DAP-ML-Deployment-Workshop

This repository contains a Python-based linear regression model for predicting housing prices based on various factors like floor area, mid storey level, flat type, and proximity to amenities among others. This guide will walk you through setting up the environment, building the Docker container, and using the application.

Prerequisites

Before you start, ensure you have Docker installed on your system. You can download it from Docker's official website.

Getting Started

Clone this repository to your local machine to get started with the app deployment.

git clone https://github.com/yourusername/DAP-ML-Deployment-Workshop.git
cd DAP-ML-Deployment-Workshop

Setting Up the Environment

It's recommended to use a virtual environment to isolate the project dependencies. Install virtualenv if you haven't already, and set up a virtual environment.

pip install virtualenv
virtualenv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`

Install the required Python packages.

pip install -r requirements.txt

Running the Application with Uvicorn

To run the application locally without Docker, use Uvicorn as follows:

uvicorn app:app --reload

This will start the FastAPI application which you can access at http://localhost:8000.

Dockerizing the Application

To containerize the application, follow these steps:

1. Build the Docker Image

   Use the Dockerfile provided in the repository to build your Docker image.

   docker build -t fastapi-app .

2. Run the Docker Container

   After the build completes, you can run the Docker container.

   docker run -p 8080:8080 fastapi-app

   This command maps port 8080 of the container to port 8080 on your host, allowing you to access the FastAPI application at http://localhost:8080.

Pushing the Docker Image to a Registry

Optionally, if you wish to share your Docker image, you can tag and push it to Docker Hub or any other Docker registry.

docker tag fastapi-app yourusername/fastapi-app
docker push yourusername/fastapi-app

Replace yourusername with your Docker Hub username.

Making Predictions

To make predictions, send a POST request to the /predict endpoint with a JSON payload. For example:

curl -X 'POST' \
  'http://localhost:8080/predict' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "floor_area_sqm": 90.0,
  "mid_storey": 11,
  "full_flat_type": "4 ROOM Model A",
  "commercial": 0,
  "planning_area": "Kallang",
  "mall_nearest_distance": 1094.090418,
  "hawker_nearest_distance": 154.7533573,
  "mrt_nearest_distance": 330.0830689707568,
  "mrt_interchange": 0,
  "pri_sch_nearest_distance": 1138.6334215866475,
  "pri_sch_name": "Geylang Methodist School",
  "pri_sch_affiliation": 1,
  "sec_sch_nearest_dist": 1138.6334215866475,
  "sec_sch_name": "Geylang Methodist School",
  "age_when_sold": 10
}'

This request sends data to the model and returns a prediction response.