app.py

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 = []
if st.button('Submit'):
	print(article)
	good_ents = []

	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
		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])