Completed the code

.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

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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"]

README.md

@@ -1 +1,161 @@
-# chest-cancer-classification
+# **Chest Cancer Classification Project with MLOps**
+
+This project showcases an **end-to-end deep learning implementation** for classifying chest cancer using CT scan images. Leveraging **MLflow** and **DVC**, this project incorporates MLOps principles, focusing on efficient experiment tracking, modular pipeline design, and deployment-ready solutions.
+
+---
+
+## **Features**
+
+- **Comprehensive Workflow**:
+  - Modular implementation of deep learning pipelines.
+  - Utilizes MLflow for experiment tracking and model registration.
+  - Tracks and optimizes data pipelines with DVC.
+
+- **Scalable Framework**:
+  - Seamless integration of MLflow with local and remote servers.
+  - CICD-based deployments using Docker and cloud platforms.
+
+- **User-Friendly Interface**:
+  - Deployable web app for cancer classification.
+  - Real-time predictions from CT scan images.
+
+---
+
+## **Tech Stack**
+
+- **Languages**: Python
+- **Libraries**: TensorFlow, Keras, MLflow, DVC
+- **Deployment**: Docker, AWS, Azure
+- **Frontend**: HTML, CSS (basic)
+- **Version Control**: GitHub
+
+---
+
+## **Directory Structure**
+
+├── app
+│   ├── api
+│   ├── pipeline
+│   ├── templates
+│   ├── utils
+├── data
+│   ├── raw
+│   └── processed
+├── experiments
+│   └── MLflow runs and metadata
+├── docker
+├── models
+├── notebooks
+│   └── experiment_notebooks
+└── requirements.txt
+
+---
+
+## **Getting Started**
+
+### **Prerequisites**
+
+1. **Python**: Install Python 3.8 or later.
+2. **MLflow**: Set up MLflow locally or on a remote server like DagsHub or AWS.
+3. **DVC**: Install DVC for data versioning.
+4. **Docker**: Required for deployment.
+5. **Cloud Accounts**: AWS and Azure credentials for deployment.
+
+### **Installation**
+
+1. Clone the repository:
+
+   ```bash
+   git clone https://github.com/yourusername/chest-cancer-classification.git
+   cd chest-cancer-classification
+   ```
+
+2. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Set up MLflow tracking URI:
+   - For local: `http://localhost:5000`
+   - For remote: Configure with your cloud-based URI.
+
+---
+
+## **How It Works**
+
+### **Pipeline Overview**
+
+1. **Data Ingestion**:
+   - Raw CT scan images are processed into labeled datasets.
+   - Metadata is managed using DVC.
+
+2. **Preprocessing**:
+   - Data augmentation techniques applied to normalize and enhance images.
+
+3. **Training and Experimentation**:
+   - Model training using TensorFlow with hyperparameter tuning.
+   - MLflow tracks all experiment parameters, metrics, and artifacts.
+
+4. **Deployment**:
+   - Web application built to accept images and provide predictions.
+   - Dockerized app deployed on AWS or Azure using CICD pipelines.
+
+---
+
+## **Project Highlights**
+
+- **MLflow Experiment Tracking**:
+  - Automatic logging of parameters, metrics, and models.
+  - Supports both local and cloud deployments.
+
+- **DVC Integration**:
+  - Tracks dataset versions and ensures data consistency.
+
+- **Web Interface**:
+  - Intuitive image uploader to classify chest cancer from CT scans.
+  - Predicts cancer types such as Adenocarcinoma.
+
+---
+
+## **Example Usage**
+
+- **Run MLflow Server**:
+
+  ```bash
+  mlflow ui
+  ```
+
+- **Execute Training Pipeline**:
+
+  ```bash
+  python app/api/train.py --config config/train_config.yaml
+  ```
+
+- **Launch Application**:
+
+  ```bash
+  docker-compose up
+  ```
+
+---
+
+## **Future Enhancements**
+
+- Expand dataset for better accuracy.
+- Improve web app UI using modern frameworks.
+- Automate pipeline with more MLOps tools.
+
+---
+
+## **Contributing**
+
+We welcome contributions! Please fork the repository and submit a pull request.
+
+---
+
+## **License**
+
+MIT License - See [LICENSE](./LICENSE) for details.
+
+---