Add application file

Dockerfile

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+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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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)

requirements.txt

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