Update app.py

app.py

@@ -5,9 +5,12 @@ import plotly.graph_objs as go
 from io import BytesIO
 from datasets import load_dataset
 
-df = load_dataset("mmmapms/Forecasts")
 
-df = df.rename(columns={
+
+@st.cache_data
+def load_data_predictions():
+    df = pd.read_csv('Predictions.csv')
+   df = df.rename(columns={
     'Price': 'Real Price',
     'DNN1': 'Neural Network 1',
     'DNN2': 'Neural Network 2',
@@ -21,33 +24,12 @@ df = df.rename(columns={
     'LEAR_Ensemble': 'Regularized Linear Model Ensemble',
     'Persis': 'Persistence Model',
     'Hybrid_Ensemble': 'Hybrid Ensemble'
-    })
-df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
-df_filtered = df.dropna(subset=['Real Price'])
-
-#@st.cache_data
-#def load_data_predictions():
-#    df = pd.read_csv('Predictions.csv')
-#   df = df.rename(columns={
-#    'Price': 'Real Price',
-#    'DNN1': 'Neural Network 1',
-#    'DNN2': 'Neural Network 2',
-#    'DNN3': 'Neural Network 3',
-#    'DNN4': 'Neural Network 4',
-#    'DNN_Ensemble': 'Neural Network Ensemble',
-#    'LEAR56': 'Regularized Linear Model 1',
-#    'LEAR84': 'Regularized Linear Model 2',
-#    'LEAR112': 'Regularized Linear Model 3',
-#    'LEAR730': 'Regularized Linear Model 4',
-#    'LEAR_Ensemble': 'Regularized Linear Model Ensemble',
-#    'Persis': 'Persistence Model',
-#    'Hybrid_Ensemble': 'Hybrid Ensemble'
-#})
-#    df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
-#    df_filtered = df.dropna(subset=['Real Price'])
-#    return df, df_filtered
-
-#df, df_filtered = load_data_predictions()
+})
+    df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
+    df_filtered = df.dropna(subset=['Real Price'])
+    return df, df_filtered
+
+df, df_filtered = load_data_predictions()
 
 min_date_allowed_pred = df_filtered['Date'].min().date()
 max_date_allowed_pred = df_filtered['Date'].max().date()