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import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain import embeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.vectorstores import faiss
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from html_templates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
import os
import pickle
from datetime import datetime
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_vectorstore(text_chunks):
embeddings = OpenAIEmbeddings()
# embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
llm = ChatOpenAI()
# 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=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):
# Display user message
if i % 2 == 0:
st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
else:
print(message)
# Display AI response
st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
# Display source document information if available in the message
if hasattr(message, 'source') and message.source:
st.write(f"Source Document: {message.source}", unsafe_allow_html=True)
def safe_vec_store():
os.makedirs('vectorstore', exist_ok=True)
filename = 'vectores' + datetime.now().strftime('%Y%m%d%H%M') + '.pkl'
file_path = os.path.join('vectorstore', filename)
vector_store = st.session_state.vectorstore
# Serialize and save the entire FAISS object using pickle
with open(file_path, 'wb') as f:
pickle.dump(vector_store, f)
def main():
load_dotenv()
st.set_page_config(page_title="DOC Verify RAG", page_icon=":hospital:")
st.write(css, unsafe_allow_html=True)
st.subheader("Your documents")
pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
filenames = [file.name for file in pdf_docs if file is not None]
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("DOC Verify RAG :hospital:")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
handle_userinput(user_question)
with st.sidebar:
st.subheader("Classification Instrucitons")
classifier_docs = st.file_uploader("Upload your instructions here and click on 'Process'", accept_multiple_files=True)
filenames = [file.name for file in classifier_docs if file is not None]
if st.button("Process"):
with st.spinner("Processing"):
loaded_vec_store = None
for filename in filenames:
if ".pkl" in filename:
file_path = os.path.join('vectorstore', filename)
with open(file_path, 'rb') as f:
loaded_vec_store = pickle.load(f)
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
vec = get_vectorstore(text_chunks)
if loaded_vec_store:
vec.merge_from(loaded_vec_store)
st.warning("loaded vectorstore")
if "vectorstore" in st.session_state:
vec.merge_from(st.session_state.vectorstore)
st.warning("merged to existing")
st.session_state.vectorstore = vec
st.session_state.conversation = get_conversation_chain(vec)
st.success("data loaded")
# Save and Load Embeddings
if st.button("Save Embeddings"):
if "vectorstore" in st.session_state:
safe_vec_store()
# st.session_state.vectorstore.save_local("faiss_index")
st.sidebar.success("safed")
else:
st.sidebar.warning("No embeddings to save. Please process documents first.")
if st.button("Load Embeddings"):
st.warning("this function is not in use, just upload the vectorstore")
if __name__ == '__main__':
main()
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