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76ae9cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.chat_models import ChatOpenAI
from langchain_community.callbacks.manager import get_openai_callback
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, user_template, bot_template
def get_pdf_text(pdf_docs):
"""Extract text from multiple uploaded PDF files."""
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
extracted_text = page.extract_text()
if extracted_text:
text += extracted_text + "\n"
return text
def get_text_chunks(text):
"""Split the extracted text into smaller chunks for vector storage."""
text_splitter = CharacterTextSplitter(
separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len
)
return text_splitter.split_text(text)
def get_vectorstore(text_chunks):
"""Convert text chunks into vector embeddings and store them in FAISS."""
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
"""Set up the conversational AI chain using a language model and vector storage."""
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.8)
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_user_input(user_question):
"""Process user input and generate a response."""
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="📚")
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 = []
st.header("📚 Chat with Multiple PDFs")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
handle_user_input(user_question)
with st.sidebar:
st.subheader("Upload Your PDFs")
pdf_docs = st.file_uploader("Upload PDFs and click Process", accept_multiple_files=True)
if st.button("Process"):
with st.spinner("Processing..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
vectorstore = get_vectorstore(text_chunks)
st.session_state.conversation = get_conversation_chain(vectorstore)
if __name__ == '__main__':
main()
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