app.py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import streamlit as st
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score

def calculate_rmse(actual, predicted):
    return np.sqrt(mean_squared_error(actual, predicted))

def calculate_r2(actual, predicted):
    return r2_score(actual, predicted)

def main():
    st.title("RMSE ve R2 Skoru Hesaplama")

    # Kontrol dosyasını yükleme
    control_file_path = "test_reel.csv"
    control_data = pd.read_csv(control_file_path)

    # Tahmin dosyasını yükleme
    prediction_file = st.file_uploader("Tahmin dosyasını yükleyin", type="csv")
    if prediction_file is not None:
        prediction_data = pd.read_csv(prediction_file)

        # Price sütunlarını seçme
        control_price = control_data["Price"].values
        prediction_price = prediction_data["Price"].values

        # RMSE skoru hesaplama
        rmse_score = calculate_rmse(control_price, prediction_price)

        # R2 skoru hesaplama
        r2_score_val = calculate_r2(control_price, prediction_price)

        # Sonuçları gösterme
        st.write("RMSE Skoru:", rmse_score)
        st.write("R2 Skoru:", r2_score_val)

if __name__ == "__main__":
    main()