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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 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()
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