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import gradio as gr
from tner import TransformersNER
from spacy import displacy
model = TransformersNER("tner/roberta-large-ontonotes5")
# DUMMY = {
# 'prediction': [['B-person', 'I-person', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-location']],
# 'probability': [[0.9967652559280396, 0.9994561076164246, 0.9986955523490906, 0.9947081804275513, 0.6129112243652344, 0.9984312653541565, 0.9868122935295105, 0.9983410835266113, 0.9995284080505371, 0.9838910698890686]],
# 'input': [['Jacob', 'Collier', 'is', 'a', 'Grammy', 'awarded', 'English', 'artist', 'from', 'London']],
# 'entity_prediction': [[
# {'type': 'person', 'entity': ['Jacob', 'Collier'], 'position': [0, 1], 'probability': [0.9967652559280396, 0.9994561076164246]},
# {'type': 'location', 'entity': ['London'], 'position': [9], 'probability': [0.9838910698890686]}
# ]]
# }
examples = [
"Jacob Collier is a Grammy awarded artist from England.",
"When Sebastian Thrun PERSON started working on self-driving cars at Google ORG in 2007 DATE , few people outside of the company took him seriously.",
"But Google ORGis starting from behind. The company made a late push into hardware, and Apple ORG’s Siri, available on iPhones, and Amazon ORG’s Alexa software, which runs on its Echo and Dot devices, have clear leads in consumer adoption."
]
def predict(text):
output = model.predict([text])
tokens = output['input'][0]
def retain_char_position(p):
if p == 0:
return 0
return len(' '.join(tokens[:p])) + 1
doc = {
"text": text,
"ents": [{
"start": retain_char_position(entity['position'][0]),
"end": retain_char_position(entity['position'][-1]) + 1 + len(entity['entity'][-1]),
"label": entity['type']
} for entity in output['entity_prediction'][0]],
"title": None
}
html = displacy.render(doc, style="ent", page=True, manual=True, minify=True)
html = (
"<div style='max-width:100%; max-height:360px; overflow:auto'>"
+ html
+ "</div>"
)
return html
iface = gr.Interface(
fn=predict,
inputs=gr.inputs.Textbox(
lines=5,
placeholder="Input Sentence",
default=examples[0],
examples=examples
),
outputs="html",
)
iface.launch()
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