init commit

.gitignore

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+run.sh

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "7860"]

api.py

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+import pandas as pd
+import joblib
+import uvicorn
+from pydantic import BaseModel
+from typing import Literal, List, Union
+from fastapi import FastAPI
+from fastapi.encoders import jsonable_encoder
+from fastapi.responses import RedirectResponse
+
+description = """
+Welcome to the GetAround Car Rental Price Predictor API!
+
+Share your car's attributes and get an estimated daily rental price based on a
+RandomForest model trained on real GetAround data.
+
+**Use the `/predict` endpoint to get a price suggestion for your car.**
+"""
+
+tags_metadata = [
+    {
+        "name": "Predictions",
+        "description": "Endpoint to predict the daily rental price of a car"
+    }
+]
+
+app = FastAPI(
+    title="GetAround — Car Rental Price Predictor",
+    description=description,
+    version="1.0",
+    openapi_tags=tags_metadata
+)
+
+
+class Car(BaseModel):
+    model_key: Literal['Citroën', 'Peugeot', 'PGO', 'Renault', 'Audi', 'BMW', 'Mercedes',
+                       'Opel', 'Volkswagen', 'Ferrari', 'Mitsubishi', 'Nissan', 'SEAT',
+                       'Subaru', 'Toyota', 'other']
+    mileage: Union[int, float]
+    engine_power: Union[int, float]
+    fuel: Literal['diesel', 'petrol', 'other']
+    paint_color: Literal['black', 'grey', 'white', 'red', 'silver', 'blue', 'beige', 'brown', 'other']
+    car_type: Literal['convertible', 'coupe', 'estate', 'hatchback', 'sedan', 'subcompact', 'suv', 'van']
+    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
+
+
+# Chargement du modèle et du preprocessor au démarrage (une seule fois)
+preprocessor = joblib.load("preprocessor.joblib")
+model = joblib.load("model.joblib")
+
+
+@app.get("/", include_in_schema=False)
+async def docs_redirect():
+    return RedirectResponse(url='/docs')
+
+
+@app.post("/predict", tags=["Predictions"])
+async def predict(cars: List[Car]):
+    """
+    Retourne le prix de location journalier estimé pour une ou plusieurs voitures.
+
+    **Input** : liste de voitures avec leurs caractéristiques
+
+    **Output** : `{"prediction": [prix_en_euros, ...]}`
+    """
+    car_features = pd.DataFrame(jsonable_encoder(cars))
+    car_features_transformed = preprocessor.transform(car_features)
+    prediction = model.predict(car_features_transformed)
+    return {"prediction": prediction.tolist()}
+
+
+if __name__ == "__main__":
+    uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True)

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:137a16b3654076082c80396e67638800496ee2eefe913db17a786ba91aac84a7
+size 44960641

preprocessor.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:132daf149c1c2f7a8e907e9a3988fd461425783c9d9c56037fa6c7f2465aaead
+size 5708

requirements.txt

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