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from typing import Dict, List, Any |
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer |
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import torch |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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tokenizer = AutoTokenizer.from_pretrained(path) |
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model = AutoModelForCausalLM.from_pretrained( |
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path, |
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return_dict=True, |
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low_cpu_mem_usage=True, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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trust_remote_code=True, |
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) |
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self.pipeline = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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max_new_tokens=2, |
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temperature=0.1, |
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device_map="auto", |
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) |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str`) |
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date (:obj: `str`) |
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Return: |
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A :obj:`list` | `dict`: will be serialized and returned |
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""" |
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inputs = data.get("inputs",data) |
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date = data.get("date", None) |
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prompt = f"""Clasifica el texto con la etiquta "1" si hay ideación/comportamiento suicida y la etiqueta "0" en otro caso, retorna la respuesta como la correspondiente etiqueta. |
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texto: {inputs} |
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etiqueta: """.strip() |
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outputs = self.pipeline(prompt) |
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pred = outputs[0]["generated_text"].split("etiqueta: ")[-1].strip() |
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label = "intencion_suicida" if pred == "1" else "no_intencion_suicida" |
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return [{"input": inputs , "clasiffication": pred, "label" : label }] |
