Update app.py

app.py

@@ -3,29 +3,33 @@ import pandas as pd
 import numpy as np
 import plotly.graph_objs as go
 from io import BytesIO
-
-
-@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
+from datasets import load_dataset
+
+df = load_dataset("mmmapms/Forecasts")
+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()