Guardar mis cambios locales

app.py

@@ -2,6 +2,8 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import joblib
 import gradio as gr
+from dateutil.relativedelta import relativedelta
+import calendar
 
 def load_model():
     try:
@@ -10,39 +12,47 @@ def load_model():
     except Exception as e:
         return None, f"Failed to load model: {str(e)}"
 
-def forecast_sales(uploaded_file, start_date, end_date, forecast_period=30):
+def parse_date(date_str):
+    """Parse the custom date format 'Month-Year' and return the start and end of the month."""
+    try:
+        date = pd.to_datetime(date_str, format="%B-%Y")
+        _, last_day = calendar.monthrange(date.year, date.month)
+        start_date = date.replace(day=1)
+        end_date = date.replace(day=last_day)
+        return start_date, end_date, None
+    except ValueError:
+        return None, None, "Date format should be 'Month-Year', e.g., 'January-2024'."
+
+def forecast_sales(uploaded_file, date_str, forecast_period=30):
     if uploaded_file is None:
-        return "No file uploaded.", None
+        return "No file uploaded.", None, "Please upload a file."
 
     try:
         df = pd.read_csv(uploaded_file)
         if 'Date' not in df.columns or 'Sale' not in df.columns:
-            return "The uploaded file must contain 'Date' and 'Sale' columns.", None
+            return None, "The uploaded file must contain 'Date' and 'Sale' columns.", "File does not have required columns."
     except Exception as e:
-        return f"Failed to read the uploaded CSV file: {str(e)}", None
+        return None, f"Failed to read the uploaded CSV file: {str(e)}", "Error reading file."
 
     df['Date'] = pd.to_datetime(df['Date'])
     df = df.rename(columns={'Date': 'ds', 'Sale': 'y'})
 
-    # Filtrar los datos según el rango de fechas introducido por el usuario
-    try:
-        start = pd.to_datetime(start_date)
-        end = pd.to_datetime(end_date)
-    except ValueError:
-        return "Please enter valid dates in YYYY-MM-DD format.", None
+    start_date, end_date, error = parse_date(date_str)
+    if error:
+        return None, error, "Invalid date format."
 
-    df_filtered = df[(df['ds'] >= start) & (df['ds'] <= end)]
+    df_filtered = df[(df['ds'] >= start_date) & (df['ds'] <= end_date)]
 
     arima_model, error = load_model()
     if arima_model is None:
-        return error, None
+        return None, error, "Failed to load ARIMA model."
 
     try:
-        forecast_index = pd.date_range(start=end, periods=forecast_period + 1, freq='D')[1:]
         forecast = arima_model.get_forecast(steps=forecast_period)
+        forecast_index = pd.date_range(start=end_date, periods=forecast_period + 1, freq='D')[1:]
         forecast_df = pd.DataFrame({'Date': forecast_index, 'Sales Forecast': forecast.predicted_mean})
     except Exception as e:
-        return f"Failed during forecasting: {str(e)}", None
+        return None, f"Failed during forecasting: {str(e)}", "Forecasting failed."
 
     try:
         fig, ax = plt.subplots(figsize=(10, 6))
@@ -52,23 +62,23 @@ def forecast_sales(uploaded_file, start_date, end_date, forecast_period=30):
         ax.set_ylabel('Sales')
         ax.set_title('Sales Forecasting with ARIMA')
         ax.legend()
-        return None, fig
+        return fig, None, "File loaded and processed successfully."
     except Exception as e:
-        return f"Failed to generate plot: {str(e)}", None
+        return None, f"Failed to generate plot: {str(e)}", "Plotting failed."
 
 def setup_interface():
     with gr.Blocks() as demo:
         gr.Markdown("## MLCast v1.1 - Intelligent Sales Forecasting System")
-        file_input = gr.File(label="Upload your store data here (must contain Date and Sales)")
-        start_date_input = gr.Textbox(label="Start Date (YYYY-MM-DD)", placeholder="Enter Start Date")
-        end_date_input = gr.Textbox(label="End Date (YYYY-MM-DD)", placeholder="Enter End Date")
-        forecast_button = gr.Button("Forecast Sales")
-        output_text = gr.Textbox(visible=True)
+        with gr.Row():
+            file_input = gr.File(label="Upload your store data here (must contain Date and Sales)")
+            date_input = gr.Textbox(label="Enter Month and Year (e.g., January-2024)")
+            forecast_button = gr.Button("Forecast Sales")
         output_plot = gr.Plot()
+        output_message = gr.Textbox(label="System Messages", visible=True)
         forecast_button.click(
-            forecast_sales, 
-            inputs=[file_input, start_date_input, end_date_input, gr.Slider(1, 60, step=1, label="Forecast Period (days)", value=30)], 
-            outputs=[output_text, output_plot]
+            forecast_sales,
+            inputs=[file_input, date_input],
+            outputs=[output_plot, output_message]
         )
     return demo