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902d14f
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Browse files- handler.py +47 -0
handler.py
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from typing import Any, Dict, List
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from app.pipelines import Pipeline
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from flair.data import Sentence
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from flair.models import SequenceTagger
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class EndpointHandler():
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def __init__(
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self,
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model_id: str,
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):
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self.tagger = SequenceTagger.load(model_id)
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def __call__(self, inputs: str) -> List[Dict[str, Any]]:
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"""
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Args:
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inputs (:obj:`str`):
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a string containing some text
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Return:
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A :obj:`list`:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing :
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- "entity_group": A string representing what the entity is.
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- "word": A substring of the original string that was detected as an entity.
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- "start": the offset within `input` leading to `answer`. context[start:stop] == word
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- "end": the ending offset within `input` leading to `answer`. context[start:stop] === word
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- "score": A score between 0 and 1 describing how confident the model is for this entity.
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"""
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inputs = data.pop("inputs", data)
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sentence: Sentence = Sentence(inputs)
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# Also show scores for recognized NEs
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self.tagger.predict(sentence, label_name="predicted")
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entities = []
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for span in sentence.get_spans("predicted"):
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if len(span.tokens) == 0:
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continue
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current_entity = {
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"entity_group": span.tag,
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"word": span.text,
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"start": span.tokens[0].start_position,
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"end": span.tokens[-1].end_position,
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"score": span.score,
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}
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entities.append(current_entity)
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return entities
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