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import streamlit as st
from dotenv import load_dotenv
import pickle
from PyPDF2 import PdfReader
from streamlit_extras.add_vertical_space import add_vertical_space
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.callbacks import get_openai_callback
import os
# Sidebar contents
with st.sidebar:
st.title(':orange[BinDoc GmbH]')
st.markdown(
"Experience the future of document interaction with the revolutionary"
)
st.markdown("**BinDocs Chat App**.")
st.markdown("Harnessing the power of a Large Language Model and AI technology,")
st.markdown("this innovative platform redefines PDF engagement,")
st.markdown("enabling dynamic conversations that bridge the gap between")
st.markdown("human and machine intelligence.")
add_vertical_space(3) # Add more vertical space between text blocks
st.write('Made with ❤️ by Anne')
openai_api_key = st.text_input("Enter your OpenAI API key:")
pdf_path = ""
def load_pdf(file_path):
pdf_reader = PdfReader(file_path)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text=text)
store_name, _ = os.path.splitext(os.path.basename(file_path))
if os.path.exists(f"{store_name}.pkl"):
with open(f"{store_name}.pkl", "rb") as f:
VectorStore = pickle.load(f)
else:
embeddings = OpenAIEmbeddings()
VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
with open(f"{store_name}.pkl", "wb") as f:
pickle.dump(VectorStore, f)
return VectorStore
def load_chatbot(openai_api_key):
openai_config = {
"api_key": openai_api_key
}
return load_qa_chain(llm=OpenAI(config=openai_config), chain_type="stuff")
def main():
st.title("BinDocs Chat App")
uploaded_pdf = st.file_uploader("Upload a PDF file:", type=["pdf"])
if uploaded_pdf is not None:
pdf_path = uploaded_pdf
if "chat_history" not in st.session_state:
st.session_state['chat_history'] = []
display_chat_history(st.session_state['chat_history'])
st.write("<!-- Start Spacer -->", unsafe_allow_html=True)
st.write("<div style='flex: 1;'></div>", unsafe_allow_html=True)
st.write("<!-- End Spacer -->", unsafe_allow_html=True)
new_messages_placeholder = st.empty()
if pdf_path is not None:
query = st.text_input("Ask questions about your PDF file (in any preferred language):")
if st.button("Ask") or (not st.session_state['chat_history'] and query) or (st.session_state['chat_history'] and query != st.session_state['chat_history'][-1][1]):
st.session_state['chat_history'].append(("User", query, "new"))
loading_message = st.empty()
loading_message.text('Bot is thinking...')
VectorStore = load_pdf(pdf_path)
chain = load_chatbot()
docs = VectorStore.similarity_search(query=query, k=3)
with get_openai_callback() as cb:
response = chain.run(input_documents=docs, question=query)
st.session_state['chat_history'].append(("Bot", response, "new"))
# Display new messages at the bottom
new_messages = st.session_state['chat_history'][-2:]
for chat in new_messages:
background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
new_messages_placeholder.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{chat[0]}: {chat[1]}</div>", unsafe_allow_html=True)
# Scroll to the latest response using JavaScript
st.write("<script>document.getElementById('response').scrollIntoView();</script>", unsafe_allow_html=True)
loading_message.empty()
# Clear the input field by setting the query variable to an empty string
query = ""
# Mark all messages as old after displaying
st.session_state['chat_history'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history']]
def display_chat_history(chat_history):
for chat in chat_history:
background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
st.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{chat[0]}: {chat[1]}</div>", unsafe_allow_html=True)
if __name__ == "__main__":
main()
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