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Flask Capstone Machine Learning Project

This is a Flask project for run ML model.

Prerequisites

Before running this project, ensure you have the following installed:

  • Python (version 3.6 or higher)
  • pip (Python package installer)

Installation

  1. Clone the repository: 
    git clone https://github.com/Ulas-Scan/UlaScan_ML.git
  2. Navigate into the project directory: 
    cd UlaScan_ML
  3. Create a virtual environment: 
    python -m venv venv
  4. Activate the virtual environment:
    • On Windows: 
      venv\Scripts\activate
    • On macOS and Linux: 
      source venv/bin/activate
  5. Install the project dependencies: 
    pip install -r requirements.txt
  6. Run the Flask application: 
    flask run
  • If you done, to exit from virtual environment: 
    deactivate

API

Only one endpoint provided in this service: /predict. This endpoint receives a request body containing a list of reviews which are going to be processed using the model. The model will classify each review whether it is a positive review or negative review. The endpoint will return a JSON which counts the number of positive and negative reviews.

Model

The model we use is BERT, pre-trained with Indonesian Wikipedia, as provided in HuggingFace (cahya/bert-base-indonesian-522M). The model is trained with TensorFlow using Adam optimizer with a learning rate of 5e-5 and sparse categorical cross entropy objective in 5 epochs. We fine-tuned the model using fancyzhx/amazon_polarity dataset, which has been translated into Indonesian. We sampled 2000 data for the fine-tuning process. This model achieves an average validation accuracy of 0.8396.

Deployment

The model is deployed with Google Cloud Run, with the model's weights file stored in Google Cloud Storage.