Update app.py

app.py

@@ -1,14 +1,10 @@
-!pip install streamlit
-!pip install pyngrok
-!pip install tensorflow
-!pip install scikit-learn
+
 
 import streamlit as st
 import pandas as pd
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from sklearn.impute import SimpleImputer
 from sklearn.preprocessing import LabelEncoder
-import numpy as np
 
 # Streamlit başlığı
 st.title("Data Corrector with AI")
@@ -64,8 +60,8 @@ if train_file and test_file:
 
         # Eksik değerleri doldurma
         imputer = SimpleImputer(strategy='mean' if model_type == "numeric" else 'most_frequent')
-        X_train = imputer.fit_transform(X_train)
-        X_test = imputer.transform(X_test)
+        X_train = pd.DataFrame(imputer.fit_transform(X_train), columns=X_train.columns)
+        X_test = pd.DataFrame(imputer.transform(X_test), columns=X_test.columns)
 
         # Modeli eğitme ve tahmin yapma
         model.fit(X_train, y_train)
@@ -84,7 +80,7 @@ if train_file and test_file:
         
         X_test = pd.get_dummies(X_test, drop_first=True)
         X_test = X_test.reindex(columns=X_train.columns, fill_value=0)
-        X_test = imputer.transform(X_test)
+        X_test = pd.DataFrame(imputer.transform(X_test), columns=X_test.columns)
         predictions = model.predict(X_test)
 
         if model_type == "categorical":