Initial Import from BitBucket

.gitignore

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+./idea/*
+./idea
+.idea
+__pycache__
+venv
+images
+data
+.DS_Store

Dockerfile

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+FROM ubuntu:latest
+MAINTAINER Rajdeep Dua "dua_rajdeep@yahoo.com"
+RUN apt-get update -y
+RUN apt-get install -y python3-pip build-essential
+COPY . /app
+WORKDIR /app
+RUN pip3 install -r requirements.txt
+ENTRYPOINT ["python3"]
+CMD ["app.py"]

Procfile

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+web: gunicorn app:app --log-file=-

app.py

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+import os
+from flask import Flask, render_template, url_for, send_from_directory
+from flask import jsonify
+from flask import request
+from flask_cors import CORS
+from flask_uploads import UploadSet, configure_uploads, IMAGES
+from boto import create_file_in_s3, create_file_in_local
+import numpy as np
+
+app = Flask(__name__)
+app.config.from_pyfile('config.py', silent=True)
+app.config['UPLOADED_PHOTOS_DEST'] = 'images'
+photos = UploadSet('photos', IMAGES)
+
+configure_uploads(app, photos)
+
+CORS(app, automatic_options=True)
+
+
+@app.route('/')
+def space_invaders():
+    return render_template('%s.html' % "spaceinvaders")
+
+@app.route('/favicon.ico')
+def favicon():
+    return send_from_directory(os.path.join(app.root_path, 'static'), 'favicon.ico', mimetype='image/vnd.microsoft.icon')
+
+@app.route('/thanks')
+def thanks():
+    return "thanks"
+
+
+@app.route('/upload', methods=['POST'], )
+def save_photos():
+    if app.config['SAVE_IMAGES']:
+        for file in request.files.keys():
+            if app.config['SAVE_TARGET'] == 'local':
+                print("Saving images to local...")
+                create_file_in_local(file, "a", request.files[file])
+            elif app.config['SAVE_TARGET'] == 's3':
+                print("Saving images to s3...")
+                create_file_in_s3(file, "a", request.files[file])
+            else:
+                print("Target configuration invalid (%s). Skipping data saving." % app.config['SAVE_TARGET'])
+    else:
+        print("Skipping images saving.")
+
+    return jsonify({"path": url_for('thanks')})
+
+
+@app.route('/upload_mobilenet_pred', methods=['POST'])
+def save_mobilenet_predictions():
+    if request.json is None:
+        print("Non JSON predictions. Quitting")
+        return jsonify({"path": url_for('thanks')})
+
+    if not app.config['SAVE_TENSORS']:
+        print("Skipping tensor saving")
+        return jsonify({"path": url_for('thanks')})
+
+    body = request.json
+    xs = body['xs']
+    ys = body['ys']
+    xs_rows, xs_columns = get_2d_shape(xs['shape'])
+    ys_rows, ys_columns = get_2d_shape(ys['shape'])
+    if xs_rows != ys_rows:
+        print("X rows count (%d) are not equal to Y rows (%d)" % (xs_rows, ys_rows))
+        jsonify({"error": "X rows count (%d) are not equal to Y rows (%d)" % (xs_rows, ys_rows)})
+
+    print("Saving tensors of %d predictions" % xs_rows)
+
+    # Save X
+    xs_2d = np.reshape(xs['data'], (xs_rows, xs_columns))
+    xs_data_file = open('data/xs.csv','ab')
+    np.savetxt(xs_data_file, xs_2d, delimiter=",")
+    xs_data_file.close()
+
+    # Save Y
+    ys_2d = np.reshape(ys['data'], (ys_rows, ys_columns))
+    ys_data_file = open('data/ys.csv','ab')
+    np.savetxt(ys_data_file, ys_2d, delimiter=",")
+    ys_data_file.close()
+    return jsonify({"path": url_for('thanks')})
+
+
+def get_2d_shape(shape):
+    rows = shape[0]
+    columns = 1
+    for i, num in enumerate(shape):
+        if i == 0:
+            continue
+        columns *= num
+    return rows, columns
+
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0")

boto.py

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+import os
+
+
+def create_file_in_s3(file_name, path, file):
+    import boto3
+    s3 = boto3.resource('s3')
+    print("fileName = %s, path = %s, file = %s" % (file_name, path, type(file)))
+    s3.Object('collect-data-for-machine-learning', file_name).put(Body=file)
+
+
+def create_file_in_local(file_name, path, file):
+    save_path = "images"
+    if not os.path.exists(save_path):
+        os.mkdir(save_path)
+    print("fileName = %s, path = %s, file = %s" % (file_name, path, type(file)))
+    file_path = os.path.join(save_path, file_name)
+    file.save(str(file_path))
+
+
+"""
+bucket = s3.Bucket('collect-data-for-machine-learning')
+exists = True
+try:
+    s3.meta.client.head_bucket(Bucket='collect-data-for-machine-learning')
+except botocore.exceptions.ClientError as e:
+    # If a client error is thrown, then check that it was a 404 error.
+    # If it was a 404 error, then the bucket does not exist.
+    error_code = int(e.response['Error']['Code'])
+    if error_code == 404:
+        exists = False
+"""

config.py

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+SAVE_IMAGES = False  # Whether to save images of users. Only if True, the SAVE_TARGET is relevant
+SAVE_TENSORS = True
+SAVE_TARGET = "local"  # For local: "local", for S3: "s3"

docker-compose.yml

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+version: "3.2"
+
+networks:
+  proxy:
+    driver: bridge
+
+services:
+  traefik:
+    image: traefik:1.6
+    #command: --web --logLevel=DEBUG --web.metrics.prometheus --web.metrics.prometheus.buckets="0.1,0.3,1.2,5.0"
+    ports:
+      - "80:80"
+      - "8080:8080"
+      - "443:443"
+    networks:
+      - proxy
+    volumes:
+      - /var/run/docker.sock:/var/run/docker.sock
+      - ./traefik.toml:/traefik.toml
+      #- ./usersFile:/usersFile
+      - ./acme.json:/acme.json
+    environment:
+      AWS_REGION: eu-central-1
+      AWS_HOSTED_ZONE_ID: 
+    labels:
+      traefik.enable: "false"
+
+  spaceinvaders:
+    image: tikalk/si-model-trainer:latest
+    #ports:
+    #  - "5000:5000"
+    networks:
+      - proxy
+    labels:
+      traefik.enable: "true"
+      traefik.port: 5000
+      traefik.frontend.passHostHeader: "true"
+      traefik.frontend.rule: "Host:si.hub.tikal.io"
+

requirements.txt

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+absl-py==0.2.2
+astor==0.6.2
+bleach==1.5.0
+boto3==1.7.35
+botocore==1.10.35
+click==6.7
+cycler==0.10.0
+docutils==0.14
+Flask==1.0.2
+Flask-Cors==3.0.4
+Flask-Uploads==0.2.1
+gast==0.2.0
+grpcio==1.12.1
+gunicorn==19.8.1
+h5py==2.7.1
+html5lib==0.9999999
+itsdangerous==0.24
+Jinja2==2.10
+jmespath==0.9.3
+Keras==2.1.4
+Keras-Applications==1.0.2
+Keras-Preprocessing==1.0.1
+kiwisolver==1.0.1
+Markdown==2.6.11
+MarkupSafe==1.0
+matplotlib==2.2.2
+numpy==1.14.1
+opencv-python==3.4.1.15
+Pillow==5.1.0
+protobuf==3.5.2.post1
+pyparsing==2.2.0
+python-dateutil==2.7.3
+pytz==2018.4
+PyYAML==3.12
+s3transfer==0.1.13
+scikit-learn==0.19.1
+scipy==1.1.0
+six==1.11.0
+sklearn==0.0
+tensorboard==1.8.0
+tensorflow==1.8.0
+tensorflow-hub==0.1.0
+tensorflowjs==0.4.2
+termcolor==1.1.0
+Werkzeug==0.14.1

runtime.txt

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+python-3.5.2