-
Notifications
You must be signed in to change notification settings - Fork 8
/
app.py
56 lines (31 loc) · 1.03 KB
/
app.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
from flask import Flask, render_template, request
from keras.models import load_model
from keras.preprocessing import image
app = Flask(__name__)
dic = {0 : 'cat', 1 : 'dog', 2 :"horse", 3:'human'}
model = load_model('model.h5')
model._make_predict_function()
def predict_label(img_path):
i = image.load_img(img_path, target_size=(100,100))
i = image.img_to_array(i)
i = i.reshape(1, 100,100,3)
p = model.predict_classes(i)
return dic[p[0]]
# routes
@app.route("/", methods=['GET', 'POST'])
def kuch_bhi():
return render_template("home.html")
@app.route("/about")
def about_page():
return "About You..!!!"
@app.route("/submit", methods = ['GET', 'POST'])
def get_hours():
if request.method == 'POST':
img = request.files['my_image']
img_path = "static/" + img.filename
img.save(img_path)
p = predict_label(img_path)
return render_template("home.html", prediction = p, img_path = img_path)
if __name__ =='__main__':
#app.debug = True
app.run(debug = True)