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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() | |