Initial commit

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

LICENSE

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+MIT License
+
+Copyright (c) 2022 Chris McComb
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+title: Cite Diversely
+emoji: 🎓
+colorFrom: orange
+colorTo: gray
+sdk: streamlit
+app_file: app.py
+pinned: false
+---

app.py

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+# This is our main interface library
+# For main things
+import types
+
+import bibtexparser
+import csv
+import gender_guesser.detector
+import nameparser
+import operator
+import os
+import pandas
+import pathlib
+import pickle
+import plotly.express
+import streamlit
+import st_aggrid
+
+
+class References(object):
+    def __init__(self, reference_text):
+        self.gender_options = ['male', 'mostly_male', 'andy', 'mostly_female', "female", "unknown",
+                               "first_name_initial"]
+        self.gender_results = {key: 0 for key in self.gender_options}
+        self.race_options = ['pctwhite', 'pctblack', 'pctapi', 'pctaian', 'pct2prace', 'pcthispanic', 'race_unknown']
+        self.ethnicity_results = {key: 0 for key in self.race_options}
+        self.raw_results = {}
+
+        pickle_path = pathlib.Path(__file__).parent / 'data' / 'ethnicity_lookup.p'
+        csv_path = pathlib.Path(__file__).parent / 'data' / 'Names_2010Census.csv'
+
+        # Load data
+        if os.path.isfile(pickle_path):
+            self.ethnicity_lookup = pickle.load(open(pickle_path, 'rb'))
+        else:
+            self.ethnicity_lookup = {}
+            with open(csv_path) as csv_file:
+                reader = csv.DictReader(csv_file)
+                for row in reader:
+                    self.ethnicity_lookup[row['name']] = {}
+                    for race in self.race_options[:-1]:
+                        try:
+                            value = float(row[race])
+                        except ValueError:
+                            value = 0
+                        self.ethnicity_lookup[row['name']][race] = value
+            pickle.dump(self.ethnicity_lookup, open(pickle_path, 'wb'))
+
+        # Parse names from input
+        self.reference_text = reference_text
+        self.references = bibtexparser.loads(reference_text)
+        self.first_names = []
+        self.last_names = []
+        self.raw_results = {'title': []}
+        for paper in self.references.entries:
+            if "author" in paper:
+                authors = paper["author"].split(' and ')
+                for person in authors:
+                    self.raw_results['title'].append(paper['title'])
+                    name = nameparser.HumanName(person)
+                    self.first_names.append(name.first)
+                    self.last_names.append(name.last)
+        self.raw_results['first_name'] = self.first_names
+        self.raw_results['last_name'] = self.last_names
+
+    def infer_ethnicity(self):
+        # Get ethnicity
+        most_likely_race = []
+        for name in self.last_names:
+            if name.upper() in self.ethnicity_lookup:
+                rr = max(self.ethnicity_lookup[name.upper()].items(), key=operator.itemgetter(1))[0]
+                most_likely_race.append(rr)
+            else:
+                most_likely_race.append('race_unknown')
+        self.raw_results['most_likely_race'] = most_likely_race
+
+        for i in most_likely_race:
+            self.ethnicity_results[i] = self.ethnicity_results.get(i, 0) + 1
+
+    def infer_gender(self):
+        # Get gender
+        most_likely_gender = []
+        d = gender_guesser.detector.Detector()
+        for name in self.first_names:
+            if (len(name) == 2 and name[1] == '.') or len(name) == 1:
+                most_likely_gender.append("first_name_initial")
+            else:
+                most_likely_gender.append(d.get_gender(name))
+        self.raw_results['most_likely_gender'] = most_likely_gender
+
+        for i in most_likely_gender:
+            self.gender_results[i] = self.gender_results.get(i, 0) + 1
+
+
+label_to_gender = {'male': "Very Likely Male",
+                   'mostly_male': "Likely Male",
+                   'andy': "Hard to Tell",
+                   'mostly_female': "Likely Female",
+                   "female": "Very Likely Female",
+                   "unknown": "Unknown (model inconclusive)",
+                   "first_name_initial": "Unknown (first name initial only)"}
+
+label_to_ethnicity = {'pctwhite': 'White',
+                      'pctblack': 'Black',
+                      'pctapi': 'Asian or Pacific Islander',
+                      'pctaian': 'American Indian or Alaskan Native',
+                      'pct2prace': 'Two or more races',
+                      'pcthispanic': 'Hispanic',
+                      'race_unknown': 'Unknown (not found in database)'}
+
+ethnicity_to_label = {v: k for k, v in label_to_ethnicity.items()}
+gender_to_label = {v: k for k, v in label_to_gender.items()}
+
+
+def make_table():
+    if 'table_data' in streamlit.session_state:
+        df = streamlit.session_state['table_data']
+    else:
+        refs = References(streamlit.session_state.bib)
+        refs.infer_gender()
+        refs.infer_ethnicity()
+
+        df = pandas.DataFrame(refs.raw_results["first_name"], columns=["First Name"])
+        df = df.join(pandas.DataFrame(refs.raw_results["last_name"], columns=["Last Name"]))
+        df = df.join(pandas.DataFrame([label_to_ethnicity[x] for x in refs.raw_results["most_likely_race"]],
+                                      columns=["Most Likely Ethnicity"]))
+        df = df.join(pandas.DataFrame([label_to_gender[x] for x in refs.raw_results["most_likely_gender"]],
+                                      columns=["Most Likely Gender"]))
+        df = df.join(pandas.DataFrame(refs.raw_results["title"], columns=["Title"]))
+        df = df.sort_values(["Last Name", "First Name"])
+        df = df.reset_index(drop=True)
+
+    gb = st_aggrid.GridOptionsBuilder.from_dataframe(df)
+    gb.configure_default_column(editable=True)
+
+    gb.configure_column('Most Likely Ethnicity',
+                        cellEditor='agRichSelectCellEditor',
+                        cellEditorParams={'values': list(label_to_ethnicity.values())},
+                        cellEditorPopup=True
+                        )
+
+    gb.configure_column('Most Likely Gender',
+                        cellEditor='agRichSelectCellEditor',
+                        cellEditorParams={'values': list(label_to_gender.values())},
+                        cellEditorPopup=True
+                        )
+
+    gb.configure_column('Title',
+                        editable=False
+                        )
+
+    # gb.configure_grid_options(enableRangeSelection=True)
+
+    response = st_aggrid.AgGrid(
+        data=df,
+        gridOptions=gb.build(),
+        fit_columns_on_grid_load=True,
+    )
+
+    streamlit.session_state['table_data'] = response.data
+    if response.column_state:
+        streamlit.experimental_rerun()
+
+
+# Define a function for addition
+def make_results():
+    data = streamlit.session_state['table_data']
+    refs = types.SimpleNamespace(
+        ethnicity_results=data['Most Likely Ethnicity'].value_counts().to_dict(),
+        gender_results=data['Most Likely Gender'].value_counts().to_dict(),
+    )
+
+    plt1 = plotly.express.pie(
+        names=list(refs.ethnicity_results.keys()),
+        values=refs.ethnicity_results.values(),
+        hole=0.5,
+    )
+    plt2 = plotly.express.pie(
+        names=list(refs.gender_results.keys()),
+        values=refs.gender_results.values(),
+        hole=0.5,
+    )
+    plt3 = plotly.express.pie(
+        names=list(refs.gender_results.keys()),
+        values=refs.gender_results.values(),
+        hole=0.5,
+    )
+    plt1.update_layout(legend=dict(orientation="h"))
+    plt2.update_layout(legend=dict(orientation="h"))
+    plt3.update_layout(legend=dict(orientation="h"))
+
+    tab1, tab2, tab3 = streamlit.tabs(["Ethnicity", "Gender", "Accuracy"])
+
+    with tab1:
+        streamlit.plotly_chart(plt1, use_container_width=True)
+    with tab2:
+        streamlit.plotly_chart(plt2, use_container_width=True)
+    with tab2:
+        streamlit.plotly_chart(plt3, use_container_width=True)
+
+
+streamlit.title("Welcome, and thank you")
+streamlit.markdown("""Simply put, many people often cite people that are like them. This is a problem because academia has historically been white male dominated, leading to the suppression of marginalized voices. If your citations are biased towards people who look like you, then you are missing out on high-quality work.
+    
+Its important to note that using this site is not a replacement for truly being diligent and engaged in citing diverse voices. Rather, this site is just a place to start, and hopefully the first step in your journey of citing more diversely. To learn more about your duty to dismantle institutional oppression through your citation practices, read up here:
+    
+- [Cite Black Women](https://www.citeblackwomencollective.org)
+- [The Racial Politics of Citation](https://www.insidehighered.com/advice/2018/04/27/racial-exclusions-scholarly-citations-opinion")
+- [Inclusive Citation: How Diverse Are Your References?](https://blog.mahabali.me/writing/inclusive-citation-how-diverse-are-your-references/")
+  
+""")
+
+streamlit.markdown("To use our tool, copy and paste your references in the box below and click on the "
+                   "`Analyze` button.")
+
+filler = """@article{Raina2019,
+    author = {Raina, Ayush and McComb, Christopher and Cagan, Jonathan},
+    title = {Learning to Design From Humans: Imitating Human Designers Through Deep Learning},
+    journal = {Journal of Mechanical Design},
+    volume = {141},
+    number = {11},
+    year = {2019},
+    month = {09},
+    issn = {1050-0472},
+    doi = {10.1115/1.4044256}
+}
+
+@article{Williams2019,
+    author = {Williams, Glen and Meisel, Nicholas A. and Simpson, Timothy W. and McComb, Christopher},
+    title = {Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing},
+    journal = {Journal of Mechanical Design},
+    volume = {141},
+    number = {11},
+    year = {2019},
+    month = {09},
+    issn = {1050-0472},
+    doi = {10.1115/1.4044199}
+}"""
+if "bib" in streamlit.session_state:
+    filler = streamlit.session_state["bib"]
+
+streamlit.text_area(".bibtex only for now, sorry!", filler, key="bib", height=250)
+details = streamlit.sidebar
+details.selectbox("Gender Inference Model", ("gender_guesser", "genderComputer"))
+details.selectbox("Ethnicity Inference Model", ("ethnicolr - census data",
+                                                  "ethnicolr - wikipedia data",
+                                                  "ethnicolr - North Carolina data",
+                                                  "ethnicolr - Florida registration data"))
+
+
+
+placeholder = streamlit.empty()
+time_to_analyze = placeholder.button("Analyze")
+if time_to_analyze or 'already_analyzed' in streamlit.session_state:
+    streamlit.session_state['already_analyzed'] = True
+    placeholder.empty()
+    with streamlit.spinner("Analyzing..."):
+        streamlit.markdown("""This table display a tabular version of your results. You can also edit the inferred 
+        ethnicity and gender to improve the accuracy of results.
+                            """)
+        make_table()
+
+        placeholder2 = streamlit.empty()
+        time_to_plot = placeholder2.button("Plot")
+        if time_to_plot or 'already_plotted' in streamlit.session_state:
+            streamlit.session_state['already_plotted'] = True
+            placeholder2.empty()
+            with streamlit.spinner("Plotting..."):
+                streamlit.markdown("These tabs summarize your results with a variety of visualizations and statistics.")
+                make_results()

requirements.txt

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+bibtexparser
+gender_guesser
+nameparser
+pandas
+pathlib
+plotly
+streamlit
+streamlit-aggrid