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| 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 = "data/aclanthology2016-23_specter2_base.json" | |
| THEMES = {"cluster": "fall", "year": "mint", "source": "phase"} | |
| st.markdown( | |
| """ | |
| <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/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: 'Permanent Marker', cursive; | |
| font-size: 2.0rem; | |
| } | |
| </style>""", | |
| unsafe_allow_html=True, | |
| ) | |
| st.sidebar.write( | |
| """<center><p class="title"> | |
| acl-spectrum | |
| </p></center>""", | |
| unsafe_allow_html=True, | |
| ) | |
| st.sidebar.write( | |
| """<p class="text-justify"> | |
| An interactive t-SNE visualization of <a href="https://huggingface.co/allenai/specter2_base">spectre2</a> embeddings | |
| featuring over 12K papers (titles and abstracts) from the <a href="https://aclanthology.org/">ACL Anthology</a> | |
| spanning 2016 to 2023. | |
| For more details, check out our <a href="https://huggingface.co/spaces/gwf-uwaterloo/acl-spectrum/blob/main/README.md">README</a> | |
| and our step-by-step guide <a href="https://huggingface.co/spaces/gwf-uwaterloo/acl-spectrum/blob/main/scipapers_scatter.ipynb">here</a>. | |
| </p>""", | |
| unsafe_allow_html=True, | |
| ) | |
| st.sidebar.markdown( | |
| "Happy exploring! :rocket::rocket:" | |
| ) | |
| 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["point2d"].apply(lambda x: x[0]) | |
| df["y"] = df["point2d"].apply(lambda x: x[1]) | |
| df["authors_trimmed"] = df.authors.apply( | |
| lambda row: to_string_authors( | |
| [(x[x.index(",") + 1 :].strip() + " " + x.split(",")[0].strip()) if "," in x else x for x in row] | |
| ) | |
| ) | |
| if "publication_type" in df.columns: | |
| df["type"] = df["publication_type"] | |
| df = df.drop(columns=["point2d", "publication_type"]) | |
| else: | |
| df = df.drop(columns=["point2d"]) | |
| return df | |
| 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: | |
| venues = st.multiselect( | |
| "Venues", | |
| ["ACL", "EMNLP", "NAACL", "TACL"], | |
| ["ACL", "EMNLP", "NAACL", "TACL"], | |
| ) | |
| 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)), | |
| ) | |
| author_names = st.text_input("Author names (separated by comma)") | |
| title = st.text_input("Title") | |
| start_year = int(start_year) | |
| end_year = int(end_year) | |
| df_mask = (DF["year"] >= start_year) & (DF["year"] <= end_year) | |
| if 0 < len(venues) < 4: | |
| selected_venues = [v.lower() for v in venues] | |
| df_mask = df_mask & DF["source"].isin(selected_venues) | |
| elif not venues: | |
| st.write(":red[Please select a venue]") | |
| 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]}") | |
| color = st.selectbox("Color", ("cluster", "year", "source")) | |
| fig = px.scatter( | |
| DF, | |
| x="x", | |
| y="y", | |
| opacity=DF["opacity"], | |
| color=color, | |
| width=1000, | |
| height=800, | |
| custom_data=("title", "authors_trimmed", "year", "source", "type"), | |
| color_continuous_scale=THEMES[color], | |
| ) | |
| fig.update_traces( | |
| hovertemplate="<b>%{customdata[0]}</b><br>%{customdata[1]}<br>%{customdata[2]}<br><i>%{customdata[3]}</i>" | |
| ) | |
| fig.update_layout( | |
| # margin=dict(l=10, r=10, t=10, b=10), | |
| 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="") | |
| st.plotly_chart(fig, use_container_width=True) | |