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from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
import os
from streamlit_chat import message
from langchain import HuggingFaceHub
def LLM_pdf(model_name = 'google/flan-t5-large'):
# st.header("Ask your PDF 💬")
# upload file
pdf = st.file_uploader("Upload your PDF", type="pdf")
# extract the text
if pdf is not None:
pdf_reader = PdfReader(pdf)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
# split into chunks
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
print(text_splitter)
chunks = text_splitter.split_text(text)
# create embeddings
embeddings = HuggingFaceEmbeddings()
knowledge_base = FAISS.from_texts(chunks, embeddings)
if 'generated' not in st.session_state:
st.session_state['generated'] = []
if 'past' not in st.session_state:
st.session_state['past'] = []
# print(st.session_state['generated'],st.session_state['past'])
chat_placeholder = st.empty()
# show user input
with st.container():
input_placeholder = st.empty()
user_question = input_placeholder.text_input("Ask a question about your PDF:")
if user_question:
docs = knowledge_base.similarity_search(user_question)
llm = HuggingFaceHub(repo_id=model_name, model_kwargs={"temperature":5,
"max_length":64})
chain = load_qa_chain(llm, chain_type="stuff")
response = chain.run(input_documents=docs,question=user_question)
#st.write(response)
# append user_input and output to state
st.session_state.past.append(user_question)
st.session_state.generated.append(response)
with chat_placeholder.container():
# If responses have been generated by the model
if st.session_state['generated']:
# Reverse iteration through the list
for i in range(len(st.session_state['generated'])-1, -1, -1):
# message from streamlit_chat
message(st.session_state['past'][::-1][i], is_user=True, key=str(i) + '_user')
message(st.session_state['generated'][::-1][i], key=str(i))
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