Spaces:
Runtime error
Runtime error
File size: 4,720 Bytes
e6e69dc 1ef3ac7 e6e69dc 1ef3ac7 e6e69dc 1ef3ac7 e6e69dc 1ef3ac7 e6e69dc 1ef3ac7 e6e69dc 1ef3ac7 818d7ad 1ef3ac7 818d7ad 1ef3ac7 818d7ad 1ef3ac7 e6e69dc 1ef3ac7 e6e69dc 1ef3ac7 818d7ad e6e69dc 1ef3ac7 e6e69dc 1ef3ac7 e6e69dc 818d7ad 1ef3ac7 e6e69dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
import csv
import json
from ast import literal_eval
from pathlib import Path
from typing import TypeVar
from docarray import DocList
from dotenv import load_dotenv
from azure_openai import AzureOpenaiEmbeddings
from data import RestaurantDescription, restaurant_index, Dish, Category, dish_index, category_index
def calculate_rating(low: str, medium: str, high: str) -> float:
low = int(low)
medium = int(medium)
high = int(high)
total = low + medium + high
return (medium*0.7 + high) / total
def normalize_dish(dish_name: str) -> str:
output = dish_name.replace('\xa0', '')
return output.title()
T = TypeVar('T')
def add_to_all(restaurant: RestaurantDescription, keys: list[str], mapping: dict[T], cls: type[T]):
keys = set(keys) # guard against duplicates
for k in keys:
v = mapping.get(k)
if v is None:
v = mapping[k] = cls(id=k, text=k, restaurants=[])
v.restaurants.append(restaurant.id)
def load_districts():
with Path('data/district_boundary.json').open('r', encoding='utf-8') as f:
districts = json.load(f)
from matplotlib.path import Path as Polyline
output = []
for d in districts['features']:
district = {
"name": d['properties']['District'],
"polygon": Polyline(d['geometry']['coordinates'][0])
}
output.append(district)
return output
DISTRICTS = load_districts()
def get_district_name(lat: str, lon: str):
lat = float(lat)
lon = float(lon)
matches = []
for district in DISTRICTS:
if district['polygon'].contains_point((lon, lat)):
matches.append(district['name'])
return matches
restaurants, dish_list, category_list = None, None, None
def main():
global restaurants, dish_list, category_list
load_dotenv()
csv_file = Path('restaurants.csv')
restaurants = DocList[RestaurantDescription]()
restaurant_names = set()
dishes = {}
categories = {}
with csv_file.open(encoding='utf-8-sig', newline='') as f:
reader = csv.DictReader(f)
for row in reader:
if row['name_lang2']:
name = row['name_lang2']
name_alt = row['name_lang1']
else:
name = row['name_lang1']
name_alt = None
# for this demo, don't add multiple locations of the same restaurant chain
if name in restaurant_names:
continue
ds = literal_eval(row['dishes'])
ds = [normalize_dish(d) for d in ds]
ds = list(set(ds)) # unique
cs = literal_eval(row['categories'])
location = get_district_name(row['map_latitude'], row['map_longitude'])
price = int(row['price'])
if price < 100:
price_bucket = 'cheap'
elif 300 > price >= 100:
price_bucket = 'moderate'
elif price >= 300:
price_bucket = 'expensive'
extra_data = ' Romantic Dining.' if 'Romantic Dining' in cs else ""
text = f"""\
Name: {name}
Intro: {row['intro']}{extra_data}
Dishes: {", ".join(ds)}
Location: {", ".join(location)}
Price: {price_bucket}\
"""
r = RestaurantDescription(
embedding=None, # batch create all embeddings later
text=text,
id=row['id'],
name=name,
name_alt=name_alt,
intro=row['intro'],
price=price,
rating=calculate_rating(row['score_cry'], row['score_o_k'], row['score_smile']),
categories=cs,
dishes=ds,
info_url=row['poi_url'],
image_url=row['door_photos'],
location=location,
)
restaurants.append(r)
restaurant_names.add(name)
add_to_all(r, ds, dishes, Dish)
add_to_all(r, cs, categories, Category)
dish_list = DocList[Dish](dishes.values())
category_list = DocList[Category](categories.values())
import IPython
IPython.embed()
embedding_settings = AzureOpenaiEmbeddings.load_from_env()
RestaurantDescription.create_embeddings(restaurants, **embedding_settings.to_settings_dict())
Dish.create_embeddings(dish_list, **embedding_settings.to_settings_dict())
Category.create_embeddings(category_list, **embedding_settings.to_settings_dict())
restaurant_index.index(restaurants)
dish_index.index(dish_list)
category_index.index(category_list)
restaurant_index.persist()
dish_index.persist()
category_index.persist()
if __name__ == '__main__':
main()
|