PyCS/pycs/frontend/endpoints/pipelines/PredictModel.py

from typing import Any

from flask import abort
from flask import make_response
from flask import request
from flask.views import View

from pycs import app
from pycs import db
from pycs.database.Database import Database
from pycs.database.Project import Project
from pycs.frontend.notifications.NotificationList import NotificationList
from pycs.frontend.notifications.NotificationManager import NotificationManager
from pycs.interfaces.MediaFile import MediaFile
from pycs.interfaces.MediaStorage import MediaStorage
from pycs.jobs.JobGroupBusyException import JobGroupBusyException
from pycs.jobs.JobRunner import JobRunner
from pycs.util.PipelineCache import PipelineCache


class PredictModel(View):
    """
    load a model and create predictions
    """
    # pylint: disable=arguments-differ
    methods = ['POST']

    def __init__(self, nm: NotificationManager, jobs: JobRunner, pipelines: PipelineCache):
        # pylint: disable=invalid-name
        self.nm = nm
        self.jobs = jobs
        self.pipelines = pipelines

    def dispatch_request(self, project_id):
        # extract request data
        data = request.get_json(force=True)

        if data.get('predict') not in ['all', 'new']:
            return abort(400)

        # find project
        project = Project.query.get(project_id)
        if project is None:
            return abort(404)

        # create job
        try:
            notifications = NotificationList(self.nm)

            self.jobs.run(project,
                          'Model Interaction',
                          f'{project.name} (create predictions)',
                          f'{project.name}/model-interaction',
                          PredictModel.load_and_predict,
                          self.pipelines, notifications,
                          project.id, data['predict'],
                          progress=self.progress)
        except JobGroupBusyException:
            return abort(400)

        return make_response()

    @staticmethod
    def load_and_predict(pipelines: PipelineCache,
                         notifications: NotificationList, project_id: int, file_filter: Any):
        pipeline = None

        # create new database instance
        try:
            project = Project.query.get(project_id)
            model = project.model
            storage = MediaStorage(project_id, notifications)

            # create a list of MediaFile
            if isinstance(file_filter, str):
                if file_filter == 'new':
                    length = project.count_files_without_results()
                    files = map(lambda f: MediaFile(f, notifications),
                                project.files_without_results())
                else:
                    length = project.count_files()
                    files = map(lambda f: MediaFile(f, notifications),
                                project.files.all())
            else:
                files = map(lambda f: MediaFile(project.file(f.id), notifications),
                            file_filter)
                length = len(file_filter)

            # load pipeline
            try:
                pipeline = pipelines.load_from_root_folder(project, model.root_folder)

                # iterate over files
                index = 0
                for file in files:
                    # remove old predictions
                    file.remove_predictions()

                    # create new predictions
                    pipeline.execute(storage, file)

                    # commit changes and yield progress
                    db.session.commit()
                    yield index / length, notifications

                    index += 1
            except Exception as e:
                import traceback
                traceback.print_exc()
                app.logger.warning(f"Pipeline Error #2: {e}")

            finally:
                if pipeline is not None:
                    pipelines.free_instance(model.root_folder)

        except Exception as e:
            import traceback
            traceback.print_exc()
            app.logger.warning(f"Pipeline Error #1: {e}")


    @staticmethod
    def progress(progress: float, notifications: NotificationList):
        notifications.fire()
        return progress