Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
+from datetime import datetime
 
 #st.set_page_config(layout="wide")
 
@@ -86,6 +87,25 @@ col1, col2 = st.columns([col1_width, col2_width])
 # Display the Forecasts values in the first column
 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']
+    
+    # Handling the sequence up to 118
+    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
+    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}"
+
 if not filtered_df.empty:
     data_string = filtered_df['Forecasts'].iloc[0]
 
@@ -93,11 +113,15 @@ if not filtered_df.empty:
     lines = data_string.split('\n')
 
     # Iterate through the lines and extract the values
-    for line in lines[:-2]:
+    for line in lines[:-2]:  # Skip the last two lines which might not contain forecast data
         period, value = line.split()
         num_float = float(value)
         monetary_value = f'R$ {num_float:,.2f}'  # Adding commas for thousands separator
-        col1.write(f"Período {period}: {monetary_value}")
+
+        # Convert period to date format
+        period_date = period_to_date(int(period))
+        
+        col1.write(f"{period_date}: {monetary_value}")
 else:
     col1.warning('No data available for the selected filters.')