├── Dockerfile
├── model.pkl
├── model.py
├── request.py
├── requirements.txt
├── server.py
└── shell.sh


/Dockerfile:
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 1 | FROM ubuntu:16.04
 2 | 
 3 | FROM python:3.6.5
 4 | 
 5 | RUN apt-get update -y && \
 6 |     apt-get install -y python-pip python-dev
 7 | 
 8 | # We copy just the requirements.txt first to leverage Docker cache
 9 | COPY ./requirements.txt /app/requirements.txt
10 | 
11 | WORKDIR /app
12 | 
13 | RUN pip install -r requirements.txt
14 | 
15 | COPY . /app
16 | 
17 | CMD python /app/model.py && python /app/server.py
18 | 
19 |  
20 | 


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/model.pkl:
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https://raw.githubusercontent.com/EkramulHoque/docker-jenkins-flask-tutorial/e4f7d1ed388e1d9c3e752a2ccc9699c6674bcad8/model.pkl


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/model.py:
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 1 | #!/usr/bin/env python
 2 | # coding: utf-8
 3 | import pickle
 4 | from  sklearn import  datasets
 5 | iris=datasets.load_iris()
 6 | x=iris.data
 7 | y=iris.target
 8 | 
 9 | #labels for iris dataset
10 | labels ={
11 |   0: "setosa",
12 |   1: "versicolor",
13 |   2: "virginica"
14 | }
15 | 
16 | #split the data set
17 | from sklearn.model_selection import train_test_split
18 | x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.25)
19 | 
20 | #Using decision tree algorithm
21 | from sklearn import tree
22 | classifier=tree.DecisionTreeClassifier()
23 | classifier.fit(x_train,y_train)
24 | predictions=classifier.predict(x_test)
25 | 
26 | #export the model
27 | pickle.dump(classifier, open('model.pkl','wb'))
28 | 
29 | #load the model and test with a custom input
30 | model = pickle.load( open('model.pkl','rb'))
31 | x = [[6.7, 3.3, 5.7, 2.1]]
32 | predict = model.predict(x)
33 | print("Hello Worlds")
34 | print(labels[predict[0]])


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/request.py:
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 1 | import requests
 2 | # Change the value of experience that you want to test
 3 | url = 'http://192.168.99.100:5000/api'
 4 | feature = [[5.8, 4.0, 1.2, 0.2]]
 5 | labels ={
 6 |   0: "setosa",
 7 |   1: "versicolor",
 8 |   2: "virginica"
 9 | }
10 | 
11 | r = requests.post(url,json={'feature': feature})
12 | print(labels[r.json()])


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/requirements.txt:
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1 | flask
2 | flask_restful
3 | sklearn 
4 | pandas
5 | numpy
6 | 


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/server.py:
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 1 | from flask import Flask, request, jsonify
 2 | import json
 3 | import pickle
 4 | import pandas as pd
 5 | import numpy as np
 6 | 
 7 | app = Flask(__name__)
 8 | 
 9 | # Load the model
10 | model = pickle.load(open('model.pkl','rb'))
11 | labels ={
12 |   0: "versicolor",   
13 |   1: "setosa",
14 |   2: "virginica"
15 | }
16 | 
17 | @app.route('/api',methods=['POST'])
18 | def predict():
19 |     # Get the data from the POST request.
20 | 	data = request.get_json(force=True)
21 | 	predict = model.predict(data['feature'])
22 | 	return jsonify(predict[0].tolist())
23 | 
24 | if __name__ == '__main__':
25 |     app.run(debug=True,host='0.0.0.0')


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/shell.sh:
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1 | #!/bin/bash
2 | set -e
3 | python model.py && python server.py


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