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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
from functions import get_vectorstore_with_doc_from_pdf, tiktoken_len, get_vectorstore_with_doc_from_word
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
from streamlit_pdf_viewer import pdf_viewer
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))
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)
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
with st.sidebar:
st.header("Hitachi Support Bot")
button = st.toggle("View Doc file.")
if button:
pdf_viewer("GPT OUTPUT.pdf")
else:
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')
vector_store = get_vectorstore_with_doc_from_word('GPT OUTPUT.docx')
st.session_state.retriever = vector_store.as_retriever(k=NUMBER_OF_VECTORS_FOR_RAG)
print("container")
# Display chat messages from history
container = st.container(height=700)
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}) |