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| import hashlib | |
| import logging | |
| import os | |
| from datetime import datetime | |
| from typing import Dict, Any, List | |
| import gradio as gr | |
| import spacy | |
| from pony.orm import db_session | |
| from spacy import displacy | |
| from app.classificator import Classificator, Classification | |
| from app.models.request import Request | |
| from app.settings import AUTH_ENDPOINT_TOKEN, ENDPOINT_URL, db | |
| class Application: | |
| examples = [ | |
| 'Things are complicated because we still live together but we have separate lives', | |
| 'My dad is a monster and took his anger out on my mom by verbally abusing her and when she left he ' | |
| 'eventually moved on to my brother', | |
| 'A two months ago, she was chatting with some random guy', | |
| 'Not I have a horrid relationship with my brother we’ve never gotten along and probably never will', | |
| 'I was outside trying to leave and he caught me to explain why Im so rude', | |
| ] | |
| def __init__(self, classificator: Classificator, options: Dict[str, Any]): | |
| self.options = options | |
| self.classificator = classificator | |
| self.nlp = spacy.load("en_core_web_md") | |
| def handle(self, input_text: str) -> str: | |
| """ | |
| Handle the input text and return the result as rendered html | |
| """ | |
| if input_text is None or input_text == '': | |
| return '' | |
| classifications = self.classificator.classify(input_text) | |
| request = self.log_request(input_text, classifications) | |
| # TODO: тут надо взять хеш или ид, прокинуть его для формирования кнопок с оценкой | |
| return self.render(input_text, classifications) | |
| def log_request(input_text: str, classifications: List[Classification]) -> Request: | |
| """ | |
| Log the request to the database | |
| """ | |
| return Request( | |
| text=input_text, | |
| hash=hashlib.md5(input_text.encode()).hexdigest(), | |
| created_at=datetime.now(), | |
| updated_at=datetime.now(), | |
| rating=0, | |
| result=[c.dict() for c in classifications] | |
| ) | |
| def render(self, input_text: str, classifications: List[Classification]) -> str: | |
| """ | |
| Render the input text and the classifications as html text with labels | |
| """ | |
| document = self.nlp(input_text) | |
| try: | |
| document.ents = [ | |
| document.char_span(classification.start, classification.end, classification.entity) for | |
| classification in classifications | |
| ] | |
| except Exception as exc: | |
| logging.exception(exc) | |
| return displacy.render(document, style="ent", options=self.options) | |
| def run(self): | |
| iface = gr.Interface( | |
| fn=self.handle, inputs=gr.Textbox( | |
| lines=5, placeholder="Enter your text here", | |
| label='Check your text for compliance with the NVC rules'), | |
| outputs=["html"], examples=self.examples | |
| ) | |
| iface.launch() | |
| if __name__ == '__main__': | |
| os.system("python -m spacy download en_core_web_md") | |
| db.generate_mapping(create_tables=True) | |
| application = Application( | |
| classificator=Classificator( | |
| config={ | |
| 'auth_endpoint_token': AUTH_ENDPOINT_TOKEN, | |
| 'endpoint_url': ENDPOINT_URL | |
| } | |
| ), | |
| options={"ents": ["Observation", "Evaluation"], "colors": {"Observation": "#9bddff", "Evaluation": "#f08080"}} | |
| ) | |
| application.run() | |