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import uvicorn | |
import pandas as pd | |
from typing import Union | |
from fastapi import FastAPI, Query | |
import joblib | |
from enum import Enum | |
from fastapi.responses import HTMLResponse | |
description = """ | |
Welcome to the GetAround Car Value Prediction API. This app provides an endpoint to predict car values based on various features! Try it out 🕹️ | |
## Machine Learning | |
This section includes a Machine Learning endpoint that predicts car values based on various features. Here is the endpoint: | |
* `/predict`: **POST** request that accepts a list of car features and returns a predicted car value. | |
Check out the documentation below 👇 for more information on each endpoint. | |
""" | |
tags_metadata = [ | |
{ | |
"name": "Machine Learning", | |
"description": "Endpoint for predicting car values based on provided features." | |
} | |
] | |
app = FastAPI( | |
title="🚗 GetAround Car Value Prediction API", | |
description=description, | |
version="0.1", | |
contact={ | |
"name": "Antoine VERDON", | |
"email": "antoineverdon.pro@gmail.com", | |
}, | |
openapi_tags=tags_metadata | |
) | |
class CarBrand(str, Enum): | |
citroen = "Citroën" | |
peugeot = "Peugeot" | |
pgo = "PGO" | |
renault = "Renault" | |
audi = "Audi" | |
bmw = "BMW" | |
other = "other" | |
mercedes = "Mercedes" | |
opel = "Opel" | |
volkswagen = "Volkswagen" | |
ferrari = "Ferrari" | |
maserati = "Maserati" | |
mitsubishi = "Mitsubishi" | |
nissan = "Nissan" | |
seat = "SEAT" | |
subaru = "Subaru" | |
toyota = "Toyota" | |
class FuelType(str, Enum): | |
diesel = "diesel" | |
petrol = "petrol" | |
hybrid_petrol = "hybrid_petrol" | |
electro = "electro" | |
class PaintColor(str, Enum): | |
black = "black" | |
grey = "grey" | |
white = "white" | |
red = "red" | |
silver = "silver" | |
blue = "blue" | |
orange = "orange" | |
beige = "beige" | |
brown = "brown" | |
green = "green" | |
class CarType(str, Enum): | |
convertible = "convertible" | |
coupe = "coupe" | |
estate = "estate" | |
hatchback = "hatchback" | |
sedan = "sedan" | |
subcompact = "subcompact" | |
suv = "suv" | |
van = "van" | |
async def index(): | |
return ( | |
"Hello world! This `/` is the most simple and default endpoint. " | |
"If you want to learn more, check out documentation of the API at " | |
"<a href='/docs'>/docs</a> or " | |
"<a href='https://2nzi-getaroundapi.hf.space/docs' target='_blank'>external docs</a>." | |
) | |
async def predict( | |
brand: CarBrand, | |
mileage: int = Query(...), | |
engine_power: int = Query(...), | |
fuel: FuelType = Query(...), | |
paint_color: PaintColor = Query(...), | |
car_type: CarType = Query(...), | |
private_parking_available: bool = Query(...), | |
has_gps: bool = Query(...), | |
has_air_conditioning: bool = Query(...), | |
automatic_car: bool = Query(...), | |
has_getaround_connect: bool = Query(...), | |
has_speed_regulator: bool = Query(...), | |
winter_tires: bool = Query(...) | |
): | |
car_data_dict = { | |
'model_key': [brand], | |
'mileage': [mileage], | |
'engine_power': [engine_power], | |
'fuel': [fuel], | |
'paint_color': [paint_color], | |
'car_type': [car_type], | |
'private_parking_available': [private_parking_available], | |
'has_gps': [has_gps], | |
'has_air_conditioning': [has_air_conditioning], | |
'automatic_car': [automatic_car], | |
'has_getaround_connect': [has_getaround_connect], | |
'has_speed_regulator': [has_speed_regulator], | |
'winter_tires': [winter_tires] | |
} | |
car_data = pd.DataFrame(car_data_dict) | |
model = joblib.load('best_model_XGBoost.pkl') | |
prediction = model.predict(car_data) | |
response = {"prediction": prediction.tolist()[0]} | |
return response | |
if __name__ == "__main__": | |
uvicorn.run(app, host="0.0.0.0", port=4000) | |