Update app.py

app.py

@@ -2,34 +2,68 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import plotly.graph_objs as go
-from io import BytesIO
-from datasets import load_dataset
-
-
-
-@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()
+import requests
+from io import StringIO
+
+# Function to load predictions from a GitHub repo
+@st.cache
+def load_data_predictions(github_token):
+    # GitHub API URL for the CSV file
+    url = 'https://api.github.com/repos/mmmapms/Forecast_DAM_V2/contents/Predictions.csv'
+    headers = {'Authorization': f'token {github_token}'}
+    
+    # Make a GET request to the GitHub API
+    response = requests.get(url, headers=headers)
+    
+    if response.status_code == 200:
+        # GitHub API response contains the file content in base64 encoding
+        file_content = response.json()['content']
+        csv_content = StringIO(file_content)
+        
+        df = pd.read_csv(csv_content, encoding='utf-8')
+        # Your renaming and preprocessing steps here
+        df = df.rename(columns={...})  # Your column renaming logic
+        df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
+        df_filtered = df.dropna(subset=['Real Price'])
+        return df, df_filtered
+    else:
+        st.error("Failed to download data. Please check your GitHub token and repository details.")
+        return pd.DataFrame(), pd.DataFrame()
+
+# Streamlit app UI for token input
+st.title("Belgium: Electricity Price Forecasting")
+github_token = st.secrets["GitHub_Token_Margarida"]
+
+if github_token:
+    df, df_filtered = load_data_predictions(github_token)
+    # Your existing logic to use df and df_filtered
+else:
+    st.warning("Please enter your GitHub Personal Access Token to proceed.")
+
+
+#@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()
 
 min_date_allowed_pred = df_filtered['Date'].min().date()
 max_date_allowed_pred = df_filtered['Date'].max().date()