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·
9455411
1
Parent(s):
e0d30f3
add .env file and dotenv lib
Browse files- .env.template +10 -0
- .github/workflows/daily_pipeline.yaml +1 -1
- README.md +2 -0
- src/requirements.txt → requirements.txt +5 -4
- src/action.py +194 -25
.env.template
ADDED
@@ -0,0 +1,10 @@
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## ArxivDigest- environment seting
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OPENAI_API_KEY=your_api_key
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## ArxivDigest- Envronment seting EMAIL SETTINGS
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SENDGRID_API_KEY=your_api_key
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FROM_EMAIL=your_email
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TO_EMAIL=your_email
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.github/workflows/daily_pipeline.yaml
CHANGED
@@ -23,7 +23,7 @@ jobs:
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r
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- name: Generate Digest
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run: |
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python src/action.py
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Generate Digest
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run: |
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python src/action.py
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README.md
CHANGED
@@ -83,7 +83,9 @@ To locally run the same UI as the Huggign Face space:
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1. Install the requirements in `src/requirements.txt` as well as `gradio`.
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2. Run `python src/app.py` and go to the local URL. From there you will be able to preview the papers from today, as well as the generated digests.
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## ✅ Roadmap
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1. Install the requirements in `src/requirements.txt` as well as `gradio`.
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2. Run `python src/app.py` and go to the local URL. From there you will be able to preview the papers from today, as well as the generated digests.
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3. If you want to run the action locally you can copy .env.template to .env and then set the environment variables in the .env file.
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> **NOTE:** This is an important note! Do not commit your .env file to your repository, it might reveal you API keys.
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## ✅ Roadmap
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src/requirements.txt → requirements.txt
RENAMED
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beautifulsoup4==4.12.2
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pytz==2023.3
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numpy==1.24.2
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openai==0.27.4
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sendgrid==6.10.0
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PyYAML==6.0
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beautifulsoup4==4.12.2
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numpy==1.25.0
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openai==0.27.8
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python-dotenv==1.0.0
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pytz==2023.3
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sendgrid==6.10.0
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tqdm==4.65.0
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src/action.py
CHANGED
@@ -6,12 +6,12 @@ from datetime import date
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import argparse
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import yaml
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import os
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from relevancy import generate_relevance_score, process_subject_fields
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from download_new_papers import get_papers
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-
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# Hackathon quality code. Don't judge too harshly.
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# Feel free to submit pull requests to improve the code.
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"Quantitative Finance": "q-fin",
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"Statistics": "stat",
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"Electrical Engineering and Systems Science": "eess",
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"Economics": "econ"
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}
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physics_topics = {
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"Nuclear Experiment": "nucl-ex",
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"Nuclear Theory": "nucl-th",
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"Physics": "physics",
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"Quantum Physics": "quant-ph"
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}
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# TODO: surely theres a better way
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category_map = {
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"Astrophysics": [
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-
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"General Relativity and Quantum Cosmology": ["None"],
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"High Energy Physics - Experiment": ["None"],
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"High Energy Physics - Lattice": ["None"],
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"High Energy Physics - Phenomenology": ["None"],
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"High Energy Physics - Theory": ["None"],
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"Mathematical Physics": ["None"],
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"Nonlinear Sciences": [
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"Nuclear Experiment": ["None"],
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"Nuclear Theory": ["None"],
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"Physics": [
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"Quantum Physics": ["None"],
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"Mathematics": [
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-
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}
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@@ -83,8 +236,10 @@ def generate_body(topic, categories, interest, threshold):
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raise RuntimeError(f"{category} is not a category of {topic}")
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papers = get_papers(abbr)
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papers = [
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t
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else:
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papers = get_papers(abbr)
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if interest:
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papers,
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query={"interest": interest},
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threshold_score=threshold,
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num_paper_in_prompt=16
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body = "<br><br>".join(
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[
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if hallucination:
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body =
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else:
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body = "<br><br>".join(
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[
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-
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return body
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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args = parser.parse_args()
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with open(args.config, "r") as f:
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config = yaml.safe_load(f)
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@@ -123,8 +292,8 @@ if __name__ == "__main__":
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body = generate_body(topic, categories, interest, threshold)
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with open("digest.html", "w") as f:
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f.write(body)
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if os.environ.get(
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sg = SendGridAPIClient(api_key=os.environ.get(
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from_email = Email(from_email) # Change to your verified sender
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to_email = To(to_email)
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subject = date.today().strftime("Personalized arXiv Digest, %d %b %Y")
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import argparse
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import yaml
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import os
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from dotenv import load_dotenv
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from relevancy import generate_relevance_score, process_subject_fields
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from download_new_papers import get_papers
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# Hackathon quality code. Don't judge too harshly.
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# Feel free to submit pull requests to improve the code.
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"Quantitative Finance": "q-fin",
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"Statistics": "stat",
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"Electrical Engineering and Systems Science": "eess",
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"Economics": "econ",
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}
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physics_topics = {
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"Nuclear Experiment": "nucl-ex",
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"Nuclear Theory": "nucl-th",
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"Physics": "physics",
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"Quantum Physics": "quant-ph",
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}
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# TODO: surely theres a better way
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category_map = {
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"Astrophysics": [
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"Astrophysics of Galaxies",
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"Cosmology and Nongalactic Astrophysics",
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"Earth and Planetary Astrophysics",
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"High Energy Astrophysical Phenomena",
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"Instrumentation and Methods for Astrophysics",
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"Solar and Stellar Astrophysics",
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],
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"Condensed Matter": [
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"Disordered Systems and Neural Networks",
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"Materials Science",
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"Mesoscale and Nanoscale Physics",
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"Other Condensed Matter",
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"Quantum Gases",
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"Soft Condensed Matter",
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"Statistical Mechanics",
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"Strongly Correlated Electrons",
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"Superconductivity",
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],
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"General Relativity and Quantum Cosmology": ["None"],
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"High Energy Physics - Experiment": ["None"],
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"High Energy Physics - Lattice": ["None"],
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"High Energy Physics - Phenomenology": ["None"],
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"High Energy Physics - Theory": ["None"],
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"Mathematical Physics": ["None"],
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"Nonlinear Sciences": [
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"Adaptation and Self-Organizing Systems",
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"Cellular Automata and Lattice Gases",
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"Chaotic Dynamics",
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"Exactly Solvable and Integrable Systems",
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"Pattern Formation and Solitons",
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],
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"Nuclear Experiment": ["None"],
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"Nuclear Theory": ["None"],
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"Physics": [
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"Accelerator Physics",
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"Applied Physics",
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"Atmospheric and Oceanic Physics",
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"Atomic and Molecular Clusters",
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"Atomic Physics",
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"Biological Physics",
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"Chemical Physics",
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"Classical Physics",
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"Computational Physics",
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"Data Analysis, Statistics and Probability",
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"Fluid Dynamics",
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"General Physics",
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"Geophysics",
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"History and Philosophy of Physics",
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"Instrumentation and Detectors",
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"Medical Physics",
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"Optics",
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"Physics and Society",
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"Physics Education",
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"Plasma Physics",
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"Popular Physics",
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"Space Physics",
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],
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"Quantum Physics": ["None"],
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"Mathematics": [
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"Algebraic Geometry",
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"Algebraic Topology",
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"Analysis of PDEs",
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"Category Theory",
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"Classical Analysis and ODEs",
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"Combinatorics",
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"Commutative Algebra",
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"Complex Variables",
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"Differential Geometry",
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"Dynamical Systems",
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"Functional Analysis",
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"General Mathematics",
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"General Topology",
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"Geometric Topology",
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"Group Theory",
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"History and Overview",
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"Information Theory",
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"K-Theory and Homology",
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"Logic",
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"Mathematical Physics",
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"Metric Geometry",
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"Number Theory",
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"Numerical Analysis",
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"Operator Algebras",
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"Optimization and Control",
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"Probability",
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"Quantum Algebra",
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"Representation Theory",
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"Rings and Algebras",
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"Spectral Theory",
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"Statistics Theory",
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"Symplectic Geometry",
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],
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"Computer Science": [
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"Artificial Intelligence",
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"Computation and Language",
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"Computational Complexity",
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"Computational Engineering, Finance, and Science",
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"Computational Geometry",
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"Computer Science and Game Theory",
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"Computer Vision and Pattern Recognition",
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"Computers and Society",
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"Cryptography and Security",
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"Data Structures and Algorithms",
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"Databases",
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"Digital Libraries",
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"Discrete Mathematics",
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"Distributed, Parallel, and Cluster Computing",
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"Emerging Technologies",
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"Formal Languages and Automata Theory",
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"General Literature",
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"Graphics",
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"Hardware Architecture",
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"Human-Computer Interaction",
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"Information Retrieval",
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"Information Theory",
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"Logic in Computer Science",
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"Machine Learning",
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"Mathematical Software",
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"Multiagent Systems",
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"Multimedia",
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"Networking and Internet Architecture",
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"Neural and Evolutionary Computing",
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"Numerical Analysis",
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"Operating Systems",
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"Other Computer Science",
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"Performance",
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"Programming Languages",
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"Robotics",
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"Social and Information Networks",
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"Software Engineering",
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"Sound",
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"Symbolic Computation",
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"Systems and Control",
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],
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"Quantitative Biology": [
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"Biomolecules",
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"Cell Behavior",
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"Genomics",
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"Molecular Networks",
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"Neurons and Cognition",
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"Other Quantitative Biology",
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"Populations and Evolution",
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"Quantitative Methods",
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"Subcellular Processes",
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"Tissues and Organs",
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],
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"Quantitative Finance": [
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"Computational Finance",
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"Economics",
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"General Finance",
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"Mathematical Finance",
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"Portfolio Management",
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"Pricing of Securities",
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"Risk Management",
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"Statistical Finance",
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"Trading and Market Microstructure",
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],
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"Statistics": [
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"Applications",
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"Computation",
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"Machine Learning",
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"Methodology",
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"Other Statistics",
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"Statistics Theory",
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],
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"Electrical Engineering and Systems Science": [
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"Audio and Speech Processing",
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"Image and Video Processing",
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"Signal Processing",
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"Systems and Control",
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],
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"Economics": ["Econometrics", "General Economics", "Theoretical Economics"],
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}
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raise RuntimeError(f"{category} is not a category of {topic}")
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papers = get_papers(abbr)
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papers = [
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t
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for t in papers
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if bool(set(process_subject_fields(t["subjects"])) & set(categories))
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]
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else:
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papers = get_papers(abbr)
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if interest:
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papers,
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query={"interest": interest},
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threshold_score=threshold,
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num_paper_in_prompt=16,
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)
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body = "<br><br>".join(
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[
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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"]}'
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for paper in relevancy
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]
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)
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if hallucination:
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body = (
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"Warning: the model hallucinated some papers. We have tried to remove them, but the scores may not be accurate.<br><br>"
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+ body
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)
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else:
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body = "<br><br>".join(
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[
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f'Title: <a href="{paper["main_page"]}">{paper["title"]}</a><br>Authors: {paper["authors"]}'
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for paper in papers
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]
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)
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return body
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if __name__ == "__main__":
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# Load the .env file.
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load_dotenv()
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parser = argparse.ArgumentParser()
|
277 |
+
parser.add_argument(
|
278 |
+
"--config", help="yaml config file to use", default="config.yaml"
|
279 |
+
)
|
280 |
args = parser.parse_args()
|
281 |
with open(args.config, "r") as f:
|
282 |
config = yaml.safe_load(f)
|
|
|
292 |
body = generate_body(topic, categories, interest, threshold)
|
293 |
with open("digest.html", "w") as f:
|
294 |
f.write(body)
|
295 |
+
if os.environ.get("SENDGRID_API_KEY", None):
|
296 |
+
sg = SendGridAPIClient(api_key=os.environ.get("SENDGRID_API_KEY"))
|
297 |
from_email = Email(from_email) # Change to your verified sender
|
298 |
to_email = To(to_email)
|
299 |
subject = date.today().strftime("Personalized arXiv Digest, %d %b %Y")
|