Update app.py

app.py

@@ -5,10 +5,10 @@ from sklearn.model_selection import train_test_split
 
 # 1. Load Real SQL Query Logs
 # Load query logs from CSV (use your own CSV file here)
-@st.cache_data
+#@st.cache_data
 def load_data():
     # Make sure the CSV file is located correctly in the Hugging Face Space
-    return pd.read_csv("Query-Performance-Predictor/data/sql_query_logs.csv")  # Adjust the path if necessary
+    return pd.read_csv("data/sql_query_logs_2025_04_25.csv")
 
 # 2. Preprocess Data and Train the Model
 def preprocess_and_train_model(df):