Update app.py

app.py

@@ -6,11 +6,11 @@ from sklearn.preprocessing import LabelEncoder, OneHotEncoder
 from sklearn.metrics import r2_score, accuracy_score
 
 # Streamlit başlığı
-st.title("Data Corrector with AI")
+st.title("Automatic Data Corrector / Quality Enhancer Tool")
 
 # Train ve Test verilerini yükleme
-train_file = st.file_uploader("Upload your train CSV", type=["csv"])
-test_file = st.file_uploader("Upload your test CSV", type=["csv"])
+train_file = st.file_uploader("Upload Your Train Data", type=["csv"])
+test_file = st.file_uploader("Upload Your Test Data", type=["csv"])
 
 if train_file and test_file:
     train_df = pd.read_csv(train_file)
@@ -110,9 +110,9 @@ if train_file and test_file:
     st.write("Test Data with Predictions", test_df)
     
     if performances:
-        st.write("Model Performance:")
+        st.write("MODEL PERFORMANCE:")
         for column, model_type, performance in performances:
             if model_type == "numeric":
-                st.write(f"Column: {column}, Type: {model_type}, R^2 Score: {performance:.2f}")
+                st.write(f"Column: {column}  , Type: {model_type}  , R² Score: {performance:.2f}")
             else:
-                st.write(f"Column: {column}, Type: {model_type}, Accuracy Score: {performance:.2f}")
+                st.write(f"Column: {column}  , Type: {model_type}  , Accuracy Score: {performance:.2f}")