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| from typing import List, Optional | |
| import streamlit as st | |
| import streamlit_pydantic as sp | |
| from pydantic import BaseModel, Field | |
| from PIL import Image | |
| import tempfile | |
| from pathlib import Path | |
| from src.Surveyor import Surveyor | |
| def get_surveyor_instance(_print_fn, _survey_print_fn): | |
| with st.spinner('Loading The-Researcher ...'): | |
| return Surveyor(print_fn=_print_fn, survey_print_fn=_survey_print_fn, high_gpu=True) | |
| def run_survey(surveyor, download_placeholder, research_keywords=None, arxiv_ids=None, max_search=None, num_papers=None): | |
| import hashlib | |
| import time | |
| hash = hashlib.sha1() | |
| hash.update(str(time.time()).encode('utf-8')) | |
| temp_hash = hash.hexdigest() | |
| survey_root = Path(temp_hash).resolve() | |
| dir_args = {f'{dname}_dir': survey_root / dname for dname in ['pdf', 'txt', 'img', 'tab', 'dump']} | |
| for d in dir_args.values(): | |
| d.mkdir(exist_ok=True, parents=True) | |
| print(survey_root) | |
| print(dir_args) | |
| dir_args = {k: f'{str(v.resolve())}/' for k, v in dir_args.items()} | |
| zip_file_name, survey_file_name = surveyor.survey(research_keywords, | |
| arxiv_ids, | |
| max_search=max_search, | |
| num_papers=num_papers, | |
| **dir_args) | |
| show_survey_download(zip_file_name, survey_file_name, download_placeholder) | |
| def show_survey_download(zip_file_name, survey_file_name, download_placeholder): | |
| with open(str(zip_file_name), "rb") as file: | |
| btn = download_placeholder.download_button( | |
| label="Download extracted topic-clustered-highlights, images and tables as zip", | |
| data=file, | |
| file_name=str(zip_file_name) | |
| ) | |
| with open(str(survey_file_name), "rb") as file: | |
| btn = download_placeholder.download_button( | |
| label="Download detailed generated survey file", | |
| data=file, | |
| file_name=str(survey_file_name) | |
| ) | |
| class KeywordsModel(BaseModel): | |
| research_keywords: Optional[str] = Field( | |
| '', description="Enter your research keywords:" | |
| ) | |
| max_search: int = Field( | |
| 10, ge=1, le=50, multiple_of=1, | |
| description="num_papers_to_search:" | |
| ) | |
| num_papers: int = Field( | |
| 3, ge=1, le=8, multiple_of=1, | |
| description="num_papers_to_select:" | |
| ) | |
| class ArxivIDsModel(BaseModel): | |
| arxiv_ids: Optional[str] = Field( | |
| '', description="Enter comma_separated arxiv ids for your curated set of papers (e.g. 2205.12755, 2205.10937, ...):" | |
| ) | |
| if __name__ == '__main__': | |
| if 'session_count' not in st.session_state: | |
| st.session_state.session_count = 0 | |
| demo_session_limit = 2 | |
| if st.session_state.session_count > demo_session_limit: | |
| st.write(f'{st.session_state.session_count} sessions running, this is a demo and only supports {demo_session_limit} parallel sessions, \n please try in sometime') | |
| st.sidebar.image(Image.open('logo_landscape.png'), use_column_width = 'always') | |
| st.title('Auto-Research') | |
| st.write('#### A no-code utility to generate a detailed well-cited survey with topic clustered sections' | |
| '(draft paper format) and other interesting artifacts from a single research query or a curated set of papers(arxiv ids).') | |
| st.write('##### Data Provider: arXiv Open Archive Initiative OAI') | |
| st.write('##### GitHub: https://github.com/sidphbot/Auto-Research') | |
| st.write(f'Note: this is only a demo on cpu-13GB RAM, hence it supports limited number of papers & only {demo_session_limit} parallel user sessions') | |
| download_placeholder = st.container() | |
| with st.sidebar.form(key="survey_keywords_form"): | |
| session_data = sp.pydantic_input(key="keywords_input_model", model=KeywordsModel) | |
| st.write('or') | |
| session_data.update(sp.pydantic_input(key="arxiv_ids_input_model", model=ArxivIDsModel)) | |
| submit = st.form_submit_button(label="Submit") | |
| st.sidebar.write('#### execution log:') | |
| run_kwargs = {'surveyor':get_surveyor_instance(_print_fn=st.sidebar.write, _survey_print_fn=st.write), | |
| 'download_placeholder':download_placeholder} | |
| if submit: | |
| if st.session_state.session_count < demo_session_limit: | |
| st.session_state.session_count = st.session_state.session_count + 1 | |
| if session_data['research_keywords'] != '': | |
| run_kwargs.update({'research_keywords':session_data['research_keywords'], | |
| 'max_search':session_data['max_search'], | |
| 'num_papers':session_data['num_papers']}) | |
| elif session_data['arxiv_ids'] != '': | |
| run_kwargs.update({'arxiv_ids':[id.strip() for id in session_data['arxiv_ids'].split(',')]}) | |
| print(run_kwargs) | |
| try: | |
| run_survey(**run_kwargs) | |
| except e: | |
| st.write(f'ERROR: {str(e)}, server might be crowded right now, please try later, thank you for your patience') | |
| pass | |
| else: | |
| st.write(f'{st.session_state.session_count} sessions running, this is a demo and only supports 2 parallel sessions, \n please try in sometime') | |