ML Prediction API using LDA and NN for EEG Dataset

 Built in Python with Flask for APIs & Deployed on Docker 

TLDR; {Build rightaway}

• Download this repo
  • api.py serves the API routes from Python-Flask application
  • train.py defines, builds and creates both NN and Classification Models and related model files
  • requirements.txt is a standard external packages dependency file for Python projects basically lists all 3rd party packages we use in the app
  • Dockerfile contains build, directory and environment info. Used to build an image of the total ML Prediction App that can be deployed as a container into any service.

Build Docker Image

docker build -t py-api -f Dockerfile .

Run built Docker Image in a Docker Container

docker run -it -p 5000:5000 py-api python3 api.py

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SOME CONTEXT

Goals

1. Build ML models to serve classifications and predictions using Flask API in Real Tine.
2. Deploy the model, package requirements for Python on a Docker Image
3. Test the image deployed on a container in localhost and on actual host on ports 5000

Test Inputs

1. test.json: 1300 rows of EEG data; 160 features[columns]; Used to the test models
2. train.csv: Partial data to train the models

APIs with Desired Outcomes

• Data Extraction

  Input: Row number
  Output: Extract data and printed out to the console
  Test Link: http://127.0.0.1:5000/line/{lineNumber}

• Results from both models

  Input: Row number
  Process: Extract the selected row, inject new data into pre-trained and ddeployed models
  Output: etrieve the classification prediction (Letter variable in the data)
  Test Link : http://127.0.0.1:5000/prediction/{lineNumber}

• Real-time Model Confidence Scores

  Input: None
  Process: Read all data from the local file {test.json}
  Output: Print classification score of both the models.
  

  Test Link: http://127.0.0.1:5000/score

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Nice to know!

• I used the idea from an article that outlined how Python-Flask-ML can be built and deployed on Docker
• Entirely coded, tested and deployed in Github Codespaces
• Output of ML may have extreme errors, which is not our focus, as we look to learn:
  1. How to develop a ML model on Python
  2. Make it accessible via Flask
  3. Package it as a Docker Image
  4. Deploy the built image as a container
• I did this in Github codespaces so had to test with in-browser terminal, outside http was not accsesible
• In the PORTS tab [Next to TERMINAL tab in Codespaces], you can set the port to be exposed to public but not guaranteed of server access
• Major packages used:
  1. scikit-learn
  2. Flask
  3. Numpy

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TODO

• Model improvement
• Faster docker image build
• Smaller Image Layers
• Remove JSON based data storage
• Add DB access
• Check network, security, monitoring, infrastructure, orchestration in real PROD apps