Spaces:
Sleeping
Sleeping
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 | |
import translators | |
# set this key as an environment variable | |
os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token'] | |
########################################################################################### | |
def trs_fa_to_en(text): | |
txt_en=translators.translate_text(text,to_language='en',from_language='auto') | |
return txt_en | |
######################################################################################### | |
def trs_en_to_fa(text): | |
txt_fa=translators.translate_text(text,to_language='fa',from_language='en') | |
return txt_fa | |
############################################################################################## | |
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("سوال کاربر: " + message.content) | |
else: | |
st.write("پاسخ ربات: " + message.content) | |
text1=message.content | |
st.write(text1) | |
txt_fa=trs_en_to_fa(text=txt1) | |
st.write(txt_fa) | |
############################################################################################################# | |
def main(): | |
st.set_page_config( | |
page_title="Chat Bot PDFs", | |
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 Bot 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) | |
#compelete build model | |
st.write("compelete build model") | |
if __name__ == "__main__": | |
main() | |