Web App

.github/workflows/main.yaml

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+name: workflow
+
+on:
+  push:
+    branches:
+      - main
+    paths-ignore:
+      - 'README.md'
+
+permissions:
+  id-token: write
+  contents: read
+
+jobs:
+  integration:
+    name: Continuous Integration
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+
+      - name: Lint code
+        run: echo "Linting repository"
+
+      - name: Run unit tests
+        run: echo "Running unit tests"
+
+  build-and-push-ecr-image:
+    name: Continuous Delivery
+    needs: integration
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+
+      - 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 }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v1
+
+      - 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
+
+      - 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 }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v1
+
+
+      - 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
+
+      - 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

Dockerfile

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+FROM python:3.8-slim-buster
+
+RUN apt update -y && apt install awscli -y
+WORKDIR /app
+
+COPY . /app
+RUN pip install -r requirements.txt
+
+CMD ["python3", "app.py"]

app.py

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+from flask import Flask, render_template, request
+import os 
+import numpy as np
+import pandas as pd
+from Project0.pipeline.prediction import PredictionPipeline
+
+app = Flask(__name__)
+
+@app.route('/',methods=['GET'])
+def homePage():
+    return render_template("index.html")
+
+@app.route('/train',methods=['GET']) 
+def training():
+    os.system("python main.py")
+    return "Training Successful!" 
+
+@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'])
+
+
+            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)
+
+            obj = PredictionPipeline()
+            predict = obj.predict(data)
+
+            return render_template('results.html', prediction = str(predict))
+
+        except Exception as e:
+            print('The Exception message is: ',e)
+            return 'something is wrong'
+
+    else:
+        return render_template('index.html')
+
+
+if __name__ == "__main__":
+	app.run(host="0.0.0.0", port = 8080, debug=True)
+	# app.run(host="0.0.0.0", port = 8080)