Price Finder API

This project contains the Price Finder API developed and deployed to Google Cloud Platform (GCP) which facilitates the image recognition capabilities through a vehicle image classification (CNN) model and market price retrieval of the Price Finder mobile application.

🔬 Overview of the tasks achieved within this project

• Intergration of Github Actions CI/CD pipeline for automated deployment to GCP.
• Automated tests using Github Actions, which runs on every push to the develop branch.
• Code test coverage of 89% which includes functional and unit tests.
• Modular project structure to facilitate seamless scalablitiy with flask blueprints and application factory pattern.
• Comprehensive exception handling to gracefully handle exceptions occured due to both client and server side issues.
• Detailed explanation of the code functionality through Docstrings, comments and documentation.
• Code standards maintained in accordance with PEP8 style guide.

🧱 Tech Stack

Framework / Library	Functionality
Flask - 2.1	Develop the API functionality
Tensorflow - 2.9	Run the image recognition model
Requests - 4.11	Retrieve current listings of the identified vehicle
Beautiful Soup - 4.11	Retrieve the current market price

⚙ Setup Instructions

Clone the repository

• Navigate to a folder in which you would like to setup the project.
• Open up a terminal in that folder and enter the command below to clone the repository.

  git clone https://github.com/donheshanthaka/Price-Finder-Flask-API.git

🐍 Setup the python virtual environment

Step 01:

• Navigate to the Price-Finder-Flask-API folder in terminal using the command below.

  cd Price-Finder-Flask-API

Step 02:

• Create the python virtual environment

    python -m venv env

Step 03:

• Navigate to the activation path

 cd env/Scripts

• Activate the virtual environment. (Run either one, not both)

  activate.bat //In CMD
  Activate.ps1 //In Powershell

Step 04:

• Move back to the project folder

  cd ../../

• Install the required dependencies

  pip install -r requirements.txt

🖥 Run Locally

Setup environment variables

• Setup flask app

  set FLASK_APP=main.py 

• Setup flask environment (development is used since the app would be running locally)

  set FLASK_ENV=development 

📌 To change the environment type : set FLASK_ENV=development/production use either development or production.

Start the local server

  flask run --host=192.168.1.100 --port=8000 

• --host=192.168.1.100 > The server listens to requests on the given IP address
• --port=8000 > The server will listen on port 8000

📡 Usage / Examples

Prerequisites:

• cURL

📌 Note: If you have Windows 10 version 1803 or later, cURL is installed by default

The current version of the api supports indentifying vehicle models and retrieving their current market price which can be accessed through a single end point. An example of that is given below.

Getting vehicle information

• Making a post request uisng cURL to the api endpoint /get-vehicle-info with an image attached to the body of the request.
• Use the command below in a terminal on the project root directory.

📍 Make sure the local server is running before running the command below

  curl -L -X POST "http://192.168.1.100:8000/get-vehicle-info" -F imageFile=@tests/images/4.jpeg

• A JSON response will be returned with the identified model of the vehicle and it's current market value.

Response:

  {"model":"Toyota Aqua 2014", "price":"RS. 6,754,400"}

📌 Note: The "Price" value returned in the response will vary since it is retrieved everytime from the web when the api is being called.

The same endpoint as above accessed using a python script:

• Run this script on the project root folder.

import requests

url = "http://192.168.1.100:8000/get-vehicle-info"

payload={}
files=[
  ('imageFile',('4.jpeg',open('tests/images/4.jpeg','rb'),'image/jpeg'))
]
headers = {}

response = requests.request("POST", url, headers=headers, data=payload, files=files)

print(response.text)

📃 Response Codes

The HTTP Status Codes used by the Price Finder API.

HTTP Status Code	Description
200 OK	Successfully identified the image and retrieved the market price.
400 Bad Request	Image file not found in the request.
404 Not Found	The requested resource was not found.
405 Not Found	The requested method is not allowed.
415 Unsupported Media Type	Invalid Image Type.
502 Bad Gateway	Unable to access price retrieval web server.

📇 API Reference

Identify vehicle and retrieve current market price.

  POST /get-vehicle-info

Parameter	Type	Description
imageFile	jpeg/png	Required. Image to be identified

🧪 Test Cases

Coverage Report

File	Stmts	Miss	Cover	Missing
app
init.py	13	3	77%	13–17
error_handlers.py	25	1	96%	16
utils.py	45	8	82%	52, 85–86, 99, 114–119
TOTAL	109	12	89%

The flask api is tested in both unit tests and functional tests using the pytest framework.

Overview of the testing criteria

Functional Tests:

Tests the functionality of the api endpoint /get-vehicle-info which takes in an image as a parameter and return the vehicle model and price as a JSON object.

Functional test modules:

• test_get_vehicle -> Given a flask application, when the '/get-vehicle-info' is requested (POST), check that a '200' response code is returned with valid response data.

• test_get_vehicle_without_image -> Given a flask application, when the '/get-vehicle-info' is requested (POST) without an image attached in the body, then check that a '400' status code is returned.

• test_get_vehicle_invalid_image_type -> Given a flask application, when the '/get-vehicle-info' is requested (POST) with an invalid image type, then check that a '415' status code is returned.

• test_page_not_found -> Given a flask application, when an invalid URL / Endpoint is requested (POST), then check that a '404 Not Found' status code is returned.

• test_method_not_allowed -> Given a flask application, when an invalid request is made to a valid endpoint (GET), then check that a '405 Method Not Allowed' status code is returned.

Unit Tests:

Tests the each individual functions used by the api, such as price retrieval, image recognition and image reshaping for the cnn model.

Unit test modules:

• test_get_price -> Given a name of vehicle, when trying to find the current market price, then check the return value is a string (cannot check for an exact value since market value is not constant,therefore checking the return type is the only option available).

• test_predict -> Given a path to an image of a vehicle, when trying to identify the vehicle, then check the identified vehicle is correct according to the given image.

• test_reshape_image -> Given a path to an image, when trying to predict the image, then check the returned image tensor is in correct shape.

A code test coverage of 89% is achieved with the implementation of above test cases.

⚗ Running Tests

Prerequisites:

• Pytest

• Coverage

• Navigate to the root folder of the project and activate the python virtual environment created during the setup process.

Step 01:

• Install pytest and coverage.

    pip install pytest, coverage

Step 02:

• Run the test modules.

    coverage run --source=app -m pytest

output:

================================== test session starts =========================================
platform win32 -- Python 3.9.5, pytest-7.1.2, pluggy-1.0.0
collected 8 items

tests\functional\test_get_vehicle_info.py ...                                             [ 37%]            
tests\functional\test_status_codes.py ..                                                  [ 62%]      
tests\unit\test_get_price.py .                                                            [ 75%]      
tests\unit\test_predict.py .                                                              [ 87%]      
tests\unit\test_reshape_image.py .                                                        [100%]      

================================== 8 passed in 7.28s ============================================

Step 03:

• Checking the test coverage report.

    coverage report

output:

Name                    Stmts   Miss  Cover
-------------------------------------------
app\__init__.py            13      3    77%
app\error_handlers.py      25      1    96%
app\utils.py               45      8    82%
app\views.py               26      0   100%
-------------------------------------------
TOTAL                     109     12    89%

🚀 Deployment (Google Cloud Platform)

The API developed in this project is deployed in Google Cloud Platform (GCP) to be accessed by the price finder mobile application. Furthermore, the deployment process is fully automated with the use of github actions CI/CD pipleline.

Prerequisites:

• Google Cloud Platform (GCP) account.

📌 Note: You have to activate the billing feauture of the account by providing your credit / debit card details and it will charge you 1-2 USD to verify your account, but that amount will be refunded and you will not have to turn on billing for the project itself and therefore, you will not be charged for the usage during this project and everything will be under the free usage limits. Moreover, you will recieve 300 USD free credits valid for 3 months.

⚒ Create new GCP project

Step 01:

• Go to the GCP console and create a new project (Make sure to give a unique relatable name for your service).

Example: vehicle-price-finder-001

📌 Note: You will not be able to use the example above since every project has to have a unique name therefore, provide a unique name and keep it noted since it will be required in the steps below.

Step 02:

Activate Cloud Run and Cloud Build API

• In the Google Cloud console, go to APIs & services for your project.
• Click on the Library page and Search Cloud Run on the search box.
• Select the API and select Enable.
• Activate Cloud Build api with the same steps.

☁ Install Google Cloud CLI

Step 01:

Download and install Google Cloud CLI using this guide by Google and complete the installing and initializing sections.

👨‍💻 Manual deployment to GCP

Step 01:

Build the docker filer in Cloud Build

PROJECT-ID -> Can be found in the project dashboard in Google Cloud console. Edit this value before executing the command below.

• Run the command below in a terminal in project root directory

    gcloud builds submit --tag gcr.io/{PROJECT-ID}/get-prediction

Step 02:

Deploying to Cloud Run

    gcloud run deploy --image gcr.io/{PROJECT-ID}/get-prediction --platform managed

• Service name -> Provide a service name for the api, use get-prediction for the current project.
• region -> Select a region based on your location with the help of this documentation for the current project i have used asia-south1 [6].
• allow unauthenticated invocations -> select 'y'

After successful deployment, the Service URL will be displayed at the bottom of the terminal. Copy that for future use since that will be the api access point.

🤖 Deployment to GCP through Github Actions CI/CD pipleine

Before following the steps below, fork this repository to your own github account.

And then make sure to complete the sections mentioned below

• Create new GCP project
• Install Google Cloud CLI

Step 01:

Create a Google Cloud Service Account.

• Go to google cloud console
• Click navigation menu on top left corner.
• IAM & Admin -> Service Accounts -> Create Service Account
• Service Account Name: api-service-account

Step 02:

Grant the Google Cloud Service Account permissions mentioned below to access Google Cloud resources.

• Cloud Run Admin
• Cloud Run Service Agent
• Cloud Build Service Agent
• Viewer

Step 03:

Enable the IAM Credentials API.

    gcloud services enable iamcredentials.googleapis.com --project "{PROJECT-ID}"

Step 04:

Create a Workload Identity Pool.

Format:

gcloud iam workload-identity-pools create `"WORKLOAD-ID-POOL-NAME"` \
  --project=`"PROJECT_ID"` \
  --location="global" \
  --display-name=`"DISPLAY_NAME_FOR_POOL"`

Example:

    gcloud iam workload-identity-pools create "price-finder-pool" --project "{PROJECT-ID}" --location="global" --display-name="price finder pool"

Step 05:

Get the full ID of the Workload Identity Pool.

Format:

gcloud iam workload-identity-pools describe `"WORKLOAD-ID-POOL-NAME"` \
  --project=`"PROJECT_ID"` \
  --location="global" \
  --format="default"

Example:

    gcloud iam workload-identity-pools describe "price-finder-pool" --project "{PROJECT-ID}" --location="global" --format="default"

Return format:

    `WORKLOAD_IDENTITY_POOL_ID` = projects/`YOUR-PROJECT-NUMBER`/locations/global/workloadIdentityPools/`"WORKLOAD-ID-POOL-NAME"`

Step 06:

Create a Workload Identity Provider in that pool.

Format:

gcloud iam workload-identity-pools providers create-oidc `"WORKLOAD-ID-POOL-PROVIDER-NAME"` \
  --project=`"PROJECT_ID"` \
  --location="global" \
  --workload-identity-pool=`"WORKLOAD-ID-POOL-NAME"` \
  --display-name=`"DISPLAY_NAME_FOR_PROVIDER"` \
  --attribute-mapping="google.subject=assertion.sub,attribute.actor=assertion.actor,attribute.repository=assertion.repository" \
  --issuer-uri="https://token.actions.githubusercontent.com"

Example:

    gcloud iam workload-identity-pools providers create-oidc “price-finder-provider”  --project "{PROJECT-ID}" --location="global" --workload-identity-pool="price-finder-pool" --display-name="price finder provider" --attribute-mapping="google.subject=assertion.sub,attribute.actor=assertion.actor,attribute.repository=assertion.repository" --issuer-uri="https://token.actions.githubusercontent.com"

Step 07:

Allow authentications from the Workload Identity Provider originating from your repository to impersonate the Service Account created above.

Format:

gcloud iam service-accounts add-iam-policy-binding "my-service-account@${PROJECT_ID}.iam.gserviceaccount.com" \
  --project=`"PROJECT_ID"` \
  --role="roles/iam.workloadIdentityUser" \
  --member="principalSet://iam.googleapis.com/`WORKLOAD_IDENTITY_POOL_ID( RETURN VALUE FROM STEP 5)`/attribute.repository/`yourgithubname/reponame`"

Example:

    gcloud iam service-accounts add-iam-policy-binding "test-service-account@{PROJECT-ID}.iam.gserviceaccount.com" --project "{PROJECT-ID}" --role="roles/iam.workloadIdentityUser" --member="principalSet://iam.googleapis.com/`WORKLOAD_IDENTITY_POOL_ID( RETURN VALUE FROM STEP 5)`/attribute.repository/`yourgithubname/reponame`"

Step 08:

Extract the Workload Identity Provider resource name.

Format:

gcloud iam workload-identity-pools providers describe "my-provider" \
  --project=`"PROJECT_ID"` \
  --location="global" \
  --workload-identity-pool=`"WORKLOAD-ID-POOL-NAME"` \
  --format="default"

Example:

    gcloud iam workload-identity-pools providers describe "price-finder-provider" --project="{PROJECT-ID}" --location="global" --workload-identity-pool=“price-finder-pool” --format="default"

Return format:

    projects/`YOUR-PROJECT-NUMBER`/locations/global/workloadIdentityPools/`WORKLOAD-ID-POOL-NAME`/providers/`WORKLOAD-ID-POOL-PROVIDER-NAME`

⭕ Important

YOU NEED TO USE THIS VALUE AS "workload_identity_provider" in main.yml file, located in .github\workflows directory under "Authenticate to Google Cloud" section.

📌 Note: When replacing the workload_identity_provider, remove the $ and curly brackets. Just include the value within single quotes.

Step 09:

Making the service public (Allow unauthenticated).

Format:

  gcloud run services add-iam-policy-binding [SERVICE_NAME] \
    --member="allUsers" \
    --role="roles/run.invoker" \
    --project=`"PROJECT_ID"`

SERVICE_NAME = Name of the cloud run service

Example:

    gcloud run services add-iam-policy-binding get-prediction --member="allUsers" --role="roles/run.invoker" --project="{PROJECT-ID}"

Step 10:

Update the cloudbuild.yaml file with current project details.

• Update the PROJECT-ID in the section below and replace the content in cloudbuild.yaml file in the project root directory.

steps:
- name: 'gcr.io/cloud-builders/docker'
  args: ['build', '-t', 'gcr.io/{PROJECT-ID}/get-prediction', '.']
images: ['gcr.io/{PROJECT-ID}/get-prediction']

Step 11:

Update the main.yml file in .github\workflows 'directory.

• PROJECT_ID -> Replace with your own project id
• REGION -> If necessary, replace it with a region suitable for you or keep as it is.
• service_account -> Can be found under IAM & Admin > Service accounts (Example: [email protected])

📌 Note: When replacing the service account, remove the $ and curly brackets. Just include the value within single quotes.

Step 12:

• Switch to the release branch
• Commit the new changes and push to the repository.

You will see the github actions running the workflow process under Actions tab in repository.

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