def userProducts(user_id):
if request.method == 'GET':
#lookup all products for in users collection
response = jsonify(userProducts=p_ctrl.get_products_by_user_id(user_id))
#respond array of products and a 200
return response, 200
if request.method == 'POST':
body = request.get_json()
#check if product is already in DB
product_id = p_ctrl.verify_product_by_name_and_brand(body['product_name'], body['brand_name'])
if product_id == None:
#Add product if not
product_id = p_ctrl.add_product_to_products(body['product_name'], body['brand_name'], body['product_category'])
#create db relationship between user and product
response = jsonify(p_ctrl.add_user_to_product(
user_id,
product_id,
body.get('product_size'),
body.get('product_status'),
body.get('product_notes'),
body.get('product_color'),
body.get('stars'),
body.get('review'),
body.get('product_image_url'),
body.get('product_category')
))
e_ctrl.add_event(user_id, "added a product", "product", product_id)
return response, 201
if request.method == 'PUT':
body = request.get_json()
p = body['product']
product_id = p_ctrl.verify_product_by_name_and_brand(p['product_name'], p['brand_name'])
response = jsonify(p_ctrl.edit_user_to_product(
user_id,
product_id,
p.get('product_size'),
p.get('product_status'),
p.get('product_notes'),
p.get('product_color'),
p.get('stars'),
p.get('review'),
p.get('product_image_url'),
p.get('product_category')
))
return response, 202
if request.method == 'DELETE':
body = request.get_json()
p_ctrl.remove_user_from_product(body['product_id'])
return "Product Removed", 204
def find_prob(user_id):
# 1885
# fetch user from database
all_prod_likes_file = open('../data/number_all_prod_likes.json', 'r')
all_prod_likes = json.loads(all_prod_likes_file.read())
all_prod_likes_file.close()
all_users_file = open('../data/number_all_user_reviews.json', 'r')
all_users = json.loads(all_users_file.read())
all_users_file.close()
counter = 0
# ******** fetch user info from database *********
from db_controller import user_controller as u_ctrl
user_info = u_ctrl.get_user_as_dictionary(user_id)
from db_controller import product_controller as p_ctrl
products = p_ctrl.get_products_by_user_id(user_id)
# age processing
if user_info['birthday'] != '':
birthday = user_info['birthday']
from datetime import date
age = int(date.today().year) - int(birthday[0:4])
else:
age = None
current_user = {
'age': age,
'location': user_info['location'],
'skin_tone': user_info['skin_tone'],
'reviews': []
}
# 'reviews':[{'rating':1,'productID':'sephorid'}]
# ********* built up current user from db fetch **************
for product in products:
current_user['reviews'].append({
'rating': product['product_rating'],
'productID': product['sephora_id']
})
# ******** fetch products ids to check against from file **********
productIDs = []
for prod in all_prod_likes:
counter += 1
productIDs.append(prod)
if counter > 1100:
break
# if counter > 11004:
# break
# if counter > productNum:
# keep store here
res = [(0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]
storage = {}
# iterate over specified prodicts and return probability values
for productID in productIDs:
prod_like_ref = all_prod_likes[productID]
user_prod_likes = prod_like_ref['L']
user_prod_dislikes = prod_like_ref['D']
# user:simIndex
storage_like_simIndex = {}
storage_dislike_simIndex = {}
for name in user_prod_likes:
# name = str(name)
b = str(name)
user = all_users[b]
index = compoundSimilarityIndex(current_user, user)
storage_like_simIndex[b] = index
for name in user_prod_dislikes:
# name = str[name]python3
b = str(name)
user = all_users[b]
index = compoundSimilarityIndex(current_user, user)
storage_dislike_simIndex[b] = index
probability = prob_user_likes_product(productID, storage_like_simIndex,
storage_dislike_simIndex)
# select maximum values algo
nextTupl = False
for index, tupl in enumerate(res):
# cascade change
if nextTupl != False:
temp = res[index]
res[index] = nextTupl
nextTupl = temp
elif probability > tupl[0]:
nextTupl = res[index]
res[index] = (probability, productID)
storage[productID] = probability
#return top 5 in a tuple
# print(res)
# unpack and push to db
r_ctrl.remove_recommendation(user_id)
# remove all recommendations from db
for index, tupl in enumerate(res):
rank = index + 1
sephora_product_id = tupl[1]
# save to database
# make sure this saves the other id, not the sephora id
product_id = p_ctrl.get_product_id_by_sephora_product_id(
sephora_product_id)
# product = p_ctrl.get_product_as_dictionary(product_id)
res = r_ctrl.add_recommendation(user_id, product_id, rank)
# print(productIDs)
# start = time.clock()
# probs = find_prob(productIDs,current_user)
# print(probs)
# end = time.clock()
# interval = end - start
# print(interval)
# print(probs[1])
# get rid of anons
# print similarityIndex(sampleUser0,sampleUser1)
# print similarityIndex(sampleUser0,sampleUser3)
# diff is high
# shift info to a mongodb
# make async worker go off and fetch stuff
# make other cool visualizations with data
# a graph of products
# make new file of usernames removed go through motions and push that up
# with rec engine
def find_prob(user_id):
# 1885
# fetch user from database
all_prod_likes_file = open('../data/number_all_prod_likes.json','r')
all_prod_likes = json.loads(all_prod_likes_file.read())
all_prod_likes_file.close()
all_users_file = open('../data/number_all_user_reviews.json','r')
all_users = json.loads(all_users_file.read())
all_users_file.close()
counter = 0
# ******** fetch user info from database *********
from db_controller import user_controller as u_ctrl
user_info = u_ctrl.get_user_as_dictionary(user_id)
from db_controller import product_controller as p_ctrl
products = p_ctrl.get_products_by_user_id(user_id)
# age processing
if user_info['birthday'] != '':
birthday = user_info['birthday']
from datetime import date
age = int(date.today().year) - int(birthday[0:4])
else:
age = None
current_user = {
'age':age,
'location':user_info['location'],
'skin_tone':user_info['skin_tone'],
'reviews':[]
}
# 'reviews':[{'rating':1,'productID':'sephorid'}]
# ********* built up current user from db fetch **************
for product in products:
current_user['reviews'].append({
'rating':product['product_rating'],
'productID':product['sephora_id']
})
# ******** fetch products ids to check against from file **********
productIDs = []
for prod in all_prod_likes:
counter +=1
productIDs.append(prod)
if counter > 1100:
break
# if counter > 11004:
# break
# if counter > productNum:
# keep store here
res = [(0,0),(0,0),(0,0),(0,0),(0,0)]
storage={}
# iterate over specified prodicts and return probability values
for productID in productIDs:
prod_like_ref = all_prod_likes[productID]
user_prod_likes = prod_like_ref['L']
user_prod_dislikes = prod_like_ref['D']
# user:simIndex
storage_like_simIndex = {}
storage_dislike_simIndex = {}
for name in user_prod_likes:
# name = str(name)
b = str(name)
user = all_users[b]
index = compoundSimilarityIndex(current_user,user)
storage_like_simIndex[b] = index
for name in user_prod_dislikes:
# name = str[name]python3
b = str(name)
user = all_users[b]
index = compoundSimilarityIndex(current_user,user)
storage_dislike_simIndex[b] = index
probability = prob_user_likes_product(productID,storage_like_simIndex,storage_dislike_simIndex)
# select maximum values algo
nextTupl = False
for index,tupl in enumerate(res):
# cascade change
if nextTupl != False:
temp = res[index]
res[index] = nextTupl
nextTupl = temp
elif probability>tupl[0]:
nextTupl = res[index]
res[index] = (probability,productID)
storage[productID] = probability
#return top 5 in a tuple
# print(res)
# unpack and push to db
r_ctrl.remove_recommendation(user_id)
# remove all recommendations from db
for index,tupl in enumerate(res):
rank = index+1
sephora_product_id = tupl[1]
# save to database
# make sure this saves the other id, not the sephora id
product_id = p_ctrl.get_product_id_by_sephora_product_id(sephora_product_id)
# product = p_ctrl.get_product_as_dictionary(product_id)
res = r_ctrl.add_recommendation(user_id, product_id, rank)
# print(productIDs)
# start = time.clock()
# probs = find_prob(productIDs,current_user)
# print(probs)
# end = time.clock()
# interval = end - start
# print(interval)
# print(probs[1])
# get rid of anons
# print similarityIndex(sampleUser0,sampleUser1)
# print similarityIndex(sampleUser0,sampleUser3)
# diff is high
# shift info to a mongodb
# make async worker go off and fetch stuff
# make other cool visualizations with data
# a graph of products
# make new file of usernames removed go through motions and push that up
# with rec engine
def userProfile(user_id):
if request.method == 'GET':
user = u_ctrl.get_user_as_dictionary(user_id)
userProducts = p_ctrl.get_products_by_user_id(user_id)
response = jsonify({'user': user, 'userProducts': userProducts})
return response, 200
