├── model.h5
├── Dockerfile
├── config.py
├── requirements.txt
├── app.yaml
└── app.py


/model.h5:
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https://raw.githubusercontent.com/department-for-transport/ValidatRKerasService/master/model.h5


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/Dockerfile:
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1 | FROM python:3.6-jessie
2 | RUN apt update
3 | WORKDIR /app
4 | ADD requirements.txt /app/requirements.txt
5 | RUN pip install -r /app/requirements.txt
6 | ADD . /app
7 | ENV PORT 8080
8 | CMD ["gunicorn", "app:app", "--config=config.py"]


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/config.py:
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 1 | from os import environ as env
 2 | import multiprocessing
 3 | 
 4 | PORT = int(env.get("PORT", 8080))
 5 | DEBUG_MODE = int(env.get("DEBUG_MODE", 1))
 6 | 
 7 | # Gunicorn config
 8 | bind = ":" + str(PORT)
 9 | workers = multiprocessing.cpu_count() * 2 + 1
10 | threads = 2 * multiprocessing.cpu_count()


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/requirements.txt:
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 1 | absl-py==0.7.1
 2 | astor==0.8.0
 3 | Click==7.0
 4 | Flask==1.0.3
 5 | gast==0.2.2
 6 | google-pasta==0.1.7
 7 | grpcio==1.21.1
 8 | gunicorn==19.9.0
 9 | h5py==2.9.0
10 | itsdangerous==1.1.0
11 | Jinja2==2.10.1
12 | joblib==0.13.2
13 | jsonify==0.5
14 | Keras==2.2.4
15 | Keras-Applications==1.0.8
16 | Keras-Preprocessing==1.1.0
17 | Markdown==3.1.1
18 | MarkupSafe==1.1.1
19 | numpy==1.16.4
20 | pandas==0.24.2
21 | protobuf==3.8.0
22 | python-dateutil==2.8.0
23 | pytz==2019.1
24 | PyYAML==5.1.1
25 | scikit-learn==0.21.2
26 | scipy==1.3.0
27 | six==1.12.0
28 | sklearn==0.0
29 | tensorboard==1.14.0
30 | tensorflow==1.14.0
31 | tensorflow-estimator==1.14.0
32 | termcolor==1.1.0
33 | Werkzeug==0.15.4
34 | wrapt==1.11.2
35 | 


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/app.yaml:
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 1 | apiVersion: apps/v1
 2 | kind: Deployment
 3 | metadata:
 4 |   name: validatr-keras-web-service
 5 |   labels:
 6 |     name: validatr-keras-web-service
 7 | spec:
 8 |   replicas: 1
 9 |   selector:
10 |     matchLabels:
11 |       name: validatr-keras-web-service
12 |   template:
13 |     metadata:
14 |       name: validatr-keras-web-service
15 |       labels:
16 |         name: validatr-keras-web-service
17 |     spec:
18 |       containers:
19 |         - name: validatr-keras-web-service
20 |           image: gcr.io/dft-da-sb-lab/flask_keras
21 |           ports:
22 |             - containerPort: 8080
23 |           resources:
24 |             requests:
25 |               memory: 256Mi
26 |             limits:
27 |               memory: 512Mi
28 |           env:
29 |             - name: DEBUG_MODE
30 |               value: "1"


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/app.py:
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 1 | from flask import Flask
 2 | from flask import request
 3 | from flask import jsonify
 4 | import json
 5 | import keras
 6 | import numpy
 7 | import pandas
 8 | import tensorflow as tf
 9 | import config
10 | 
11 | #load model
12 | model = keras.models.load_model('model.h5')
13 | graph = tf.get_default_graph()
14 | classes = ['Cycle', 'HGV','LGV','car', 'largecar']
15 | 
16 | app = Flask(__name__)
17 | 
18 | @app.route("/predict", methods=['POST'])
19 | def predict():
20 |     parameters = request.json
21 |     
22 |     X = pandas.DataFrame.from_dict(parameters,orient='index').values
23 |     X = tf.keras.utils.normalize(X)
24 |     with graph.as_default():
25 |         predictions = model.predict(X)
26 |     predictions = numpy.argmax(predictions, axis=1)
27 | 
28 |     
29 |     
30 |     class_list = [classes[prediction] for prediction in predictions]
31 | 
32 | 
33 |     class_json = json.dumps({ i : class_list[i] for i in range(0, len(class_list) ) })
34 |     
35 |     return jsonify(class_json)
36 | 
37 | if __name__ == "__main__":
38 |     app.run(host="0.0.0.0", port=config.PORT, debug=config.DEBUG_MODE)


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