forked from mediacloud/news-search-api
/
ui.py
executable file
·160 lines (136 loc) · 5.06 KB
/
ui.py
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#!/usr/bin/env python3
import os
import random
from urllib.parse import quote_plus
import altair as alt
import matplotlib.pyplot as plt
import pandas as pd
import requests
import streamlit as st
from wordcloud import WordCloud
from utils import env_to_list, load_config
config = load_config()
config["title"] = (
os.getenv("TITLE", config.get("title", "Collection Search API")) + " Explorer"
)
config["apiurl"] = os.getenv(
"APIURL", config.get("apiurl", "http://localhost:8000/v1")
).rstrip("/")
config["termfields"] = env_to_list("TERMFIELDS") or config.get("termfields", [])
config["termaggrs"] = env_to_list("TERMAGGRS") or config.get("termaggrs", [])
config["maxwc"] = int(os.getenv("MAXWC", config.get("maxwc", 30)))
st.set_page_config(page_title=config["title"], layout="wide")
st.title(config["title"])
# @st.cache(ttl=300)
def load_data(cname, qstr, ep="search/overview"):
r = requests.get(
f"{config['apiurl']}/{cname}/{ep}?q={quote_plus(qstr)}", timeout=60
)
if r.ok:
return r.json()
return None
def load_collections():
r = requests.get(f"{config['apiurl']}/collections", timeout=60)
if r.ok:
return r.json()
return None
COLLECTIONS = load_collections()
qp = st.experimental_get_query_params()
for p in ("col", "q"):
if p not in st.session_state and qp.get(p):
st.session_state[p] = qp.get(p, [""])[0]
cols = st.columns([20, 80])
col = cols[0].selectbox("Collection", COLLECTIONS, key="col")
q = cols[1].text_input("Search Query", key="q", placeholder="covid -vaccine title:usa")
if not q or not col:
st.stop()
st.experimental_set_query_params(**st.session_state) # type: ignore [misc]
d = load_data(col, q)
if not d:
st.warning("No results returned!")
st.stop()
ov = {
"total": d["total"],
"topdomains": pd.DataFrame(d["topdomains"].items(), columns=["Domain", "Articles"]),
"toptlds": pd.DataFrame(d["toptlds"].items(), columns=["TLD", "Articles"]),
"toplangs": pd.DataFrame(d["toplangs"].items(), columns=["Language", "Articles"]),
"dailycounts": pd.DataFrame(d["dailycounts"].items(), columns=["Date", "Articles"]),
"matches": d["matches"],
}
cols = st.columns(4)
cols[0].metric("Hits", f"{ov['total']:,}")
cols[1].metric(
"Languages", f"{'100+' if len(ov['toplangs'])>=100 else len(ov['toplangs'])}"
)
cols[2].metric(
"Domains", f"{'100+' if len(ov['topdomains'])>=100 else len(ov['topdomains'])}"
)
cols[3].metric("Days", f"{len(ov['dailycounts']):,}")
tbs = st.tabs(["Top Hits", "Data"])
res = [
"Title | Domain | Published | Archived | Language",
":---|:---|:---:|:---:|:---:",
]
for m in ov["matches"]:
t = m.get("article_title", "UNKNOWN").replace("|", "|")
res.append(
" | ".join(
[
f"[{t}]({m.get('archive_playback_url') or '#'})",
f"`{m.get('canonical_domain') or '~'}` | `{m.get('publication_date') or '~'}`",
f"`{(m.get('capture_time') or '~')[:10]}` | `{m.get('language') or '~'}`",
]
)
)
tbs[0].write("\n".join(res))
tbs[1].write(ov["matches"])
tbs = st.tabs(["Temporal Attention", "Data"])
ov["dailycounts"]["Day"] = ov["dailycounts"]["Date"] + "T12:00:00Z"
c = (
alt.Chart(ov["dailycounts"], height=250)
.mark_line(point=alt.OverlayMarkDef(color="#e74c3c"))
.encode(x="Day:T", y="Articles:Q", tooltip=["Day:T", "Articles"])
.interactive(bind_y=False)
.configure_axisX(grid=False)
)
tbs[0].altair_chart(c, use_container_width=True)
tbs[1].write(ov["dailycounts"][["Date", "Articles"]])
fmap = {"Domain": "topdomains", "TLD": "toptlds", "Language": "toplangs"}
cols = st.columns(len(fmap))
for i, (k, v) in enumerate(fmap.items()):
with cols[i]:
tbs = st.tabs([f"Top {k}s", "Data"])
c = (
alt.Chart(ov[v].head(20), height=300)
.mark_bar()
.encode(
x="Articles:Q",
y=alt.Y(f"{k}:N", sort="-x"),
tooltip=[f"{k}:N", "Articles:Q"],
)
)
tbs[0].altair_chart(c, use_container_width=True)
tbs[1].write(ov[v])
for fld in config["termfields"]:
cols = st.columns(3)
for i, aggr in enumerate(config["termaggrs"]):
with cols[i]:
tbs = st.tabs([f"{aggr} {fld} terms".title(), "Data"])
tt = load_data(col, q, f"terms/{fld}/{aggr}")
if tt:
sample = tt
if len(tt) > config["maxwc"]:
if aggr == "rare":
sample = dict(random.sample(list(tt.items()), config["maxwc"]))
else:
sample = dict(list(tt.items())[: config["maxwc"]])
wc = WordCloud(background_color="white")
wc.generate_from_frequencies(sample)
fig, ax = plt.subplots()
ax.imshow(wc)
ax.axis("off")
tbs[0].pyplot(fig)
tbs[1].write(pd.DataFrame(tt.items(), columns=["Term", "Frequency"]))
else:
tbs[0].info("No related terms found!")
tbs[1].info("No related terms found!")