this

app.py

@@ -1,15 +1,17 @@
 import streamlit as st 
 import os
-from bardapi import Bard
+import pandas as pd
 
+from streamlit_option_menu import option_menu 
+from bardapi import Bard
 from  getvalues import getValues
 from pymongo import MongoClient
 from transformers import pipeline, Conversation
 
 
 
-classifyr = pipeline("zero-shot-classification")
-convo = pipeline("conversational")
+# classifyr = pipeline("zero-shot-classification")
+# convo = pipeline("conversational")
 
 uri = os.environ["MONGO_CONNECTION_STRING"]
 client = MongoClient(uri, tlsCertificateKeyFile="database/cert.pem") 
@@ -21,13 +23,19 @@ col = db["reminders"]
 bardkey = os.environ.get("BARD_API_KEY")
 
 bard = Bard(token=bardkey)
+
+def view_rem():
+    allrem = list(col.find())
+    remdata = pd.DataFrame(allrem)
+    st.dataframe(remdata)
+    
  
 def Chatbot():
     st.title("Chatbot")
     if query :=st.chat_input("Enter your message"):
         ans = classifyr(query,candidate_labels=["reminders", "general conversation"])
         if ans["labels"][0] == "reminders":
-            values = getValues(ans.lower())
+            values = getValues(query.lower())
             with st.chat_message("assistant"):
                 st.write(values)
                 col.insert_one(values)
@@ -35,7 +43,6 @@ def Chatbot():
                 
         elif ans["labels"][0] == "general conversation":
             umsg = bard.get_answer(message)["content"]
-          
             with st.chat_message("assistant"):
                 st.write(umsg)