Update index.py

index.py

@@ -14,6 +14,12 @@ from datetime import date
 import dash_bootstrap_components as dbc
 import plotly.express as px
 
+
+from dateutil.parser import parse
+ 
+def convert_to_datetime(input_str, parserinfo=None):
+    return parse(input_str, parserinfo=parserinfo)
+ 
 server = app.server
 
 url='https://drive.google.com/file/d/1NaXOYHQFF5UO5rQr4rn8Lr3bkYMSOq4_/view?usp=sharing'
@@ -212,15 +218,17 @@ app.layout = dbc.Container([
 )
 def update_output(selected_topic, selected_domain, start_date, end_date):
     #log
-    print("topic:",selected_topic,"domain:",selected_domain,"start:", start_date,"end:", end_date)
+    print("topic:",selected_topic,"domain:",selected_domain,"start:", start_date,"end:", end_date,"\n\n")
 
     # This is a hack to filter dates to confine to respective topic boundaries
     min_topic_date  = df[df["Topic"] == selected_topic]["date"].min()
     max_topic_date = df[df["Topic"] == selected_topic]["date"].max()
 
     #if start visualisation from where the topic starts
-    start_date = min_topic_date if (min_topic_date > start_date) else start_date
-    end_date = max_topic_date if (max_topic_date < end_date ) else  end_date
+    start_date = min_topic_date.dt.date if (min_topic_date > convert_to_datetime(start_date)) else start_date
+    end_date = max_topic_date.dt.date if (max_topic_date < convert_to_datetime(end_date)) else  end_date
+    
+    print("After: Sd",start_date,"Ed",end_date)
     
     # filter dataframes based on updated data range
     mask_1 = ((df["Topic"] == selected_topic) & (df['date'] >= start_date) & (df['date'] <= end_date))