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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, HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models.gigachat import GigaChat
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub, LlamaCpp
from huggingface_hub import snapshot_download, hf_hub_download
# from prompts import CONDENSE_QUESTION_PROMPT
repo_name = "IlyaGusev/saiga_mistral_7b_gguf"
model_name = "model-q4_K.gguf"
#snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
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, # 1000
chunk_overlap=200, # 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")
embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
#embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore, model_name):
#llm = LlamaCpp(model_path=model_name,
# temperature=0.1,
# top_k=30,
# top_p=0.9,
# streaming=True,
# n_ctx=2048,
# n_parts=1,
# echo=True
# )
#llm = ChatOpenAI()
llm = GigaChat(profanity=False,
verify_ssl_certs=False
)
memory = ConversationBufferMemory(memory_key='chat_history',
input_key='question',
output_key='answer',
return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory,
return_source_documents=True
)
return conversation_chain
def handle_userinput(user_question):
response = st.session_state.conversation({'question': user_question})
st.session_state.chat_history = response['chat_history']
st.session_state.retrieved_text = response['source_documents']
for i, (message, text) in enumerate(zip(st.session_state.chat_history, st.session_state.retrieved_text)):
if i % 3 == 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)
print(text)
st.write(bot_template.replace(
"{{MSG}}", str(text.page_content)), unsafe_allow_html=True)
#for text in enumerate(st.session_state.retrieved_text):
# st.write(text[1].page_content, '\n')
#print(response['source_documents'][0])
# main code
load_dotenv()
st.set_page_config(page_title="Chat with multiple 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 multiple PDFs :books:")
user_question = st.text_input("Ask a question about your documents: ")
if user_question:
handle_userinput(user_question)
with st.sidebar:
st.subheader("Your documents")
pdf_docs = st.file_uploader(
"Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
if st.button("Process"):
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
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore, model_name)
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