# Copyright 2016 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file or at
# https://developers.google.com/open-source/licenses/bsd
""" Tasks and handlers for maintaining the spam classifier model. These
    should be run via cron and task queue rather than manually.
"""
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import

import csv
import logging
import webapp2
import cloudstorage
import json

from datetime import date
from datetime import datetime
from datetime import timedelta
from google.appengine.api import app_identity

from framework import cloud_tasks_helpers
from framework import gcs_helpers
from framework import servlet
from framework import urls

class TrainingDataExport(webapp2.RequestHandler):
  """Trigger a training data export task"""
  def get(self):
    task = cloud_tasks_helpers.generate_simple_task(
        urls.SPAM_DATA_EXPORT_TASK + '.do', {})
    cloud_tasks_helpers.create_task(task)


BATCH_SIZE = 1000

class TrainingDataExportTask(servlet.Servlet):
  """Export any human-labeled ham or spam from the previous day. These
     records will be used by a subsequent task to create an updated model.
  """
  CHECK_SECURITY_TOKEN = False

  def ProcessFormData(self, mr, post_data):
    logging.info("Training data export initiated.")

    bucket_name = app_identity.get_default_gcs_bucket_name()
    date_str = date.today().isoformat()
    export_target_path = '/' + bucket_name + '/spam_training_data/' + date_str
    total_issues = 0

    with cloudstorage.open(export_target_path, mode='w',
        content_type=None, options=None, retry_params=None) as gcs_file:

      csv_writer = csv.writer(gcs_file, delimiter=',', quotechar='"',
          quoting=csv.QUOTE_ALL, lineterminator='\n')

      since = datetime.now() - timedelta(days=7)

      # TODO: Further pagination.
      issues, first_comments, _count = (
          self.services.spam.GetTrainingIssues(
              mr.cnxn, self.services.issue, since, offset=0, limit=BATCH_SIZE))
      total_issues += len(issues)
      for issue in issues:
        # Cloud Prediction API doesn't allow newlines in the training data.
        fixed_summary = issue.summary.replace('\r\n', ' ')
        fixed_comment = first_comments[issue.issue_id].replace('\r\n', ' ')
        email = self.services.user.LookupUserEmail(mr.cnxn, issue.reporter_id)
        csv_writer.writerow([
            'spam' if issue.is_spam else 'ham',
            fixed_summary.encode('utf-8'), fixed_comment.encode('utf-8'), email,
        ])

      comments = (
          self.services.spam.GetTrainingComments(
              mr.cnxn, self.services.issue, since, offset=0, limit=BATCH_SIZE))
      total_comments = len(comments)
      for comment in comments:
        # Cloud Prediction API doesn't allow newlines in the training data.
        fixed_comment = comment.content.replace('\r\n', ' ')
        email = self.services.user.LookupUserEmail(mr.cnxn, comment.user_id)
        csv_writer.writerow([
            'spam' if comment.is_spam else 'ham',
            # Comments don't have summaries, so it's blank:
            '', fixed_comment.encode('utf-8'), email
        ])

    self.response.body = json.dumps({
        "exported_issue_count": total_issues,
        "exported_comment_count": total_comments,
    })