-
Notifications
You must be signed in to change notification settings - Fork 0
/
app (2).py
135 lines (108 loc) · 4.92 KB
/
app (2).py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
from flask import Flask, render_template, url_for, flash, redirect, request
import pandas as pd
from sklearn.neighbors import NearestNeighbors
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import pickle
label_encoder = LabelEncoder()
df=pd.read_csv("C:\\Users\\Sanpa Solutions\\Downloads\\Restaurant-Recommendation-System-main\\Restaurant-Recommendation-System-main\\food1.csv")
df['cuisine_encoded'] = label_encoder.fit_transform(df['cuisines'])
df['locality_encoded'] = label_encoder.fit_transform(df['locality'])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
df['average_cost_for_one'] = scaler.fit_transform(df[['average_cost_for_one']])
X = df[['cuisine_encoded', 'average_cost_for_one', 'locality_encoded']]
knn = NearestNeighbors(n_neighbors=5)
knn.fit(X)
input_data = [[57, 0.418182, 12]]
distances, indices = knn.kneighbors(input_data)
recommended_restaurants = df.iloc[indices[0]]
print(recommended_restaurants)
#model = pickle.load(open(r"C:\Users\Sanpa Solutions\Downloads\Restaurant-Recommendation-System-main\knn_model.pkl", 'rb'))
app = Flask(__name__)
#import pandas as pd
@app.route('/first')
def first_page():
return render_template('first_page.html')
lko_rest = pd.read_csv("food1.csv")
def fav(lko_rest1):
lko_rest1 = lko_rest1.reset_index()
from sklearn.feature_extraction.text import CountVectorizer
count1 = CountVectorizer(stop_words='english')
count_matrix = count1.fit_transform(lko_rest1['highlights'])
from sklearn.metrics.pairwise import cosine_similarity
cosine_sim2 = cosine_similarity(count_matrix, count_matrix)
sim = list(enumerate(cosine_sim2[0]))
sim = sorted(sim, key=lambda x: x[1], reverse=True)
sim = sim[1:11]
indi = [i[0] for i in sim]
final = lko_rest1.copy().iloc[indi[0]]
final = pd.DataFrame(final)
final = final.T
for i in range(1, len(indi)):
final1 = lko_rest1.copy().iloc[indi[i]]
final1 = pd.DataFrame(final1)
final1 = final1.T
final = pd.concat([final, final1])
return final
def rest_rec(cost, people=2, min_cost=0, cuisine=[], Locality=[], fav_rest="", lko_rest=lko_rest):
cost = cost + 200
x = cost / people
y = min_cost / people
lko_rest1 = lko_rest.copy().loc[lko_rest['locality'] == Locality[0]]
for i in range(1, len(Locality)):
lko_rest2 = lko_rest.copy().loc[lko_rest['locality'] == Locality[i]]
lko_rest1 = pd.concat([lko_rest1, lko_rest2])
lko_rest1.drop_duplicates(subset='name', keep='last', inplace=True)
lko_rest_locale = lko_rest1.copy()
lko_rest_locale = lko_rest_locale.loc[lko_rest_locale['average_cost_for_one'] <= x]
lko_rest_locale = lko_rest_locale.loc[lko_rest_locale['average_cost_for_one'] >= y]
lko_rest_locale['Start'] = lko_rest_locale['cuisines'].str.find(cuisine[0])
lko_rest_cui = lko_rest_locale.copy().loc[lko_rest_locale['Start'] >= 0]
for i in range(1, len(cuisine)):
lko_rest_locale['Start'] = lko_rest_locale['cuisines'].str.find(cuisine[i])
lko_rest_cu = lko_rest_locale.copy().loc[lko_rest_locale['Start'] >= 0]
lko_rest_cui = pd.concat([lko_rest_cui, lko_rest_cu])
lko_rest_cui.drop_duplicates(subset='name', keep='last', inplace=True)
if fav_rest != "":
favr = lko_rest.loc[lko_rest['name'] == fav_rest].drop_duplicates()
favr = pd.DataFrame(favr)
lko_rest3 = pd.concat([favr, lko_rest_cui])
lko_rest3.drop('Start', axis=1, inplace=True)
rest_selected = fav(lko_rest3)
else:
lko_rest_cui = lko_rest_cui.sort_values('scope', ascending=False)
rest_selected = lko_rest_cui.head(10)
return rest_selected
def calc(max_Price, people, min_Price, cuisine, locality):
rest_sugg = rest_rec(max_Price, people, min_Price, [cuisine], [locality])
rest_list1 = rest_sugg.copy().loc[:,
['name', 'address', 'locality', 'timings', 'aggregate_rating', 'url', 'cuisines']]
rest_list = pd.DataFrame(rest_list1)
rest_list = rest_list.reset_index()
rest_list = rest_list.rename(columns={'index': 'res_id'})
rest_list.drop('res_id', axis=1, inplace=True)
rest_list = rest_list.T
rest_list = rest_list
ans = rest_list.to_dict()
res = [value for value in ans.values()]
return res
@app.route("/")
@app.route("/home", methods=['POST'])
def home():
return render_template('home.html')
@app.route("/search", methods=['POST'])
def search():
if request.method == 'POST':
people = int(request.form['people'])
min_Price = int(request.form['min_Price'])
max_Price =int(request.form['max_Price'])
cuisine1 = request.form['cuisine']
locality1 = request.form['locality']
res = calc(max_Price, people, min_Price,cuisine1, locality1)
return render_template('search.html', title='Search', restaurants=res)
#return res
else:
return redirect(url_for('home'))
if __name__ == '__main__':
app.run(debug=True)