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import spacy.displacy
import streamlit as st
from flair.models import SequenceTagger
from flair.splitter import SegtokSentenceSplitter
from colorhash import ColorHash
# st.title("Flair NER Demo")
st.set_page_config(layout="centered")
# models to choose from
model_map = {
"find Entities (default)": "ner-large",
"find Entities (18-class)": "ner-ontonotes-large",
"find Parts-of-Speech": "pos-multi",
}
# Block 1: Users can select a model
st.subheader("Select a model")
selected_model_id = st.selectbox("This is a check box",
model_map.keys(),
label_visibility="collapsed",
)
# Block 2: Users can input text
st.subheader("Input your text here")
input_text = st.text_area('Write or Paste Text Below',
value="George was born in Washington.",
height=128,
max_chars=None,
label_visibility="collapsed")
@st.cache(allow_output_mutation=True)
def get_model(model_name):
return SequenceTagger.load(model_map[model_name])
def get_html(html: str):
WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
html = html.replace("\n", " ")
return WRAPPER.format(html)
def color_variant(hex_color, brightness_offset=1):
""" takes a color like #87c95f and produces a lighter or darker variant
taken from: https://chase-seibert.github.io/blog/2011/07/29/python-calculate-lighterdarker-rgb-colors.html
"""
if len(hex_color) != 7:
raise Exception("Passed %s into color_variant(), needs to be in #87c95f format." % hex_color)
rgb_hex = [hex_color[x:x + 2] for x in [1, 3, 5]]
new_rgb_int = [int(hex_value, 16) + brightness_offset for hex_value in rgb_hex]
new_rgb_int = [min([255, max([0, i])]) for i in new_rgb_int] # make sure new values are between 0 and 255
# hex() produces "0x88", we want just "88"
return "#" + "".join([hex(i)[2:] for i in new_rgb_int])
# Block 3: Output is displayed
button_clicked = st.button("**Click here** to tag the input text", key=None)
if button_clicked:
# get a sentence splitter and split text into sentences
splitter = SegtokSentenceSplitter()
sentences = splitter.split(input_text)
# get the model and predict
model = get_model(selected_model_id)
model.predict(sentences)
spacy_display = {"ents": [], "text": input_text, "title": None}
predicted_labels = set()
for sentence in sentences:
for prediction in sentence.get_labels():
spacy_display["ents"].append(
{"start": prediction.data_point.start_position + sentence.start_position,
"end": prediction.data_point.end_position + sentence.start_position,
"label": prediction.value})
predicted_labels.add(prediction.value)
# create colors for each label
colors = {}
for label in predicted_labels:
colors[label] = color_variant(ColorHash(label).hex, brightness_offset=85)
# use displacy to render
html = spacy.displacy.render(spacy_display,
style="ent",
minify=True,
manual=True,
options={
"colors": colors,
},
)
style = "<style>mark.entity { display: inline-block }</style>"
st.subheader("Found entities")
st.write(f"{style}{get_html(html)}", unsafe_allow_html=True)
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