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import streamlit as st
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 html_template import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
import os
FREE_RUN = False
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_vector_store(text_chunks):
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") if FREE_RUN else OpenAIEmbeddings()
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={
"temperature": 0.5, "max_length": 512}) if FREE_RUN else ChatOpenAI()
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():
st.set_page_config(page_title="WhisperChain 🔗", page_icon=":link:")
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("WhisperChain 🔗")
user_question = st.text_input("Ask a question about your documents.")
if user_question:
handle_userinput(user_question)
with st.sidebar:
###
OPENAI_API_KEY = st.sidebar.text_input("Enter OpenAI API Key", type="password")
HUGGINGFACEHUB_API_KEY = st.sidebar.text_input("Enter Hugging Face API Key", type="password")
if not OPENAI_API_KEY or not HUGGINGFACEHUB_API_KEY:
st.sidebar.error("Please enter your API keys")
st.stop()
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
os.environ["HUGGINGFACEHUB_API_KEY"] = HUGGINGFACEHUB_API_KEY
#Toggle free run
global FREE_RUN
FREE_RUN = st.sidebar.checkbox("Free run", value=False)
###
pdf_docs = st.file_uploader(
"Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
if st.button("Process"):
if pdf_docs:
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
vector_store = get_vector_store(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(vector_store)
else:
st.error("Please upload at least one PDF")
if __name__ == '__main__':
main()
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