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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 #facebook AI similarity search
from langchain.chains.question_answering import load_qa_chain
from langchain import HuggingFaceHub
import docx
import os
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_core.callbacks import StdOutCallbackHandler
from streamlit_chat import message
def main():
load_dotenv()
st.set_page_config(page_title="Ask your PDF")
st.header("Ask Your PDF")
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
if "processComplete" not in st.session_state:
st.session_state.processComplete = None
with st.sidebar:
uploaded_files = st.file_uploader("Upload your file",type=['pdf','docx'],accept_multiple_files=True)
process = st.button("Process")
# pdf = st.file_uploader("Upload your pdf",type="pdf")
if process:
files_text = get_files_text(uploaded_files)
# get text chunks
text_chunks = get_text_chunks(files_text)
# create vetore stores
vetorestore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(vetorestore) #for openAI
# st.session_state.conversation = get_conversation_chain(vetorestore) #for huggingface
st.session_state.processComplete = True
if st.session_state.processComplete == True:
user_question = st.chat_input("Ask Question about your files.")
if user_question:
handel_userinput(user_question)
def get_files_text(uploaded_files):
text = ""
for uploaded_file in uploaded_files:
split_tup = os.path.splitext(uploaded_file.name)
file_extension = split_tup[1]
if file_extension == ".pdf":
text += get_pdf_text(uploaded_file)
elif file_extension == ".docx":
text += get_docx_text(uploaded_file)
else:
text += get_csv_text(uploaded_file)
return text
def get_pdf_text(pdf):
pdf_reader = PdfReader(pdf)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
def get_docx_text(file):
doc = docx.Document(file)
allText = []
for docpara in doc.paragraphs:
allText.append(docpara.text)
text = ' '.join(allText)
return text
def get_csv_text(file):
return "a"
def get_text_chunks(text):
# spilit ito chuncks
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=900,
chunk_overlap=100,
length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
embeddings = HuggingFaceEmbeddings()
knowledge_base = FAISS.from_texts(text_chunks,embeddings)
return knowledge_base
def get_conversation_chain(vetorestore):
handler = StdOutCallbackHandler()
llm = HuggingFaceHub(
repo_id="google/flan-t5-large",
task="text2text-generation",
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=vetorestore.as_retriever(),
memory=memory,
callbacks=[handler]
)
return conversation_chain
def handel_userinput(user_question):
response = st.session_state.conversation({'question':user_question})
st.session_state.chat_history = response['chat_history']
# Layout of input/response containers
response_container = st.container()
with response_container:
for i, messages in enumerate(st.session_state.chat_history):
if i % 2 == 0:
message(messages.content, is_user=True, key=str(i))
else:
message(messages.content, key=str(i))
if __name__ == '__main__':
main() |