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| Original file line number | Diff line number | Diff line change |
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| name: workflow | ||
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| on: | ||
| push: | ||
| branches: | ||
| - main | ||
| paths-ignore: | ||
| - "README.md" | ||
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| permissions: | ||
| id-token: write | ||
| contents: read | ||
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| jobs: | ||
| integration: | ||
| name: Continuous Integration | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - name: Checkout Code | ||
| uses: actions/checkout@v3 | ||
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| - name: Lint code | ||
| run: echo "Linting repository" | ||
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| - name: Run unit tests | ||
| run: echo "Running unit tests" | ||
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| build-and-push-ecr-image: | ||
| name: Continuous Delivery | ||
| needs: integration | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - name: Checkout Code | ||
| uses: actions/checkout@v3 | ||
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| - name: Install Utilities | ||
| run: | | ||
| sudo apt-get update | ||
| sudo apt-get install -y jq unzip | ||
| - name: Configure AWS credentials | ||
| uses: aws-actions/configure-aws-credentials@v1 | ||
| with: | ||
| aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} | ||
| aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} | ||
| aws-region: ${{ secrets.AWS_REGION }} | ||
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| - name: Login to Amazon ECR | ||
| id: login-ecr | ||
| uses: aws-actions/amazon-ecr-login@v1 | ||
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| - name: Build, tag, and push image to Amazon ECR | ||
| id: build-image | ||
| env: | ||
| ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }} | ||
| ECR_REPOSITORY: ${{ secrets.ECR_REPOSITORY_NAME }} | ||
| IMAGE_TAG: latest | ||
| run: | | ||
| # Build a docker container and | ||
| # push it to ECR so that it can | ||
| # be deployed to ECS. | ||
| docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG . | ||
| docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG | ||
| echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG" | ||
| Continuous-Deployment: | ||
| needs: build-and-push-ecr-image | ||
| runs-on: self-hosted | ||
| steps: | ||
| - name: Checkout | ||
| uses: actions/checkout@v3 | ||
|
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| - name: Configure AWS credentials | ||
| uses: aws-actions/configure-aws-credentials@v1 | ||
| with: | ||
| aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} | ||
| aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} | ||
| aws-region: ${{ secrets.AWS_REGION }} | ||
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| - name: Login to Amazon ECR | ||
| id: login-ecr | ||
| uses: aws-actions/amazon-ecr-login@v1 | ||
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| - name: Pull latest images | ||
| run: | | ||
| docker pull ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest | ||
| # - name: Stop and remove container if running | ||
| # run: | | ||
| # docker ps -q --filter "name=mlproj" | grep -q . && docker stop mlproj && docker rm -fv mlproj | ||
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| - name: Run Docker Image to serve users | ||
| run: | | ||
| docker run -d -p 8080:8080 --name=mlproj -e 'AWS_ACCESS_KEY_ID=${{ secrets.AWS_ACCESS_KEY_ID }}' -e 'AWS_SECRET_ACCESS_KEY=${{ secrets.AWS_SECRET_ACCESS_KEY }}' -e 'AWS_REGION=${{ secrets.AWS_REGION }}' ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest | ||
| - name: Clean previous images and containers | ||
| run: | | ||
| docker system prune -f |
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| @@ -0,0 +1,9 @@ | ||
| FROM python:3.8-slim-buster | ||
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| RUN apt update -y && apt install awscli -y | ||
| WORKDIR /app | ||
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| COPY . /app | ||
| RUN pip install -r requirements.txt | ||
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| CMD ["python3", "app.py"] |
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| @@ -0,0 +1,53 @@ | ||
| from flask import Flask, render_template, request | ||
| import os | ||
| import numpy as np | ||
| import pandas as pd | ||
| from src.mlProject.pipeline.prediction import PredictionPipeline | ||
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| app = Flask(__name__) | ||
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| @app.route('/', methods=['GET']) | ||
| def homePage(): | ||
| return render_template("index.html") | ||
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| @app.route('/train', methods=['GET']) | ||
| def training(): | ||
| os.system("python main.py") | ||
| return "Training Successful" | ||
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| @app.route('/predict',methods=['POST','GET']) # route to show the predictions in a web UI | ||
| def index(): | ||
| if request.method == 'POST': | ||
| try: | ||
| # reading the inputs given by the user | ||
| fixed_acidity =float(request.form['fixed_acidity']) | ||
| volatile_acidity =float(request.form['volatile_acidity']) | ||
| citric_acid =float(request.form['citric_acid']) | ||
| residual_sugar =float(request.form['residual_sugar']) | ||
| chlorides =float(request.form['chlorides']) | ||
| free_sulfur_dioxide =float(request.form['free_sulfur_dioxide']) | ||
| total_sulfur_dioxide =float(request.form['total_sulfur_dioxide']) | ||
| density =float(request.form['density']) | ||
| pH =float(request.form['pH']) | ||
| sulphates =float(request.form['sulphates']) | ||
| alcohol =float(request.form['alcohol']) | ||
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| data = [fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,pH,sulphates,alcohol] | ||
| data = np.array(data).reshape(1, 11) | ||
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| obj = PredictionPipeline() | ||
| predict = obj.predict(data) | ||
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| return render_template('results.html', prediction = str(predict)) | ||
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| except Exception as e: | ||
| print('The Exception message is: ',e) | ||
| return 'something is wrong' | ||
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| else: | ||
| return render_template('index.html') | ||
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| if __name__ == '__main__': | ||
| app.run(host='0.0.0.0', port=8080, debug=True) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,13 @@ | ||
| import joblib | ||
| import numpy as np | ||
| import pandas as pd | ||
| from pathlib import Path | ||
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| class PredictionPipeline: | ||
| def __init__(self): | ||
| self.model = joblib.load(Path('artifacts/model_trainer/model.joblib')) | ||
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| def predict(self, data): | ||
| prediction = self.model.predict(data) | ||
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| return prediction |
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