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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 OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
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.callbacks import get_openai_callback
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
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
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
def get_conversation_chain(vectorstore):
# llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
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
def handle_userinput(user_question):
response = st.session_state.conversation({'question': user_question})
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()
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:")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
handle_userinput(user_question)
with st.sidebar:
st.subheader("Your documents")
pdf_docs = st.file_uploader(
"Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
if st.button("Process"):
if(len(pdf_docs) == 0):
st.error("Please upload at least one PDF")
else:
with st.spinner("Processing"):
# get pdf text
raw_text = get_pdf_text(pdf_docs)
# get the text chunks
text_chunks = get_text_chunks(raw_text)
# create vector store
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(
vectorstore)
if __name__ == '__main__':
main()
# import os
# import getpass
# import streamlit as st
# from langchain.document_loaders import PyPDFLoader
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# from langchain.embeddings import HuggingFaceEmbeddings
# from langchain.vectorstores import Chroma
# from langchain import HuggingFaceHub
# from langchain.chains import RetrievalQA
# # __import__('pysqlite3')
# # import sys
# # sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
# # load huggingface api key
# hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]
# # use streamlit file uploader to ask user for file
# # file = st.file_uploader("Upload PDF")
# path = "Geeta.pdf"
# loader = PyPDFLoader(path)
# pages = loader.load()
# # st.write(pages)
# splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
# docs = splitter.split_documents(pages)
# embeddings = HuggingFaceEmbeddings()
# doc_search = Chroma.from_documents(docs, embeddings)
# repo_id = "tiiuae/falcon-7b"
# llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})
# from langchain.schema import retriever
# retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
# if query := st.chat_input("Enter a question: "):
# with st.chat_message("assistant"):
# st.write(retireval_chain.run(query)) |