add app

action.py

@@ -0,0 +1,142 @@
+from sendgrid import SendGridAPIClient
+from sendgrid.helpers.mail import Mail, Email, To, Content
+
+from datetime import date
+
+import argparse
+import yaml
+import os
+
+from relevancy import generate_relevance_score, process_subject_fields
+from download_new_papers import get_papers
+
+
+
+# Hackathon quality code. Don't judge too harshly.
+# Feel free to submit pull requests to improve the code.
+
+topics = {
+    "Physics": "",
+    "Mathematics": "math",
+    "Computer Science": "cs",
+    "Quantitative Biology": "q-bio",
+    "Quantitative Finance": "q-fin",
+    "Statistics": "stat",
+    "Electrical Engineering and Systems Science": "eess",
+    "Economics": "econ"
+}
+
+physics_topics = {
+    "Astrophysics": "astro-ph",
+    "Condensed Matter": "cond-mat",
+    "General Relativity and Quantum Cosmology": "gr-qc",
+    "High Energy Physics - Experiment": "hep-ex",
+    "High Energy Physics - Lattice": "hep-lat",
+    "High Energy Physics - Phenomenology": "hep-ph",
+    "High Energy Physics - Theory": "hep-th",
+    "Mathematical Physics": "math-ph",
+    "Nonlinear Sciences": "nlin",
+    "Nuclear Experiment": "nucl-ex",
+    "Nuclear Theory": "nucl-th",
+    "Physics": "physics",
+    "Quantum Physics": "quant-ph"
+}
+
+
+# TODO: surely theres a better way
+category_map = {
+    "Astrophysics": ["Astrophysics of Galaxies", "Cosmology and Nongalactic Astrophysics", "Earth and Planetary Astrophysics", "High Energy Astrophysical Phenomena", "Instrumentation and Methods for Astrophysics", "Solar and Stellar Astrophysics"],
+    "Condensed Matter": ["Disordered Systems and Neural Networks", "Materials Science", "Mesoscale and Nanoscale Physics", "Other Condensed Matter", "Quantum Gases", "Soft Condensed Matter", "Statistical Mechanics", "Strongly Correlated Electrons", "Superconductivity"],
+    "General Relativity and Quantum Cosmology": ["None"],
+    "High Energy Physics - Experiment": ["None"],
+    "High Energy Physics - Lattice": ["None"],
+    "High Energy Physics - Phenomenology": ["None"],
+    "High Energy Physics - Theory": ["None"],
+    "Mathematical Physics": ["None"],
+    "Nonlinear Sciences": ["Adaptation and Self-Organizing Systems", "Cellular Automata and Lattice Gases", "Chaotic Dynamics", "Exactly Solvable and Integrable Systems", "Pattern Formation and Solitons"],
+    "Nuclear Experiment": ["None"],
+    "Nuclear Theory": ["None"],
+    "Physics": ["Accelerator Physics", "Applied Physics", "Atmospheric and Oceanic Physics", "Atomic and Molecular Clusters", "Atomic Physics", "Biological Physics", "Chemical Physics", "Classical Physics", "Computational Physics", "Data Analysis, Statistics and Probability", "Fluid Dynamics", "General Physics", "Geophysics", "History and Philosophy of Physics", "Instrumentation and Detectors", "Medical Physics", "Optics", "Physics and Society", "Physics Education", "Plasma Physics", "Popular Physics", "Space Physics"],
+    "Quantum Physics": ["None"],
+    "Mathematics": ["Algebraic Geometry", "Algebraic Topology", "Analysis of PDEs", "Category Theory", "Classical Analysis and ODEs", "Combinatorics", "Commutative Algebra", "Complex Variables", "Differential Geometry", "Dynamical Systems", "Functional Analysis", "General Mathematics", "General Topology", "Geometric Topology", "Group Theory", "History and Overview", "Information Theory", "K-Theory and Homology", "Logic", "Mathematical Physics", "Metric Geometry", "Number Theory", "Numerical Analysis", "Operator Algebras", "Optimization and Control", "Probability", "Quantum Algebra", "Representation Theory", "Rings and Algebras", "Spectral Theory", "Statistics Theory", "Symplectic Geometry"],
+    "Computer Science": ["Artificial Intelligence", "Computation and Language", "Computational Complexity", "Computational Engineering, Finance, and Science", "Computational Geometry", "Computer Science and Game Theory", "Computer Vision and Pattern Recognition", "Computers and Society", "Cryptography and Security", "Data Structures and Algorithms", "Databases", "Digital Libraries", "Discrete Mathematics", "Distributed, Parallel, and Cluster Computing", "Emerging Technologies", "Formal Languages and Automata Theory", "General Literature", "Graphics", "Hardware Architecture", "Human-Computer Interaction", "Information Retrieval", "Information Theory", "Logic in Computer Science", "Machine Learning", "Mathematical Software", "Multiagent Systems", "Multimedia", "Networking and Internet Architecture", "Neural and Evolutionary Computing", "Numerical Analysis", "Operating Systems", "Other Computer Science", "Performance", "Programming Languages", "Robotics", "Social and Information Networks", "Software Engineering", "Sound", "Symbolic Computation", "Systems and Control"],
+    "Quantitative Biology": ["Biomolecules", "Cell Behavior", "Genomics", "Molecular Networks", "Neurons and Cognition", "Other Quantitative Biology", "Populations and Evolution", "Quantitative Methods", "Subcellular Processes", "Tissues and Organs"],
+    "Quantitative Finance": ["Computational Finance", "Economics", "General Finance", "Mathematical Finance", "Portfolio Management", "Pricing of Securities", "Risk Management", "Statistical Finance", "Trading and Market Microstructure"],
+    "Statistics": ["Applications", "Computation", "Machine Learning", "Methodology", "Other Statistics", "Statistics Theory"],
+    "Electrical Engineering and Systems Science": ["Audio and Speech Processing", "Image and Video Processing", "Signal Processing", "Systems and Control"],
+    "Economics": ["Econometrics", "General Economics", "Theoretical Economics"]
+}
+
+
+def generate_body(topic, categories, interest, threshold):
+    if topic == "Physics":
+        raise RuntimeError("You must choose a physics subtopic.")
+    elif topic in physics_topics:
+        abbr = physics_topics[topic]
+    elif topic in topics:
+        abbr = topics[topic]
+    else:
+        raise RuntimeError(f"Invalid topic {topic}")
+    if categories:
+        for category in categories:
+            if category not in category_map[topic]:
+                raise RuntimeError(f"{category} is not a category of {topic}")
+        papers = get_papers(abbr)
+        papers = [
+            t for t in papers
+            if bool(set(process_subject_fields(t['subjects'])) & set(categories))]
+    else:
+        papers = get_papers(abbr)
+    if interest:
+        relevancy, hallucination = generate_relevance_score(
+            papers,
+            query={"interest": interest},
+            threshold_score=threshold,
+            num_paper_in_prompt=8)
+        body = "<br><br>".join(
+            [f'Title: <a href="{paper["main_page"]}">{paper["title"]}</a><br>Authors: {paper["authors"]}<br>Score: {paper["Relevancy score"]}<br>Reason: {paper["Reasons for match"]}'
+             for paper in relevancy])
+        if hallucination:
+            body = "Warning: the model hallucinated some papers. We have tried to remove them, but the scores may not be accurate.<br><br>" + body
+    else:
+        body = "<br><br>".join(
+            [f'Title: <a href="{paper["main_page"]}">{paper["title"]}</a><br>Authors: {paper["authors"]}'
+             for paper in papers])
+    return body
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", help="yaml config file to use", default="config.yaml")
+    args = parser.parse_args()
+    with open(args.config, "r") as f:
+        config = yaml.safe_load(f)
+    if "OPENAI_API_KEY" not in os.environ:
+        raise RuntimeError("No openai api key found")
+
+    topic = config["topic"]
+    categories = config["categories"]
+    from_email = config.get("from_email") or os.environ.get("FROM_EMAIL")
+    to_email = config.get("to_email") or os.environ.get("TO_EMAIL")
+    threshold = config["threshold"]
+    interest = config["interest"]
+    with open("digest.html", "w") as f:
+        body = generate_body(topic, categories, interest, threshold)
+        f.write(body)
+        if os.environ.get('SENDGRID_API_KEY', None):
+            sg = SendGridAPIClient(api_key=os.environ.get('SENDGRID_API_KEY'))
+            from_email = Email(from_email)  # Change to your verified sender
+            to_email = To(to_email)
+            subject = date.today().strftime("Personalized arXiv Digest, %d %b %Y")
+            content = Content("text/html", body)
+            mail = Mail(from_email, to_email, subject, content)
+            mail_json = mail.get()
+
+            # Send an HTTP POST request to /mail/send
+            response = sg.client.mail.send.post(request_body=mail_json)
+            if response.status_code >= 200 and response.status_code <= 300:
+                print("Send test email: Success!")
+            else:
+                print("Send test email: Failure ({response.status_code}, {response.text})")
+        else:
+            print("No sendgrid api key found. Skipping email")

app.py

@@ -0,0 +1,186 @@
+import gradio as gr
+from download_new_papers import get_papers
+import utils
+from relevancy import generate_relevance_score, process_subject_fields
+from sendgrid.helpers.mail import Mail, Email, To, Content
+import sendgrid
+import os
+import openai
+
+topics = {
+    "Physics": "",
+    "Mathematics": "math",
+    "Computer Science": "cs",
+    "Quantitative Biology": "q-bio",
+    "Quantitative Finance": "q-fin",
+    "Statistics": "stat",
+    "Electrical Engineering and Systems Science": "eess",
+    "Economics": "econ"
+}
+
+physics_topics = {
+    "Astrophysics": "astro-ph",
+    "Condensed Matter": "cond-mat",
+    "General Relativity and Quantum Cosmology": "gr-qc",
+    "High Energy Physics - Experiment": "hep-ex",
+    "High Energy Physics - Lattice": "hep-lat",
+    "High Energy Physics - Phenomenology": "hep-ph",
+    "High Energy Physics - Theory": "hep-th",
+    "Mathematical Physics": "math-ph",
+    "Nonlinear Sciences": "nlin",
+    "Nuclear Experiment": "nucl-ex",
+    "Nuclear Theory": "nucl-th",
+    "Physics": "physics",
+    "Quantum Physics": "quant-ph"
+}
+
+categories_map = {
+    "Astrophysics": ["Astrophysics of Galaxies", "Cosmology and Nongalactic Astrophysics", "Earth and Planetary Astrophysics", "High Energy Astrophysical Phenomena", "Instrumentation and Methods for Astrophysics", "Solar and Stellar Astrophysics"],
+    "Condensed Matter": ["Disordered Systems and Neural Networks", "Materials Science", "Mesoscale and Nanoscale Physics", "Other Condensed Matter", "Quantum Gases", "Soft Condensed Matter", "Statistical Mechanics", "Strongly Correlated Electrons", "Superconductivity"],
+    "General Relativity and Quantum Cosmology": ["None"],
+    "High Energy Physics - Experiment": ["None"],
+    "High Energy Physics - Lattice": ["None"],
+    "High Energy Physics - Phenomenology": ["None"],
+    "High Energy Physics - Theory": ["None"],
+    "Mathematical Physics": ["None"],
+    "Nonlinear Sciences": ["Adaptation and Self-Organizing Systems", "Cellular Automata and Lattice Gases", "Chaotic Dynamics", "Exactly Solvable and Integrable Systems", "Pattern Formation and Solitons"],
+    "Nuclear Experiment": ["None"],
+    "Nuclear Theory": ["None"],
+    "Physics": ["Accelerator Physics", "Applied Physics", "Atmospheric and Oceanic Physics", "Atomic and Molecular Clusters", "Atomic Physics", "Biological Physics", "Chemical Physics", "Classical Physics", "Computational Physics", "Data Analysis, Statistics and Probability", "Fluid Dynamics", "General Physics", "Geophysics", "History and Philosophy of Physics", "Instrumentation and Detectors", "Medical Physics", "Optics", "Physics and Society", "Physics Education", "Plasma Physics", "Popular Physics", "Space Physics"],
+    "Quantum Physics": ["None"],
+    "Mathematics": ["Algebraic Geometry", "Algebraic Topology", "Analysis of PDEs", "Category Theory", "Classical Analysis and ODEs", "Combinatorics", "Commutative Algebra", "Complex Variables", "Differential Geometry", "Dynamical Systems", "Functional Analysis", "General Mathematics", "General Topology", "Geometric Topology", "Group Theory", "History and Overview", "Information Theory", "K-Theory and Homology", "Logic", "Mathematical Physics", "Metric Geometry", "Number Theory", "Numerical Analysis", "Operator Algebras", "Optimization and Control", "Probability", "Quantum Algebra", "Representation Theory", "Rings and Algebras", "Spectral Theory", "Statistics Theory", "Symplectic Geometry"],
+    "Computer Science": ["Artificial Intelligence", "Computation and Language", "Computational Complexity", "Computational Engineering, Finance, and Science", "Computational Geometry", "Computer Science and Game Theory", "Computer Vision and Pattern Recognition", "Computers and Society", "Cryptography and Security", "Data Structures and Algorithms", "Databases", "Digital Libraries", "Discrete Mathematics", "Distributed, Parallel, and Cluster Computing", "Emerging Technologies", "Formal Languages and Automata Theory", "General Literature", "Graphics", "Hardware Architecture", "Human-Computer Interaction", "Information Retrieval", "Information Theory", "Logic in Computer Science", "Machine Learning", "Mathematical Software", "Multiagent Systems", "Multimedia", "Networking and Internet Architecture", "Neural and Evolutionary Computing", "Numerical Analysis", "Operating Systems", "Other Computer Science", "Performance", "Programming Languages", "Robotics", "Social and Information Networks", "Software Engineering", "Sound", "Symbolic Computation", "Systems and Control"],
+    "Quantitative Biology": ["Biomolecules", "Cell Behavior", "Genomics", "Molecular Networks", "Neurons and Cognition", "Other Quantitative Biology", "Populations and Evolution", "Quantitative Methods", "Subcellular Processes", "Tissues and Organs"],
+    "Quantitative Finance": ["Computational Finance", "Economics", "General Finance", "Mathematical Finance", "Portfolio Management", "Pricing of Securities", "Risk Management", "Statistical Finance", "Trading and Market Microstructure"],
+    "Statistics": ["Applications", "Computation", "Machine Learning", "Methodology", "Other Statistics", "Statistics Theory"],
+    "Electrical Engineering and Systems Science": ["Audio and Speech Processing", "Image and Video Processing", "Signal Processing", "Systems and Control"],
+    "Economics": ["Econometrics", "General Economics", "Theoretical Economics"]
+}
+
+
+def sample(email, topic, physics_topic, categories, interest):
+    if subject == "Physics":
+        if isinstance(physics_topic, list):
+            raise gr.Error("You must choose a physics topic.")
+        topic = physics_topic
+        abbr = physics_topics[topic]
+    else:
+        abbr = topics[topic]
+    if categories:
+        papers = get_papers(abbr)
+        papers = [
+            t for t in papers
+            if bool(set(process_subject_fields(t['subjects'])) & set(categories))][:4]
+    else:
+        papers = get_papers(abbr, limit=4)
+    if interest:
+        if not openai.api_key: raise gr.Error("Set your OpenAI api key on the left first")
+        relevancy, _ = generate_relevance_score(
+            papers,
+            query={"interest": interest},
+            threshold_score=0,
+            num_paper_in_prompt=4)
+        return "\n\n".join([paper["summarized_text"] for paper in relevancy])
+    else:
+        return "\n\n".join(f"Title: {paper['title']}\nAuthors: {paper['authors']}" for paper in papers)
+
+
+def change_subsubject(subject, physics_subject):
+    if subject != "Physics":
+        return gr.Dropdown.update(choices=categories_map[subject], value=[], visible=True)
+    else:
+        if physics_subject and not isinstance(physics_subject, list):
+            return gr.Dropdown.update(choices=categories_map[physics_subject], value=[], visible=True)
+        else:
+            return gr.Dropdown.update(choices=[], value=[], visible=False)
+
+
+def change_physics(subject):
+    if subject != "Physics":
+        return gr.Dropdown.update(visible=False, value=[])
+    else:
+        return gr.Dropdown.update(physics_topics, visible=True)
+
+
+def test(email, topic, physics_topic, categories, interest, key):
+    if not email: raise gr.Error("Set your email")
+    if not key: raise gr.Error("Set your SendGrid key")
+    if topic == "Physics":
+        if isinstance(physics_topic, list):
+            raise gr.Error("You must choose a physics topic.")
+        topic = physics_topic
+        abbr = physics_topics[topic]
+    else:
+        abbr = topics[topic]
+    if categories:
+        papers = get_papers(abbr)
+        papers = [
+            t for t in papers
+            if bool(set(process_subject_fields(t['subjects'])) & set(categories))][:4]
+    else:
+        papers = get_papers(abbr, limit=4)
+    if interest:
+        if not openai.api_key: raise gr.Error("Set your OpenAI api key on the left first")
+        relevancy, hallucination = generate_relevance_score(
+            papers,
+            query={"interest": interest},
+            threshold_score=7,
+            num_paper_in_prompt=8)
+        body = "<br><br>".join([f'Title: <a href="{paper["main_page"]}">{paper["title"]}</a><br>Authors: {paper["authors"]}<br>Score: {paper["Relevancy score"]}<br>Reason: {paper["Reasons for match"]}' for paper in relevancy])
+        if hallucination:
+            body = "Warning: the model hallucinated some papers. We have tried to remove them, but the scores may not be accurate.<br><br>" + body
+    else:
+        body = "<br><br>".join([f'Title: <a href="{paper["main_page"]}">{paper["title"]}</a><br>Authors: {paper["authors"]}' for paper in papers])
+    sg = sendgrid.SendGridAPIClient(api_key=key)
+    from_email = Email(email)
+    to_email = To(email)
+    subject = "arXiv digest"
+    content = Content("text/html", body)
+    mail = Mail(from_email, to_email, subject, content)
+    mail_json = mail.get()
+
+    # Send an HTTP POST request to /mail/send
+    response = sg.client.mail.send.post(request_body=mail_json)
+    if response.status_code >= 200 and response.status_code <= 300:
+        return "Success!"
+    else:
+        return "Failure: ({response.status_code})"
+
+
+def register_openai_token(token):
+    print(f"registering new key: {token[:5]}")
+    openai.api_key = token
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=0.40):
+            with gr.Box():
+                token = gr.Textbox(label="OpenAI API Key", type="password")
+            with gr.Box():
+                description = gr.HTML(value="Send an email to the below address using the configuration on the right. Requires a sendgrid token")
+                email = gr.Textbox(label="Email address", type="email", placeholder="")
+                sendgrid_token = gr.Textbox(label="SendGrid API Key", type="password")
+                with gr.Row():
+                    test_btn = gr.Button("Send email")
+                    output = gr.Textbox(show_label=False, placeholder="email status")
+        with gr.Column(scale=1):
+            subject = gr.Radio(
+                list(topics.keys()), label="Topic"
+            )
+            physics_subject = gr.Dropdown(physics_topics, value=[], multiselect=False, label="Physics category", visible=False, info="")
+            subsubject = gr.Dropdown(
+                    [], value=[], multiselect=True, label="Subtopic", info="", visible=False)
+            subject.change(fn=change_physics, inputs=[subject], outputs=physics_subject)
+            subject.change(fn=change_subsubject, inputs=[subject, physics_subject], outputs=subsubject)
+            physics_subject.change(fn=change_subsubject, inputs=[subject, physics_subject], outputs=subsubject)
+
+            interest = gr.Textbox(label="A natural language description of what you are interested in. Press shift-enter to update.", lines=7)
+            sample_output = gr.Textbox(label="Examples")
+    test_btn.click(fn=test, inputs=[email, subject, physics_subject, subsubject, interest, sendgrid_token], outputs=output)
+    token.change(fn=register_openai_token, inputs=[token])
+    subject.change(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
+    physics_subject.change(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
+    subsubject.change(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
+    interest.submit(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
+
+demo.launch()

download_new_papers.py

@@ -0,0 +1,64 @@
+# encoding: utf-8
+import os
+import tqdm
+from bs4 import BeautifulSoup as bs
+import urllib.request
+import json
+import datetime
+import pytz
+
+
+def _download_new_papers(field_abbr):
+    NEW_SUB_URL = f'https://arxiv.org/list/{field_abbr}/new'  # https://arxiv.org/list/cs/new
+    page = urllib.request.urlopen(NEW_SUB_URL)
+    soup = bs(page)
+    content = soup.body.find("div", {'id': 'content'})
+
+    # find the first h3 element in content
+    h3 = content.find("h3").text   # e.g: New submissions for Wed, 10 May 23
+    date = h3.replace("New submissions for", "").strip()
+
+    dt_list = content.dl.find_all("dt")
+    dd_list = content.dl.find_all("dd")
+    arxiv_base = "https://arxiv.org/abs/"
+
+    assert len(dt_list) == len(dd_list)
+    new_paper_list = []
+    for i in tqdm.tqdm(range(len(dt_list))):
+        paper = {}
+        paper_number = dt_list[i].text.strip().split(" ")[2].split(":")[-1]
+        paper['main_page'] = arxiv_base + paper_number
+        paper['pdf'] = arxiv_base.replace('abs', 'pdf') + paper_number
+
+        paper['title'] = dd_list[i].find("div", {"class": "list-title mathjax"}).text.replace("Title: ", "").strip()
+        paper['authors'] = dd_list[i].find("div", {"class": "list-authors"}).text \
+                            .replace("Authors:\n", "").replace("\n", "").strip()
+        paper['subjects'] = dd_list[i].find("div", {"class": "list-subjects"}).text.replace("Subjects: ", "").strip()
+        paper['abstract'] = dd_list[i].find("p", {"class": "mathjax"}).text.replace("\n", " ").strip()
+        new_paper_list.append(paper)
+
+
+    #  check if ./data exist, if not, create it
+    if not os.path.exists("./data"):
+        os.makedirs("./data")
+
+    # save new_paper_list to a jsonl file, with each line as the element of a dictionary
+    date = datetime.date.fromtimestamp(datetime.datetime.now(tz=pytz.timezone("America/New_York")).timestamp())
+    date = date.strftime("%a, %d %b %y")
+    with open(f"./data/{field_abbr}_{date}.jsonl", "w") as f:
+        for paper in new_paper_list:
+            f.write(json.dumps(paper) + "\n")
+
+
+def get_papers(field_abbr, limit=None):
+    date = datetime.date.fromtimestamp(datetime.datetime.now(tz=pytz.timezone("America/New_York")).timestamp())
+    date = date.strftime("%a, %d %b %y")
+    if not os.path.exists(f"./data/{field_abbr}_{date}.jsonl"):
+        _download_new_papers(field_abbr)
+    results = []
+    with open(f"./data/{field_abbr}_{date}.jsonl", "r") as f:
+        for i, line in enumerate(f.readlines()):
+            if limit and i == limit:
+                return results
+            results.append(json.loads(line))
+    return results

relevancy.py

@@ -0,0 +1,174 @@
+"""
+run:
+python -m relevancy run_all_day_paper \
+  --output_dir ./data \
+  --model_name="gpt-3.5-turbo" \
+"""
+import time
+import json
+import os
+import random
+import re
+import string
+from datetime import datetime
+
+import numpy as np
+import tqdm
+import utils
+
+
+def encode_prompt(query, prompt_papers):
+    """Encode multiple prompt instructions into a single string."""
+    prompt = open("src/relevancy_prompt.txt").read() + "\n"
+    prompt += query['interest']
+
+    for idx, task_dict in enumerate(prompt_papers):
+        (title, authors, abstract) = task_dict["title"], task_dict["authors"], task_dict["abstract"]
+        if not title:
+            raise
+        prompt += f"###\n"
+        prompt += f"{idx + 1}. Title: {title}\n"
+        prompt += f"{idx + 1}. Authors: {authors}\n"
+        prompt += f"{idx + 1}. Abstract: {abstract}\n"
+    prompt += f"\n Generate response:\n1."
+    print(prompt)
+    return prompt
+
+
+def post_process_chat_gpt_response(paper_data, response, threshold_score=8):
+    selected_data = []
+    if response is None:
+        return []
+    json_items = response['message']['content'].replace("\n\n", "\n").split("\n")
+    pattern = r"^\d+\. |\\"
+    import pprint
+    try:
+        score_items = [
+            json.loads(re.sub(pattern, "", line))
+            for line in json_items if "relevancy score" in line.lower()]
+    except Exception:
+        pprint.pprint([re.sub(pattern, "", line) for line in json_items if "relevancy score" in line.lower()])
+        raise RuntimeError("failed")
+    pprint.pprint(score_items)
+    scores = []
+    for item in score_items:
+        temp = item["Relevancy score"]
+        if "/" in temp:
+            scores.append(int(temp.split("/")[0]))
+        else:
+            scores.append(int(temp))
+    if len(score_items) != len(paper_data):
+        score_items = score_items[:len(paper_data)]
+        hallucination = True
+    else:
+        hallucination = False
+
+    for idx, inst in enumerate(score_items):
+        # if the decoding stops due to length, the last example is likely truncated so we discard it
+        if scores[idx] < threshold_score:
+            continue
+        output_str = "Title: " + paper_data[idx]["title"] + "\n"
+        output_str += "Authors: " + paper_data[idx]["authors"] + "\n"
+        output_str += "Link: " + paper_data[idx]["main_page"] + "\n"
+        for key, value in inst.items():
+            paper_data[idx][key] = value
+            output_str += key + ": " + value + "\n"
+        paper_data[idx]['summarized_text'] = output_str
+        selected_data.append(paper_data[idx])
+    return selected_data, hallucination
+
+
+def find_word_in_string(w, s):
+    return re.compile(r"\b({0})\b".format(w), flags=re.IGNORECASE).search(s)
+
+
+def process_subject_fields(subjects):
+    all_subjects = subjects.split(";")
+    all_subjects = [s.split(" (")[0] for s in all_subjects]
+    return all_subjects
+
+def generate_relevance_score(
+    all_papers,
+    query,
+    model_name="gpt-3.5-turbo",
+    threshold_score=8,
+    num_paper_in_prompt=4,
+    temperature=0.4,
+    top_p=1.0,
+    sorting=True
+):
+    ans_data = []
+    request_idx = 1
+    hallucination = False
+    for id in tqdm.tqdm(range(0, len(all_papers), num_paper_in_prompt)):
+        prompt_papers = all_papers[id:id+num_paper_in_prompt]
+        # only sampling from the seed tasks
+        prompt = encode_prompt(query, prompt_papers)
+
+        decoding_args = utils.OpenAIDecodingArguments(
+            temperature=temperature,
+            n=1,
+            max_tokens=1072,  # hard-code to maximize the length. the requests will be automatically adjusted
+            top_p=top_p,
+        )
+        request_start = time.time()
+        response = utils.openai_completion(
+            prompts=prompt,
+            model_name=model_name,
+            batch_size=1,
+            decoding_args=decoding_args,
+            logit_bias={"100257": -100},  # prevent the <|endoftext|> from being generated
+            # "100265":-100, "100276":-100 for <|im_end|> and <endofprompt> token 
+        )
+        print ("response", response['message']['content'])
+        request_duration = time.time() - request_start
+
+        process_start = time.time()
+        batch_data, hallu = post_process_chat_gpt_response(prompt_papers, response, threshold_score=threshold_score)
+        hallucination = hallucination or hallu
+        ans_data.extend(batch_data)
+
+        print(f"Request {request_idx+1} took {request_duration:.2f}s")
+        print(f"Post-processing took {time.time() - process_start:.2f}s")
+
+    if sorting:
+        ans_data = sorted(ans_data, key=lambda x: x["Relevancy score"], reverse=True)
+    
+    return ans_data, hallucination
+
+def run_all_day_paper(
+    query={"interest":"", "subjects":["Computation and Language", "Artificial Intelligence"]},
+    date=None,
+    data_dir="../data",
+    model_name="gpt-3.5-turbo",
+    threshold_score=8,
+    num_paper_in_prompt=8,
+    temperature=0.4,
+    top_p=1.0
+):
+    if date is None:
+        date = datetime.today().strftime('%a, %d %b %y')
+        # string format such as Wed, 10 May 23
+    print ("the date for the arxiv data is: ", date)
+
+    all_papers = [json.loads(l) for l in open(f"{data_dir}/{date}.jsonl", "r")]
+    print (f"We found {len(all_papers)}.")
+
+    all_papers_in_subjects = [
+        t for t in all_papers
+        if bool(set(process_subject_fields(t['subjects'])) & set(query['subjects']))
+    ]
+    print(f"After filtering subjects, we have {len(all_papers_in_subjects)} papers left.")
+    ans_data = generate_relevance_score(all_papers_in_subjects, query, model_name, threshold_score, num_paper_in_prompt, temperature, top_p)
+    utils.write_ans_to_file(ans_data, date, output_dir="../outputs")
+    return ans_data
+
+
+if __name__ == "__main__":
+    query = {"interest":"""
+    1. Large language model pretraining and finetunings
+    2. Multimodal machine learning
+    3. Do not care about specific application, for example, information extraction, summarization, etc.
+    4. Not interested in paper focus on specific languages, e.g., Arabic, Chinese, etc.\n""",
+    "subjects":["Computation and Language"]}
+    ans_data = run_all_day_paper(query)

utils.py

@@ -0,0 +1,149 @@
+import dataclasses
+import logging
+import math
+import os
+import io
+import sys
+import time
+import json
+from typing import Optional, Sequence, Union
+
+import openai
+import tqdm
+from openai import openai_object
+import copy
+
+StrOrOpenAIObject = Union[str, openai_object.OpenAIObject]
+
+
+openai_org = os.getenv("OPENAI_ORG")
+if openai_org is not None:
+    openai.organization = openai_org
+    logging.warning(f"Switching to organization: {openai_org} for OAI API key.")
+
+
+@dataclasses.dataclass
+class OpenAIDecodingArguments(object):
+    max_tokens: int = 1800
+    temperature: float = 0.2
+    top_p: float = 1.0
+    n: int = 1
+    stream: bool = False
+    stop: Optional[Sequence[str]] = None
+    presence_penalty: float = 0.0
+    frequency_penalty: float = 0.0
+    # logprobs: Optional[int] = None
+
+
+def openai_completion(
+    prompts, #: Union[str, Sequence[str], Sequence[dict[str, str]], dict[str, str]],
+    decoding_args: OpenAIDecodingArguments,
+    model_name="text-davinci-003",
+    sleep_time=2,
+    batch_size=1,
+    max_instances=sys.maxsize,
+    max_batches=sys.maxsize,
+    return_text=False,
+    **decoding_kwargs,
+) -> Union[Union[StrOrOpenAIObject], Sequence[StrOrOpenAIObject], Sequence[Sequence[StrOrOpenAIObject]],]:
+    """Decode with OpenAI API.
+
+    Args:
+        prompts: A string or a list of strings to complete. If it is a chat model the strings should be formatted
+            as explained here: https://github.com/openai/openai-python/blob/main/chatml.md. If it is a chat model
+            it can also be a dictionary (or list thereof) as explained here:
+            https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
+        decoding_args: Decoding arguments.
+        model_name: Model name. Can be either in the format of "org/model" or just "model".
+        sleep_time: Time to sleep once the rate-limit is hit.
+        batch_size: Number of prompts to send in a single request. Only for non chat model.
+        max_instances: Maximum number of prompts to decode.
+        max_batches: Maximum number of batches to decode. This argument will be deprecated in the future.
+        return_text: If True, return text instead of full completion object (which contains things like logprob).
+        decoding_kwargs: Additional decoding arguments. Pass in `best_of` and `logit_bias` if you need them.
+
+    Returns:
+        A completion or a list of completions.
+        Depending on return_text, return_openai_object, and decoding_args.n, the completion type can be one of
+            - a string (if return_text is True)
+            - an openai_object.OpenAIObject object (if return_text is False)
+            - a list of objects of the above types (if decoding_args.n > 1)
+    """
+    is_chat_model = "gpt-3.5" in model_name or "gpt-4" in model_name
+    is_single_prompt = isinstance(prompts, (str, dict))
+    if is_single_prompt:
+        prompts = [prompts]
+
+    if max_batches < sys.maxsize:
+        logging.warning(
+            "`max_batches` will be deprecated in the future, please use `max_instances` instead."
+            "Setting `max_instances` to `max_batches * batch_size` for now."
+        )
+        max_instances = max_batches * batch_size
+
+    prompts = prompts[:max_instances]
+    num_prompts = len(prompts)
+    prompt_batches = [
+        prompts[batch_id * batch_size : (batch_id + 1) * batch_size]
+        for batch_id in range(int(math.ceil(num_prompts / batch_size)))
+    ]
+
+    completions = []
+    for batch_id, prompt_batch in tqdm.tqdm(
+        enumerate(prompt_batches),
+        desc="prompt_batches",
+        total=len(prompt_batches),
+    ):
+        batch_decoding_args = copy.deepcopy(decoding_args)  # cloning the decoding_args
+
+        while True:
+            try:
+                shared_kwargs = dict(
+                    model=model_name,
+                    **batch_decoding_args.__dict__,
+                    **decoding_kwargs,
+                )
+                if is_chat_model:
+                    completion_batch = openai.ChatCompletion.create(
+                        messages=[
+                            {"role": "system", "content": "You are a helpful assistant."},
+                            {"role": "user", "content": prompt_batch[0]}
+                        ],
+                        **shared_kwargs
+                    )
+                else:
+                    completion_batch = openai.Completion.create(prompt=prompt_batch, **shared_kwargs)
+
+                choices = completion_batch.choices
+    
+                for choice in choices:
+                    choice["total_tokens"] = completion_batch.usage.total_tokens
+                completions.extend(choices)
+                break
+            except openai.error.OpenAIError as e:
+                logging.warning(f"OpenAIError: {e}.")
+                if "Please reduce your prompt" in str(e):
+                    batch_decoding_args.max_tokens = int(batch_decoding_args.max_tokens * 0.8)
+                    logging.warning(f"Reducing target length to {batch_decoding_args.max_tokens}, Retrying...")
+                else:
+                    logging.warning("Hit request rate limit; retrying...")
+                    time.sleep(sleep_time)  # Annoying rate limit on requests.
+
+    if return_text:
+        completions = [completion.text for completion in completions]
+    if decoding_args.n > 1:
+        # make completions a nested list, where each entry is a consecutive decoding_args.n of original entries.
+        completions = [completions[i : i + decoding_args.n] for i in range(0, len(completions), decoding_args.n)]
+    if is_single_prompt:
+        # Return non-tuple if only 1 input and 1 generation.
+        (completions,) = completions
+    return completions
+
+
+def write_ans_to_file(ans_data, file_prefix, output_dir="./output"):
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    filename = os.path.join(output_dir, file_prefix + ".txt")
+    with open(filename, "w") as f:
+        for ans in ans_data:
+            f.write(ans + "\n")