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import gradio as gr
import os
import requests
from spacy import displacy
os.system("python -m spacy download en_core_web_md")
import spacy
options = {"ents": ["Observation",
"Evaluation"],
"colors" : {
"Observation": "#9bddff",
"Evaluation": "#f08080",
}
}
nlp = spacy.load("en_core_web_md")
def postprocess(classifications):
entities = []
for i in range(len(classifications)):
if classifications[i]['entity'] != 0:
if classifications[i]['entity'][0] == 'B':
j = i + 1
while j < len(classifications) and classifications[j]['entity'][0] == 'I':
j += 1
entities.append((classifications[i]['entity'].split('-')[1], classifications[i]['start'],
classifications[j - 1]['end']))
while True:
merged = False
to_remove = []
merged_entities = []
for i in range(len(entities)):
for j in range(i + 1, len(entities)):
if entities[i] != entities[j] and entities[i][0] == entities[j][0] and \
(entities[i][2] == entities[j][1] or entities[i][1] == entities[j][2]):
to_remove.append(entities[i])
to_remove.append(entities[j])
new_start = min(entities[i][1], entities[j][1])
new_end = max(entities[i][2], entities[j][2])
merged_entities.append((entities[i][0], new_start, new_end))
merged = True
break
if merged:
break
for ent in to_remove:
entities.remove(ent)
entities += merged_entities
if not merged:
break
return entities
def set_entities(sentence, entities):
doc = nlp(sentence)
ents = []
for label, start, end in entities:
ents.append(doc.char_span(start, end, label))
try:
doc.ents = ents
except TypeError:
pass
return doc
def apply_ner(input_text_message: str):
endpoint_url = 'https://on1m82uknekghqeh.us-east-1.aws.endpoints.huggingface.cloud'
headers = {
'Authorization': 'Bearer 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',
'Content-Type': 'application/json',
}
json_data = {
'inputs': input_text_message,
}
response = requests.post(endpoint_url, headers=headers, json=json_data)
classifications = response.json()
entities = postprocess(classifications)
doc = set_entities(input_text_message, entities)
displacy_html = displacy.render(doc, style="ent", options=options)
return displacy_html
examples = ['You are dick','Today i broke my leg and my dad is a dick',
'My dad is an asshole and took his anger out on my mom by verbally abusing her',
'He eventually moved on to my brother']
iface = gr.Interface(fn=apply_ner, inputs=gr.inputs.Textbox(lines=5, placeholder="Enter your text here",
label='Check your text for compliance with the NVC rules'),
outputs="html", examples=examples)
iface.launch()
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