def recommend():
user_id = str(request.form['user_id'])
#recmd_result = db.query_userid(user_id)
per = recommenderDB.minidatabase('recommend')
recmd_result = per.query_userid(user_id)
per.close()
#review_dataset = pd.read_csv('yelp_academic_dataset_review.csv')
#business_dataset = pd.read_csv('yelp_academic_dataset_business.csv')
plot_sun_1 = individual_table.individ_selfexplore(user_id)
plot_li_1 = user_individual_table.user_info_table(user_id)
plot_li_2 = user_individual_barchart.user_info_bars(user_id)
plot_sun_2 = word_cloud.individ_wc(user_id)
return render_template('recommend.html',
user_id=user_id,
plot_sun_2=plot_sun_2,
recmd=recmd_result,
plot_sun_1=plot_sun_1,
plot_li_1=plot_li_1,
plot_li_2=plot_li_2)
def individ_wc(user_id):
db = recommenderDB.minidatabase('recommend')
#text_data = review_dataset[review_dataset['user_id'] == user_id]['text'].tolist()
individual_data = db.query_review(user_id)
a = ''
for i in range(len(individual_data)):
a += individual_data[i][2]
wordcloud = WordCloud(background_color="white", max_words=2000, mask=food_mask,
stopwords=STOPWORDS.add("food"))
# generate word cloud
wordcloud.generate(a)
cwd = os.getcwd()
wc_path = cwd + '/static/images/test/individual_wc'+user_id+'.png'
wordcloud.to_file(wc_path)
#print(cwd)
db.close()
#print(wc_path)
return '/static/images/test/individual_wc'+user_id+'.png'
#individ_wc('qEE5EvV-f-s7yHC0Z4ydJQ')
def individ_selfexplore(user_id):
db = recommenderDB.minidatabase('recommend')
individual_data = db.query_review(user_id)
business_user = []
business_time = []
business_name = []
business_categories = []
business_city = []
business_stars = []
business_address = []
for i in range(min(len(individual_data), 10)):
business_user.append(individual_data[i][1])
business_time.append(individual_data[i][3])
business_name.append(individual_data[i][11])
business_categories.append(individual_data[i][18].split(',')[0])
business_city.append(individual_data[i][16])
business_stars.append(individual_data[i][15])
business_address.append(individual_data[i][14])
individual_info = list(
zip(business_time, business_name, business_categories, business_city,
business_stars, business_address))
#individual_info = title.append(individual_info)
individual_info = np.array(individual_info)
individual_info = np.insert(individual_info, 0, title, axis=0)
table = FF.create_table(individual_info)
#table.layout.height = 600
#individual_plot = py.plot(table, filename='individual_selfexplore')
table.layout.width = 1700
cwd = os.getcwd()
wc_path = cwd + '/static/images/test/individual_selfexplore' + user_id + '.png'
py.image.save_as(table, filename=wc_path)
db.close()
return '/static/images/test/individual_selfexplore' + user_id + '.png'
from os import path
import os
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
import pandas as pd
import recommenderDB
from scipy.misc import imread
food_mask = imread("Yelp_Logo.jpg")
db = recommenderDB.minidatabase('recommend')
def individ_wc(user_id):
#text_data = review_dataset[review_dataset['user_id'] == user_id]['text'].tolist()
individual_dataset = db.query_review(user_id)
a = ''
for i in range(len(individual_data)):
a += individual_data[i][2]
wordcloud = WordCloud(background_color="white",
max_words=2000,
mask=food_mask,
stopwords=STOPWORDS.add("food"))
# generate word cloud
wordcloud.generate(a)
cwd = os.getcwd()