Update app.py

app.py

@@ -83,8 +83,11 @@ if uploaded_file is not None:
         X = df_cleaned[features]
         y = df_cleaned[target]
         
-        # Determine if the target is continuous or categorical
-        is_classification = len(y.unique()) <= 10  # If target has fewer than or equal to 10 unique values, treat as classification
+        # Check if the target is continuous (for regression) or categorical (for classification)
+        if y.dtype == 'O' or len(y.unique()) <= 10:  # Treat as classification if target is categorical or has <= 10 unique values
+            is_classification = True
+        else:
+            is_classification = False
         
         # Ensure there is enough data before proceeding with train-test split
         if len(X) == 0 or len(y) == 0:
@@ -98,7 +101,6 @@ if uploaded_file is not None:
             results = []
 
             # Model Selection and Evaluation
-            models = []
             if is_classification:
                 model_choices = [
                     ("Random Forest", RandomForestClassifier(n_estimators=50)),
@@ -116,7 +118,7 @@ if uploaded_file is not None:
                     class_report = classification_report(y_test, y_pred)
                     results.append([name, accuracy, class_report])
                 
-            else:
+            else:  # Regression models
                 model_choices = [
                     ("Random Forest", RandomForestRegressor(n_estimators=50)),
                     ("Linear Regression", LinearRegression()),