modified

app.py

@@ -10,13 +10,28 @@ import os
 import sklearn as sns
 
 print(os.getcwd())
+
 # Load dataset
 @st.cache_data
 def load_data():
-    df = pd.read_csv("pokemon_cleaned.csv")
+    file_path = "pokemon_cleaned.csv"
+
+    if not os.path.exists(file_path):
+        st.error(f"❌ File not found: {file_path}")
+        return None  # Return None if file doesn't exist
+
+    try:
+        df = pd.read_csv(file_path)
+        return df
+    except Exception as e:
+        st.error(f"❌ Failed to load the CSV: {e}")
+        return None  # Return None in case of an error
 
 df = load_data()
 
+if df is None:
+    st.stop() 
+
 # Load machine learning models for prediction
 model = joblib.load("random_forest.pkl")
 model = joblib.load("logistic_regression.pkl")