app.py

import streamlit as st 
import os
# 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

st.set_page_config(layout="wide")



#Mongo connection
url = os.environ["MONGO_CONNECTION_STRING"]
client = MongoClient(url,tlsCerificateKeyFile ="cert.pem")

#Fetch database 
db = client["myapp"]

#Fetch collections
remcol = db["Reminders"]
usrcol = db["Users"]
notecol = db["Notes"]

#Connect with Bard
uri = os.environ["BARD_API_KEY"]
bard = Bard(token = uri )

#Store user bot chat messages
def chat_message(ques, ans):
    chat = {[{
            "user": ques,
            "bot": ans
        },
        {
            "test": "ques",
            "bot": "ans"
        }]}
    usrcol.insert_many(chat)

#Creating reminders from the described goal
def create_rem(ans):
    remlist = bard.get_answer(f"Create a daily routine to achieve this goal below and make a list of reminders with values for reminder_message, time, repetition, days\n\nGoal = {ans}")



def Chatbot():
    st.title("chatbot")
    if query:= st.chat_input("Describe your goal"):
        answer = bard.get_answer(query)
        chat_message(query, answer)
        create_rem(answer)

        with st.chat_message("assistant"):
            st.write(answer["content"])


Chatbot()

# classifyr = pipeline("zero-shot-classification")

# convo = pipeline("conversational")

# # classifi = pipeline(model="facebook/bart-large-mnli")

# uri = os.environ["MONGO_CONNECTION_STRING"]
# client = MongoClient(uri, tlsCertificateKeyFile="database/cert.pem") 


# def view_rem():
#     allrem = list(col.find())
#     remdata = pd.DataFrame(allrem)
#     st.dataframe(remdata)
    
 
# def Chatbot():
#     st.title("Chatbot")
#     if user_input := st.chat_input("Describe your goal. e.g: I want to achieve this goal in this time. Be as specific and explanatory as you can."):
#         bardans = bard.get_answer(user_input)['content']
#         anslist = bard.get_answer(f"Make a list of this answer: \n{bardans} \nfor this goal: \n{user_input}\n\nThe list should be in two section, section 1 for all the reminders to track called Daily Routine and section 2 for all information that should be consumed to achieve the goal and stay very focused and motivated with excitement and this section is called Notes")['content']
#         listrem = bard.get_answer(f"Act as a ToDo Reminder AI who sets reminders or daily routine based upon the daily routine provided below:\n{anslist} \n\nMake a list of reminders with exact message, time, repetation frequecy, day/s kind of neccessary detail that would be required to set a reminder notification. Make it a numeric list.")['content']
#         # result = classifyr(user_input,candidate_labels=["reminders", "notes"])
#         with st.chat_message("assistant"):
#             st.write(f"What to do to achive the goal:\n{bardans}\n\nHow to do it:\n{anslist}\n\nList of Reminders you should make:\n{listrem}")

#         # with st.chat_message("user"):
#         #     st.write(result["labels"][0])
        
#         # if ans["labels"][0] == "reminders":
#         #     values = getValues(query.lower())
#         #     with st.chat_message("assistant"):
#         #         st.write(values)
#         #         col.insert_one(values)
                
                
#         # elif ans["labels"][0] == "general conversation":
#         #     umsg = bard.get_answer(query)["content"]
#         #     with st.chat_message("assistant"):
#         #         st.write(umsg)
        
#         # elif ans["labels"][0] ==  "notes":
            
#         #      Notes = query.lower().replace( " create a new note", "",).replace(" no new note", "")
            
            
            
# Chatbot()
    
    
# def Create_Reminder():
#     st.title("Create Reminder")
#     message = st.text_input("Share your plan of today")
#     time = str(st.time_input("Time"))
#     date = str(st.date_input("Date"))