api.py

import os

from flask import Flask, Response, request
from flask_cors import CORS
import pandas as pd
import pickle

app = Flask(__name__)

CORS(app)


training_data = pd.read_csv(os.path.join("data", "auto-mpg.csv"))

# laden des Models
file_to_open = open(os.path.join("data", "models", "regressor_mpg.pickle"), "rb")
trained_model = pickle.load(file_to_open)
file_to_open.close()


@app.route("/", methods=["GET"])
def index():
    return {"hello": "world"}


@app.route("/hello_world", methods=["GET"])
def hello_world():
    return "<p>Hello World!</p>"


@app.route("/training_data", methods=["GET"])
def get_training_data():
    return Response(training_data.to_json(), mimetype="application/json")


@app.route("/predict", methods=["GET"])
def predict():
    # Lese die Variablen aus
    zylinder = request.args.get("zylinder")
    ps = request.args.get("ps")
    gewicht = request.args.get("gewicht")
    beschleunigung = request.args.get("beschleunigung")
    baujahr = request.args.get("baujahr")

    # Erstellen der Vorhersage
    prediction = trained_model.predict(
        [[int(zylinder), int(ps), int(gewicht), int(beschleunigung), int(baujahr)]]
    )

    return {"result": prediction[0]}