Update app.py

app.py

@@ -76,6 +76,7 @@ def get_month_label(i):
 #df['revenue'] = (df['vintage_unique_cases'] / df['predicted_monthly_payment_rate']).round(2)
 #df['Month'] = [get_month_label(i) for i in range(len(df))]
 df = pd.DataFrame()
+df_2 = pd.DataFrame()
     
 def update_table(start_date, end_date, window, user_text):
     #############################################################################################
@@ -121,10 +122,10 @@ def update_table(start_date, end_date, window, user_text):
             time.sleep(30)
         
         print(f"Run ID: {run_id}")
-        #data_payments = get_databricks_file("dbfs:/dbfs/FileStore/forecast_alleviatetax_payments.csv")
+        data_payments = get_databricks_file("dbfs:/dbfs/FileStore/forecast_alleviatetax_payments_"+str(user_text).split('@')[0]+".csv")
         #data_metrics = get_databricks_file("dbfs:/dbfs/FileStore/forecast_alleviatetax_metrics.csv")
         data_predictions = get_databricks_file("dbfs:/dbfs/FileStore/forecast_alleviatetax_predictions_"+str(user_text).split('@')[0]+".csv")
-        #df_payments = pd.read_csv(StringIO(data_payments.decode('utf-8')))
+        df_payments = pd.read_csv(StringIO(data_payments.decode('utf-8')))
         #new_columns = ['vintage', 'vintage_unique_cases'] + [f'M{col}' for col in df_payments.columns[2:]] 
         #df_metrics = pd.read_csv(StringIO(data_metrics.decode('utf-8')))
         df_predictions = pd.read_csv(StringIO(data_predictions.decode('utf-8')))
@@ -134,8 +135,10 @@ def update_table(start_date, end_date, window, user_text):
         return {error: str(e)}
     #############################################################################################
     global df  # Use global variable
+    global df_2
     df = df_predictions.copy()
-    return df
+    df_2 = df_payments.copy() 
+    return df, df_2
 
 def save_csv(file_name):
     global df  # Use global variable
@@ -177,10 +180,11 @@ with gr.Blocks(fill_height=True) as demo:
                 range_input = gr.Slider(3, 12, 6, label="Window / Moving Average Period")
                 gr.Markdown("##### Window = 3-period/months, the predictive model reacts more quickly to recent monthly payment fluctuations, but it may also include more noise.<br><br>Window = 12-period/months, the Forecast adjusts more slowly and is less sensitive to small fluctuations, making it more reliable, but also slower to react to sharp changes.")
         
-        table_ = gr.DataFrame(value=df, label="Predictions (consult 'revenue' column):")
+        table_1 = gr.DataFrame(value=df, label="Predictions (consult 'revenue' column):")
         btn_update = gr.Button("Run Forecast")
+        table_2 = gr.DataFrame(value=df_2, label="Forecast Inputs:")
         
-        btn_update.click(fn=update_table, inputs=[start_input, prediction_input, range_input, user_text], outputs=table_)
+        btn_update.click(fn=update_table, inputs=[start_input, prediction_input, range_input, user_text], outputs=[table_1,table_2])
     
     # Configure the buttons and the panel visibility
     btn_1.click(check_words, inputs=[user_text, input_text], outputs=[panel_2, panel_1])