Using the REST API

The REST API provides programmatic access to the linear regressor pipeline, allowing you to train models and make predictions.

Getting Started

Local API Setup

Start the API locally:

# Using Make
make run-api

# Using Poetry
poe run-api

Docker API Setup

1. Build and deploy with Docker:

# Build the Docker image
make docker-build

# Deploy the API container
make docker-deploy-api

API Base URL

Local: http://localhost:8000
Docker: http://localhost:8000

Endpoints

Model information

Prediction

Make Single Prediction

POST /predict

Example request

import requests
import pandas as pd

# Prepare data
data = {
    "data": {
        "feature1": [1.0],
        "feature2": [2.0]
    }
}

# Make prediction
response = requests.post(
    "http://localhost:8000/predict",
    json=data
)

print(response.json())

Batch Prediction

POST /predict_batch

Example request

import requests
import pandas as pd

# Read CSV file
df = pd.read_csv("./data/sample.csv")

# Prepare data
data = {
    "data": df.to_dict(orient="list")
}

# Make batch predictions
response = requests.post(
    "http://localhost:8000/predict_batch",
    json=data
)

print(response.json())

Train models

POST /train

Example request

import requests
import pandas as pd

# Read training data
df = pd.read_csv("./data/sample.csv")

# Prepare training data
data = {
    "data": df.to_dict(orient="list")
}

# Train model
response = requests.post(
    "http://localhost:8000/train",
    json=data
)

print(response.json())

Docker Management

Stop the API

make docker-stop-api

Remove the API container

make docker-down-api