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import uvicorn
import pandas as pd
from pydantic import BaseModel
from typing import List, Union
from fastapi import FastAPI
import joblib
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": "[email protected]", # Replace with actual email
},
openapi_tags=tags_metadata
)
class PredictionFeatures(BaseModel):
CarData: List[Union[str, int, bool]] = ["Renault", 193231, 85, "diesel", "black", "estate", False, True, False, False, False, False, True]
@app.get("/", tags=["Introduction Endpoints"])
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 `/docs`"
@app.post("/predict", tags=["Machine Learning"])
async def predict(predictionFeatures: PredictionFeatures):
columns = [
'model_key', 'mileage', 'engine_power', 'fuel', 'paint_color',
'car_type', 'private_parking_available', 'has_gps',
'has_air_conditioning', 'automatic_car', 'has_getaround_connect',
'has_speed_regulator', 'winter_tires'
]
car_data_dict = {col: [val] for col, val in zip(columns, predictionFeatures.CarData)}
car_data = pd.DataFrame(car_data_dict)
# model_file = hf_hub_download(repo_id="2nzi/GetAround-CarPrediction", filename="best_model_XGBoost.pkl")
# with open(model_file, 'rb') as f:
# model = pickle.load(f)
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)
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