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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_diary(): | |
with open('diary/diary_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 π") | |
st.write("Ask any questions about all the journal entries with OpenAI's Embeddings API 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 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=["Diary", "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 == "Diary": | |
text = extract_text_from_diary() | |
if file is not None or file_type == "Diary": | |
# 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_input("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() | |