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26c3f0d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | """
API de prédiction du prix de location - Projet Getaround.
Cette API expose un point de terminaison /predict qui renvoie le prix
de location journalier suggéré pour un véhicule, à partir de ses
caractéristiques.
"""
from pathlib import Path
from typing import List, Union
import joblib
import pandas as pd
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
# ---------------------------------------------------------------------------
# Configuration de l'application
# ---------------------------------------------------------------------------
APP_TITLE = "API Getaround - Prédiction du prix de location"
APP_DESCRIPTION = (
"Cette API met à disposition un modèle de Machine Learning entraîné "
"pour suggérer le prix journalier optimal d'une location de véhicule "
"sur la plateforme Getaround.\n\n"
"### Performances du modèle (XGBoost Regressor) :\n"
"* **Erreur absolue moyenne (MAE) :** 9,18 EUR\n"
"* **RMSE :** 12,82 EUR\n"
"* **R² :** 0,846\n"
)
APP_VERSION = "1.0.0"
MODEL_PATH = Path(__file__).resolve().parent / "best_model.joblib"
FEATURE_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",
]
BOOL_COLUMNS = [
"private_parking_available", "has_gps", "has_air_conditioning",
"automatic_car", "has_getaround_connect", "has_speed_regulator",
"winter_tires",
]
# ---------------------------------------------------------------------------
# Initialisation
# ---------------------------------------------------------------------------
app = FastAPI(
title=APP_TITLE,
description=APP_DESCRIPTION,
version=APP_VERSION,
)
# Chargement du modèle
try:
model = joblib.load(MODEL_PATH)
MODEL_LOADED = True
except Exception as exc:
print(f"Erreur de chargement du modèle : {exc}")
model = None
MODEL_LOADED = False
# ---------------------------------------------------------------------------
# Schémas Pydantic
# ---------------------------------------------------------------------------
class PredictionInput(BaseModel):
"""Schéma d'entrée pour l'endpoint /predict."""
input: List[List[Union[str, int, float, bool]]] = Field(
...,
examples=[[[
"Citroën", 140411, 100, "diesel", "black", "convertible",
True, True, False, False, True, True, True,
]]],
)
class PredictionOutput(BaseModel):
"""Schéma de sortie pour l'endpoint /predict."""
prediction: List[float]
# ---------------------------------------------------------------------------
# Endpoints
# ---------------------------------------------------------------------------
@app.get("/", tags=["Accueil"])
async def root():
"""Point d'entrée par défaut."""
return {
"message": "API Getaround opérationnelle",
"version": APP_VERSION,
"model_loaded": MODEL_LOADED,
"documentation": "/docs",
}
@app.get("/health", tags=["Accueil"])
async def health():
"""Indicateur de santé du service."""
return {"status": "ok" if MODEL_LOADED else "model_missing"}
@app.post(
"/predict",
tags=["Prédiction"],
response_model=PredictionOutput,
summary="Prédire le prix de location journalier",
)
async def predict(payload: PredictionInput):
"""Renvoie la prédiction du prix de location journalier (EUR/jour)."""
if not MODEL_LOADED:
raise HTTPException(
status_code=503,
detail="Modèle non chargé sur le serveur.",
)
try:
df_input = pd.DataFrame(payload.input, columns=FEATURE_COLUMNS)
# Conversion des types
for col in BOOL_COLUMNS:
df_input[col] = df_input[col].astype(int)
df_input["mileage"] = df_input["mileage"].astype(int)
df_input["engine_power"] = df_input["engine_power"].astype(int)
preds = model.predict(df_input)
return {"prediction": [round(float(p), 2) for p in preds]}
except Exception as exc:
raise HTTPException(
status_code=400,
detail=f"Erreur lors de la prédiction : {exc}",
)
if __name__ == "__main__":
import uvicorn
uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=False)
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