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from dotenv import load_dotenv
import os
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
def extract_text_from_pdf(pdf):
pdf_reader = PdfReader(pdf)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
def extract_text_from_txt(txt):
text = txt.read().decode("utf-8")
return text
def extract_text_from_brain():
with open('brain/brain_journal.txt', 'r', encoding='utf-8') as file:
text = file.read()
return text
def main():
load_dotenv()
hide_streamlit_style = """
<style>
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
st.title("Digital Brain Journal Search 🔍")
st.write("Ask any questions about all the journal entries with OpenAI's Embeddings and Langchain. The virtual brain keeps track of everything in a user's life. If you have another TXT or PDF file you'd like to search for answers, click on the dropdown and select eithter TXT or PDF option in file type.")
# Add API key input
api_key = st.text_input("Enter your OpenAI API key:", type="password")
os.environ["OPENAI_API_KEY"] = api_key
if not api_key:
st.warning("Please enter your OpenAI API key to continue.")
else:
file_type = st.selectbox("Choose the file type", options=["Brain", "PDF", "TXT"])
file = None
text = None
if file_type == "PDF":
file = st.file_uploader("Upload your PDF", type="pdf")
if file is not None:
text = extract_text_from_pdf(file)
elif file_type == "TXT":
file = st.file_uploader("Upload your TXT", type="txt")
if file is not None:
text = extract_text_from_txt(file)
elif file_type == "Brain":
text = extract_text_from_brain()
if file is not None or file_type == "Brain":
# split into chunks
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text)
# create embeddings
embeddings = OpenAIEmbeddings()
knowledge_base = FAISS.from_texts(chunks, embeddings)
# show user input
user_question = st.text_area("Ask a question about your document:")
if st.button("Submit"):
if user_question:
docs = knowledge_base.similarity_search(user_question)
llm = OpenAI()
chain = load_qa_chain(llm, chain_type="stuff")
with get_openai_callback() as cb:
response = chain.run(input_documents=docs, question=user_question)
print(cb)
st.markdown("### Response:")
st.write(response)
st.write(cb)
st.markdown("---")
st.markdown("")
st.markdown("<p style='text-align: center'><a href='https://github.com/Kaludii'>Github</a> | <a href='https://huggingface.co/Kaludi'>HuggingFace</a></p>", unsafe_allow_html=True)
if __name__ == '__main__':
main()
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