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How to use finished_processing method in localstack

Best Python code snippet using localstack_python

Video.py

Source:Video.py

1from typing import Iterable2from .. import db3from .BaseModel import BaseModel4import enum5class VideoType(str, enum.Enum):6    Trailer = "Trailer"7    Reaction = "Reaction"8class Video(BaseModel):9    """10    Video Model11    """12    __tablename__ = 'Video'13    id = db.Column(db.String(64), primary_key=True)14    type = db.Column(db.Enum(VideoType), nullable=False, default=VideoType.Reaction, primary_key=True)15    filename = db.Column(db.String(256), nullable=False)16    trailer_id = db.Column(db.String(64), db.ForeignKey('Video.id'), nullable=True, default=None)17    frame_count = db.Column(db.Integer, nullable=False)18    duration = db.Column(db.Float, nullable=False)19    finished_processing = db.Column(db.Boolean, nullable=False, default=False)20    progress = db.Column(db.Float, nullable=True, default=0.0)21    persons = db.relationship('Person', backref='video', lazy=True)22    key_moments = db.relationship('KeyMoment', backref='video', lazy=True)23    mood = db.relationship('OverallMood', backref='video', lazy=True)24    def __init__(self, id: str, filename: str, frame_count: int, duration: float, progress: float = 0.0,25                 finished_processing: bool = False):26        self.id = str(id)27        self.filename = str(filename)28        self.frame_count = int(frame_count)29        self.duration = float(duration)30        self.progress = float(progress)31        self.finished_processing = bool(finished_processing)32    @property33    def trailer(self):34        return db.object_session(self).query(Video).filter_by(id=self.trailer_id, type=VideoType.Trailer).first()35    @property36    def reactions(self):37        return db.object_session(self).query(Video).filter_by(trailer_id=self.id, type=VideoType.Reaction).all()38    def to_json(self, populate: Iterable = (), exclude_columns: Iterable = (), include_foreign_keys=False,39                base_url: str = None):40        json = BaseModel.to_json(self, populate, exclude_columns, include_foreign_keys)41        json['trailer_id'] = self.trailer_id42        json['reaction_ids'] = [reaction.id for reaction in self.reactions]43        if base_url is not None:44            json["img_url"] = f"{base_url}/{self.id}/thumbnail"45        return json46    def __repr__(self):...

consumer.py

Source:consumer.py

1import logging2import typing3from queue import Queue, Empty4from threading import Thread, Event5from enum import Enum6from gender_classification_pipeline.ml_assignment_proto.prediction_pb2 import Prediction7log = logging.getLogger(__file__)8logging.basicConfig(level=logging.INFO)9class Gender(Enum):10    MALE = 011    FEMALE = 112class PredictionConsumer(Thread):13    def __init__(self,14                 predictions_queue: Queue,      # The queue to read predictions from15                 finished_processing: Event,    # Signal to stop running16                 num_predictions_to_expect: typing.Optional[int] = None17                 ):18        super(PredictionConsumer, self).__init__()19        self.predictions_queue = predictions_queue20        self.finished_processing = finished_processing21        self.num_predictions_to_expect = None22        self.num_predictions_processed = 023        self.predicted_genders = {24            Gender.MALE: 0,25            Gender.FEMALE: 026        }27    @property28    def num_consumed(self):29        return self.num_predictions_processed30    def run(self) -> None:31        while not self.finished_processing.is_set() or not self.predictions_queue.empty():32            try:33                prediction = self.predictions_queue.get(timeout=5.0)34            except Empty:35                continue36            log.info(f"[ Consumer ] Acquired prediction (name = {prediction.user.name})")37            gender = prediction.predicted_gender38            self.num_predictions_processed += 139            if gender == Prediction.Gender.Value("MALE"):40                self.predicted_genders[Gender.MALE] += 141            else:42                self.predicted_genders[Gender.FEMALE] += 143            log.info(f"[ Consumer ] Processed {self.num_predictions_processed} so far, "44                     f"{self.predicted_genders[Gender.MALE]} males and {self.predicted_genders[Gender.FEMALE]} females")...

pipeline.py

Source:pipeline.py

1import logging2import time3from queue import Queue4import os5from gender_classification_pipeline.consumer import PredictionConsumer6from gender_classification_pipeline.generator import SampleGenerator7from threading import Event8log = logging.getLogger(__file__)9logging.basicConfig(level=logging.INFO)10class GenderClassificationPipeline(object):11    data_path = os.path.join(os.path.dirname(__file__), "data/features.dat")12    def __init__(self, predictor_class, num_samples_to_generate=100):13        self.num_samples_to_generate = num_samples_to_generate14        self.finished_processing = Event()15        self.samples_queue = Queue()16        self.predictions_queue = Queue()17        self.predictor = predictor_class(self.samples_queue, self.predictions_queue, self.finished_processing)18        self.generator = SampleGenerator(self.samples_queue, GenderClassificationPipeline.data_path,19                                         num_samples_to_generate=num_samples_to_generate)20        self.consumer = PredictionConsumer(self.predictions_queue, self.finished_processing)21    def run(self):22        log.info(f"[ Pipeline ] Starting execution - processing {self.num_samples_to_generate} examples.")23        self.generator.start()24        self.predictor.start()25        self.consumer.start()26        while self.consumer.num_consumed < self.num_samples_to_generate:27            time.sleep(1)28        self.finished_processing.set()29        self.generator.join()30        self.predictor.join()31        self.consumer.join()...

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