Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-from datetime import datetime
+from datetime import datetime, timedelta
 
 #st.set_page_config(layout="wide")
 
@@ -88,23 +88,32 @@ col1, col2 = st.columns([col1_width, col2_width])
 col1.header('Valores previstos')
 
 def period_to_date(period):
-    # Define a list of month names
-    months = ['Janeiro', 'Fevereiro', 'Março', 'Abril', 'Maio', 'Junho', 'Julho', 'Agosto', 'Setembro', 'Outubro', 'Novembro', 'Dezembro']
+    # Define start point for datetime-like integers scenario (December 2024 as a reference point)
+    datetime_ref_period = 403460
+    datetime_ref_date = datetime(2024, 12, 1)
     
-    # Handling the sequence up to 118
+    # Sequential period numbers scenario (1 up to 118 for December 2024)
     if period <= 118:
-        # Assuming 118 corresponds to December 2024 and counting backwards
-        year = 2024
-        month_index = 118 - period
-        return f"{months[-(month_index + 1)]}/{year}"
-
-    # Handling the datetime integer sequence
+        # Calculate difference in months from December 2024
+        months_diff = 118 - period
+        date = datetime_ref_date - timedelta(days=months_diff * 30)  # Rough approximation
+    # Datetime-like integers scenario
     else:
-        # Convert period to datetime object assuming the period is a Julian date
-        date = datetime.fromordinal(period)
-        month_name = months[date.month - 1]
-        year = date.year
-        return f"{month_name}/{year}"
+        # Calculate difference in periods from the reference period and convert to date
+        periods_diff = datetime_ref_period - period
+        date = datetime_ref_date - timedelta(days=periods_diff)  # Assuming each unit difference represents one day
+        
+    # Format and return the date
+    month_name = date.strftime('%B')
+    year = date.year
+    # Portuguese month names
+    month_translation = {
+        'January': 'Janeiro', 'February': 'Fevereiro', 'March': 'Março',
+        'April': 'Abril', 'May': 'Maio', 'June': 'Junho',
+        'July': 'Julho', 'August': 'Agosto', 'September': 'Setembro',
+        'October': 'Outubro', 'November': 'Novembro', 'December': 'Dezembro'
+    }
+    return f"{month_translation[month_name]}/{year}"
 
 if not filtered_df.empty:
     data_string = filtered_df['Forecasts'].iloc[0]