added in some timers to measure time to forecast, in addition to remo…

…ving some comments.

app.py

@@ -9,6 +9,7 @@
 import pandas as pd
 from helper_v4 import forecastr,determine_timeframe,get_summary_stats,validate_model
 import logging
+import time
 
 
 
@@ -210,8 +211,6 @@ def main(message):
     # Send data back to the client in the form of a label detected or text extracted.
     emit('render_uploaded_csv_data', {'data': [column_headers,message, timeframe, summary_stats,original_data]})
 
-    # Probably need some sort of date checker function to properly format the date.
-    data[time_unit] = data[time_unit].apply(lambda x: pd.to_datetime(str(x)))
 
 
 

helper_v4.py

@@ -3,7 +3,8 @@
 from fbprophet.diagnostics import performance_metrics
 import pandas as pd
 import numpy as np
-#import time
+from flask_socketio import SocketIO, emit
+import time
 
 
 def forecastr(data,forecast_settings,column_headers,freq_val,build_settings):
@@ -122,10 +123,14 @@ def forecastr(data,forecast_settings,column_headers,freq_val,build_settings):
     m = Prophet(**prophet_arg_vals)
 
     # Fit the Model - Side Note it would be interesting to time how long this takes by file size #start = time.time()
-    #start = time.time()
+    start = time.time()
     m.fit(data)
-    #end = time.time()
-    #print(end-start)
+    end = time.time()
+    print(end-start)
+
+    # Status update
+    emit('processing', {'data': 'model has been fit'})
+
 
     # Let's create a new data frame for the forecast which includes how long the user requested to forecast out in time units and by time unit type (eg. "D", "M","Y")
     future = m.make_future_dataframe(periods=fs_period, freq=freq_val)