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paige

Generate tuned research highlights using AI

https://github.com/crvernon/paige

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.2%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Generate tuned research highlights using AI

Basic Info
  • Host: GitHub
  • Owner: crvernon
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 2.5 MB
Statistics
  • Stars: 7
  • Watchers: 1
  • Forks: 6
  • Open Issues: 2
  • Releases: 2
Created almost 3 years ago · Last pushed 11 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

build DOI

highlight

Generate publication highlights using AI

Setting Up OpenAI API Key

To use the OpenAI API, you need to set up an API key and set it as an environment variable.

  1. Obtain your OpenAI API key from the OpenAI website.

  2. Set the API key as an environment variable:

    • On Windows: bash set OPENAI_API_KEY=your_api_key_here
    • On macOS and Linux: bash export OPENAI_API_KEY=your_api_key_here

Replace your_api_key_here with your actual OpenAI API key.

Installation

Clone this repository

Navigate to the directory you want to store this repo in and run:

bash git clone https://github.com/crvernon/highlight.git

To install this Python package in a virtual environment, you can use either pip or Anaconda.

Using pip

  1. Create a virtual environment: bash python -m venv highlight_env

  2. Activate the virtual environment:

    • On Windows: bash highlight_env\Scripts\activate
    • On macOS and Linux: bash source highlight_env/bin/activate

  3. Install the package from the cloned highlight directory: bash pip install .

Using Anaconda

  1. Create a virtual environment: bash conda create --name highlight_env python=3.9

  2. Activate the virtual environment: bash conda activate highlight_env

  3. Install the package from the cloned highlight directory: bash pip install .

Running the App

To run the app using Streamlit, follow these steps:

  1. Ensure your virtual environment is activated.

  2. Run the Streamlit app from the highlight directory: bash streamlit run app.py

Owner

  • Name: Chris Vernon
  • Login: crvernon
  • Kind: user
  • Location: Remote
  • Company: @JGCRI @IMMM-SFA @PNNL @multisectordynamics @openjournals

Data scientist specializing in all things geospatial who spends a lot of time developing open-source software ecosystems and mentoring

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Vernon"
  given-names: "Chris R."
  orcid: "https://orcid.org/0000-0002-3406-6214"
- family-names: "Rice"
  given-names: "Jennie S."
  orcid: "https://orcid.org/0000-0002-7833-9456"
title: "highlight: Generate tuned research highlights using AI"
version: 0.1.1
url: "https://github.com/crvernon/highlight"

GitHub Events

Total
  • Push event: 15
Last Year
  • Push event: 15

Dependencies

.github/workflows/build.yml actions
  • actions/checkout v1 composite
  • actions/setup-python master composite
pyproject.toml pypi
  • docxtpl >=0.16.7
  • openai >=1.35.14
  • pypdf >=3.0.1
  • python-pptx >=0.6.23
  • streamlit >=1.36.0
  • tiktoken >=0.7.0
  • tqdm >=4.66.1