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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() |