Update app.py

app.py

@@ -81,7 +81,8 @@ def calculate_profit_loss(stock_data,days_to_monitor):
     dft = pd.DataFrame(buy_sell_actions)
 
     dff = pd.DataFrame(columns = ['+ve trade probability','Median returns','Mean returns','Best return','Worst return'])
-    dff['+ve trade probability'] = len(dft[dft['Profit/Loss (%)'] > 0])
+    
+    dff['+ve trade probability'] = len(dft[dft['Profit/Loss (%)'] > 0]) / len(dft)
     dff['Mean returns'] = dft['Profit/Loss (%)'].mean()
     dff['Median returns'] =  dft['Profit/Loss (%)'].median()
     dff['Best return'] =  dft['Profit/Loss (%)'].max()
@@ -166,7 +167,7 @@ def save_to_csv(stock_input,rsi_window,days_to_monitor,previous_n_days,rsi_thres
     # fstock['Date'] = pd.to_datetime(fstock['Date']).dt.date
     # profit_data['Date'] = pd.to_datetime(profit_data['Date']).dt.date
 
-    return fstock, profit_data, summary_data, csv_file_path
+    return profit_data, summary_data, csv_file_path
 
 # Create the Gradio interface
 with gr.Blocks() as demo:
@@ -194,13 +195,13 @@ with gr.Blocks() as demo:
         
         with gr.Column():
             gr.Markdown("<h3 style='text-align: center;'>Output</h3>")
-            output_stock_data = gr.DataFrame(label="Dates where Conditions met", interactive=False)
+            # output_stock_data = gr.DataFrame(label="Dates where Conditions met", interactive=False)
             output_pl_data = gr.DataFrame(label="Profit and Loss Statement", interactive=False)
             output_summary_data = gr.DataFrame(label="Returns Summary", interactive=False)
             csv_download = gr.File(label="Download the full CSV")
 
     # When the button is clicked, show the two dataframes and provide a downloadable CSV
-    submit_button.click(save_to_csv, inputs=[stock_input,rsi_window_slider,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2], outputs=[output_stock_data, output_pl_data, output_summary_data,csv_download])
+    submit_button.click(save_to_csv, inputs=[stock_input,rsi_window_slider,days_to_monitor,previous_n_days,rsi_threshold1,rsi_threshold2], outputs=[output_pl_data, output_summary_data,csv_download])
 
 # Launch the Gradio interface
 demo.launch()