Update app.py

app.py

@@ -80,12 +80,14 @@ def calculate_profit_loss(stock_data,days_to_monitor):
 
     dft = pd.DataFrame(buy_sell_actions)
 
-    dff = pd.DataFrame()
+    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['Mean returns'] = dft['Profit/Loss (%)'].mean()
     dff['Median returns'] =  dft['Profit/Loss (%)'].median()
     dff['Best return'] =  dft['Profit/Loss (%)'].max()
-    dff['worst return'] =  dft['Profit/Loss (%)'].min()
+    dff['Worst return'] =  dft['Profit/Loss (%)'].min()
+
+    print(dff.head())
 
     
     return dft, dff
@@ -180,8 +182,8 @@ with gr.Blocks() as demo:
             stock_input = gr.Textbox(label="Enter Stock Symbol", placeholder="e.g., CANBK", lines=1)
             rsi_window_slider = gr.Slider(minimum=1, maximum=30, value=14, label="RSI Window (Days)", step=1)
             days_to_monitor = gr.Slider(minimum=1, maximum=30, value=8, label="Days to monitor stock once condition is met", step=1)
-            previous_n_days = gr.Slider(minimum=1, maximum=180, value=30, label="Previous n days RSI threshold to check", step=1)
             rsi_threshold1 = gr.Slider(minimum=1, maximum=100, value=65, label="Previous RSI threshold", step=1)
+            previous_n_days = gr.Slider(minimum=1, maximum=180, value=30, label="N -> days to check for RSI threshold", step=1)
             rsi_threshold2 = RangeSlider(label="Current RSI Range", minimum=0, maximum=100, value=[65, 70])
             text1 = "## The range is: {min} to {max}"
             range_ = gr.Markdown(value=text1.format(min=0, max=100))