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import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
# set this key as an environment variable
HUGGINGFACEHUB_API_TOKEN=os.environ["HUGGINGFACEHUB_API_TOKEN"]
def get_pdf_text(pdf_docs : list) -> str:
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:str) ->list:
text_splitter = CharacterTextSplitter(
separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks : list) -> FAISS:
model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
encode_kwargs = {
"normalize_embeddings": True
} # set True to compute cosine similarity
embeddings = HuggingFaceBgeEmbeddings(
model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
)
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore:FAISS) -> ConversationalRetrievalChain:
# llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
llm = HuggingFaceHub(
repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
#repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF"
model_kwargs={"temperature": 0.5, "max_length": 1048},
)
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:str):
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(" Usuario: " + message.content)
else:
st.write("🤖 ChatBot: " + message.content)
def main():
st.set_page_config(
page_title="Chat with your Document!",
page_icon=":books:",
)
#st.markdown("# Chat with a Bot")
#st.markdown("This bot tries to answer questions about multiple PDFs. Let the processing of the PDF finish before adding your question. 🙏🏾")
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 your Document! 🤖")
user_question = st.text_input("Ask a question related to your document:")
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)
if __name__ == "__main__":
main()
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