Guardar mis cambios locales

app.py

@@ -1,6 +1,5 @@
 import pandas as pd
 import matplotlib.pyplot as plt
-from statsmodels.tsa.arima.model import ARIMA
 import pickle
 import gradio as gr
 
@@ -8,7 +7,7 @@ def load_model():
     try:
         with open('arima_sales_model.pkl', 'rb') as f:
             arima_model = pickle.load(f)
-        return arima_model
+        return arima_model, None
     except Exception as e:
         return None, f"Failed to load model: {str(e)}"
 
@@ -18,20 +17,19 @@ def forecast_sales(uploaded_file, forecast_period=30):
 
     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
     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
+    df['Date'] = pd.to_datetime(df['Date'])
+    df = df.rename(columns={'Date': 'ds', 'Sale': 'y'})
 
-    try:
-        df['Date'] = pd.to_datetime(df['Date'])
-        df = df.rename(columns={'Date': 'ds', 'Sale': 'y'})
-
-        arima_model, error = load_model()
-        if arima_model is None:
-            return error, None
+    arima_model, error = load_model()
+    if arima_model is None:
+        return error, None
 
+    try:
         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})
@@ -56,9 +54,9 @@ def setup_interface():
         gr.Markdown("## MLCast v1.1 - Intelligent Sales Forecasting System")
         file_input = gr.File(label="Upload your store data here (must contain Date and Sales)")
         forecast_button = gr.Button("Forecast Sales")
+        output_text = gr.Textbox(visible=True)
         output_plot = gr.Plot()
-        output_text = gr.Textbox()
-        forecast_button.click(forecast_sales, inputs=[file_input], outputs=[output_text, output_plot])
+        forecast_button.click(forecast_sales, inputs=[file_input, gr.Slider(1, 60, 1, label="Forecast Period (days)", default=30)], outputs=[output_text, output_plot])
 
     return demo