patrickbrus

CloudFormationElasticBeanstalkApplication:
  Type: AWS::ElasticBeanstalk::Application
  Properties: 
    ApplicationName: !Sub ${ElasticApplicationName}-app

CloudFormationElasticBeanstalkEvnironmentStaging:
  Type: AWS::ElasticBeanstalk::Environment
  Properties:
    ApplicationName: !Ref CloudFormationElasticBeanstalkApplication
    EnvironmentName: !Sub ${ElasticApplicationName}-env-staging

patrickbrus

CodeBuildDockerBirdsClassifierImage:
  Type: AWS::CodeBuild::Project
  Properties: 
    Name: !Ref CodeBuildProjectName
    ServiceRole: !Ref CodeBuildBirdsClassifierImageServiceRole
    Artifacts:
      Type: CODEPIPELINE
    LogsConfig:
      CloudWatchLogs:
        Status: ENABLED

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version: 0.2

phases:
  pre_build:
    commands:
      - echo Build started on `date`
      - echo List all available files
      - ls -ll
      - echo Delete current model folder and fetch new model from s3
      - rm -rf model

patrickbrus

#!/bin/bash
BUCKET_NAME=birds-classifier-model-bucket

AWS_ID=$(aws sts get-caller-identity --query Account --output text | cat)
AWS_REGION=$(aws configure get region)

echo "Creating bucket "
aws s3api create-bucket --acl public-read --bucket $BUCKET_NAME --create-bucket-configuration LocationConstraint=$AWS_REGION 

echo "Enable versioning"

patrickbrus

CloudFormationCodePipeline:
  Type: AWS::CodePipeline::Pipeline
  Properties:
    Name: !Ref CodePipelineProjectName
    RoleArn: !GetAtt BirdsClassifierCodePipelineServiceRole.Arn
    Stages:
      - Name: SourceStage
        Actions:
          - Name: SourceAction
            ActionTypeId:

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name: Docker Image CI

on:
  push:
    branches: [ master ]
  pull_request:
    branches: [ master ]

jobs:

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version: '3'
services:
  deploy:
    build:
      context: .
      dockerfile: Dockerfile
    ports:
      - '80:5000'

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FROM python:3.9

WORKDIR /usr/src/app

# We copy just the requirements.txt first to leverage Docker cache
COPY ./requirements.txt ./

RUN pip install --upgrade pip
RUN pip install --no-cache-dir -r requirements.txt

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def load(dataset):
    dataset.to_csv("final_dataset.csv", index=False)

load(df_final)

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# Compute GDP by using GDP per capita and the Population columns
df_final["GDP"] = df_final["GDP per capita"] * df_final["Population"]

# Remove % sign of Population under 20 years old column and convert it to be of type float
def transform_col(col_val):
    try: 
        return float(col_val.replace(" %", ""))
    except: # value is NaN
        return col_val