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import subprocess
import sys
# Function to install a package
def install_package(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])
# Install required packages if they are not already installed
required_packages = [
"PyPDF2", "streamlit", "langchain", "faiss-cpu",
"sentence-transformers", "python-dotenv"
]
for package in required_packages:
try:
__import__(package)
except ImportError:
install_package(package)
import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import HuggingFaceHub
from langchain.embeddings import HuggingFaceEmbeddings
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
try:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
except Exception as e:
st.error(f"Error reading {pdf.name}: {e}. Skipping this file.")
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):
try:
embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embedding)
return vectorstore
except Exception as e:
st.error(f"Error creating vector store: {e}")
return None
def get_conversation_chain(vectorstore):
# Fetch the HuggingFace API token from environment variable
api_token = os.getenv("HUGGINGFACE_API_TOKEN")
if not api_token:
st.error("HuggingFace API token not found. Please ensure it is set in the environment variables.")
return None
llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512}, huggingfacehub_api_token=api_token)
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']
def main():
load_dotenv()
st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
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"):
with st.spinner("Processing"):
# get pdf text
raw_text = get_pdf_text(pdf_docs)
if raw_text: # Proceed only if there is valid text
# get the text chunks
text_chunks = get_text_chunks(raw_text)
# create vector store
vectorstore = get_vectorstore(text_chunks)
if vectorstore: # Check if vectorstore is valid
# create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore)
if __name__ == '__main__':
main()
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