Guardar mis cambios locales

app.py

@@ -5,21 +5,40 @@ import pickle
 import gradio as gr
 
 def load_model():
-    with open('arima_sales_model.pkl', 'rb') as f:
-        arima_model = pickle.load(f)
-    return arima_model
+    try:
+        with open('arima_sales_model.pkl', 'rb') as f:
+            arima_model = pickle.load(f)
+        return arima_model
+    except Exception as e:
+        return None, f"Failed to load model: {str(e)}"
 
 def forecast_sales(uploaded_file, forecast_period=30):
-    df = pd.read_csv(uploaded_file)
-    if 'Date' in df.columns and 'Sale' in df.columns:
+    if uploaded_file is None:
+        return "No file uploaded.", None
+
+    try:
+        df = pd.read_csv(uploaded_file)
+    except Exception as e:
+        return f"Failed to read the uploaded CSV file: {str(e)}", None
+
+    if 'Date' not in df.columns or 'Sale' not in df.columns:
+        return "The uploaded file must contain 'Date' and 'Sale' columns.", None
+
+    try:
         df['Date'] = pd.to_datetime(df['Date'])
         df = df.rename(columns={'Date': 'ds', 'Sale': 'y'})
 
-        arima_model = load_model()
+        arima_model, error = load_model()
+        if arima_model is None:
+            return error, None
+
         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:
         # Create the plot
         fig, ax = plt.subplots(figsize=(10, 6))
         ax.plot(df['ds'], df['y'], label='Historical Sales', color='blue')
@@ -28,19 +47,19 @@ def forecast_sales(uploaded_file, forecast_period=30):
         ax.set_ylabel('Sales')
         ax.set_title('Sales Forecasting with ARIMA')
         ax.legend()
-        return fig
-    else:
-        return "The uploaded file must contain at least 'Date' and 'Sales' columns."
+        return None, fig
+    except Exception as e:
+        return f"Failed to generate plot: {str(e)}", None
 
 def setup_interface():
     with gr.Blocks() as demo:
         gr.Markdown("## MLCast v1.1 - Intelligent Sales Forecasting System")
-        with gr.Row():
-            file_input = gr.File(label="Upload your store data here (must contain Date and Sales)")
-            forecast_button = gr.Button("Forecast Sales")
+        file_input = gr.File(label="Upload your store data here (must contain Date and Sales)")
+        forecast_button = gr.Button("Forecast Sales")
         output_plot = gr.Plot()
-        forecast_button.click(forecast_sales, inputs=[file_input], outputs=[output_plot])
-    
+        output_text = gr.Textbox()
+        forecast_button.click(forecast_sales, inputs=[file_input], outputs=[output_text, output_plot])
+
     return demo
 
 if __name__ == "__main__":