Spaces:
Running
Running
| import streamlit as st #Web App | |
| import os | |
| from PIL import Image | |
| from unitls import * | |
| import pickle | |
| docs = None | |
| api_key = ' ' | |
| st.set_page_config(layout="wide") | |
| image = Image.open('arxiv_decode.png') | |
| st.image(image, width=1000) | |
| #title | |
| st.title("Answering questions from scientific papers") | |
| st.markdown("##### This tool will allow you to ask questions and get answers based on scientific papers. It uses OpenAI's GPT models, and you must have your own API key. Each query is about 10k tokens, which costs about only $0.20 on your own API key which is charged by OpenAI.") | |
| st.markdown("##### Current version searches on [ArXiv](https://arxiv.org) papers only. 🚧Under development🚧") | |
| st.markdown("Used libraries:\n * [PaperQA](https://github.com/whitead/paper-qa) \n* [langchain](https://github.com/hwchase17/langchain)") | |
| api_key_url = 'https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key' | |
| api_key = st.text_input('OpenAI API Key', | |
| placeholder='sk-...', | |
| help=f"['What is that?']({api_key_url})", | |
| type="password") | |
| os.environ["OPENAI_API_KEY"] = f"{api_key}" # | |
| if len(api_key) != 51: | |
| st.warning('Please enter a valid OpenAI API key.', icon="⚠️") | |
| max_results_current = 5 | |
| max_results = max_results_current | |
| def search_click_callback(search_query, max_results): | |
| global pdf_info, pdf_citation | |
| pdf_info, pdf_citation = call_arXiv_API(f'{search_query}', max_results=max_results) | |
| download_pdf(pdf_info) | |
| return pdf_info | |
| with st.form(key='columns_in_form', clear_on_submit = False): | |
| c1, c2 = st.columns([8,1]) | |
| with c1: | |
| search_query = st.text_input("Input search query here:", placeholder='Keywords for most relevant search...', value='' | |
| )#search_query, max_results_current)) | |
| with c2: | |
| max_results = st.text_input("Max papers", value=max_results_current) | |
| max_results_current = max_results_current | |
| searchButton = st.form_submit_button(label = 'Search') | |
| if searchButton: | |
| global pdf_info | |
| pdf_info = search_click_callback(search_query, max_results) | |
| if 'pdf_info' not in st.session_state: | |
| st.session_state.key = 'pdf_info' | |
| st.session_state['pdf_info'] = pdf_info | |
| def answer_callback(question_query): | |
| import paperqa | |
| global docs | |
| progress_text = "Please wait..." | |
| # my_bar = st.progress(0, text = progress_text) | |
| st.info('Please wait...', icon="🔥") | |
| if docs is None: | |
| # my_bar.progress(0.2, "Please wait...") | |
| pdf_info = st.session_state['pdf_info'] | |
| # print('buliding docs') | |
| docs = paperqa.Docs() | |
| pdf_paths = [f"{p[4]}/{p[0]}.pdf" for p in pdf_info] | |
| pdf_citations = [p[5] for p in pdf_info] | |
| print(list(zip(pdf_paths, pdf_citations))) | |
| for d, c in zip(pdf_paths, pdf_citations): | |
| # print(d,c) | |
| docs.add(d, c) | |
| # docs._build_faiss_index() | |
| answer = docs.query(question_query) | |
| # print(answer.formatted_answer) | |
| # my_bar.progress(1.0, "Done!") | |
| st.success('Voila!') | |
| return answer.formatted_answer | |
| form = st.form(key='question_form') | |
| question_query = form.text_input("What do you wanna know from these papers?", placeholder='Input questions here...', | |
| value='') | |
| submitButton = form.form_submit_button('Submit') | |
| if submitButton: | |
| with st.expander("Found papers:", expanded=True): | |
| st.write(f"{st.session_state['all_reference_text']}") | |
| st.text_area("Answer:", answer_callback(question_query), height=600) | |