Update app.py

app.py

@@ -14,30 +14,24 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_auc_score
 import gradio as gr
 
-
-def load_csv(file_obj):
+def load_csv(file_path):
     try:
-        file_obj.seek(0)
-        df = pd.read_csv(file_obj)
+        df = pd.read_csv(file_path)
     except Exception as e:
         try:
-            file_obj.seek(0)
-            df = pd.read_excel(file_obj)
+            df = pd.read_excel(file_path)
         except Exception as e2:
-            return None, f"Failed to read file as CSV or Excel: {e} / {e2}"
+            return None, f"Failed to read file: {e} / {e2}"
     return df, None
 
-
-# Step 1: upload file -> return column choices
 def on_upload(file):
     if file is None:
         return gr.Dropdown.update(choices=[]), "No file uploaded", None
-    df, err = load_csv(file)
+    df, err = load_csv(file.name)  # file.name = temp file path
     if err:
         return gr.Dropdown.update(choices=[]), f"Error: {err}", None
     cols = df.columns.tolist()
-    return gr.Dropdown.update(choices=cols, value=cols[-1] if len(cols)>0 else None), f"Loaded {len(df)} rows, {len(cols)} columns", df
-
+    return gr.Dropdown.update(choices=cols, value=cols[-1] if len(cols) > 0 else None), f"Loaded {len(df)} rows, {len(cols)} columns", df
 
 # Helper: build preprocessing + model pipeline
 def build_pipeline(df, target_col, impute_strategy, apply_scaling, encode_categorical):