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2 Commits

Author SHA1 Message Date
olgkwo
ca2f7ab42e correct project interface 2023-10-17 18:13:17 +02:00
Julia Krzemien
ae5fd6be72 added picture 2023-10-17 17:42:41 +02:00
18 changed files with 0 additions and 6484 deletions

3
.gitignore vendored
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# ignoruj pliki .bak
*.bak

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Jenkinsfile vendored
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pipeline {
agent {
docker { image 'python:3.10' }
}
stages {
stage('Test') {
steps {
sh 'pip install --user black'
sh 'black ./'
}
}
}
}

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## Systemy Informatyczne <br />
### Grupa B <br />
Celem projektu jest zaznajomienie się studentów z systemem kontroli wersji Git. <br />
### W ramach projektu przeprowadzone zostaną następujące czynności: <br />
1. Utworzenie repozytorium
2. Sklonowanie repozytorium przez wszystkich członków grupy.
3. Modyfikacja plików będących efektem pracy podczas poprzednich laboratoriów zgodnie z komentarzami prowadzących.
4. Na gałęzi master znajdują się pliki README, gitignore oraz kody źródłowe, natomiast rysunki znajdują się na gałęzi with-pictures.
### Instalacja i uruchamianie aplikacji: <br />
1. W pierwszej kolejności należy skolonować repozytorium
2. Następnie należy wykonać polecenie: pip install -r requirements.txt
3. W celu skorzystania z kalkulatora mieszkań należy uruchomić plik interface.py.
### Podręcznik użytkownika: <br />
- Interfejs programu zawiera dwa pola do wprowadzania parametrów. Użytkownik wpisuje metraż mieszkania oraz piętro, na którym znajduje się mieszkanie.
- Oba pola nie mogą być puste, a wpisywane wartości muszą być typu numerycznego. Wprowadzone wartości metrażu powinny zawierać się w przedziale od 0 do 100 000 metrów kwadratowych, a liczba pięter powinna zawierać sie w przedziale od 0 do 100. Jeśli wprowadzone dane nie zawierją się w tych przedziałach, to wyświetlony zostaje komunikat: "Please provid numbers between 0 to 100 for floors and 0 to 100 000 for metric area.". Jeśli któreś z pól pozostanie puste to pojawi się komunikat: "Please provide valid data".
- Po prawidłowym uzupełnieniu pól można uruchomić kalkulator za pomocą przycisku "start". Program poprosi o potwierdzenie startu. Wynikiem działania programu jest prognozowana cena mieszkania, wyliczona na podstawie działania modelu regresji liniowej.

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test

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

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asttokens==2.4.0
backcall==0.2.0
comm==0.1.4
debugpy==1.8.0
decorator==5.1.1
exceptiongroup==1.1.3
executing==2.0.0
ipykernel==6.25.2
ipython==8.16.1
jedi==0.19.1
joblib==1.3.2
matplotlib-inline==0.1.6
nest-asyncio==1.5.8
numpy==1.26.0
packaging==23.2
pandas==2.1.1
parso==0.8.3
pexpect==4.8.0
pickleshare==0.7.5
platformdirs==3.11.0
prompt-toolkit==3.0.39
psutil==5.9.5
ptyprocess==0.7.0
pure-eval==0.2.2
Pygments==2.16.1
python-dateutil==2.8.2
pytz==2023.3.post1
pyzmq==25.1.1
scikit-learn==1.3.1
scipy==1.11.3
six==1.16.0
stack-data==0.6.3
threadpoolctl==3.2.0
tornado==6.3.3
traitlets==5.11.2
tzdata==2023.3
wcwidth==0.2.8
black==23.10.1

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