Added app.py step and flask web application step

.github/workflows/main.yaml

@@ -0,0 +1,98 @@
+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=cnncls" | grep -q . && docker stop cnncls && docker rm -fv cnncls
+
+      - name: Run Docker Image to serve users
+        run: |
+         docker run -d -p 8080:8080 --name=cnncls -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

@@ -0,0 +1,9 @@
+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"]

README.md

@@ -40,3 +40,5 @@ DVC cmd
 dvc init
 dvc repro
 dvc dag
+
+

app.py

@@ -0,0 +1,53 @@
+from flask import Flask, request, jsonify, render_template
+import os
+from flask_cors import CORS, cross_origin
+from cnnClassifier.utils.common import decodeImage
+from cnnClassifier.pipeline.prediction import PredictionPipeline
+
+
+os.putenv('LANG', 'en_US.UTF-8')
+os.putenv('LC_ALL', 'en_US.UTF-8')
+
+app = Flask(__name__)
+CORS(app)
+
+
+class ClientApp:
+    def __init__(self):
+        self.filename = "inputImage.jpg"
+        self.classifier = PredictionPipeline(self.filename)
+
+
+
+
+
+@app.route("/", methods=['GET'])
+@cross_origin()
+def home():
+    return render_template('index.html')
+
+
+
+@app.route("/train", methods=['GET','POST'])
+@cross_origin()
+def trainRoute():
+    os.system("python main.py")
+    # os.system("dvc repro")
+    return "Training done successfully!"
+
+
+
+
+@app.route("/predict", methods=['POST'])
+@cross_origin()
+def predictRoute():
+    image = request.json['image']
+    decodeImage(image, clApp.filename)
+    result = clApp.classifier.predict()
+    return jsonify(result)
+
+
+
+if __name__ == "__main__":
+    clApp = ClientApp()
+    app.run(host='0.0.0.0', port=8080) #for AWS

src/cnnClassifier/pipeline/prediction.py

@@ -0,0 +1,32 @@
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import os
+
+
+
+class PredictionPipeline:
+    def __init__(self,filename):
+        self.filename =filename
+
+
+
+    def predict(self):
+        ## load model
+
+        # model = load_model(os.path.join("artifacts","training", "model.h5"))
+        model = load_model(os.path.join("model", "model.h5"))
+
+        imagename = self.filename
+        test_image = image.load_img(imagename, target_size = (224,224))
+        test_image = image.img_to_array(test_image)
+        test_image = np.expand_dims(test_image, axis = 0)
+        result = np.argmax(model.predict(test_image), axis=1)
+        print(result)
+
+        if result[0] == 1:
+            prediction = 'Normal'
+            return [{ "image" : prediction}]
+        else:
+            prediction = 'Adenocarcinoma Cancer'
+            return [{ "image" : prediction}]