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import streamlit as st
import streamlit.components.v1 as components
import requests
import spacy
import hashlib
nlp = spacy.load("en_core_web_md")
# add pipeline (declared through entry_points in setup.py)
nlp.add_pipe("entityfishing")
st.title('Entity Linking Demo')
article = st.text_area('Article to analyze:', value=open("example.txt").read())
seen_entities = []
seen_surnames = []
if st.button('Submit'):
good_ents = []
with st.spinner(text="Analysing..."):
doc = nlp(article)
for ent in doc.ents:
if ent._.kb_qid is None or ent.label_ not in ["ORG", "PERSON", "GPE"] or ent.text in seen_entities:
continue
if ent.label_ == "PERSON":
if len(ent.text.split()) == 1:
# Single name
if ent.text in seen_surnames:
continue
else:
# Multipart name
seen_surnames.append(ent.text.split()[-1])
seen_entities.append(ent.text)
print((ent.text, ent.label_, ent._.kb_qid, ent._.url_wikidata, ent._.nerd_score))
r = requests.get("https://www.wikidata.org/w/api.php?action=wbgetclaims&format=json&property=P18&entity=" + ent._.kb_qid)
data = r.json()["claims"]
if "P18" in data.keys():
data = data["P18"][0]["mainsnak"]
img_name = data["datavalue"]["value"].replace(" ", "_")
img_name_hash = hashlib.md5(img_name.encode("utf-8")).hexdigest()
a = img_name_hash[0]
b = img_name_hash[1]
url= f"https://upload.wikimedia.org/wikipedia/commons/{a}/{a}{b}/{img_name}"
good_ents.append((ent.text, ent.label_, ent._.kb_qid, ent._.url_wikidata, ent._.nerd_score, url))
cols = st.columns(len(good_ents))
for i, ent in enumerate(good_ents):
# st.image(url)
with cols[i]:
components.html(f"<image style='border-radius: 50%;object-fit:cover;width:100px;height:100px' src='{ent[-1]}'/>", height=110, width=110)
st.caption(ent[0])
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