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| import os | |
| import gradio as gr | |
| from gradio import FlaggingCallback | |
| from gradio.components import IOComponent | |
| from transformers import pipeline | |
| from typing import List, Optional, Any | |
| import argilla as rg | |
| import os | |
| nlp = pipeline("ner", model="deprem-ml/deprem-ner") | |
| examples = [ | |
| ["Lütfen yardım Akevler mahallesi Rüzgar sokak Tuncay apartmanı zemin kat Antakya akrabalarım göçük altında #hatay #Afad"] | |
| ] | |
| def create_record(input_text, feedback): | |
| # define the record status based on feedback | |
| # default means it needs to be reviewed --> "Incorrect" or "Ambiguous" | |
| # validated means it's correct and has been checked --> "Correct" | |
| status = "Validated" if feedback == "Doğru" else "Default" | |
| # Making the prediction | |
| predictions = nlp(input_text, aggregation_strategy="first") | |
| # Creating the predicted entities as a list of tuples (entity, start_char, end_char, score) | |
| prediction = [(pred["entity_group"], pred["start"], pred["end"], pred["score"]) for pred in predictions] | |
| # Create word tokens | |
| batch_encoding = nlp.tokenizer(input_text) | |
| word_ids = sorted(set(batch_encoding.word_ids()) - {None}) | |
| words = [] | |
| for word_id in word_ids: | |
| char_span = batch_encoding.word_to_chars(word_id) | |
| words.append(input_text[char_span.start:char_span.end]) | |
| # Building a TokenClassificationRecord | |
| record = rg.TokenClassificationRecord( | |
| text=input_text, | |
| tokens=words, | |
| prediction=prediction, | |
| prediction_agent="deprem-ml/deprem-ner", | |
| status=status, | |
| metadata={"feedback": feedback} | |
| ) | |
| print(record) | |
| return record | |
| class ArgillaLogger(FlaggingCallback): | |
| def __init__(self, api_url, api_key, dataset_name): | |
| rg.init(api_url=api_url, api_key=api_key) | |
| self.dataset_name = dataset_name | |
| def setup(self, components: List[IOComponent], flagging_dir: str): | |
| pass | |
| def flag( | |
| self, | |
| flag_data: List[Any], | |
| flag_option: Optional[str] = None, | |
| flag_index: Optional[int] = None, | |
| username: Optional[str] = None, | |
| ) -> int: | |
| text = flag_data[0] | |
| inference = flag_data[1] | |
| rg.log(name=self.dataset_name, records=create_record(text, flag_option)) | |
| gr.Interface.load( | |
| "models/deprem-ml/deprem-ner", | |
| examples=examples, | |
| title = "NER Adres Aktif Öğrenme Arayüzü", | |
| description = "Aşağıda veri girişi yapıp modelin çıktısına göre Doğru/Yanlış/Belirsiz olarak işaretleyerek modelimizi değerlendirmemize yardımcı olabilirsiniz. Not: flag'lere bir kez tıklamanız yeterlidir. Şu an arayüzü flag alındığında size feedback verecek şekilde düzeltiyoruz. ", | |
| allow_flagging="manual", | |
| flagging_callback=ArgillaLogger( | |
| api_url="https://sandbox.argilla.io", | |
| api_key=os.getenv("TEAM_API_KEY"), | |
| dataset_name="ner-flags" | |
| ), | |
| flagging_options=["Doğru", "Yanlış", "Belirsiz"] | |
| ).launch() |