Correction endpoint racine et ajout gunicorn

app.py

@@ -1,69 +1,77 @@
-import pandas as pd
+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 ====
+from joblib import load
 
 description = """
-# 🚗 GetAround Rental Price Predictor API
+Welcome to the GetAround API, designed to assist you in predicting the rental price for your car!
 
-This API predicts the **rental price per day (in €)** for a car based on various features.
+Here are the available endpoints:
+
+* `/`: This endpoint is provided as an example. You can explore its functionality.
+* `/predict`: This endpoint accepts a POST request with JSON input data. You can use this endpoint to make predictions by providing the necessary information about your car.
+Feel free to use the `/predict` endpoint by sending a POST request with the required JSON data to obtain accurate rental price predictions for your vehicule
 """
 
+tags_metadata = [
+    {
+        "name": "Simple Endpoint",
+        "description": "Simple endpoint to try out!",
+    },
+    {
+        "name": "Prediction",
+        "description": "Prediction of the rental price based"
+    }
+]
+
 app = FastAPI(
-    title="GetAround Price Prediction API",
+    title="🚙 GetAround price prediction API ",
     description=description,
-    version="1.0",
-    contact={"name": "Ton Nom"},
+    version="0.1",
+    contact={
+        "name": "GetAround API - by Delphine Cesar",
+        "url": "https://github.com/delphinecesar",
+    }, 
+    openapi_tags=tags_metadata,
 )
 
-# ==== 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}
-
-if __name__=="__main__":
+# Data types for prediction
+class PredictionFeatures(BaseModel):
+    model_key: str = "Peugeot"
+    mileage: int = 13131
+    engine_power: int = 110
+    fuel: str = "diesel"
+    paint_color: str = "grey"
+    car_type: str = "convertible"
+    private_parking_available: bool = False
+    has_gps: bool = True
+    has_air_conditioning: bool = True
+    automatic_car: bool = False
+    has_getaround_connect: bool = True
+    has_speed_regulator: bool = False
+    winter_tires: bool = True
+
+@app.get("/", tags=["Simple Endpoint"])
+async def index():
+    return {"message": "Hello World!"}
+
+@app.post("/predict", tags=["Prediction"])
+async def predict(features: PredictionFeatures):
+    # Convert input data to DataFrame
+    information = pd.DataFrame(features.dict(), index=[0])
+    
+    # Load model
+    model = load('model.joblib')
+    
+    # Make prediction
+    prediction = model.predict(information)
+
+    # Return result
+    return {"prediction": prediction.tolist()[0]}
+
+if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=4000)
 
 
+