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API: /predict Endpoint

The /predict endpoint accepts penguin measurements and returns a predicted species.

The server must be running locally to use this endpoint. See the project README for instructions on how to start the server.

Endpoint

POST http://127.0.0.1:8000/predict

Request

Content-Type: application/json

Field Type Description
bill_length_mm float Bill length in millimeters
bill_depth_mm float Bill depth in millimeters
flipper_length_mm float Flipper length in millimeters
body_mass_g float Body mass in grams

Example Request

{
  "bill_length_mm": 39.1,
  "bill_depth_mm": 18.7,
  "flipper_length_mm": 181,
  "body_mass_g": 3750
}

Response

Field Type Description
prediction string Predicted species label

Example Response

{ "prediction": "Adelie" }

Error Response

If a required field is missing or non-numeric, the server returns HTTP 400:

{ "detail": "Missing required feature: 'bill_depth_mm'" }

Interactive Docs

When the server is running, FastAPI provides interactive documentation at:

http://127.0.0.1:8000/docs

Free Server Hosting Options

Many hosting options require a credit card to sign up, even if you can host your function for free.

Possible options for hosting a free live endpoint:

  • Render - simplest deploy, free tier, point at a GitHub repo and it builds automatically
  • Railway - free tier, similar to Render, good developer experience
  • Fly.io - free tier, more control, slightly more setup
  • Hugging Face Spaces - free, no credit card, popular in ML circles, supports FastAPI

Cloud Hosting Options

The major cloud providers all offer free tiers that can host a FastAPI endpoint, but all require a credit card to sign up (even for free usage).

  • AWS Lambda + API Gateway - 1M requests/month free; FastAPI works via the Mangum adapter
  • Azure Functions - 1M requests/month free; FastAPI works via a similar adapter
  • Google Cloud Run - 2M requests/month free; easiest of the three for FastAPI; build a Docker container and deploy

For this, Render or Hugging Face Spaces are the easiest starting points. For production experience closest to what industry uses, Google Cloud Run may be a good option to explore.