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"""
Reference: https://huggingface.co/spaces/gwf-uwaterloo/acl-spectrum (By Ehsan Khamallo)
"""
import os
import re
import pandas as pd
import plotly.express as px
import streamlit as st
st.set_page_config(layout="wide")
DATA_FILE = "hess_papers_details.json"
st.markdown(
"""
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Permanent+Marker&display=swap" rel="stylesheet">
<style>
.title {
font-family: 'Arial';
font-size: 2.0rem;
}
</style>""",
unsafe_allow_html=True,
)
st.sidebar.write(
"""<center><p class="title">
Clustering on HESS Papers 🌎🌿
</p></center>""",
unsafe_allow_html=True,
)
st.sidebar.write(
"""<p class="text-justify">
A clustered visualization of all papers submitted to the
<a href=https://www.hydrology-and-earth-system-sciences.net/>Hydrology and Earth System Sciences</a> (HESS) conference.
5318 papers are embedded using <a href="https://huggingface.co/allenai/specter2_base">spectre2</a> and reduced with
t-SNE. Papers span from as early as 1997 to 2023.
</p>""",
unsafe_allow_html=True,
)
def to_string_authors(list_of_authors):
if len(list_of_authors) > 5:
return ", ".join(list_of_authors[:5]) + ", et al."
elif len(list_of_authors) > 2:
return ", ".join(list_of_authors[:-1]) + ", and " + list_of_authors[-1]
else:
return " and ".join(list_of_authors)
def load_df(data_file: os.PathLike):
df = pd.read_json(data_file, orient="records")
df["x"] = df["t-SNE1"]
df["y"] = df["t-SNE2"]
df["authors_trimmed"] = df["authors_trimmed"]
#sort dataframe by year
df['year'] = pd.to_datetime(df['year'])
df = df.sort_values('year', ascending=True)
df['year'] = df['year'].dt.strftime('%Y')
df['year'] = df['year'].astype(int)
return df
@st.cache_data
def load_dataframe():
return load_df(DATA_FILE)
DF = load_dataframe()
DF["opacity"] = 0.04
min_year, max_year = DF["year"].min(), DF["year"].max()
with st.sidebar:
author_names = st.text_input("Author names (separated by comma)")
title = st.text_input("Title")
# Work on this
# topics = st.multiselect(
# "Topics",
# ["Topics 1: "],
# ["Topics 2: "],
# )
# start_year, end_year = st.select_slider(
# "Publication year",
# options=[str(y) for y in range(min_year, max_year + 1)],
# value=(str(min_year), str(max_year)),
# )
start_year = int(start_year)
end_year = int(end_year)
df_mask = (DF["year"] >= start_year) & (DF["year"] <= end_year)
if author_names:
authors = [a.strip() for a in author_names.split(",")]
author_mask = DF.authors.apply(
lambda row: all(any(re.match(rf".*{a}.*", x, re.IGNORECASE) for x in row) for a in authors)
)
df_mask = df_mask & author_mask
if title:
df_mask = df_mask & DF.title.apply(lambda x: title.lower() in x.lower())
DF.loc[df_mask, "opacity"] = 1.0
st.write(f"Number of points: {DF[df_mask].shape[0]}")
fig = px.scatter(
DF,
x="x",
y="y",
opacity=DF["opacity"],
color=DF["cluster"],
animation_frame=DF["year"],
width=1000,
height=800,
custom_data=("title", "authors_trimmed", "year"),
color_continuous_scale="haline",
)
fig.update_traces(
hovertemplate="<b>%{customdata[0]}</b><br>%{customdata[1]}<br>%{customdata[2]}<br><i>"
)
fig.update_layout(
showlegend=False,
font=dict(
family="Times New Roman",
size=30,
),
hoverlabel=dict(
align="left",
font_size=14,
font_family="Rockwell",
namelength=-1,
),
)
fig.update_xaxes(title="")
fig.update_yaxes(title="")
fig.update_layout(transition = {'duration': 1000})
st.plotly_chart(fig, use_container_width=True)
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