Update app.py

app.py

@@ -50,7 +50,7 @@ def predict_values(start_doc, end_doc, days_option):
     close_future_rate = close_rate_model.make_future_dataframe(periods=days_option, freq='D')
 
     # Generate DOC values within the specified range
-    doc_values = np.linspace(start_doc, end_doc, num=len(farm_future_rate))  # Adjusted line
+    doc_values = np.linspace(start_doc, end_doc, num=days_option)  # Generate DOC values for the number of days
 
     # Assign the generated DOC values to the future dataframes
     farm_future_rate['DOC'] = doc_values
@@ -64,25 +64,25 @@ def predict_values(start_doc, end_doc, days_option):
 
     # Filter to get only the future predictions (last `days_option` days)
     farm_output = farm_forecast_rate[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(days_option).copy()
-    farm_output['DOC'] = farm_future_rate['DOC'].tail(days_option)
+    farm_output['DOC'] = np.round(doc_values, 1)  # Round DOC values
 
     open_output = open_forecast_rate[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(days_option).copy()
-    open_output['DOC'] = open_future_rate['DOC'].tail(days_option)
+    open_output['DOC'] = np.round(doc_values, 1)  # Round DOC values
 
     close_output = close_forecast_rate[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(days_option).copy()
-    close_output['DOC'] = close_future_rate['DOC'].tail(days_option)
+    close_output['DOC'] = np.round(doc_values, 1)  # Round DOC values
 
     # Create Plotly graphs
     fig_farm = go.Figure()
-    fig_farm.add_trace(go.Scatter(x=farm_forecast_rate['ds'], y=farm_forecast_rate['yhat'], mode='lines+markers', name='Farm Rate Prediction'))
+    fig_farm.add_trace(go.Scatter(x=farm_forecast_rate['ds'], y=farm_forecast_rate['yhat'].round(1), mode='lines+markers', name='Farm Rate Prediction'))
     fig_farm.update_layout(title='Farm Rate Predictions over Time', xaxis_title='Date', yaxis_title='Predicted Farm Rate')
 
     fig_open = go.Figure()
-    fig_open.add_trace(go.Scatter(x=open_forecast_rate['ds'], y=open_forecast_rate['yhat'], mode='lines+markers', name='Open Rate Prediction'))
+    fig_open.add_trace(go.Scatter(x=open_forecast_rate['ds'], y=open_forecast_rate['yhat'].round(1), mode='lines+markers', name='Open Rate Prediction'))
     fig_open.update_layout(title='Open Rate Predictions over Time', xaxis_title='Date', yaxis_title='Predicted Open Rate')
 
     fig_close = go.Figure()
-    fig_close.add_trace(go.Scatter(x=close_forecast_rate['ds'], y=close_forecast_rate['yhat'], mode='lines+markers', name='Close Rate Prediction'))
+    fig_close.add_trace(go.Scatter(x=close_forecast_rate['ds'], y=close_forecast_rate['yhat'].round(1), mode='lines+markers', name='Close Rate Prediction'))
     fig_close.update_layout(title='Close Rate Predictions over Time', xaxis_title='Date', yaxis_title='Predicted Close Rate')
 
     return farm_output, open_output, close_output, fig_farm, fig_open, fig_close
@@ -93,7 +93,7 @@ def interface(start_doc, end_doc, days_option):
     if days_option is None:
         return "Please select a valid option for days.", None, None, None, None, None
     
-    days_map = {'7 days': 7, '10 days': 10, '15 days': 15,'40 days':40}
+    days_map = {'7 days': 7, '10 days': 10, '15 days': 15, '40 days': 40}
     days_selected = days_map.get(days_option, 7)  # Default to 7 days if no valid option is provided
     results_farm, results_open, results_close, plot_farm, plot_open, plot_close = predict_values(start_doc, end_doc, days_selected)
     return results_farm, results_open, results_close, plot_farm, plot_open, plot_close
@@ -101,7 +101,7 @@ def interface(start_doc, end_doc, days_option):
 # Create Gradio inputs and outputs
 start_doc_input = gr.components.Number(label="Start DOC Value")
 end_doc_input = gr.components.Number(label="End DOC Value")
-days_dropdown = gr.components.Dropdown(choices=['7 days', '10 days', '15 days','40 days'], label="Select Number of Days",value='7 days')
+days_dropdown = gr.components.Dropdown(choices=['7 days', '10 days', '15 days', '40 days'], label="Select Number of Days", value='7 days')
 
 # Define output components
 output_table_farm = gr.components.Dataframe(label="Predicted Farm Rate Values")