def nls_result(request):
q = request.GET['q']
p = int(request.GET['p'])
tfidf = NLP_LSI_TFIDF()
result = tfidf.getResult(query=q, page=p)
pages = []
f = result[1]
total_p = (result[0] / 10) + 1
t_p = int(total_p)
p_p = max(p - 5, 1)
n_p = min(p + 5, total_p)
while total_p > 0:
pages.append(0)
total_p -= 1
files = []
for f_f in f:
f_name = f_f[0]
temp = fci.to_fciObject(config.configs['paths']['FCI_path'] + "/so/" + f_name)
m_l = ''
for t_f_f in f_f[1]:
m_l += str(t_f_f + 1)
m_l += ','
m_l = m_l[0:len(m_l) - 1]
fei = FrontEndInterface(temp, m_l)
files.append(fei)
return render(request, 'nlp-result.html',
{'results': files, 'q': q, 'p': p, 'pages': pages, 'p_p': p_p, 'n_p': n_p, 'pre': p - 1,
'next': p + 1, 't_p': t_p})
def detail(request):
id = request.GET['id']
ml = request.GET['ml']
m_l = ml
fci_obj = fci.to_fciObject(config.configs['paths']['FCI_path'] + "/lsi/" +
id + '.json')
return render(request, 'detail.html', {
'detail': fci_obj,
'match_lines': m_l
})
def search(request):
q = request.GET['q']
p = int(request.GET['p'])
timestamp = time.time()
client = Client("as", "137.43.92.9", 9609, {
'operate_type': 1,
'query': q,
'page': p,
'timestamp': str(timestamp)
})
client.send_message()
server = CommunicationServer()
message = server.receive_message(socket_name=str(timestamp))
result = message['result']
# tfidf = LSI_TFIDF()
# result = tfidf.getResult(query=q, page=p)
pages = []
f = result[1]
total_p = (result[0] / configs['others']['page_num']) + 1
t_p = int(total_p)
p_p = max(p - 5, 1)
n_p = min(p + 5, total_p)
while total_p > 0:
pages.append(0)
total_p -= 1
files = []
for f_f in f:
f_name = f_f[0]
temp = fci.to_fciObject(config.configs['paths']['FCI_path'] + "/lsi/" +
f_name)
m_l = ''
for t_f_f in f_f[1]:
m_l += str(t_f_f + 1)
m_l += ','
m_l = m_l[0:len(m_l) - 1]
fei = FrontEndInterface(temp, m_l)
files.append(fei)
return render(
request, 'search-result.html', {
'results': files,
'q': q,
'p': p,
'pages': pages,
'p_p': p_p,
'n_p': n_p,
'pre': p - 1,
'next': p + 1,
't_p': t_p
})
def snippet_detail(request):
id = request.GET['id']
ml = request.GET['ml']
qml = request.GET['qml']
timestamp = request.GET['ts']
l = int(request.GET['l'])
r = redis.Redis(host='localhost', port=6379, decode_responses=True)
q = r.get(timestamp)
language = ''
print(l)
if l == 3:
language = 'python'
else:
language = 'java'
m_l = ml
fci_obj = fci.to_fciObject(config.configs['paths']['FCI_path'] + "/" + language + "/" + id + '.json')
return render(request, 'snippet-detail.html',
{'detail': fci_obj, 'match_lines': m_l, 'query_match_lines': qml, 'query': q})
def plagiarizeResult(request):
snippet = request.POST['snippet']
page = int(request.POST['p'])
operate_type = request.POST['l']
timestamp = time.time()
m = hashlib.md5()
m.update((str(timestamp) + snippet).encode("utf8"))
ts = m.hexdigest()
r = redis.Redis(host='localhost', port=6379, decode_responses=True)
r.set(ts, snippet, ex=3000)
operate_type = int(operate_type)
# timestamp = time.time()
# client = Client("yeats.ucd.ie", "10.141.131.14", 9609,
# {'operate_type': operate_type, 'query': snippet, 'page': page, 'timestamp': timestamp})
# client.send_message()
# server = CommunicationServer()
# message = server.receive_message(socket_name=str(timestamp))
# result = message['result']
ast = None
language = ''
if operate_type == 3:
# ast = ASTSearching()
ast = 3
language = 'python'
else:
# ast = JavaAST()
ast = 4
language = 'java'
timestamp = time.time()
client = Client(
"as", "137.43.92.9", 9609, {
'operate_type': ast,
'query': snippet,
'page': page,
'timestamp': str(timestamp)
})
client.send_message()
server = CommunicationServer()
message = server.receive_message(socket_name=str(timestamp))
result = message['result']
# result = ast.getResults(snippet, page)
if result == 0:
return render(request, 'snippet-result.html', {
'snippet': snippet,
})
else:
result = result.to_dict()
is_global = False
plagiarize_list = []
document_list = []
component_document = []
global_similarity = 0
if result != None:
total_num = result['numOfResults']
total_page = (total_num / config.configs['others']['page_num']) + 1
matching_blocks = result['matchingBlocks']
global_similarity = result['globalSimilarity']
matching_lines = result['matchingLines']
blockWeights = result['blockWeights']
if global_similarity != None and global_similarity > 0:
is_global = True
cd = result['componentDocuments']
component_document = []
for c in cd:
qml = str(matching_blocks[c][0]) + '-' + str(
matching_blocks[c][1])
ml = ''
for mls in matching_lines[c]:
ml += str(mls[2]) + '-' + str(mls[3]) + ','
print(qml)
fobj = fci.to_fciObject(config.configs['paths']['FCI_path'] +
"/" + language + "/" + c)
fei = FrontEndInterface(fobj, ml)
fei.set_query_match_lines(qml)
print(fei.get_query_match_lines(), '==========')
component_document.append(fei)
for t in result['plagiarismList']:
ml = ''
qml = ''
for mls in matching_lines[t]:
qml += str(mls[0]) + '-' + str(mls[1]) + ','
ml += str(mls[2]) + '-' + str(mls[3]) + ','
fobj = fci.to_fciObject(config.configs['paths']['FCI_path'] + "/" +
language + "/" + t)
fei = FrontEndInterface(fobj, ml)
fei.set_query_match_lines(qml)
plagiarize_list.append(fei)
for t in result['documentList']:
ml = ''
qml = ''
for mls in matching_lines[t]:
qml += str(mls[0]) + '-' + str(mls[1]) + ','
ml += str(mls[2]) + '-' + str(mls[3]) + ','
fobj = fci.to_fciObject(config.configs['paths']['FCI_path'] + "/" +
language + "/" + t)
fei = FrontEndInterface(fobj, ml)
fei.set_query_match_lines(qml)
document_list.append(fei)
if global_similarity != None:
global_similarity *= 100
global_similarity = '%.2f' % global_similarity
return render(
request, 'snippet-result.html', {
'snippet': snippet,
"is_global": is_global,
'component_documents': component_document,
"global_similarity": global_similarity,
"plagiarize_list": plagiarize_list,
"document_list": document_list,
"l": operate_type,
'ts': ts
})