app.py

import streamlit as st
import random
from app_config import SYSTEM_PROMPT, NLP_MODEL_NAME, NUMBER_OF_VECTORS_FOR_RAG, NLP_MODEL_TEMPERATURE, NLP_MODEL_MAX_TOKENS, VECTOR_MAX_TOKENS,my_vector_store,chat,tiktoken_len
from langchain.memory import ConversationSummaryBufferMemory
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain.chains.summarize import load_summarize_chain
from langchain.prompts import PromptTemplate
from langchain_groq import ChatGroq
from dotenv import load_dotenv
from pathlib import Path
import os
env_path = Path('.') / '.env'
load_dotenv(dotenv_path=env_path)

def response_generator(prompt: str) -> str:
    """this function can be used for general quetion answers which are related to tyrex and tyre recycling 

    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """

    try:
        retriever = st.session_state.retriever
        docs = retriever.invoke(prompt)
        my_context = [doc.page_content for doc in docs]
        my_context = '\n\n'.join(my_context)
        system_message = SystemMessage(content = SYSTEM_PROMPT.format(context=my_context, previous_message_summary=st.session_state.rag_memory.moving_summary_buffer))
        print(system_message)
        chat_messages = (system_message + st.session_state.rag_memory.chat_memory.messages + HumanMessage(content=prompt)).messages
        print("total tokens: ", tiktoken_len(str(chat_messages)))
        # print("my_context*********",my_context)
        response = st.session_state.llm.invoke(chat_messages)
        return response.content
    
    except Exception as error:
        print(error, "ERROR")
        return "Oops! something went wrong, please try again."


st.markdown(
    """
<style>
    .st-emotion-cache-janbn0 {
        flex-direction: row-reverse;
        text-align: right;
    }
</style>
""",
    unsafe_allow_html=True,
)

# When user gives input

print("SYSTEM MESSAGE")
if "messages" not in st.session_state:
    st.session_state.messages=[{"role": "system", "content": SYSTEM_PROMPT}]
    
print("SYSTEM MODEL")
if "llm" not in st.session_state:
    st.session_state.llm = ChatGroq(temperature=NLP_MODEL_TEMPERATURE, groq_api_key=str(os.getenv('GROQ_API_KEY')), model_name=NLP_MODEL_NAME)

print("rag")
if "rag_memory" not in st.session_state:
    st.session_state.rag_memory = ConversationSummaryBufferMemory(llm=st.session_state.llm, max_token_limit= 5000)

print("retrival")
if "retriever" not in st.session_state:
    # vector_store = get_vectorstore_with_doc_from_pdf('GPT OUTPUT.pdf')
    st.session_state.retriever = my_vector_store.as_retriever(k=NUMBER_OF_VECTORS_FOR_RAG)


st.title("Insurance Bot")
print("container")
# Display chat messages from history
container =  st.container(height=600)
for message in st.session_state.messages:
    if message["role"] != "system":
        with container.chat_message(message["role"]):
            st.write(message["content"])

if prompt := st.chat_input("Enter your query here... "):
    with container.chat_message("user"):
        st.write(prompt)
    st.session_state.messages.append({"role":"user" , "content":prompt})
    
    with container.chat_message("assistant"):  
        response = response_generator(prompt=prompt)
        print("******************************************************** Response ********************************************************")
        print("MY RESPONSE IS:", response)
        st.write(response)
    
    print("Response is:", response)
    st.session_state.rag_memory.save_context({'input': prompt}, {'output': response})
    st.session_state.messages.append({"role":"assistant" , "content":response})