Ajout du modèle, du préprocesseur

Dockerfile

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+FROM python:3.10.10
+
+WORKDIR /home/app
+
+COPY requirements.txt /dependencies/requirements.txt
+RUN pip install -r /dependencies/requirements.txt
+
+COPY . /home/app
+
+CMD gunicorn app:app  --bind 0.0.0.0:$PORT --worker-class uvicorn.workers.UvicornWorker

app.py

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+import uvicorn
+import pandas as pd
+from pydantic import BaseModel
+from fastapi import FastAPI
+import numpy as np
+import joblib
+from typing import List
+
+# ==== FastAPI Description ====
+
+description = """
+# 🚗 GetAround Rental Price Predictor API
+
+This API predicts the **rental price per day (in €)** for a car based on various features.
+"""
+
+app = FastAPI(
+    title="GetAround Price Prediction API",
+    description=description,
+    version="1.0",
+    contact={"name": "Ton Nom"},
+)
+
+# ==== Input Data Models ====
+
+class CarCriteria(BaseModel):
+    model_key: str
+    mileage: int
+    engine_power: int
+    fuel: str
+    paint_color: str
+    car_type: str
+    private_parking_available: bool
+    has_gps: bool
+    has_air_conditioning: bool
+    automatic_car: bool
+    has_getaround_connect: bool
+    has_speed_regulator: bool
+    winter_tires: bool
+
+class CarOptions(BaseModel):
+    car_options: List[CarCriteria]
+
+# ==== Load pipeline (model + preprocessor ensemble) ====
+
+def load_model():
+    model = joblib.load("model.joblib")  # Pipeline: preprocessor + LinearRegression
+    return model
+
+# ==== Predict endpoint ====
+
+@app.post("/predict", tags=["Machine Learning"])
+async def predict(car_options: CarOptions):
+    model = load_model()
+
+    # Convertir les données en DataFrame
+    df_input = pd.DataFrame([option.dict() for option in car_options.car_options])
+
+    # Prédiction directe (le modèle contient déjà le préprocesseur)
+    predictions = model.predict(df_input)
+
+    # Retourner les résultats formatés
+    formatted = [f"Option {i+1}: {round(pred)} €" for i, pred in enumerate(predictions)]
+    return {"predictions": formatted}
+
+# ==== Exécution locale ====
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=4000)
+

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb2d31c2bdf37b3d683e65da884bac4714bfbc736145be1590b106c2cfa01994
+size 5993

preprocessor.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:91f63c94fe85c7a7144d6f608787f2b1a26bc2ea53f82725d6726f1bd400c175
+size 5156

requirements.txt

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+fastapi
+uvicorn[standard]
+pydantic
+pandas
+numpy
+scikit-learn==1.0.2
+joblib==1.2.0