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| import os | |
| import re | |
| import copy | |
| import datasets | |
| import pandas as pd | |
| import gradio as gr | |
| from datetime import datetime, timedelta | |
| from datasets import Dataset | |
| from huggingface_hub import HfApi | |
| from huggingface_hub import create_repo | |
| from huggingface_hub.utils import HfHubHTTPError | |
| from paper.download import ( | |
| download_pdf_from_arxiv, | |
| get_papers_from_hf_daily_papers, | |
| get_papers_from_arxiv_ids | |
| ) | |
| from paper.parser import extract_text_and_figures | |
| from gen.gemini import get_basic_qa, get_deep_qa | |
| import utils | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| STYLE = """ | |
| @media only screen and (max-width: 700px) { | |
| .main { | |
| width: 80% !important; | |
| margin: 0 auto; /* Center the container */ | |
| } | |
| } | |
| .small-font{ | |
| font-size: 12pt !important; | |
| } | |
| .small-font:hover { | |
| font-size: 20px !important; | |
| transition: font-size 0.3s ease-out; | |
| transition-delay: 1.5s; | |
| } | |
| .group { | |
| padding-top: 10px; | |
| padding-left: 10px; | |
| padding-right: 10px; | |
| padding-bottom: 10px; | |
| border: 2px dashed gray; | |
| border-radius: 20px; | |
| box-shadow: 5px 3px 10px 1px rgba(0, 0, 0, 0.4) !important; | |
| } | |
| .accordion > button > span{ | |
| font-size: 12pt !important; | |
| } | |
| .accordion { | |
| border-style: dashed !important; | |
| border-left-width: 2px !important; | |
| border-bottom-width: 2.5px !important; | |
| border-top: none !important; | |
| border-right: none !important; | |
| box-shadow: none !important; | |
| } | |
| .no-gap { | |
| gap: 0px; | |
| } | |
| .no-radius { | |
| border-radius: 0px; | |
| } | |
| .textbox-no-label > label > span { | |
| display: none; | |
| } | |
| .exp-type > span { | |
| display: none; | |
| } | |
| .conv-type > span { | |
| display: none; | |
| } | |
| .conv-type .wrap:nth-child(3) { | |
| width: 167px; | |
| margin: auto; | |
| } | |
| button { | |
| font-size: 10pt !important; | |
| } | |
| h3 { | |
| font-size: 13pt !important; | |
| } | |
| """ | |
| gemini_api_key = os.getenv("GEMINI_API_KEY") | |
| hf_token = os.getenv("HF_TOKEN") | |
| dataset_repo_id = "chansung/auto-paper-qa2" | |
| request_arxiv_repo_id="chansung/requested-arxiv-ids-3" | |
| ds = datasets.load_dataset(dataset_repo_id) | |
| request_ds = datasets.load_dataset(request_arxiv_repo_id) | |
| requested_arxiv_ids = [] | |
| for request_d in request_ds['train']: | |
| arxiv_ids = request_d['Requested arXiv IDs'] | |
| requested_arxiv_ids = requested_arxiv_ids + arxiv_ids | |
| requested_arxiv_ids_df = pd.DataFrame({'Requested arXiv IDs': requested_arxiv_ids}) | |
| title2qna = {} | |
| date2qna = {} | |
| longest_qans = 0 | |
| def filter_function(example, ids): | |
| ids_e = example['Requested arXiv IDs'] | |
| for iid in ids: | |
| if iid in ids_e: | |
| ids_e.remove(iid) | |
| example['Requested arXiv IDs'] = ids_e | |
| print(example) | |
| return example | |
| def process_arxiv_ids(gemini_api, hf_repo_id, req_hf_repo_id, hf_token, how_many=10): | |
| arxiv_ids = [] | |
| ds1 = datasets.load_dataset(req_hf_repo_id) | |
| for d in ds1['train']: | |
| req_arxiv_ids = d['Requested arXiv IDs'] | |
| if len(req_arxiv_ids) > 0 and req_arxiv_ids[0] != "top": | |
| arxiv_ids = arxiv_ids + req_arxiv_ids | |
| arxiv_ids = arxiv_ids[:how_many] | |
| if arxiv_ids is not None and len(arxiv_ids) > 0: | |
| print(f"1. Get metadata for the papers [{arxiv_ids}]") | |
| papers = get_papers_from_arxiv_ids(arxiv_ids) | |
| print("...DONE") | |
| print("2. Generating QAs for the paper") | |
| for paper in papers: | |
| try: | |
| title = paper['title'] | |
| target_date = paper['target_date'] | |
| abstract = paper['paper']['summary'] | |
| arxiv_id = paper['paper']['id'] | |
| authors = paper['paper']['authors'] | |
| print(f"...PROCESSING ON[{arxiv_id}, {title}]") | |
| print(f"......Downloading the paper PDF") | |
| filename = download_pdf_from_arxiv(arxiv_id) | |
| print(f"......DONE") | |
| print(f"......Extracting text and figures") | |
| texts, figures = extract_text_and_figures(filename) | |
| text =' '.join(texts) | |
| print(f"......DONE") | |
| print(f"......Generating the seed(basic) QAs") | |
| qnas = get_basic_qa(text, gemini_api_key=gemini_api, trucate=30000) | |
| qnas['title'] = title | |
| qnas['abstract'] = abstract | |
| qnas['authors'] = ','.join(authors) | |
| qnas['arxiv_id'] = arxiv_id | |
| qnas['target_date'] = target_date | |
| qnas['full_text'] = text | |
| print(f"......DONE") | |
| print(f"......Generating the follow-up QAs") | |
| qnas = get_deep_qa(text, qnas, gemini_api_key=gemini_api, trucate=30000) | |
| del qnas["qna"] | |
| print(f"......DONE") | |
| print(f"......Exporting to HF Dataset repo at [{hf_repo_id}]") | |
| utils.push_to_hf_hub(qnas, hf_repo_id, hf_token) | |
| print(f"......DONE") | |
| print(f"......Updating request arXiv HF Dataset repo at [{req_hf_repo_id}]") | |
| ds1 = ds1['train'].map( | |
| lambda example: filter_function(example, [arxiv_id]) | |
| ).filter( | |
| lambda example: len(example['Requested arXiv IDs']) > 0 | |
| ) | |
| ds1.push_to_hub(req_hf_repo_id, token=hf_token) | |
| print(f"......DONE") | |
| except Exception as e: | |
| print(f".......failed due to exception {e}") | |
| continue | |
| HfApi(token=hf_token).restart_space( | |
| repo_id="chansung/paper_qa", token=hf_token | |
| ) | |
| def push_to_hf_hub( | |
| df, repo_id, token, append=True | |
| ): | |
| exist = False | |
| ds = Dataset.from_pandas(df) | |
| try: | |
| create_repo(request_arxiv_repo_id, repo_type="dataset", token=hf_token) | |
| except HfHubHTTPError as e: | |
| exist = True | |
| if exist and append: | |
| existing_ds = datasets.load_dataset(repo_id) | |
| ds = datasets.concatenate_datasets([existing_ds['train'], ds]) | |
| ds.push_to_hub(repo_id, token=token) | |
| def _filter_duplicate_arxiv_ids(arxiv_ids_to_be_added): | |
| ds1 = datasets.load_dataset("chansung/requested-arxiv-ids-3") | |
| ds2 = datasets.load_dataset("chansung/auto-paper-qa2") | |
| unique_arxiv_ids = set() | |
| for d in ds1['train']: | |
| arxiv_ids = d['Requested arXiv IDs'] | |
| unique_arxiv_ids = set(list(unique_arxiv_ids) + arxiv_ids) | |
| for d in ds2['train']: | |
| arxiv_id = d['arxiv_id'] | |
| unique_arxiv_ids.add(arxiv_id) | |
| return list(set(arxiv_ids_to_be_added) - unique_arxiv_ids) | |
| def _is_arxiv_id_valid(arxiv_id): | |
| pattern = r"^\d{4}\.\d{5}$" | |
| return bool(re.match(pattern, arxiv_id)) | |
| def _get_valid_arxiv_ids(arxiv_ids_str): | |
| valid_arxiv_ids = [] | |
| invalid_arxiv_ids = [] | |
| for arxiv_id in arxiv_ids_str.split(","): | |
| arxiv_id = arxiv_id.strip() | |
| if _is_arxiv_id_valid(arxiv_id): | |
| valid_arxiv_ids.append(arxiv_id) | |
| else: | |
| invalid_arxiv_ids.append(arxiv_id) | |
| return valid_arxiv_ids, invalid_arxiv_ids | |
| def add_arxiv_ids_to_queue(queue, arxiv_ids_str): | |
| print(0) | |
| valid_arxiv_ids, invalid_arxiv_ids = _get_valid_arxiv_ids(arxiv_ids_str) | |
| print("01") | |
| if len(invalid_arxiv_ids) > 0: | |
| gr.Warning(f"found invalid arXiv ids as in {invalid_arxiv_ids}") | |
| if len(valid_arxiv_ids) > 0: | |
| valid_arxiv_ids = _filter_duplicate_arxiv_ids(valid_arxiv_ids) | |
| if len(valid_arxiv_ids) > 0: | |
| valid_arxiv_ids = [[arxiv_id] for arxiv_id in valid_arxiv_ids] | |
| gr.Warning(f"Processing on [{valid_arxiv_ids}]. Other requested arXiv IDs not found on this list should be already processed or being processed...") | |
| valid_arxiv_ids = pd.DataFrame({'Requested arXiv IDs': valid_arxiv_ids}) | |
| queue = pd.concat([queue, valid_arxiv_ids]) | |
| queue.reset_index(drop=True) | |
| push_to_hf_hub(valid_arxiv_ids, request_arxiv_repo_id, hf_token) | |
| else: | |
| gr.Warning(f"All requested arXiv IDs are already processed or being processed...") | |
| else: | |
| gr.Warning(f"No valid arXiv IDs found...") | |
| return queue | |
| def count_nans(row): | |
| count = 0 | |
| for _, (k, v) in enumerate(data.items()): | |
| if v is None: | |
| count = count + 1 | |
| return count | |
| for data in ds["train"]: | |
| date = data["target_date"].strftime("%Y-%m-%d") | |
| if date in date2qna: | |
| papers = copy.deepcopy(date2qna[date]) | |
| for paper in papers: | |
| if paper["title"] == data["title"]: | |
| if count_nans(paper) > count_nans(data): | |
| date2qna[date].remove(paper) | |
| date2qna[date].append(data) | |
| del papers | |
| else: | |
| date2qna[date] = [data] | |
| for date in date2qna: | |
| papers = date2qna[date] | |
| for paper in papers: | |
| title2qna[paper["title"]] = paper | |
| titles = title2qna.keys() | |
| sorted_dates = sorted(date2qna.keys()) | |
| last_date = sorted_dates[-1] | |
| last_papers = date2qna[last_date] | |
| selected_paper = last_papers[0] | |
| def get_papers(date): | |
| papers = [paper["title"] for paper in date2qna[date]] | |
| return gr.Dropdown( | |
| papers, | |
| value=papers[0] | |
| ) | |
| def set_paper(date, paper_title): | |
| selected_paper = None | |
| for paper in date2qna[date]: | |
| if paper["title"] == paper_title: | |
| selected_paper = paper | |
| break | |
| return ( | |
| gr.Markdown(f"# {selected_paper['title']}"), gr.Markdown(selected_paper["summary"]), | |
| gr.Markdown(f"### 🙋 {selected_paper['0_question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['0_answers:expert']}"), | |
| gr.Markdown(f"### 🙋🙋 {selected_paper['0_additional_depth_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_depth_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_depth_q:answers:expert']}"), | |
| gr.Markdown(f"### 🙋🙋 {selected_paper['0_additional_breath_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_breath_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_breath_q:answers:expert']}"), | |
| gr.Markdown(f"### 🙋 {selected_paper['1_question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['1_answers:expert']}"), | |
| gr.Markdown(f"### 🙋🙋 {selected_paper['1_additional_depth_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_depth_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_depth_q:answers:expert']}"), | |
| gr.Markdown(f"### 🙋🙋 {selected_paper['1_additional_breath_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_breath_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_breath_q:answers:expert']}"), | |
| gr.Markdown(f"### 🙋 {selected_paper['2_question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['2_answers:expert']}"), | |
| gr.Markdown(f"### 🙋🙋 {selected_paper['2_additional_depth_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_depth_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_depth_q:answers:expert']}"), | |
| gr.Markdown(f"### 🙋🙋 {selected_paper['2_additional_breath_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_breath_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_breath_q:answers:expert']}"), | |
| ) | |
| def change_exp_type(exp_type): | |
| if exp_type == "ELI5": | |
| return ( | |
| gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), | |
| gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), | |
| gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), | |
| ) | |
| else: | |
| return ( | |
| gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), | |
| gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), | |
| gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), | |
| ) | |
| def search(search_in, max_results=3): | |
| results = [] | |
| for title in titles: | |
| if len(results) > 3: | |
| break | |
| else: | |
| if search_in in title: | |
| results.append(title) | |
| return ( | |
| gr.Textbox( | |
| visible=True if len(results) > 0 else False, | |
| value=results[0] if len(results) > 0 else "" | |
| ), | |
| gr.Textbox( | |
| visible=True if len(results) > 1 else False, | |
| value=results[1] if len(results) > 1 else "" | |
| ), | |
| gr.Textbox( | |
| visible=True if len(results) > 2 else False, | |
| value=results[2] if len(results) > 2 else "" | |
| ) | |
| ) | |
| UPDATE_SEARCH_RESULTS = f""" | |
| function search(searchIn, maxResults = 3) {{ | |
| if (searchIn.trim().length > 0) {{ | |
| const results = []; | |
| let titles = {list(titles)}; | |
| for (const title of titles) {{ // Assuming 'titles' is an array defined elsewhere | |
| if (results.length > 10) {{ | |
| break; | |
| }} else {{ | |
| if (title.toLowerCase().includes(searchIn.toLowerCase())) {{ // JavaScript's equivalent to Python's 'in' | |
| results.push(title); | |
| }} | |
| }} | |
| }} | |
| // Handle UI elements (Explanation below) | |
| const resultElements = [1,2,3,4,5,6,7,8,9,10].map(index => {{ | |
| return results[index - 1] || ''; | |
| }}); | |
| if (resultElements[0] == '') {{ | |
| document.getElementById('search_r1').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r1').style.display = 'block'; | |
| }} | |
| if (resultElements[1] == '') {{ | |
| document.getElementById('search_r2').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r2').style.display = 'block'; | |
| }} | |
| if (resultElements[2] == '') {{ | |
| document.getElementById('search_r3').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r3').style.display = 'block'; | |
| }} | |
| if (resultElements[3] == '') {{ | |
| document.getElementById('search_r4').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r4').style.display = 'block'; | |
| }} | |
| if (resultElements[4] == '') {{ | |
| document.getElementById('search_r5').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r5').style.display = 'block'; | |
| }} | |
| if (resultElements[5] == '') {{ | |
| document.getElementById('search_r6').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r6').style.display = 'block'; | |
| }} | |
| if (resultElements[6] == '') {{ | |
| document.getElementById('search_r7').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r7').style.display = 'block'; | |
| }} | |
| if (resultElements[7] == '') {{ | |
| document.getElementById('search_r8').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r8').style.display = 'block'; | |
| }} | |
| if (resultElements[8] == '') {{ | |
| document.getElementById('search_r9').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r9').style.display = 'block'; | |
| }} | |
| if (resultElements[9] == '') {{ | |
| document.getElementById('search_r10').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r10').style.display = 'block'; | |
| }} | |
| return resultElements; | |
| }} else {{ | |
| document.getElementById('search_r1').style.display = 'none'; | |
| document.getElementById('search_r2').style.display = 'none'; | |
| document.getElementById('search_r3').style.display = 'none'; | |
| document.getElementById('search_r4').style.display = 'none'; | |
| document.getElementById('search_r5').style.display = 'none'; | |
| document.getElementById('search_r6').style.display = 'none'; | |
| document.getElementById('search_r7').style.display = 'none'; | |
| document.getElementById('search_r8').style.display = 'none'; | |
| document.getElementById('search_r9').style.display = 'none'; | |
| document.getElementById('search_r10').style.display = 'none'; | |
| return ['', '', '', '', '', '', '', '', '', ''] | |
| }} | |
| }} | |
| """ | |
| UPDATE_IF_TYPE = f""" | |
| function chage_if_type(if_type) {{ | |
| if (if_type == 'Q&As') {{ | |
| document.getElementById('chat_block').style.display = 'none'; | |
| document.getElementById('qna_block').style.display = 'block'; | |
| }} else {{ | |
| document.getElementById('chat_block').style.display = 'block'; | |
| document.getElementById('qna_block').style.display = 'none'; | |
| }} | |
| }} | |
| """ | |
| def set_date(title): | |
| paper = title2qna[title] | |
| date = paper["target_date"].strftime("%Y-%m-%d") | |
| return date | |
| def set_papers(date, title): | |
| papers = [paper["title"] for paper in date2qna[date]] | |
| return ( | |
| gr.Dropdown(choices=papers, value=title), | |
| gr.Textbox("") | |
| ) | |
| with gr.Blocks(css=STYLE, theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# Let's explore papers with auto generated Q&As") | |
| with gr.Column(elem_classes=["group"]): | |
| with gr.Row(): | |
| date_dd = gr.Dropdown( | |
| sorted_dates, | |
| value=last_date, | |
| label="Select date", | |
| interactive=True, | |
| scale=3, | |
| ) | |
| papers_dd = gr.Dropdown( | |
| [paper["title"] for paper in last_papers], | |
| value=selected_paper["title"], | |
| label="Select paper title", | |
| interactive=True, | |
| scale=7, | |
| ) | |
| with gr.Column(elem_classes=["no-gap"]): | |
| search_in = gr.Textbox("", placeholder="Enter keywords to search...", elem_classes=["textbox-no-label"]) | |
| search_r1 = gr.Button(visible=False, elem_id="search_r1", elem_classes=["no-radius"]) | |
| search_r2 = gr.Button(visible=False, elem_id="search_r2", elem_classes=["no-radius"]) | |
| search_r3 = gr.Button(visible=False, elem_id="search_r3", elem_classes=["no-radius"]) | |
| search_r4 = gr.Button(visible=False, elem_id="search_r4", elem_classes=["no-radius"]) | |
| search_r5 = gr.Button(visible=False, elem_id="search_r5", elem_classes=["no-radius"]) | |
| search_r6 = gr.Button(visible=False, elem_id="search_r6", elem_classes=["no-radius"]) | |
| search_r7 = gr.Button(visible=False, elem_id="search_r7", elem_classes=["no-radius"]) | |
| search_r8 = gr.Button(visible=False, elem_id="search_r8", elem_classes=["no-radius"]) | |
| search_r9 = gr.Button(visible=False, elem_id="search_r9", elem_classes=["no-radius"]) | |
| search_r10 = gr.Button(visible=False, elem_id="search_r10", elem_classes=["no-radius"]) | |
| conv_type = gr.Radio(choices=["Q&As", "Chat"], value="Q&As", interactive=True, visible=False, elem_classes=["conv-type"]) | |
| with gr.Column(scale=7): | |
| title = gr.Markdown(f"# {selected_paper['title']}") | |
| summary = gr.Markdown(f"{selected_paper['summary']}", elem_classes=["small-font"]) | |
| with gr.Column(elem_id="chat_block", visible=False): | |
| gr.Chatbot([("hello", "world"), ("how", "are you?")]) | |
| with gr.Column(elem_id="qna_block", visible=True): | |
| with gr.Row(): | |
| with gr.Column(scale=7): | |
| gr.Markdown("## Auto generated Questions & Answers") | |
| exp_type = gr.Radio(choices=["ELI5", "Technical"], value="ELI5", elem_classes=["exp-type"], scale=3) | |
| # 1 | |
| with gr.Column(elem_classes=["group"], visible=True) as q_0: | |
| basic_q_0 = gr.Markdown(f"### 🙋 {selected_paper['0_question']}") | |
| basic_q_eli5_0 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_answers:eli5']}", elem_classes=["small-font"]) | |
| basic_q_expert_0 = gr.Markdown(f"↪ **(Technical)** {selected_paper['0_answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_0_0: | |
| depth_q_0 = gr.Markdown(f"### 🙋🙋 {selected_paper['0_additional_depth_q:follow up question']}") | |
| depth_q_eli5_0 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_depth_q:answers:eli5']}", elem_classes=["small-font"]) | |
| depth_q_expert_0 = gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_0_1: | |
| breath_q_0 = gr.Markdown(f"### 🙋🙋 {selected_paper['0_additional_breath_q:follow up question']}") | |
| breath_q_eli5_0 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_breath_q:answers:eli5']}", elem_classes=["small-font"]) | |
| breath_q_expert_0 = gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| # 2 | |
| with gr.Column(elem_classes=["group"], visible=True) as q_1: | |
| basic_q_1 = gr.Markdown(f"### 🙋 {selected_paper['1_question']}") | |
| basic_q_eli5_1 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_answers:eli5']}", elem_classes=["small-font"]) | |
| basic_q_expert_1 = gr.Markdown(f"↪ **(Technical)** {selected_paper['1_answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_1_0: | |
| depth_q_1 = gr.Markdown(f"### 🙋🙋 {selected_paper['1_additional_depth_q:follow up question']}") | |
| depth_q_eli5_1 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_depth_q:answers:eli5']}", elem_classes=["small-font"]) | |
| depth_q_expert_1 = gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_1_1: | |
| breath_q_1 = gr.Markdown(f"### 🙋🙋 {selected_paper['1_additional_breath_q:follow up question']}") | |
| breath_q_eli5_1 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_breath_q:answers:eli5']}", elem_classes=["small-font"]) | |
| breath_q_expert_1 = gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| # 3 | |
| with gr.Column(elem_classes=["group"], visible=True) as q_2: | |
| basic_q_2 = gr.Markdown(f"### 🙋 {selected_paper['2_question']}") | |
| basic_q_eli5_2 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_answers:eli5']}", elem_classes=["small-font"]) | |
| basic_q_expert_2 = gr.Markdown(f"↪ **(Technical)** {selected_paper['2_answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_2_0: | |
| depth_q_2 = gr.Markdown(f"### 🙋🙋 {selected_paper['2_additional_depth_q:follow up question']}") | |
| depth_q_eli5_2 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_depth_q:answers:eli5']}", elem_classes=["small-font"]) | |
| depth_q_expert_2 = gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_2_1: | |
| breath_q_2 = gr.Markdown(f"### 🙋🙋 {selected_paper['2_additional_breath_q:follow up question']}") | |
| breath_q_eli5_2 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_breath_q:answers:eli5']}", elem_classes=["small-font"]) | |
| breath_q_expert_2 = gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"]) | |
| gr.Markdown("## Request any arXiv ids") | |
| arxiv_queue = gr.Dataframe( | |
| headers=["Requested arXiv IDs"], col_count=(1, "fixed"), | |
| value=requested_arxiv_ids_df, | |
| datatype=["str"], | |
| interactive=False | |
| ) | |
| arxiv_id_enter = gr.Textbox(placeholder="Enter comma separated arXiv IDs...", elem_classes=["textbox-no-label"]) | |
| arxiv_id_enter.submit( | |
| add_arxiv_ids_to_queue, | |
| [arxiv_queue, arxiv_id_enter], | |
| arxiv_queue | |
| ) | |
| gr.Markdown("The target papers are collected from [Hugging Face 🤗 Daily Papers](https://huggingface.co/papers) on a daily basis. " | |
| "The entire data is generated by [Google's Gemini 1.0](https://deepmind.google/technologies/gemini/) Pro. " | |
| "If you are curious how it is done, visit the [Auto Paper Q&A Generation project repository](https://github.com/deep-diver/auto-paper-analysis) " | |
| "Also, the generated dataset is hosted on Hugging Face 🤗 Dataset repository as well([Link](https://huggingface.co/datasets/chansung/auto-paper-qa2)). ") | |
| search_r1.click(set_date, search_r1, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r1], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r2.click(set_date, search_r2, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r2], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r3.click(set_date, search_r3, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r3], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r4.click(set_date, search_r4, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r4], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r5.click(set_date, search_r5, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r5], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r6.click(set_date, search_r6, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r6], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r7.click(set_date, search_r7, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r7], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r8.click(set_date, search_r8, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r8], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r9.click(set_date, search_r9, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r9], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r10.click(set_date, search_r10, date_dd).then( | |
| set_papers, | |
| inputs=[date_dd, search_r10], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| date_dd.input(get_papers, date_dd, papers_dd).then( | |
| set_paper, | |
| [date_dd, papers_dd], | |
| [ | |
| title, summary, | |
| basic_q_0, basic_q_eli5_0, basic_q_expert_0, | |
| depth_q_0, depth_q_eli5_0, depth_q_expert_0, | |
| breath_q_0, breath_q_eli5_0, breath_q_expert_0, | |
| basic_q_1, basic_q_eli5_1, basic_q_expert_1, | |
| depth_q_1, depth_q_eli5_1, depth_q_expert_1, | |
| breath_q_1, breath_q_eli5_1, breath_q_expert_1, | |
| basic_q_2, basic_q_eli5_2, basic_q_expert_2, | |
| depth_q_2, depth_q_eli5_2, depth_q_expert_2, | |
| breath_q_2, breath_q_eli5_2, breath_q_expert_2 | |
| ] | |
| ) | |
| papers_dd.change( | |
| set_paper, | |
| [date_dd, papers_dd], | |
| [ | |
| title, summary, | |
| basic_q_0, basic_q_eli5_0, basic_q_expert_0, | |
| depth_q_0, depth_q_eli5_0, depth_q_expert_0, | |
| breath_q_0, breath_q_eli5_0, breath_q_expert_0, | |
| basic_q_1, basic_q_eli5_1, basic_q_expert_1, | |
| depth_q_1, depth_q_eli5_1, depth_q_expert_1, | |
| breath_q_1, breath_q_eli5_1, breath_q_expert_1, | |
| basic_q_2, basic_q_eli5_2, basic_q_expert_2, | |
| depth_q_2, depth_q_eli5_2, depth_q_expert_2, | |
| breath_q_2, breath_q_eli5_2, breath_q_expert_2 | |
| ] | |
| ) | |
| search_in.change( | |
| inputs=[search_in], | |
| outputs=[ | |
| search_r1, search_r2, search_r3, search_r4, search_r5, | |
| search_r6, search_r7, search_r8, search_r9, search_r10 | |
| ], | |
| js=UPDATE_SEARCH_RESULTS, | |
| fn=None | |
| ) | |
| exp_type.select( | |
| change_exp_type, | |
| exp_type, | |
| [ | |
| basic_q_eli5_0, basic_q_expert_0, depth_q_eli5_0, depth_q_expert_0, breath_q_eli5_0, breath_q_expert_0, | |
| basic_q_eli5_1, basic_q_expert_1, depth_q_eli5_1, depth_q_expert_1, breath_q_eli5_1, breath_q_expert_1, | |
| basic_q_eli5_2, basic_q_expert_2, depth_q_eli5_2, depth_q_expert_2, breath_q_eli5_2, breath_q_expert_2 | |
| ] | |
| ) | |
| conv_type.select( | |
| inputs=[conv_type], | |
| js=UPDATE_IF_TYPE, | |
| outputs=None, | |
| fn=None | |
| ) | |
| start_date = datetime.now() + timedelta(minutes=1) | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job( | |
| process_arxiv_ids, | |
| trigger='interval', | |
| seconds=3600, | |
| args=[ | |
| gemini_api_key, | |
| dataset_repo_id, | |
| request_arxiv_repo_id, | |
| hf_token | |
| ], | |
| start_date=start_date | |
| ) | |
| scheduler.start() | |
| demo.launch(share=True, debug=True) |