def all_restaurants():
id_list = np.unique(reviews_df.business_id)
data = []
for _id in id_list:
_dict = {}
_dict['id'] = _id
_dict['name'] = np.unique(reviews_df[reviews_df.business_id == _id].name)[0]
print _id
reviews = get_reviews(_id)
try:
cat_score = get_business_score(reviews,_id)
_dict['cat_score'] = cat_score
data.append(_dict)
except:
pass
return render_template("all_restaurants.html",
title = "Yelp Review Categorization",
data = data)
def index():
#bus_id = "vcNAWiLM4dR7D2nwwJ7nCA"
#bus_id = "mVHrayjG3uZ_RLHkLj-AMg"
#bus_id = "1qCuOcks5HRv67OHovAVpg"
bus_id = "wJr6kSA5dchdgOdwH6dZ2w"
res = get_reviews(bus_id)
try:
cat_score = get_business_score(res,bus_id)
name = np.unique(reviews_df[reviews_df.business_id == bus_id].name)[0]
print res
return render_template("index.html",
title = "Yelp Review Categorization",
cat_score = cat_score,
res = res,
name = name)
except Exception as e:
print e
print "nan",res
return render_template("index.html",
title = "Yelp Review Categorization",
cat_score = {},
res = [],
name = " ")
import numpy as np
import pandas as pd
from JsonToDF import get_data
from review import reviews_df
from review.review_score import get_reviews
from business import get_business_score
import pickle
count = 1000
df = get_data(count)
print df.shape
id_list = np.unique(reviews_df.business_id)
data = []
for _id in id_list:
_dict = {}
_dict['id'] = _id
_dict['name'] = np.unique(reviews_df[reviews_df.business_id == _id].name)[0]
print _id
reviews = get_reviews(_id)
try:
cat_score = get_business_score(reviews,_id)
_dict['cat_score'] = cat_score
data.append(_dict)
except:
pass
pickle.dump(data,open('data.p','w'))