├── Dockerfile
├── README.md
├── app.py
├── deployment_28042020.pkl
├── requirements.txt
├── static
└── style.css
└── templates
└── home.html
/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM python:3.7
2 |
3 | RUN pip install virtualenv
4 | ENV VIRTUAL_ENV=/venv
5 | RUN virtualenv venv -p python3
6 | ENV PATH="VIRTUAL_ENV/bin:$PATH"
7 |
8 | WORKDIR /app
9 | ADD . /app
10 |
11 | # Install dependencies
12 | RUN pip install -r requirements.txt
13 |
14 | # Expose port
15 | EXPOSE 5000
16 |
17 | # Run the application:
18 | CMD ["python", "app.py"]
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Deployment on Microsoft Azure using docker container
--------------------------------------------------------------------------------
/app.py:
--------------------------------------------------------------------------------
1 | from flask import Flask,request, url_for, redirect, render_template, jsonify
2 | from pycaret.regression import *
3 | import pandas as pd
4 | import pickle
5 | import numpy as np
6 |
7 | app = Flask(__name__)
8 |
9 | model=load_model('deployment_28042020')
10 |
11 | @app.route('/')
12 | def home():
13 | return render_template("home.html")
14 |
15 |
16 | @app.route('/predict',methods=['POST'])
17 | def predict():
18 | int_features=[x for x in request.form.values()]
19 | final=np.array(int_features)
20 | col = ['age', 'sex', 'bmi', 'children', 'smoker', 'region']
21 | data_unseen = pd.DataFrame([final], columns = col)
22 | print(int_features)
23 | print(final)
24 | prediction=predict_model(model, data=data_unseen, round = 0)
25 | prediction=int(prediction.Label[0])
26 | return render_template('home.html',pred='Expected Bill will be {}'.format(prediction))
27 |
28 | @app.route('/predict_api',methods=['POST'])
29 | def predict_api():
30 | '''
31 | For direct API calls trought request
32 | '''
33 | data = request.get_json(force=True)
34 | data_unseen = pd.DataFrame([data])
35 | prediction = predict_model(model, data=data_unseen)
36 | output = prediction.Label[0]
37 | return jsonify(output)
38 |
39 | if __name__ == '__main__':
40 | app.run(host='0.0.0.0', debug = True)
--------------------------------------------------------------------------------
/deployment_28042020.pkl:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/pycaret/pycaret-deployment-azure/8efc8feb8ba6e6ddc4efc3682890b75d666aa651/deployment_28042020.pkl
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | pycaret==1.0.0
2 | Flask==1.1.1
3 | Jinja2==2.10.1
4 | gunicorn==19.9.0
5 | certifi==2019.11.28
6 | itsdangerous==1.1.0
7 | numpy>=1.17
--------------------------------------------------------------------------------
/static/style.css:
--------------------------------------------------------------------------------
1 | @import url(https://fonts.googleapis.com/css?family=Open+Sans);
2 | .btn { display: inline-block; *display: inline; *zoom: 1; padding: 4px 10px 4px; margin-bottom: 0; font-size: 13px; line-height: 18px; color: #333333; text-align: center;text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); vertical-align: middle; background-color: #f5f5f5; background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); background-image: -ms-linear-gradient(top, #ffffff, #e6e6e6); background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); background-image: linear-gradient(top, #ffffff, #e6e6e6); background-repeat: repeat-x; filter: progid:dximagetransform.microsoft.gradient(startColorstr=#ffffff, endColorstr=#e6e6e6, GradientType=0); border-color: #e6e6e6 #e6e6e6 #e6e6e6; border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); border: 1px solid #e6e6e6; -webkit-border-radius: 4px; -moz-border-radius: 4px; border-radius: 4px; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); cursor: pointer; *margin-left: .3em; }
3 | .btn:hover, .btn:active, .btn.active, .btn.disabled, .btn[disabled] { background-color: #e6e6e6; }
4 | .btn-large { padding: 9px 14px; font-size: 15px; line-height: normal; -webkit-border-radius: 5px; -moz-border-radius: 5px; border-radius: 5px; }
5 | .btn:hover { color: #333333; text-decoration: none; background-color: #e6e6e6; background-position: 0 -15px; -webkit-transition: background-position 0.1s linear; -moz-transition: background-position 0.1s linear; -ms-transition: background-position 0.1s linear; -o-transition: background-position 0.1s linear; transition: background-position 0.1s linear; }
6 | .btn-primary, .btn-primary:hover { text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); color: #ffffff; }
7 | .btn-primary.active { color: rgba(255, 255, 255, 0.75); }
8 | .btn-primary { background-color: #4a77d4; background-image: -moz-linear-gradient(top, #6eb6de, #4a77d4); background-image: -ms-linear-gradient(top, #6eb6de, #4a77d4); background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#6eb6de), to(#4a77d4)); background-image: -webkit-linear-gradient(top, #6eb6de, #4a77d4); background-image: -o-linear-gradient(top, #6eb6de, #4a77d4); background-image: linear-gradient(top, #6eb6de, #4a77d4); background-repeat: repeat-x; filter: progid:dximagetransform.microsoft.gradient(startColorstr=#6eb6de, endColorstr=#4a77d4, GradientType=0); border: 1px solid #3762bc; text-shadow: 1px 1px 1px rgba(0,0,0,0.4); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.5); }
9 | .btn-primary:hover, .btn-primary:active, .btn-primary.active, .btn-primary.disabled, .btn-primary[disabled] { filter: none; background-color: #4a77d4; }
10 | .btn-block { width: 100%; display:block; }
11 |
12 | * { -webkit-box-sizing:border-box; -moz-box-sizing:border-box; -ms-box-sizing:border-box; -o-box-sizing:border-box; box-sizing:border-box; }
13 |
14 | html { width: 100%; height:100%; overflow:hidden; }
15 |
16 | body {
17 | width: 100%;
18 | height:100%;
19 | font-family: 'Open Sans', sans-serif;
20 | background: #092756;
21 | color: #fff;
22 | font-size: 18px;
23 | text-align:center;
24 | letter-spacing:1.2px;
25 | background: -moz-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%),-moz-linear-gradient(top, rgba(57,173,219,.25) 0%, rgba(42,60,87,.4) 100%), -moz-linear-gradient(-45deg, #670d10 0%, #092756 100%);
26 | background: -webkit-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%), -webkit-linear-gradient(top, rgba(57,173,219,.25) 0%,rgba(42,60,87,.4) 100%), -webkit-linear-gradient(-45deg, #670d10 0%,#092756 100%);
27 | background: -o-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%), -o-linear-gradient(top, rgba(57,173,219,.25) 0%,rgba(42,60,87,.4) 100%), -o-linear-gradient(-45deg, #670d10 0%,#092756 100%);
28 | background: -ms-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%), -ms-linear-gradient(top, rgba(57,173,219,.25) 0%,rgba(42,60,87,.4) 100%), -ms-linear-gradient(-45deg, #670d10 0%,#092756 100%);
29 | background: -webkit-radial-gradient(0% 100%, ellipse cover, rgba(104,128,138,.4) 10%,rgba(138,114,76,0) 40%), linear-gradient(to bottom, rgba(57,173,219,.25) 0%,rgba(42,60,87,.4) 100%), linear-gradient(135deg, #670d10 0%,#092756 100%);
30 | filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#3E1D6D', endColorstr='#092756',GradientType=1 );
31 |
32 | }
33 | .login {
34 | position: absolute;
35 | top: 40%;
36 | left: 50%;
37 | margin: -150px 0 0 -150px;
38 | width:400px;
39 | height:400px;
40 | }
41 |
42 | .login h1 { color: #fff; text-shadow: 0 0 10px rgba(0,0,0,0.3); letter-spacing:1px; text-align:center; }
43 |
44 | input {
45 | width: 100%;
46 | margin-bottom: 10px;
47 | background: rgba(0,0,0,0.3);
48 | border: none;
49 | outline: none;
50 | padding: 10px;
51 | font-size: 13px;
52 | color: #fff;
53 | text-shadow: 1px 1px 1px rgba(0,0,0,0.3);
54 | border: 1px solid rgba(0,0,0,0.3);
55 | border-radius: 4px;
56 | box-shadow: inset 0 -5px 45px rgba(100,100,100,0.2), 0 1px 1px rgba(255,255,255,0.2);
57 | -webkit-transition: box-shadow .5s ease;
58 | -moz-transition: box-shadow .5s ease;
59 | -o-transition: box-shadow .5s ease;
60 | -ms-transition: box-shadow .5s ease;
61 | transition: box-shadow .5s ease;
62 | }
63 | input:focus { box-shadow: inset 0 -5px 45px rgba(100,100,100,0.4), 0 1px 1px rgba(255,255,255,0.2); }
--------------------------------------------------------------------------------
/templates/home.html:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 |
6 | Predict Insurance Bill
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
Predict Insurance Bill
18 |
19 |
20 |
31 |
32 |
33 |
34 |
35 |
36 |
37 | {{pred}}
38 |
39 |
40 |
--------------------------------------------------------------------------------