def translate(request, ip_address):
template = loader.get_template('translator/translate.html')
if request.method == 'POST':
request_str = request.POST.get('request_str')
else:
request_str = request.GET.get('request_str')
if not request_str or not request_str.strip():
return redirect('/')
while request_str.endswith('/'):
request_str = request_str[:-1]
trans_list = []
html_strs = []
if CACHE_TRANSLATIONS and NLRequest.objects.filter(
request_str=request_str).exists():
# if the natural language request string has been translated before,
# directly output previously cached translations
if Translation.objects.filter(
request__request_str=request_str).exists():
# model translations exist
cached_trans = Translation.objects.filter(
request__request_str=request_str)
for trans in cached_trans:
print(trans.pred_cmd)
pred_tree = data_tools.bash_parser(trans.pred_cmd)
if pred_tree is not None:
trans_list.append(trans)
html_str = tokens2html(pred_tree)
html_strs.append(html_str)
try:
nl_request = NLRequest.objects.get(request_str=request_str)
except ObjectDoesNotExist:
nl_request = NLRequest.objects.create(request_str=request_str)
try:
user = User.objects.get(ip_address=ip_address)
except ObjectDoesNotExist:
r = requests.get('http://ipinfo.io/{}/json'.format(ip_address))
organization = r.json()['org']
city = r.json()['city']
region = r.json()['region']
country = r.json()['country']
user = User.objects.create(
ip_address=ip_address,
organization=organization,
city=city,
region=region,
country=country
)
# check if the natural language request has been issued by the IP
# address before
# if not, save the natural language request issued by this IP Address
if not NLRequestIPAddress.objects.filter(
request=nl_request, user=user).exists():
NLRequestIPAddress.objects.create(
request=nl_request, user=user)
if not trans_list:
if not WEBSITE_DEVELOP:
# call learning model and store the translations
batch_outputs, output_logits = translate_fun(request_str)
if batch_outputs:
top_k_predictions = batch_outputs[0]
top_k_scores = output_logits[0]
for i in range(len(top_k_predictions)):
pred_tree, pred_cmd, outputs = top_k_predictions[i]
score = top_k_scores[i]
trans = Translation.objects.create(
request=nl_request, pred_cmd=pred_cmd, score=score)
trans_list.append(trans)
html_str = tokens2html(pred_tree)
html_strs.append(html_str)
translation_list = []
for trans, html_str in zip(trans_list, html_strs):
upvoted, downvoted, starred = "", "", ""
if Vote.objects.filter(translation=trans, ip_address=ip_address).exists():
v = Vote.objects.get(translation=trans, ip_address=ip_address)
upvoted = 1 if v.upvoted else ""
downvoted = 1 if v.downvoted else ""
starred = 1 if v.starred else ""
translation_list.append((trans, upvoted, downvoted, starred,
trans.pred_cmd.replace('\\', '\\\\'), html_str))
# sort translation_list based on voting results
translation_list.sort(
key=lambda x: x[0].num_votes + x[0].score, reverse=True)
context = {
'nl_request': nl_request,
'trans_list': translation_list
}
return HttpResponse(template.render(context, request))
def translate(request, ip_address):
template = loader.get_template('translator/translate.html')
if request.method == 'POST':
request_str = request.POST.get('request_str')
else:
request_str = request.GET.get('request_str')
if not request_str or not request_str.strip():
return redirect('/')
while request_str.endswith('/'):
request_str = request_str[:-1]
# check if the natural language request is in the database
nl = get_nl(request_str)
trans_list = []
annotated_trans_list = []
if CACHE_TRANSLATIONS and \
Translation.objects.filter(nl=nl).exists():
# model translations exist
cached_trans = Translation.objects.filter(nl=nl).order_by('score')
count = 0
for trans in cached_trans:
pred_tree = data_tools.bash_parser(trans.pred_cmd.str)
if pred_tree is not None:
trans_list.append(trans)
annotated_trans_list.append(tokens2html(pred_tree))
count += 1
if count >= NUM_TRANSLATIONS:
break
# check if the user is in the database
try:
user = User.objects.get(ip_address=ip_address)
except ObjectDoesNotExist:
if ip_address == '123.456.789.012':
organization = ''
city = '--'
region = '--'
country = '--'
else:
r = requests.get('http://ipinfo.io/{}/json'.format(ip_address))
organization = '' if r.json()['org'] is None else r.json()['org']
city = '--' if r.json()['city'] is None else r.json()['city']
region = '--' if r.json()['region'] is None else r.json()['region']
country = '--' if r.json()['country'] is None else r.json(
)['country']
user = User.objects.create(ip_address=ip_address,
organization=organization,
city=city,
region=region,
country=country)
# save the natural language request issued by this IP Address
nl_request = NLRequest.objects.create(nl=nl, user=user)
if not trans_list:
if not WEBSITE_DEVELOP:
# call learning model and store the translations
batch_outputs, output_logits = translate_fun(request_str)
if batch_outputs:
top_k_predictions = batch_outputs[0]
top_k_scores = output_logits[0]
for i in range(len(top_k_predictions)):
pred_tree, pred_cmd = top_k_predictions[i]
score = top_k_scores[i]
cmd = get_command(pred_cmd)
trans_set = Translation.objects.filter(nl=nl, pred_cmd=cmd)
if not trans_set.exists():
trans = Translation.objects.create(nl=nl,
pred_cmd=cmd,
score=score)
else:
for trans in trans_set:
break
trans.score = score
trans.save()
trans_list.append(trans)
start_time = time.time()
annotated_trans_list.append(tokens2html(pred_tree))
print(time.time() - start_time)
start_time = time.time()
translation_list = []
for trans, annotated_cmd in zip(trans_list, annotated_trans_list):
upvoted, downvoted, starred = "", "", ""
if Vote.objects.filter(translation=trans,
ip_address=ip_address).exists():
v = Vote.objects.get(translation=trans, ip_address=ip_address)
upvoted = 1 if v.upvoted else ""
downvoted = 1 if v.downvoted else ""
starred = 1 if v.starred else ""
translation_list.append(
(trans, upvoted, downvoted, starred,
trans.pred_cmd.str.replace('\\', '\\\\'), annotated_cmd))
# sort translation_list based on voting results
translation_list.sort(key=lambda x: x[0].num_votes + x[0].score,
reverse=True)
context = {'nl_request': nl_request, 'trans_list': translation_list}
return HttpResponse(template.render(context, request))