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import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
from langchain_community.llms import Ollama
from langchain_groq import ChatGroq
import os
#extraction of the text from the pdfs
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
#dividing the raw text in different chunks
def get_text_chunks(text):
text_splitter = CharacterTextSplitter(
separator= "\n" ,
chunk_size=1000,
chunk_overlap=200,
length_function= len
)
chunks = text_splitter.split_text(text)
return chunks
#creating a vector store embeddings from huggingface
def get_vectorstore(text_chunks):
# embeddings = OpenAIEmbeddings()
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
#creating a conversation chain to store the context for follow up question
def get_conversation_chain(vectorstore, groq_api_key):
#llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
#llm = Ollama(model="llama2")
llm=ChatGroq(groq_api_key=groq_api_key,
model_name="llama3-70b-8192")
memory = ConversationBufferMemory(
memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
return conversation_chain
#handling the user input
def handle_userinput(user_question):
response = st.session_state.conversation({'question' : user_question})
#st.write(response)
st.session_state.chat_history = response['chat_history']
for i , message in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html= True)
else:
st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html= True)
def main():
load_dotenv()
#os.environ['OPENAI_API_KEY']=os.getenv("OPENAI_API_KEY")
groq_api_key=os.getenv('GROQ_API_KEY')
st.set_page_config("Chat with your pdf!!!!", 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 your pdf!!! :books:")
#question section
user_question = st.text_input("Wanna ask something???")
if user_question:
handle_userinput(user_question)
with st.sidebar:
st.subheader("Your documents")
#generally supports single file at a time. Need the enable the option to access multiple files
pdf_docs = st.file_uploader("Upload your pdf file", type=["pdf"], accept_multiple_files=True)
if st.button("Process"):
with st.spinner("Processing"):
#get the pdf text
raw_text = get_pdf_text(pdf_docs)
#get the text chunks
text_chunks = get_text_chunks(raw_text)
#create the vector store with embeddings
vectorstore = get_vectorstore(text_chunks)
#create the conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore, groq_api_key)
if __name__ == '__main__':
main() |