Guardar mis cambios locales

app.py

@@ -4,16 +4,16 @@ import joblib
 import gradio as gr
 from dateutil.relativedelta import relativedelta
 import calendar
-import emoji  # Importa la librería de emoji
 
 def load_model():
     try:
         model = joblib.load('arima_sales_model.pkl')
         return model, None
     except Exception as e:
-        return None, emoji.emojize(f"Failed to load model :warning: {str(e)}")
+        return None, f"Failed to load model: {str(e)}"
 
 def parse_date(date_str):
+    """Parse the custom date format 'Month-Year'."""
     try:
         date = pd.to_datetime(date_str, format="%B-%Y")
         _, last_day = calendar.monthrange(date.year, date.month)
@@ -21,18 +21,18 @@ def parse_date(date_str):
         end_date = date.replace(day=last_day)
         return start_date, end_date, None
     except ValueError:
-        return None, None, emoji.emojize("Date format should be 'Month-Year', e.g., 'January-2024' :calendar:")
+        return None, None, "Date format should be 'Month-Year', e.g., 'January-2024'."
 
 def forecast_sales(uploaded_file, start_date_str, end_date_str):
     if uploaded_file is None:
-        return emoji.emojize("No file uploaded :file_folder:"), None, emoji.emojize("Please upload a file :upside_down_face:")
+        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 None, emoji.emojize("The uploaded file must contain 'Date' and 'Sale' columns :x:"), "File does not have required columns."
+            return None, "The uploaded file must contain 'Date' and 'Sale' columns.", "File does not have required columns."
     except Exception as e:
-        return None, emoji.emojize(f"Failed to read the uploaded CSV file :disappointed: {str(e)}"), "Error reading file."
+        return None, f"Failed to read the uploaded CSV file: {str(e)}", "Error reading file."
 
     start_date, _, error = parse_date(start_date_str)
     _, end_date, error_end = parse_date(end_date_str)
@@ -60,20 +60,22 @@ def forecast_sales(uploaded_file, start_date_str, end_date_str):
         ax.set_ylabel('Sales')
         ax.set_title('Sales Forecasting with ARIMA')
         ax.legend()
-        return fig, emoji.emojize("File loaded and processed successfully :check_mark:")
+        return fig, "File loaded and processed successfully."
     except Exception as e:
-        return None, emoji.emojize(f"Failed to generate plot :cry: {str(e)}"), "Plotting failed."
+        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")
         with gr.Row():
-            file_input = gr.File(label=emoji.emojize("Upload your store data here :open_file_folder:"))
-            start_date_input = gr.Textbox(label="Start Date", placeholder="January-2024")
-            end_date_input = gr.Textbox(label="End Date", placeholder="December-2024")
-            forecast_button = gr.Button("Forecast Sales")
-        output_plot = gr.Plot()
-        output_message = gr.Textbox(label="Notifications", visible=True, lines=2)
+            with gr.Column(scale=1):
+                file_input = gr.File(label="Upload your store data")
+                start_date_input = gr.Textbox(label="Start Date", placeholder="January-2024")
+                end_date_input = gr.Textbox(label="End Date", placeholder="December-2024")
+                forecast_button = gr.Button("Forecast Sales")
+            with gr.Column(scale=2):
+                output_plot = gr.Plot()
+                output_message = gr.Textbox(label="Notifications", visible=True, lines=2)
         forecast_button.click(
             forecast_sales,
             inputs=[file_input, start_date_input, end_date_input],