def search():
form = LoginForm()
#form.username.data= 7536970
if request.method == 'POST':
acptcrt = clean_text(getacptnccriteria(str(
form.username.data))).replace("given",
"").replace("when",
"").replace("user", "")
result = findsimilartc(acptcrt)
tc_ids = result[0]
accuracy = result[1]
to_be_rendered = list(zip(tc_ids, accuracy))
if request.method == 'GET':
acptcrt = clean_text(getacptnccriteria(str(USERSTRYID))).replace(
"given", "").replace("when", "").replace("user", "")
result = findsimilartc(acptcrt)
tc_ids = result[0]
accuracy = result[1]
to_be_rendered = list(zip(tc_ids, accuracy))
# if form.validate_on_submit():
# acptcrt = clean_text(getacptnccriteria(str(form.username.data))).replace("given", "").replace("when","").replace("user", "")
# return render_template('homechart.html', title='Sign In', form=form, tc_ids=tc_ids)
#return render_template('home.html', title='Sign In', form=form, tc_ids=tc_ids, accuracy=accuracy)
return render_template('home.html',
title='Sign In',
form=form,
result=to_be_rendered)
def personal():
data = request.get_json()
uid = data['id']
sender = data['sender']
subject = data['subject']
message = data['message']
text = sender + ' ' + subject + ' ' + message
text_clean = clean_text(text)
text_vect = personal_vect.transform([text_clean])
predict = personal_model.predict(text_vect)
print(predict[0])
email = {
'message-id': uid,
'from': sender,
'subject': subject,
'message': message,
'personal': predict.tolist()
}
return jsonify(email)
def hello_world(id):
id = int(id)
acptcrt = clean_text(getacptnccriteria(id)).replace("give", "").replace(
"when", "").replace("user", "")
return jsonify({"test_case_ids": findsimilartcwitacc(acptcrt)})
#basic1 is dsa model
model.save("dellcom.model")
# print("Model Saved")
model = Doc2Vec.load("dellcom.model")
# to find the vector of a document which is not in training data
# test_data = word_tokenize(acptcrtlst[0].lower())
# v1 = model.infer_vector(test_data)
# print("V1_infer", v1)
# to find most similar doc using tags
# similar_doc = model.docvecs.most_similar('1')
# print(similar_doc)
print(acptcrtlst[0])
tokens = [clean_text(str(acptcrtlst[0]))]
print(listToString(tokens))
# tokens = tokens.remove("Given")
new_vector = model.infer_vector(listToString(tokens).split())
# sims = model.docvecs.most_similar([new_vector])
# print(sims)
most_similar_docs = []
# for d in model.docvecs.most_similar([new_vector]):
# print(int(d[0]))
# most_similar_docs.append(coupleddata[d[0]])
MOST_SIMILAR_TCS =model.docvecs.most_similar([new_vector])
similar_tcs = [tc_tuple[0] for tc_tuple in MOST_SIMILAR_TCS]
for item in similar_tcs:
print(item+"===="+tfsconnect.getworkitemtitle(item))