README.md

metadata

title: My Fastapi Endpoint
emoji: 🏃
colorFrom: gray
colorTo: purple
sdk: docker
pinned: false
license: mit

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

title: My FastAPI App emoji: 🌍 colorFrom: red colorTo: red sdk: docker pinned: false license: mit short_description: This is a taxi predictor!

---

FastAPI Taxi Trip Duration Prediction API

This project provides a FastAPI-based REST API for predicting taxi trip durations in different cities using pre-trained Ridge regression models.

How to Use

1. Running the API

• Locally:
  Start the server with:

  uvicorn main:app --host 0.0.0.0 --port 7860
  

• On Hugging Face Spaces:
  The API will be available at
  https://<your-username>-<your-space-name>.hf.space/predict

2. Sending a Prediction Request

You can send a POST request to the /predict endpoint using any HTTP client (such as curl, Postman, or Python's requests library).

Example Python snippet

import requests

API_URL = "https://<your-username>-<your-space-name>.hf.space/predict"
# If your Space is private, uncomment and set your token:
# HF_TOKEN = "hf_xxx..."
# headers = {"Authorization": f"Bearer {HF_TOKEN}"}
headers = {}

data = {
    "vendor_id": "Bogotá UberX",
    "dist_meters": 18.976,
    "wait_sec": 1640,
    "geodetic_dist": 15.439039,
    "mean_velocity": 17.172851,
    "is_rush_hour": False,
    "model_name": "bog"
}

response = requests.post(API_URL, json=data, headers=headers)
print(response.json())

3. Datapoint Format

The API expects a JSON object with the following fields:

Field	Type	Example Value	Description
vendor_id	string	"Bogotá UberX"	The taxi vendor or service name
dist_meters	float	18.976	Distance of the trip in meters
wait_sec	float	1640	Waiting time in seconds
geodetic_dist	float	15.439039	Geodetic (straight-line) distance
mean_velocity	float	17.172851	Mean velocity during the trip
is_rush_hour	bool	false	Whether the trip occurred during rush hour
model_name	string	"bog"	Which model to use: "bog", "mex", or "uio"

Example JSON datapoint:

{
  "vendor_id": "Bogotá UberX",
  "dist_meters": 18.976,
  "wait_sec": 1640,
  "geodetic_dist": 15.439039,
  "mean_velocity": 17.172851,
  "is_rush_hour": false,
  "model_name": "bog"
}

4. Response Format

The API will return a JSON response like:

{
  "trip_duration": 123.45,
  "model_used": "bog",
  "message": "Inference successful using BOG model."
}

---

531d625 (Commiiitt!)