Update app.py

app.py

@@ -9,12 +9,23 @@ def convert_df_to_csv(df):
     return df.to_csv(index=False).encode('utf-8')
 
 # Load your data
-df = pd.read_csv('Predictions.csv')
-df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
-df_filtered = df.dropna(subset=['Price'])
+@st.cache_data
+def load_data_elia():
+    df = pd.read_csv('DATA_ELIA.csv')
+    df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
+    return df
+
+# Caching data loading for Predictions.csv
+@st.cache_data
+def load_data_predictions():
+    df = pd.read_csv('Predictions.csv')
+    df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
+    df_filtered = df.dropna(subset=['Price'])
+    return df, df_filtered
 
-df_input = pd.read_csv('DATA_ELIA.csv')
-df_input['Date'] = pd.to_datetime(df_input['Date'], dayfirst=True)
+# Load your data
+df_input = load_data_elia()
+df, df_filtered = load_data_predictions()
 
 # Determine the first and last date
 min_date_allowed = df_input['Date'].min().date()