| import pandas as pd | |
| from pandas import DataFrame | |
| from typing import Optional | |
| from annotation.src.utils import extract_before_parenthesis | |
| class Restaurants: | |
| def __init__(self, path="../database/restaurants/clean_restaurant_2022.csv"): | |
| self.path = path | |
| self.data = pd.read_csv(self.path).dropna()[['Name','Average Cost','Cuisines','Aggregate Rating','City']] | |
| print("Restaurants loaded.") | |
| def load_db(self): | |
| self.data = pd.read_csv(self.path).dropna() | |
| def run(self, | |
| city: str, | |
| ) -> DataFrame: | |
| """Search for restaurant .""" | |
| results = self.data[self.data["City"] == city] | |
| # results = results[results["date"] == date] | |
| # if price_order == "asc": | |
| # results = results.sort_values(by=["Average Cost"], ascending=True) | |
| # elif price_order == "desc": | |
| # results = results.sort_values(by=["Average Cost"], ascending=False) | |
| # if rating_order == "asc": | |
| # results = results.sort_values(by=["Aggregate Rating"], ascending=True) | |
| # elif rating_order == "desc": | |
| # results = results.sort_values(by=["Aggregate Rating"], ascending=False) | |
| if len(results) == 0: | |
| return "There is no restaurant in this city." | |
| return results | |
| def run_for_annotation(self, | |
| city: str, | |
| ) -> DataFrame: | |
| """Search for restaurant .""" | |
| results = self.data[self.data["City"] == extract_before_parenthesis(city)] | |
| # results = results[results["date"] == date] | |
| # if price_order == "asc": | |
| # results = results.sort_values(by=["Average Cost"], ascending=True) | |
| # elif price_order == "desc": | |
| # results = results.sort_values(by=["Average Cost"], ascending=False) | |
| # if rating_order == "asc": | |
| # results = results.sort_values(by=["Aggregate Rating"], ascending=True) | |
| # elif rating_order == "desc": | |
| # results = results.sort_values(by=["Aggregate Rating"], ascending=False) | |
| return results |