handler.py

from typing import Dict, List, Any
from scipy.special import softmax

from utils import clean_str, clean_str_nopunct
import torch
from transformers import BertTokenizer
from utils import MultiHeadModel, BertInputBuilder, get_num_words

MODEL_CHECKPOINT='ddemszky/uptake-model'

class EndpointHandler():
    def __init__(self, path="."):
        print("Loading models...")
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
        self.input_builder = BertInputBuilder(tokenizer=self.tokenizer)
        self.model = MultiHeadModel.from_pretrained(path, head2size={"nsp": 2})
        self.model.to(self.device)
        self.max_length = 120

    def get_clean_text(self, text, remove_punct=False):
        if remove_punct:
            return clean_str_nopunct(text)
        return clean_str(text)

    def get_prediction(self, instance):
        instance["attention_mask"] = [[1] * len(instance["input_ids"])]
        for key in ["input_ids", "token_type_ids", "attention_mask"]:
            instance[key] = torch.tensor(instance[key]).unsqueeze(0)  # Batch size = 1
            instance[key].to(self.device)

        output = self.model(input_ids=instance["input_ids"],
                       attention_mask=instance["attention_mask"],
                       token_type_ids=instance["token_type_ids"],
                       return_pooler_output=False)
        return output

    def get_uptake_score(self, textA, textB):

        textA = self.get_clean_text(textA, remove_punct=False)
        textB = self.get_clean_text(textB, remove_punct=False)

        instance = self.input_builder.build_inputs([textA], textB,
                                              max_length=self.max_length,
                                              input_str=True)
        output = self.get_prediction(instance)
        uptake_score = softmax(output["nsp_logits"][0].tolist())[1]
        return uptake_score

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            utterances (:obj: `list`)
            parameters (:obj: `dict`)
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        # get inputs
        utterances = data.pop("inputs", data)
        params = data.pop("parameters", None)

        print("EXAMPLES")
        for utt in utterances[:3]:
            print("speaker %s: %s" % (utt["speaker"], utt["text"]))

        print("Running inference on %d examples..." % len(utterances))
        self.model.eval()
        prev_num_words = 0
        prev_text = ""
        uptake_scores = {}
        with torch.no_grad():
            for i, utt in enumerate(utterances):
                if utt["speaker"] == params["speaker_2"] and (prev_num_words >= params["speaker_1_min_num_words"]):
                    uptake_scores[utt["id"]] = self.get_uptake_score(textA=prev_text, textB=utt["text"])
                prev_num_words = get_num_words(utt["text"])
                prev_text = utt["text"]

        return uptake_scores