feat: user & restaurant mock data (#48)

Co-authored-by: Ben Petrillo <benpetrillo.bp@gmail.com>

Author: sierrawelsch

backend/cmd/db/script/__pycache__/restaurant_setup.cpython-311.pyc

@@ -1,14 +1,21 @@
 from pymongo import MongoClient
-from restaurant_setup import convert_to_mongo_db_format, load_env_variable
+from restaurant_setup import convert_to_mongo_db_format, load_env_variable, generate_mock_restaurant_data
+from users_setup import generate_user_data
 
 mongo_db_uri = load_env_variable("backend/.env", "MONGO_DB_URI")
 client = MongoClient(mongo_db_uri)
-db = client['dev-restaurants-api']
+db = client['Production']
 collection = db['restaurants']
 
 def insert_db(restaurants):
     for place in restaurants:
             collection.insert_one(place)
+            
+def delete_documents():
+    collection.delete_many({})
 
-restaurants_for_mongo = convert_to_mongo_db_format()
-insert_db(restaurants_for_mongo)
+#restaurants_for_mongo = convert_to_mongo_db_format()
+# users_for_mongo = generate_user_data()
+mock_restaurant_data = generate_mock_restaurant_data()
+insert_db(mock_restaurant_data)
+# delete_documents()

backend/cmd/db/script/__pycache__/users_setup.cpython-311.pyc

@@ -1,6 +1,14 @@
 import requests
 #from googlemaps import GooglePlaces, types, lang
 import json
+import pandas as pd
+import random
+
+min_num = 0 # Minimum value of review id
+max_num = 50  # Maximum value of review id
+min_size = 0  # Minimum size of the amount of reviews 
+max_size = 50  # Maximum size of the amount of reviews
+style_list = ['Pub', 'Restaurant', 'Bar', 'Cafe']
 
 def load_env_variable(file_path, key):
     with open(file_path, "r") as f:
@@ -118,7 +126,52 @@ def convert_to_mongo_db_format():
         restaurants_for_mongo.append(restaurant_for_mongo)
     return restaurants_for_mongo
 
+def generate_mock_restaurant_data():
+    mock_restaurants = []
+    df = pd.read_csv("backend/cmd/db/script/mock_restaurant_data.csv")
+    tags = {}
+    tags['servesBreakfast'] = 'Serves Breakfast'
+    tags['servesLunch'] = 'Serves Lunch'
+    tags['servesDinner'] = 'Serves Dinner'
+    tags['servesBeer'] = 'Serves Beer'
+    tags['servesWine'] = 'Serves Wine'
+    tags['servesBrunch'] = 'Serves Brunch'
+    tags['servesVegetarianFood'] = 'Serves Vegetarian Food'
+    tags['servesCocktails'] = 'Serves Cocktails'
+    tags['servesDessert'] = 'Serves Dessert'
+    tags['servesCoffee'] = 'Serves Coffee'
+    available_tags = ['servesBreakfast', 'servesLunch', 'servesDinner', 'servesBeer', 'servesWine', 'servesBrunch', 'servesVegetarianFood', 'servesCocktails', 'servesDessert', 'servesCoffee']
+    
+    for index, row in df.iterrows():
+        mock_restaurants_for_mongo = {}
+        mock_restaurants_for_mongo['name'] = row['name']
+        mock_restaurants_for_mongo['address'] = {}
+        mock_restaurants_for_mongo['address']['street'] = row['address.street']
+        mock_restaurants_for_mongo['address']['zipcode'] = row['address.zipcode']
+        mock_restaurants_for_mongo['address']['state'] = row['address.state'] 
+        mock_restaurants_for_mongo['address']['location'] = {}
+        mock_restaurants_for_mongo['address']['location']['latitude'] = row['address.location.latitude']
+        mock_restaurants_for_mongo['address']['location']['longitude'] = row['address.location.longitude']
+        list_size = random.randint(min_size, max_size)
+        menu_list = list(set([random.randint(min_num, max_num) for _ in range(list_size)]))
+        mock_restaurants_for_mongo['menuItems'] = menu_list
+        mock_restaurants_for_mongo['ratingAvg'] = {}
+        mock_restaurants_for_mongo['ratingAvg']['overall'] = row['ratingAvg.overall']
+        mock_restaurants_for_mongo['ratingAvg']['return'] = row['ratingAvg.return']
+        mock_restaurants_for_mongo['style'] = style_list[random.randint(0, 3)]
+        mock_restaurants_for_mongo['picture'] = row['picture']
+        mock_restaurants_for_mongo['restaurant'] = row['description']
+        list_size = random.randint(0, 9)
+        tags_idx_list = list(set([random.randint(0, 9) for _ in range(list_size)]))
+        tag_list = []
+        for idx in tags_idx_list:
+            tag_list.append(available_tags[idx])
+        mock_restaurants_for_mongo['tags'] = []
+        for tag in tag_list:
+            mock_restaurants_for_mongo['tags'].append(tag)
+        mock_restaurants.append(mock_restaurants_for_mongo)
+    return mock_restaurants
+    
 
 #google_places_search()
-#convert_to_mongo_db_format()
-
+#convert_to_mongo_db_format()

backend/cmd/db/script/insert_db.py

@@ -0,0 +1,39 @@
+# create restrcution options, choose from random 
+# have user ids choose from random following/followers, etc.
+# use some of mock data
+import pandas as pd
+import random
+
+restrictions = ['vegetarian', 'gluten free', 'vegan', 'keto', 'low calorie']
+user_dict = []
+min_num = 0 # Minimum value of review id
+max_num = 10000  # Maximum value of review id
+min_size = 0  # Minimum size of the amount of reviews 
+max_size = 50  # Maximum size of the amount of reviews
+
+def generate_user_data():
+    df = pd.read_csv("backend/cmd/db/script/mock_user_data.csv")
+    print(len(df))
+    for index, row in df.iterrows():
+       user_entry = {}
+       user_entry['username'] = row['username']
+       user_entry['password'] = row['password']
+       list_size = random.randint(min_size, max_size)
+       random_list = list(set([random.randint(min_num, max_num) for _ in range(list_size)]))
+       user_entry['reviews'] = random_list
+       list_size = random.randint(min_size, max_size)
+       list_size = random.randint(0, 4)
+       preferences_idx_list = list(set([random.randint(0, 4) for _ in range(list_size)]))
+       user_entry['preferences'] = {}
+       preferences_list = []
+       for idx in preferences_idx_list:
+           preferences_list.append(restrictions[idx])
+       user_entry['preferences']['restrictions'] = preferences_list
+       user_entry['following'] = list(set([random.randint(min_num, max_num) for _ in range(list_size)]))
+       user_entry['followingCount'] = len(user_entry['following'])
+       list_size = random.randint(min_size, max_size)
+       user_entry['followers'] = list(set([random.randint(min_num, max_num) for _ in range(list_size)]))
+       user_entry['followersCount'] = len(user_entry['followers'])
+       user_entry['profile_picture'] = ""
+       user_dict.append(user_entry)
+    return user_dict

backend/cmd/db/script/restaurant_setup.py

@@ -0,0 +1,43 @@
+/* global use, db */
+// MongoDB Playground
+// Use Ctrl+Space inside a snippet or a string literal to trigger completions.
+
+const database = 'Production';
+const collection = 'restaurants';
+
+// The current database to use.
+use(database);
+
+// Create a new collection.
+db.createCollection(collection);
+
+// The prototype form to create a collection:
+/* db.createCollection( <name>,
+  {
+    capped: <boolean>,
+    autoIndexId: <boolean>,
+    size: <number>,
+    max: <number>,
+    storageEngine: <document>,
+    validator: <document>,
+    validationLevel: <string>,
+    validationAction: <string>,
+    indexOptionDefaults: <document>,
+    viewOn: <string>,
+    pipeline: <pipeline>,
+    collation: <document>,
+    writeConcern: <document>,
+    timeseries: { // Added in MongoDB 5.0
+      timeField: <string>, // required for time series collections
+      metaField: <string>,
+      granularity: <string>,
+      bucketMaxSpanSeconds: <number>, // Added in MongoDB 6.3
+      bucketRoundingSeconds: <number>, // Added in MongoDB 6.3
+    },
+    expireAfterSeconds: <number>,
+    clusteredIndex: <document>, // Added in MongoDB 5.3
+  }
+)*/
+
+// More information on the `createCollection` command can be found at:
+// https://www.mongodb.com/docs/manual/reference/method/db.createCollection/