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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.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from htmlTemplates import bot_template, user_template, css
from transformers import pipeline
def get_pdf_text(pdf_files):
text = ""
for pdf_file in pdf_files:
reader = PdfReader(pdf_file)
for page in reader.pages:
text += page.extract_text()
return text
def get_chunk_text(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_vector_store(text_chunks):
# For OpenAI Embeddings
embeddings = OpenAIEmbeddings()
# For Huggingface Embeddings
# embeddings = HuggingFaceInstructEmbeddings(model_name = "hkunlp/instructor-xl")
vectorstore = FAISS.from_texts(texts = text_chunks, embedding = embeddings)
return vectorstore
def get_conversation_chain(vector_store):
# OpenAI Model
llm = ChatOpenAI()
# HuggingFace Model
# 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 = vector_store.as_retriever(),
memory = memory
)
return conversation_chain
def handle_user_input(question):
response = st.session_state.conversation({'question':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 Your own 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 Your own PDFs :books:')
question = st.text_input("Ask anything to your PDF: ")
if question:
handle_user_input(question)
with st.sidebar:
st.subheader("Upload your Documents Here: ")
pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)
if st.button("OK"):
with st.spinner("Processing your PDFs..."):
# Get PDF Text
raw_text = get_pdf_text(pdf_files)
# Get Text Chunks
text_chunks = get_chunk_text(raw_text)
# Create Vector Store
vector_store = get_vector_store(text_chunks)
st.write("DONE")
# Create conversation chain
st.session_state.conversation = get_conversation_chain(vector_store)
if __name__ == '__main__':
main() |