Mercurial > p > roundup > code
view website/issues/extensions/spambayes.py @ 5010:0428d2004a86
Fix exception handling to be python2.5 compatible
The exception handling introduced in commit hg:494d2550:
Display errors containing HTML with RejectRaw (issue2550847)
contained the new style of exception handling that is not compatible
with python2.5. Rework these exception handlers to use the old style to
maintain compatibility with python2.5
| author | John Kristensen <john@jerrykan.com> |
|---|---|
| date | Wed, 06 Jan 2016 16:02:34 +1100 |
| parents | ca692423e401 |
| children | 198b6e810c67 |
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import re, math from roundup.cgi.actions import Action from roundup.cgi.exceptions import * import xmlrpclib, socket REVPAT = re.compile(r'(r[0-9]+\b|rev(ision)? [0-9]+\b)') def extract_classinfo(db, classname, nodeid): node = db.getnode(classname, nodeid) authorage = node['creation'].timestamp() - \ db.getnode('user', node.get('author', node.get('creator')))['creation'].timestamp() authorid = node.get('author', node.get('creator')) content = db.getclass(classname).get(nodeid, 'content') tokens = ["klass:%s" % classname, "author:%s" % authorid, "authorage:%d" % int(math.log(authorage)), "hasrev:%s" % (REVPAT.search(content) is not None)] return (content, tokens) def train_spambayes(db, content, tokens, is_spam): spambayes_uri = db.config.detectors['SPAMBAYES_URI'] server = xmlrpclib.ServerProxy(spambayes_uri, verbose=False) try: server.train({'content':content}, tokens, {}, is_spam) return (True, None) except (socket.error, xmlrpclib.Error), e: return (False, str(e)) class SpambayesClassify(Action): permissionType = 'SB: May Classify' def handle(self): (content, tokens) = extract_classinfo(self.db, self.classname, self.nodeid) if self.form.has_key("trainspam"): is_spam = True elif self.form.has_key("trainham"): is_spam = False (status, errmsg) = train_spambayes(self.db, content, tokens, is_spam) node = self.db.getnode(self.classname, self.nodeid) props = {} if status: if node.get('spambayes_misclassified', False): props['spambayes_misclassified'] = True props['spambayes_score'] = 1.0 s = " SPAM" if not is_spam: props['spambayes_score'] = 0.0 s = " HAM" self.client.add_ok_message(self._('Message classified as') + s) else: self.client.add_error_message(self._('Unable to classify message, got error:') + errmsg) klass = self.db.getclass(self.classname) klass.set(self.nodeid, **props) self.db.commit() def sb_is_spam(obj): cutoff_score = float(obj._db.config.detectors['SPAMBAYES_SPAM_CUTOFF']) try: score = obj['spambayes_score'] except KeyError: return False return score >= cutoff_score def init(instance): instance.registerAction("spambayes_classify", SpambayesClassify) instance.registerUtil('sb_is_spam', sb_is_spam)
