Guardar mis cambios locales

app.py

@@ -10,7 +10,7 @@ def load_model():
     except Exception as e:
         return None, f"Failed to load model: {str(e)}"
 
-def forecast_sales(uploaded_file, forecast_period=30):
+def forecast_sales(uploaded_file, start_date, end_date, forecast_period=30):
     if uploaded_file is None:
         return "No file uploaded.", None
 
@@ -24,21 +24,26 @@ def forecast_sales(uploaded_file, forecast_period=30):
     df['Date'] = pd.to_datetime(df['Date'])
     df = df.rename(columns={'Date': 'ds', 'Sale': 'y'})
 
+    # Filtrar los datos según el rango de fechas seleccionado por el usuario
+    df_filtered = df[(df['ds'] >= pd.to_datetime(start_date)) & (df['ds'] <= pd.to_datetime(end_date))]
+
     arima_model, error = load_model()
     if arima_model is None:
         return error, None
 
     try:
+        forecast_index = pd.date_range(start=pd.to_datetime(end_date), periods=forecast_period + 1, freq='D')[1:]
         forecast = arima_model.get_forecast(steps=forecast_period)
-        forecast_index = pd.date_range(df['ds'].max(), 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
 
     try:
         fig, ax = plt.subplots(figsize=(10, 6))
-        ax.plot(df['ds'], df['y'], label='Historical Sales', color='blue')
-        ax.plot(forecast_df['Date'], forecast_df['Sales Forecast'], label='Sales Forecast', color='red', linestyle='--')
+        # Dibujar las ventas actuales en rojo
+        ax.plot(df_filtered['ds'], df_filtered['y'], label='Actual Sales', color='red')
+        # Dibujar la predicción en azul
+        ax.plot(forecast_df['Date'], forecast_df['Sales Forecast'], label='Sales Forecast', color='blue', linestyle='--')
         ax.set_xlabel('Date')
         ax.set_ylabel('Sales')
         ax.set_title('Sales Forecasting with ARIMA')
@@ -51,12 +56,14 @@ 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.Date(label="Start Date")
+        end_date_input = gr.Date(label="End Date")
         forecast_button = gr.Button("Forecast Sales")
         output_text = gr.Textbox(visible=True)
         output_plot = gr.Plot()
         forecast_button.click(
             forecast_sales, 
-            inputs=[file_input, gr.Slider(1, 60, step=1, label="Forecast Period (days)", value=30)], 
+            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]
         )
     return demo