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# importing dependencies
from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import faiss
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from htmlTemplates import css, bot_template, user_template
from langchain.embeddings import openai
from langchain.embeddings.openai import OpenAIEmbeddings
import os
openai.api_key = os.getenv["OPENAI_API_KEY"]
# creating custom template to guide llm model
custom_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)
# extracting text from pdf
def get_pdf_text(docs):
text=""
for pdf in docs:
pdf_reader=PdfReader(pdf)
for page in pdf_reader.pages:
text+=page.extract_text()
return text
# converting text to chunks
def get_chunks(raw_text):
text_splitter=CharacterTextSplitter(separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len)
chunks=text_splitter.split_text(raw_text)
return chunks
# using all-MiniLm embeddings model and faiss to get vectorstore
def get_vectorstore(chunks):
embeddings=OpenAIEmbeddings()
vectorstore=faiss.FAISS.from_texts(texts=chunks,embedding=embeddings)
return vectorstore
# generating conversation chain
def get_conversationchain(vectorstore):
llm=ChatOpenAI(temperature=0.2,model_name='gpt-4-turbo')
memory = ConversationBufferMemory(memory_key='chat_history',
return_messages=True,
output_key='answer') # using conversation buffer memory to hold past information
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
condense_question_prompt=CUSTOM_QUESTION_PROMPT,
memory=memory)
return conversation_chain
# generating response from user queries and displaying them accordingly
def handle_question(question):
response=st.session_state.conversation({'question': question})
st.session_state.chat_history=response["chat_history"]
for i,msg in enumerate(st.session_state.chat_history):
if i%2==0:
st.write(user_template.replace("{{MSG}}",msg.content,),unsafe_allow_html=True)
else:
st.write(bot_template.replace("{{MSG}}",msg.content),unsafe_allow_html=True)
def main():
load_dotenv()
st.set_page_config(page_title="Chat with multiple PDFs",page_icon=":books:")
st.write(css,unsafe_allow_html=True)
if "conversation" not in st.session_state:
st.session_state.conversation=None
if "chat_history" not in st.session_state:
st.session_state.chat_history=None
st.header("Chat with multiple PDFs :books:")
question=st.text_input("Ask question from your document:")
if question:
handle_question(question)
with st.sidebar:
st.subheader("Your documents")
docs=st.file_uploader("Upload your PDF here and click on 'Process'",accept_multiple_files=True)
if st.button("Process"):
with st.spinner("Processing"):
#get the pdf
raw_text=get_pdf_text(docs)
#get the text chunks
text_chunks=get_chunks(raw_text)
#create vectorstore
vectorstore=get_vectorstore(text_chunks)
#create conversation chain
st.session_state.conversation=get_conversationchain(vectorstore)
if __name__ == '__main__':
main() |