add more typing, add tag to file upload, and rename form generation service
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brmnjsh
259
app/services/form_generation_service.py
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259
app/services/form_generation_service.py
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import os, json, random, io, logging
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from pulp import LpProblem, LpVariable, LpMinimize, LpMaximize, LpAffineExpression, LpConstraint, LpStatus, lpSum
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from lib.application_configs import ApplicationConfigs
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from helpers import aws_helper, tar_helper, csv_helper, service_helper, solver_helper
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from lib.errors.item_generation_error import ItemGenerationError
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from models.solver_run import SolverRun
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from models.solution import Solution
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from models.form import Form
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from models.item import Item
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from models.target import Target
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from services.base import Base
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class FormGenerationService(Base):
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def process(self):
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try:
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self.solver_run = self.create_solver_run_from_attributes()
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self.solver_run.generate_bundles()
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self.solution = self.generate_solution()
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self.result = self.stream_to_s3_bucket()
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except ItemGenerationError as error:
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self.result = self.stream_to_s3_bucket(error)
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except TypeError as error:
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logging.error(error)
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self.result = self.stream_to_s3_bucket(
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ItemGenerationError(
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"Provided params causing error in calculation results"))
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def create_solver_run_from_attributes(self) -> SolverRun:
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logging.info('Retrieving attributes from message...')
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# get s3 object
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self.key = aws_helper.get_key_from_message(self.source)
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s3_object = aws_helper.get_object(
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self.key, aws_helper.get_bucket_from_message(self.source))
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# convert to tar
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self.tar = tar_helper.raw_to_tar(s3_object)
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# get attributes file and convert to dict
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attributes = json.loads(
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tar_helper.extract_file_from_tar(
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self.tar, 'solver_run_attributes.json').read())
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# create solver run
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solver_run = SolverRun.parse_obj(attributes)
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# get items file and convert to dict
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items_csv = tar_helper.extract_file_from_tar(self.tar, 'items.csv')
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items_csv_reader = csv_helper.file_stream_reader(items_csv)
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# add items to solver run
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solver_run.items = service_helper.csv_to_item(items_csv_reader,
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solver_run)
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logging.info('Processed Attributes...')
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return solver_run
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def generate_solution(self) -> Solution:
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logging.info('Generating Solution...')
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solution = Solution(response_id=random.randint(100, 5000),
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forms=[]) # unsolved solution
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# iterate for number of forms that require creation
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for form_count in range(self.solver_run.total_forms):
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form_number = form_count + 1
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current_drift = 0 # FF Tokyo Drift
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# adding an element of randomness to the items and bundles used
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selected_items = self.solver_run.select_items_by_percent(30)
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selected_bundles = self.solver_run.select_bundles_by_percent(
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30)
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# setup common Solver variables
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items = LpVariable.dicts("Item",
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[item.id for item in selected_items],
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lowBound=0,
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upBound=1,
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cat='Binary')
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bundles = LpVariable.dicts("Bundle",
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[bundle.id for bundle in selected_bundles],
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lowBound=0,
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upBound=1,
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cat='Binary')
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logging.info(f'Generating Solution for Form {form_number}')
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while current_drift <= Target.max_drift():
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drift_percent = current_drift / 100
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self.solver_run.objective_function.update_targets_drift(
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drift_percent)
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# create problem
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problem = LpProblem('ata-form-generate', LpMinimize)
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# objective function
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problem += lpSum([
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bundle.count * bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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items[item.id]
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for item in selected_items
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])
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# Form Constraints
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problem += lpSum(
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[
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bundle.count * bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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1 * items[item.id]
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for item in selected_items
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]
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) == self.solver_run.total_form_items, f'Total bundle form items for form {form_number}'
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# Dynamic constraints.. currently we only support Metadata and Bundles(Cases/Passages)
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problem = solver_helper.build_constraints(
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self.solver_run, problem, items, bundles, selected_items, selected_bundles)
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# form uniqueness constraints
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# for form in solution.forms:
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# form_item_options = [
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# bundles[bundle.id]
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# for bundle in selected_bundles
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# ] + [
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# items[item.id]
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# for item in selected_items
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# ]
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# problem += len(
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# set(form.solver_variables)
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# & set(form_item_options)) / float(
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# len(
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# set(form.solver_variables)
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# | set(form_item_options))) * 100 >= 10
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logging.info('Creating TIF and TCC Elastic constraints')
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# Behold our very own Elastic constraints!
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for tif_target in self.solver_run.objective_function.tif_targets:
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problem += lpSum([
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bundle.tif(self.solver_run.irt_model, tif_target.theta)
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* bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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item.iif(self.solver_run, tif_target.theta) *
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items[item.id]
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for item in selected_items
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]) >= tif_target.minimum(
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), f'Min TIF theta({tif_target.theta}) at target {tif_target.value} drift at {current_drift}%'
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problem += lpSum([
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bundle.tif(self.solver_run.irt_model, tif_target.theta)
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* bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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item.iif(self.solver_run, tif_target.theta) *
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items[item.id]
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for item in selected_items
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]) <= tif_target.maximum(
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), f'Max TIF theta({tif_target.theta}) at target {tif_target.value} drift at {current_drift}%'
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for tcc_target in self.solver_run.objective_function.tcc_targets:
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problem += lpSum([
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bundle.trf(self.solver_run.irt_model, tcc_target.theta)
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* bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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item.irf(self.solver_run, tcc_target.theta) *
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items[item.id]
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for item in selected_items
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]) >= tcc_target.minimum(
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), f'Min TCC theta({tcc_target.theta}) at target {tcc_target.value} drift at {current_drift}%'
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problem += lpSum([
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bundle.trf(self.solver_run.irt_model, tcc_target.theta)
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* bundles[bundle.id]
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for bundle in selected_bundles
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] + [
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item.irf(self.solver_run, tcc_target.theta) *
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items[item.id]
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for item in selected_items
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]) <= tcc_target.maximum(
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), f'Max TCC theta({tcc_target.theta}) at target {tcc_target.value} drift at {current_drift}%'
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logging.info(
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f'Solving for Form {form_number} with a drift of {current_drift}%'
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)
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problem.solve()
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if LpStatus[problem.status] == 'Infeasible':
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logging.info(
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f'attempt infeasible for drift of {current_drift}%')
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if current_drift >= Target.max_drift(
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): # this is the last attempt, so lets finalize the solution
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if ApplicationConfigs.local_dev_env:
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service_helper.print_problem_variables(problem)
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logging.info(
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f'No feasible solution found for Form {form_number}!'
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)
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self.add_form_to_solution(problem, solution)
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break
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current_drift += Target.max_drift_increment()
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else:
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if ApplicationConfigs.local_dev_env:
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service_helper.print_problem_variables(problem)
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logging.info(
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f'Optimal solution found with drift of {current_drift}%!'
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)
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self.add_form_to_solution(problem, solution)
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break
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logging.info('Solution Generated.')
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return solution
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def add_form_to_solution(self, problem: LpProblem, solution: Solution):
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# add return items and create as a form
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form_items, solver_variables = service_helper.solution_items(
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problem.variables(), self.solver_run)
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form = Form.create(form_items, self.solver_run,
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LpStatus[problem.status], solver_variables)
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solution.forms.append(form)
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logging.info('Form generated and added to solution...')
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def stream_to_s3_bucket(self, error=None):
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self.file_name = f'{service_helper.key_to_uuid(self.key)}.csv'
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solution_file = None
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# setup writer buffer and write processed forms to file
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buffer = io.StringIO()
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if error:
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logging.info('Streaming %s error response to s3 bucket - %s',
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self.file_name,
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ApplicationConfigs.s3_processed_bucket)
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solution_file = service_helper.error_to_file(buffer, error)
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else:
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logging.info('Streaming %s to s3 bucket - %s', self.file_name,
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ApplicationConfigs.s3_processed_bucket)
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solution_file = service_helper.solution_to_file(
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buffer, self.solver_run.total_form_items, self.solution.forms)
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# upload generated file to s3 and return result
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return aws_helper.file_stream_upload(
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solution_file, self.file_name,
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ApplicationConfigs.s3_processed_bucket, 'formGeneration')
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