model.py

import pandas as pd
import joblib
'''
from sklearn.ensemble import RandomForestRegressor

def train_model():
    data = pd.read_csv("ResponseTimes.csv")

    X = data[["EmergencyStations", "Area", "Urban"]]
    Y = data[["ResponseTime"]]

    Y = Y.to_numpy()
    Y = Y.flatten()

    clf = RandomForestRegressor()
    clf.fit(X, Y)

    predicted = clf.predict(X)

    avg=0
    for i in range(len(predicted)-1):
        avg = avg + abs(predicted[i] - Y[[i]])
        print(avg)

    print("Loss: ", avg/len(predicted))

    ans = input("Save?")
    if ans == "y":
        filename = 'model.sav'
        joblib.dump(clf, "./random_forest.joblib")
'''


def predict(stations, area, urban):
    clf = joblib.load("./random_forest.joblib")
    prediction = clf.predict([[stations, area, urban]])
    prediction = prediction.flatten()
    return prediction[0].item()

'''print(predict(429,789,0.5))
print(type(predict(429,789,0.5)))'''