Spaces:
Sleeping
Sleeping
import streamlit as st | |
from dotenv import load_dotenv | |
from PyPDF2 import PdfReader | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.embeddings import HuggingFaceInstructEmbeddings | |
from langchain.vectorstores import FAISS | |
from langchain.memory import ConversationBufferMemory | |
from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain | |
from langchain.llms.huggingface_hub import HuggingFaceHub | |
css = ''' | |
<style> | |
.chat-message { | |
padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex | |
} | |
.chat-message.user { | |
background-color: #2b313e | |
} | |
.chat-message.bot { | |
background-color: #475063 | |
} | |
.chat-message .avatar { | |
width: 20%; | |
} | |
.chat-message .avatar img { | |
max-width: 78px; | |
max-height: 78px; | |
border-radius: 50%; | |
object-fit: cover; | |
} | |
.chat-message .message { | |
width: 80%; | |
padding: 0 1.5rem; | |
color: #fff; | |
} | |
''' | |
bot_template = ''' | |
<div class="chat-message bot"> | |
<div class="avatar"> | |
<img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;"> | |
</div> | |
<div class="message">{{MSG}}</div> | |
</div> | |
''' | |
user_template = ''' | |
<div class="chat-message user"> | |
<div class="avatar"> | |
<img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png"> | |
</div> | |
<div class="message">{{MSG}}</div> | |
</div> | |
''' | |
st.set_page_config( | |
page_icon=':balloon:', | |
page_title= 'dump', | |
layout='wide' | |
) | |
st.title(body='*Streamlit*') | |
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 = 500, | |
chunk_overlap = 200, | |
length_function = len | |
) | |
chunks = text_splitter.split_text(text) | |
return chunks | |
def get_vectorstore(text_chunks): | |
embeddings = HuggingFaceInstructEmbeddings(model_name='hkunlp/instructor-xl') | |
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
return vectorstore | |
def get_conversation_chain(vectorstore): | |
llm = HuggingFaceHub( | |
repo_id = 'google/flan-t5-xxl', | |
model_kwargs = {"temperature":0.5, "max_length":256} | |
) | |
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): | |
if i %2 == 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) | |
def main(): | |
load_dotenv() | |
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( | |
label="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() | |