='検索', variant='primary')
result_tb = gr.Textbox(label='正規化後')
result_df = gr.Dataframe(label="正規化後(分割)", wrap=True)
search_result_df = gr.Dataframe(label="町丁目まで検索結果")
chiban_result_df = gr.Dataframe(label="地番・住居表示検索結果")
def search_address(query_address, top_k):
splitted, hits, splits, best_addresses, best_similarities = normalize_address_v2(query_address, top_k)
search_result_df = pd.DataFrame(hits, columns=['Top-k', '類似度', '住所', '都道府県', '郡', '市区町村', '政令市区', '大字・町', '丁目', '小字'])
result_df = pd.DataFrame([splits.values()], columns=splits.keys())
chiban_result_df = pd.DataFrame({
'Top-k': range(1, len(best_similarities) + 1),
'類似度': best_similarities,
'住所': [best_address + splitted['building'] for best_address in best_addresses]
})
best_address = best_addresses[0] + splitted['building']
return search_result_df, chiban_result_df, best_address, result_df
search_button.click(
fn=search_address,
inputs=[address_input, top_k_input],
outputs=[search_result_df, chiban_result_df, result_tb, result_df],
)
with gr.Blocks() as demo:
create_function_test_tab()
create_vector_search_tab()
create_vector_search_v2_tab()
create_digital_agency_tab()
app = gr.mount_gradio_app(app, demo, path='/') |