Update app.py

app.py

@@ -87,50 +87,21 @@ 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 start point for datetime-like integers scenario (December 2024 as a reference point)
-    datetime_ref_period = 403460
-    datetime_ref_date = datetime(2024, 12, 1)
-    
-    # Sequential period numbers scenario (1 up to 118 for December 2024)
-    if period <= 119:
-        # Calculate difference in months from December 2024
-        months_diff = 119 - period
-        date = datetime_ref_date - timedelta(days=months_diff * 30)  # Rough approximation
-    # Datetime-like integers scenario
-    else:
-        # Calculate difference in periods from the reference period and convert to date
-        periods_diff = 119 - (period * (119/datetime_ref_period))
-        date = datetime_ref_date - timedelta(days=periods_diff * 30)
-        
-    # 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]
 
     # Split the string into lines
     lines = data_string.split('\n')
 
+    mes = 0
+
     # Iterate through the lines and extract the values
     for line in lines[:-1]:  # Skip the last two lines which might not contain forecast data
-        period, value = line.split()
+        value = line.split()
         num_float = float(value)
         monetary_value = f'R$ {num_float:,.2f}'  # Adding commas for thousands separator
-
-        # Convert period to date format
-        period_date = period_to_date(int(period))
-        
-        col1.write(f"{period_date}: {monetary_value}")
+        mes += 1
+        col1.write(f"{mes}: {monetary_value}")
 else:
     col1.warning('No data available for the selected filters.')