add files

.dockerignore

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+artifact
+saved_models
+venv
+docs
+flowcharts
+notebooks
+.git
+aps_failure_training_set1.csv

.github/workflows/main.yml

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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 sensor container if running
+        run: |
+         docker ps -q --filter "name=sensor" | grep -q . && docker stop sensor && docker rm -fv sensor
+       
+      - name: Run Docker Image to serve users
+        run: |
+         docker run -d -p 80:8080 --name=sensor -e 'MONGO_DB_URL=${{ secrets.MONGO_DB_URL }}' -e 'AWS_ACCESS_KEY_ID=${{ 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

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+artifact
+.vscode
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+saved_models/*
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

Dockerfile

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+FROM python:3.8.5-slim-buster
+RUN apt update -y && apt install awscli -y
+WORKDIR /app
+
+COPY . /app
+RUN pip install -r requirements.txt
+
+CMD ["python3", "main.py"]

README.md

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+# Sensor-Fault-Detection
+
+### Problem Statement
+The Air Pressure System (APS) is a critical component of a heavy-duty vehicle that uses compressed air to force a piston to provide pressure to the brake pads, slowing the vehicle down. The benefits of using an APS instead of a hydraulic system are the easy availability and long-term sustainability of natural air.
+
+This is a Binary Classification problem, in which the affirmative class indicates that the failure was caused by a certain component of the APS, while the negative class
+indicates that the failure was caused by something else.
+
+### Solution Proposed 
+In this project, the system in focus is the Air Pressure system (APS) which generates pressurized air that are utilized in various functions in a truck, such as braking and gear changes. The datasets positive class corresponds to component failures for a specific component of the APS system. The negative class corresponds to trucks with failures for components not related to the APS system.
+
+The problem is to reduce the cost due to unnecessary repairs. So it is required to minimize the false predictions.
+## Tech Stack Used
+1. Python 
+2. FastAPI 
+3. Machine learning algorithms
+4. Docker
+5. MongoDB
+
+## Infrastructure Required.
+
+1. AWS S3
+2. AWS EC2
+3. AWS ECR
+4. Git Actions
+5. Terraform
+
+## How to run?
+Before we run the project, make sure that you are having MongoDB in your local system, with Compass since we are using MongoDB for data storage. You also need AWS account to access the service like S3, ECR and EC2 instances.
+
+## Data Collections
+![image](https://user-images.githubusercontent.com/57321948/193536736-5ccff349-d1fb-486e-b920-02ad7974d089.png)
+
+
+## Project Archietecture
+![image](https://user-images.githubusercontent.com/57321948/193536768-ae704adc-32d9-4c6c-b234-79c152f756c5.png)
+
+
+## Deployment Archietecture
+![image](https://user-images.githubusercontent.com/57321948/193536973-4530fe7d-5509-4609-bfd2-cd702fc82423.png)
+
+
+### Step 1: Clone the repository
+```bash
+git clone https://github.com/mihirkudale/Sensor-Fault-Detection.git
+```
+
+### Step 2- Create a conda environment after opening the repository
+
+```bash
+conda create -n sensor python=3.7.6 -y
+```
+
+```bash
+conda activate sensor
+```
+
+### Step 3 - Install the requirements
+```bash
+pip install -r requirements.txt
+```
+
+### Step 4 - Export the environment variable
+```bash
+export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
+
+export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>
+
+export AWS_DEFAULT_REGION=<AWS_DEFAULT_REGION>
+
+export MONGODB_URL="mongodb+srv://<username>:<password>@ineuron-ai-projects.7eh1w4s.mongodb.net/?retryWrites=true&w=majority"
+
+```
+
+### Step 5 - Run the application server
+```bash
+python app.py
+```
+
+### Step 6. Train application
+```bash
+http://localhost:8080/train
+
+```
+
+### Step 7. Prediction application
+```bash
+http://localhost:8080/predict
+
+```
+
+## Run locally
+
+1. Check if the Dockerfile is available in the project directory
+
+2. Build the Docker image
+```
+docker build --build-arg AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID> --build-arg AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY> --build-arg AWS_DEFAULT_REGION=<AWS_DEFAULT_REGION> --build-arg MONGODB_URL=<MONGODB_URL> . 
+
+```
+
+3. Run the Docker image
+```
+docker run -d -p 8080:8080 <IMAGE_NAME>
+```
+
+To run the project  first execute the below commmand.
+MONGO DB URL: 
+```
+mongodb+srv://mihir:[email protected]/admin?authSource=admin&replicaSet=atlas-okvkrd-shard-0&w=majority&readPreference=primary&appname=MongoDB%20Compass&retryWrites=true&ssl=true
+```
+windows user
+
+```
+MONGO_DB_URL=mongodb+srv://mihir:[email protected]/admin?authSource=admin&replicaSet=atlas-okvkrd-shard-0&w=majority&readPreference=primary&appname=MongoDB%20Compass&retryWrites=true&ssl=true
+```
+
+Linux user
+
+```
+export MONGO_DB_URL=mongodb+srv://mihir:[email protected]/admin?authSource=admin&replicaSet=atlas-okvkrd-shard-0&w=majority&readPreference=primary&appname=MongoDB%20Compass&retryWrites=true&ssl=true
+```
+
+then run 
+```
+python main.py
+```