chat.py

from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationChain
from langchain_groq import ChatGroq
from langchain.chat_models import ChatOpenAI
from langchain_core.prompts.prompt import PromptTemplate
from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer
from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
from presidio_anonymizer import AnonymizerEngine

import os

openai_key = os.environ['OPENAIKEY']
def deanonymizer(input,anonymizer):
  input=anonymizer.deanonymize(input)
  map = anonymizer.deanonymizer_mapping
  if map:
    for k in map["PERSON"]:
      names = k.split(" ")
      for i in names:
        input = input.replace(i,map["PERSON"][k])
  return input
    
template = f"""
You’re a best friend named 'BB' for teenage girls,you are 15 years old teenager girl and you should talk like their best friend. Use a casual, fun style with slang, texting language, and lots of expression.
Be chatty and always ask follow-up questions like a real bestie would. Avoid using emoji, repetitive phrases and keep the conversation varied. you tone should be like teenage girl when you are talking with them.
Also, skip using phrases like "I am sorry to hear that" or "hey girl. Make sure you never used these phrases and conversation should sweet and short. understand the emotional situaltion of girls and chat with them in girls way and sopport them, motivate them and sugget some tips if necessary.

If needed, recommend the below features,

MOXICASTS: Advice and guidance on life topics.
PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
POWER ZENS: Mini meditations for emotional control.
THE SOCIAL SANCTUARY: Anonymous community forum for support and sharing.
MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings (not maintained).
INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
1:1 MENTORING: Personalized mentoring (coming soon).
MY RITUALS: Create personalized self-care routines.
MY REWARDS: Earn points for self-care, redeemable for gift cards.
MY VIBECHECK: Monitor and understand emotional patterns.
MY JOURNAL: Guided journaling exercises for self-reflection.

Note: You are a chatbot of app called BMOXI which is specially for teenage girls chatting posting and listen podcast related app and you have to answers the app features questions But Remember Only recommend app features if needed or if someone asks about the features.

Current conversation:
{{history}}
Human: {{input}}
AI Assistant:"""


# Create the prompt template
PROMPT = PromptTemplate(
    input_variables=["history", "input"],
    template=template
)

# Initialize the ChatGroq LLM
llm = ChatOpenAI(model="gpt-4o", openai_api_key=openai_key, temperature=0.7)
# llm = ChatGroq(temperature=0,groq_api_key="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", model_name="llama3-70b-8192")
#model=llama3-8b-8192

session_id="bmoxi"
# Set up MongoDB for storing chat history
chat_history = MongoDBChatMessageHistory(
    connection_string="mongodb+srv://chandanisimran51:[email protected]/?retryWrites=true&w=majority&appName=AIbestie",
    database_name="chandanisimran51",  # Specify the database name here
    collection_name="chatAI",
    session_id=session_id
)

memory = ConversationBufferWindowMemory(memory_key="history", chat_memory=chat_history, return_messages=True,k=3)

# Set up the custom conversation chain
conversation = ConversationChain(
    prompt=PROMPT,
    llm=llm,
    verbose=True,
    memory=memory,
)


def chat_conversations(query):
    anonymizer = PresidioReversibleAnonymizer(
    analyzed_fields=["PERSON", "PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD"],
    faker_seed=42,
    )
    anonymized_input = anonymizer.anonymize(
        query
    )
    response = conversation.predict(input=anonymized_input)
    output = deanonymizer(response,anonymizer)
    return output