76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
from tkinter import *
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from tkinter import messagebox
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import pickle
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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window = Tk()
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window.minsize(width=600, height=600)
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window.config(padx=50, pady=50)
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window.config(background="#ffffff")
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window.title("Regresja")
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canvas = Canvas(width=250, height=150, highlightthickness=0, background="#ffffff")
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house = PhotoImage(file="house.png")
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canvas.create_image(140,75, image=house)
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canvas.grid(column=0, row=0, columnspan=2, padx=60)
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tytul = Label()
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tytul.config(text="Wycena wartości mieszkania na podstawie danych z serwisu otodom.pl", padx=70, pady=50, background="#ffffff")
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tytul.grid(column=0, row=1, columnspan=2)
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metraz = Label()
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metraz.config(text="Metraż", pady=20, background="#ffffff")
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metraz.grid(column=0, row=2)
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metraz_entry = Entry()
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metraz_entry.grid(column=0, row=3)
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pietro_entry = Entry()
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pietro_entry.grid(column=1, row=3)
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pietro = Label()
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pietro.config(text="Piętro", pady=20, background="#ffffff")
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pietro.grid(column=1, row=2)
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przewidywania = Label()
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przewidywania.config(text="Przewidywanie:", pady=20, padx=5, background="#ffffff")
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przewidywania.grid(column=0, row=5, sticky='e')
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wartosc_regresji = Label()
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wartosc_regresji.config(text="Wartosć przewidywana", background="#ffffff")
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wartosc_regresji.grid(column=1, row=5, sticky='w')
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model = pickle.load(open("model.pkl", 'rb'))
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def val_numbers():
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max_metraz = 100000
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max_piętro = 100
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metraz = metraz_entry.get()
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pietro = pietro_entry.get()
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if metraz == "" or not metraz.isdigit() or pietro=="" or not pietro.isdigit():
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messagebox.showinfo(title="Error", message=f"Please provide valid data")
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elif int(metraz) < 0 or int(metraz) > max_metraz or int(pietro) < 0 or int(pietro) > max_piętro:
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messagebox.showinfo(title="Error", message=f"Please provide numbers between 0 to 100 for floors and 0 to 100.000 for metric area.")
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else:
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confirm = messagebox.askyesno(title="Confirm", message="Do you want to start prediction?")
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if confirm == True:
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array = np.array([[int(pietro_entry.get()),float(metraz_entry.get())]])
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scaler = StandardScaler()
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transformed_data = scaler.fit_transform(array)
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prediction = model.predict(transformed_data)
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wartosc_regresji.config(text=f'{round(prediction[0][0], 2)} PLN')
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start_button = Button(command=val_numbers)
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start_button.config(text="Start", background="#000000", foreground="#ffffff")
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start_button.grid(column=0, row=4, columnspan=2, sticky="s", pady=20)
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window.mainloop()
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