def story_score(self, story, classifiers):
score = compute_story_score(story, classifier_titles=classifiers.get('titles', []),
classifier_authors=classifiers.get('authors', []),
classifier_tags=classifiers.get('tags', []),
classifier_feeds=classifiers.get('feeds', []))
return score
def story_score(self, story, classifiers):
score = compute_story_score(story, classifier_titles=classifiers.get('titles', []),
classifier_authors=classifiers.get('authors', []),
classifier_tags=classifiers.get('tags', []),
classifier_feeds=classifiers.get('feeds', []))
return score
def api_shared_story(request):
user = request.user
body = request.body_json
after = body.get('after', None)
before = body.get('before', None)
limit = body.get('limit', 50)
fields = body.get('triggerFields')
blurblog_user = fields['blurblog_user']
entries = []
if isinstance(blurblog_user, int) or blurblog_user.isdigit():
social_user_ids = [int(blurblog_user)]
elif blurblog_user == "all":
socialsubs = MSocialSubscription.objects.filter(user_id=user.pk)
social_user_ids = [ss.subscription_user_id for ss in socialsubs]
mstories = MSharedStory.objects(
user_id__in=social_user_ids
).order_by('-shared_date')[:limit]
stories = Feed.format_stories(mstories)
found_feed_ids = list(set([story['story_feed_id'] for story in stories]))
share_user_ids = list(set([story['user_id'] for story in stories]))
users = dict([(u.pk, u.username)
for u in User.objects.filter(pk__in=share_user_ids).only('pk', 'username')])
feeds = dict([(f.pk, {
"title": f.feed_title,
"website": f.feed_link,
"address": f.feed_address,
}) for f in Feed.objects.filter(pk__in=found_feed_ids)])
classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk,
social_user_id__in=social_user_ids))
classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk,
social_user_id__in=social_user_ids))
classifier_titles = list(MClassifierTitle.objects(user_id=user.pk,
social_user_id__in=social_user_ids))
classifier_tags = list(MClassifierTag.objects(user_id=user.pk,
social_user_id__in=social_user_ids))
# Merge with feed specific classifiers
classifier_feeds = classifier_feeds + list(MClassifierFeed.objects(user_id=user.pk,
feed_id__in=found_feed_ids))
classifier_authors = classifier_authors + list(MClassifierAuthor.objects(user_id=user.pk,
feed_id__in=found_feed_ids))
classifier_titles = classifier_titles + list(MClassifierTitle.objects(user_id=user.pk,
feed_id__in=found_feed_ids))
classifier_tags = classifier_tags + list(MClassifierTag.objects(user_id=user.pk,
feed_id__in=found_feed_ids))
for story in stories:
if before and int(story['shared_date'].strftime("%s")) > before: continue
if after and int(story['shared_date'].strftime("%s")) < after: continue
score = compute_story_score(story, classifier_titles=classifier_titles,
classifier_authors=classifier_authors,
classifier_tags=classifier_tags,
classifier_feeds=classifier_feeds)
if score < 0: continue
feed = feeds.get(story['story_feed_id'], None)
entries.append({
"StoryTitle": story['story_title'],
"StoryContent": story['story_content'],
"StoryURL": story['story_permalink'],
"StoryAuthor": story['story_authors'],
"PublishedAt": story['story_date'].strftime("%Y-%m-%dT%H:%M:%SZ"),
"StoryScore": score,
"Comments": story['comments'],
"Username": users.get(story['user_id']),
"SharedAt": story['shared_date'].strftime("%Y-%m-%dT%H:%M:%SZ"),
"Site": feed and feed['title'],
"SiteURL": feed and feed['website'],
"SiteRSS": feed and feed['address'],
"ifttt": {
"id": story['story_hash'],
"timestamp": int(story['shared_date'].strftime("%s"))
},
})
if after:
entries = sorted(entries, key=lambda s: s['ifttt']['timestamp'])
logging.user(request, "~FMChecking shared stories from ~SB~FCIFTTT~SN~FM: ~SB~FM%s~FM~SN - ~SB%s~SN stories" % (blurblog_user, len(entries)))
return {"data": entries}
def api_unread_story(request, trigger_slug=None):
user = request.user
body = request.body_json
after = body.get('after', None)
before = body.get('before', None)
limit = body.get('limit', 50)
fields = body.get('triggerFields')
feed_or_folder = fields['feed_or_folder']
entries = []
if isinstance(feed_or_folder, int) or feed_or_folder.isdigit():
feed_id = int(feed_or_folder)
usersub = UserSubscription.objects.get(user=user, feed_id=feed_id)
found_feed_ids = [feed_id]
found_trained_feed_ids = [feed_id] if usersub.is_trained else []
stories = usersub.get_stories(order="newest", read_filter="unread",
offset=0, limit=limit,
default_cutoff_date=user.profile.unread_cutoff)
else:
folder_title = feed_or_folder
if folder_title == "Top Level":
folder_title = " "
usf = UserSubscriptionFolders.objects.get(user=user)
flat_folders = usf.flatten_folders()
feed_ids = None
if folder_title != "all":
feed_ids = flat_folders.get(folder_title)
usersubs = UserSubscription.subs_for_feeds(user.pk, feed_ids=feed_ids,
read_filter="unread")
feed_ids = [sub.feed_id for sub in usersubs]
params = {
"user_id": user.pk,
"feed_ids": feed_ids,
"offset": 0,
"limit": limit,
"order": "newest",
"read_filter": "unread",
"usersubs": usersubs,
"cutoff_date": user.profile.unread_cutoff,
}
story_hashes, unread_feed_story_hashes = UserSubscription.feed_stories(**params)
mstories = MStory.objects(story_hash__in=story_hashes).order_by('-story_date')
stories = Feed.format_stories(mstories)
found_feed_ids = list(set([story['story_feed_id'] for story in stories]))
trained_feed_ids = [sub.feed_id for sub in usersubs if sub.is_trained]
found_trained_feed_ids = list(set(trained_feed_ids) & set(found_feed_ids))
if found_trained_feed_ids:
classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk,
feed_id__in=found_trained_feed_ids))
classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk,
feed_id__in=found_trained_feed_ids))
classifier_titles = list(MClassifierTitle.objects(user_id=user.pk,
feed_id__in=found_trained_feed_ids))
classifier_tags = list(MClassifierTag.objects(user_id=user.pk,
feed_id__in=found_trained_feed_ids))
feeds = dict([(f.pk, {
"title": f.feed_title,
"website": f.feed_link,
"address": f.feed_address,
}) for f in Feed.objects.filter(pk__in=found_feed_ids)])
for story in stories:
if before and int(story['story_date'].strftime("%s")) > before: continue
if after and int(story['story_date'].strftime("%s")) < after: continue
score = 0
if found_trained_feed_ids and story['story_feed_id'] in found_trained_feed_ids:
score = compute_story_score(story, classifier_titles=classifier_titles,
classifier_authors=classifier_authors,
classifier_tags=classifier_tags,
classifier_feeds=classifier_feeds)
if score < 0: continue
if trigger_slug == "new-unread-focus-story" and score < 1: continue
feed = feeds.get(story['story_feed_id'], None)
entries.append({
"StoryTitle": story['story_title'],
"StoryContent": story['story_content'],
"StoryURL": story['story_permalink'],
"StoryAuthor": story['story_authors'],
"PublishedAt": story['story_date'].strftime("%Y-%m-%dT%H:%M:%SZ"),
"StoryScore": score,
"Site": feed and feed['title'],
"SiteURL": feed and feed['website'],
"SiteRSS": feed and feed['address'],
"ifttt": {
"id": story['story_hash'],
"timestamp": int(story['story_date'].strftime("%s"))
},
})
if after:
entries = sorted(entries, key=lambda s: s['ifttt']['timestamp'])
logging.user(request, "~FYChecking unread%s stories with ~SB~FCIFTTT~SN~FY: ~SB%s~SN - ~SB%s~SN stories" % (" ~SBfocus~SN" if trigger_slug == "new-unread-focus-story" else "", feed_or_folder, len(entries)))
return {"data": entries[:limit]}
def api_shared_story(request):
user = request.user
body = request.body_json
after = body.get('after', None)
before = body.get('before', None)
limit = body.get('limit', 50)
fields = body.get('triggerFields')
blurblog_user = fields['blurblog_user']
entries = []
if isinstance(blurblog_user, int) or blurblog_user.isdigit():
social_user_ids = [int(blurblog_user)]
elif blurblog_user == "all":
socialsubs = MSocialSubscription.objects.filter(user_id=user.pk)
social_user_ids = [ss.subscription_user_id for ss in socialsubs]
mstories = MSharedStory.objects(
user_id__in=social_user_ids
).order_by('-shared_date')[:limit]
stories = Feed.format_stories(mstories)
found_feed_ids = list(set([story['story_feed_id'] for story in stories]))
share_user_ids = list(set([story['user_id'] for story in stories]))
users = dict([(u.pk, u.username)
for u in User.objects.filter(pk__in=share_user_ids).only('pk', 'username')])
feeds = dict([(f.pk, {
"title": f.feed_title,
"website": f.feed_link,
"address": f.feed_address,
}) for f in Feed.objects.filter(pk__in=found_feed_ids)])
classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk,
social_user_id__in=social_user_ids))
classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk,
social_user_id__in=social_user_ids))
classifier_titles = list(MClassifierTitle.objects(user_id=user.pk,
social_user_id__in=social_user_ids))
classifier_tags = list(MClassifierTag.objects(user_id=user.pk,
social_user_id__in=social_user_ids))
# Merge with feed specific classifiers
classifier_feeds = classifier_feeds + list(MClassifierFeed.objects(user_id=user.pk,
feed_id__in=found_feed_ids))
classifier_authors = classifier_authors + list(MClassifierAuthor.objects(user_id=user.pk,
feed_id__in=found_feed_ids))
classifier_titles = classifier_titles + list(MClassifierTitle.objects(user_id=user.pk,
feed_id__in=found_feed_ids))
classifier_tags = classifier_tags + list(MClassifierTag.objects(user_id=user.pk,
feed_id__in=found_feed_ids))
for story in stories:
if before and int(story['shared_date'].strftime("%s")) > before: continue
if after and int(story['shared_date'].strftime("%s")) < after: continue
score = compute_story_score(story, classifier_titles=classifier_titles,
classifier_authors=classifier_authors,
classifier_tags=classifier_tags,
classifier_feeds=classifier_feeds)
if score < 0: continue
feed = feeds.get(story['story_feed_id'], None)
entries.append({
"StoryTitle": story['story_title'],
"StoryContent": story['story_content'],
"StoryURL": story['story_permalink'],
"StoryAuthor": story['story_authors'],
"PublishedAt": story['story_date'].strftime("%Y-%m-%dT%H:%M:%SZ"),
"StoryScore": score,
"Comments": story['comments'],
"Username": users.get(story['user_id']),
"SharedAt": story['shared_date'].strftime("%Y-%m-%dT%H:%M:%SZ"),
"Site": feed and feed['title'],
"SiteURL": feed and feed['website'],
"SiteRSS": feed and feed['address'],
"meta": {
"id": story['story_hash'],
"timestamp": int(story['shared_date'].strftime("%s"))
},
})
if after:
entries = sorted(entries, key=lambda s: s['meta']['timestamp'])
logging.user(request, "~FMChecking shared stories from ~SB~FCIFTTT~SN~FM: ~SB~FM%s~FM~SN - ~SB%s~SN stories" % (blurblog_user, len(entries)))
return {"data": entries}
def api_unread_story(request, trigger_slug=None):
user = request.user
body = request.body_json
after = body.get('after', None)
before = body.get('before', None)
limit = body.get('limit', 50)
fields = body.get('triggerFields')
feed_or_folder = fields['feed_or_folder']
entries = []
if isinstance(feed_or_folder, int) or feed_or_folder.isdigit():
feed_id = int(feed_or_folder)
try:
usersub = UserSubscription.objects.get(user=user, feed_id=feed_id)
except UserSubscription.DoesNotExist:
return dict(data=[])
found_feed_ids = [feed_id]
found_trained_feed_ids = [feed_id] if usersub.is_trained else []
stories = usersub.get_stories(order="newest", read_filter="unread",
offset=0, limit=limit,
default_cutoff_date=user.profile.unread_cutoff)
else:
folder_title = feed_or_folder
if folder_title == "Top Level":
folder_title = " "
usf = UserSubscriptionFolders.objects.get(user=user)
flat_folders = usf.flatten_folders()
feed_ids = None
if folder_title != "all":
feed_ids = flat_folders.get(folder_title)
usersubs = UserSubscription.subs_for_feeds(user.pk, feed_ids=feed_ids,
read_filter="unread")
feed_ids = [sub.feed_id for sub in usersubs]
params = {
"user_id": user.pk,
"feed_ids": feed_ids,
"offset": 0,
"limit": limit,
"order": "newest",
"read_filter": "unread",
"usersubs": usersubs,
"cutoff_date": user.profile.unread_cutoff,
}
story_hashes, unread_feed_story_hashes = UserSubscription.feed_stories(**params)
mstories = MStory.objects(story_hash__in=story_hashes).order_by('-story_date')
stories = Feed.format_stories(mstories)
found_feed_ids = list(set([story['story_feed_id'] for story in stories]))
trained_feed_ids = [sub.feed_id for sub in usersubs if sub.is_trained]
found_trained_feed_ids = list(set(trained_feed_ids) & set(found_feed_ids))
if found_trained_feed_ids:
classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk,
feed_id__in=found_trained_feed_ids))
classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk,
feed_id__in=found_trained_feed_ids))
classifier_titles = list(MClassifierTitle.objects(user_id=user.pk,
feed_id__in=found_trained_feed_ids))
classifier_tags = list(MClassifierTag.objects(user_id=user.pk,
feed_id__in=found_trained_feed_ids))
feeds = dict([(f.pk, {
"title": f.feed_title,
"website": f.feed_link,
"address": f.feed_address,
}) for f in Feed.objects.filter(pk__in=found_feed_ids)])
for story in stories:
if before and int(story['story_date'].strftime("%s")) > before: continue
if after and int(story['story_date'].strftime("%s")) < after: continue
score = 0
if found_trained_feed_ids and story['story_feed_id'] in found_trained_feed_ids:
score = compute_story_score(story, classifier_titles=classifier_titles,
classifier_authors=classifier_authors,
classifier_tags=classifier_tags,
classifier_feeds=classifier_feeds)
if score < 0: continue
if trigger_slug == "new-unread-focus-story" and score < 1: continue
feed = feeds.get(story['story_feed_id'], None)
entries.append({
"StoryTitle": story['story_title'],
"StoryContent": story['story_content'],
"StoryURL": story['story_permalink'],
"StoryAuthor": story['story_authors'],
"PublishedAt": story['story_date'].strftime("%Y-%m-%dT%H:%M:%SZ"),
"StoryScore": score,
"Site": feed and feed['title'],
"SiteURL": feed and feed['website'],
"SiteRSS": feed and feed['address'],
"meta": {
"id": story['story_hash'],
"timestamp": int(story['story_date'].strftime("%s"))
},
})
if after:
entries = sorted(entries, key=lambda s: s['meta']['timestamp'])
logging.user(request, "~FYChecking unread%s stories with ~SB~FCIFTTT~SN~FY: ~SB%s~SN - ~SB%s~SN stories" % (" ~SBfocus~SN" if trigger_slug == "new-unread-focus-story" else "", feed_or_folder, len(entries)))
return {"data": entries[:limit]}
