E-Hospital
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df17865
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Parent(s):
f4a9a7f
Upload handler.py
Browse files- handler.py +35 -0
handler.py
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from typing import Dict, List, Any
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
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import torch
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class EndpointHandler:
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def __init__(self, path=""):
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# load model and processor from path
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self.model = AutoModelForSeq2SeqLM.from_pretrained(path, device_map="auto")
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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self.pipeline = pipeline(task="text-generation", tokenizer=self.tokenizer, device=0, device_map="auto", framework="pt", model=self.model, max_length=512)
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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"""
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Args:
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data (:obj:):
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includes the deserialized image file as PIL.Image
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"""
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# process input
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", None)
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# preprocess
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input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
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# pass inputs with all kwargs in data
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if parameters is not None:
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prediction = self.pipeline(inputs, device=0, **parameters)
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else:
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prediction = self.pipeline(inputs, device=0)
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# postprocess the prediction
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prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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return [{"generated_text": prediction}]
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