def upload():
if request.method == 'POST':
print(request.files['fileUploaded'])
File = request.files['fileUploaded']
if File.filename == '':
return '<h1>No File</h1>'
if File:
filename = secure_filename(File.filename)
File.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
data = ResumeParser(
os.path.join(app.config['UPLOAD_FOLDER'],
filename)).get_extracted_data()
conn = sqlite3.connect('users.db')
skillset = data.get('skills')
skills = ""
for s in skillset:
skills = skills + ";" + s
row = [
listToString(data.get('name')),
listToString(data.get('email')),
listToString(data.get('mobile_number')), skills,
listToString(data.get('experience')),
listToString(data.get('college_name')),
listToString(data.get('degree')),
listToString(data.get('designation')),
listToString(data.get('company_names')), filename
]
print(row)
conn.execute("INSERT INTO USERS VALUES (NULL,?,?,?,?,?,?,?,?,?,?)",
(row))
conn.commit()
print("Records created successfully")
conn.close()
return '<h1>File saved!</h1>'
def get_prediction(filename, details):
name = 'Sample DE'
data = ResumeParser(filename,
skills_file='skills.csv').get_extracted_data()
df = pd.DataFrame({'Skills': data.get("skills")})
df['Email'] = data.get("email")
df['Contact_Number'] = data.get("mobile_number")
df['Name'] = name
try:
df['Experience'] = data.get("total_experience")
except:
df['Experience'] = 0
try:
df['Education'] = data.get("education")[0]
except:
df['Education'] = 'Did not catch that!'
a = []
a = df["Skills"].tolist()
b = concatenate_list_data(a)
df['Skills'] = b
df = df[[
"Name", "Email", "Contact_Number", "Education", "Experience", "Skills"
]]
df = df.drop_duplicates()
model = load(open('MB.pkl', 'rb'))
wn = WordNetLemmatizer()
corpus1 = []
for i in range(len(df)):
review = re.sub('[^a-zA-Z]', ' ', str(df['Skills'][i]))
review = review.lower()
review = review.split()
review = [
wn.lemmatize(word) for word in review
if not word in stopwords.words('english')
]
review = ' '.join(review)
corpus1.append(review)
tf_fit = load(open('TF_FIT.pkl', 'rb'))
X = tf_fit.transform(corpus1)
pred = model.predict(X)
df['Best_Suited_Role'] = pred
df['Name'] = details['name']
df['Email'] = details['email']
df['Education'] = details['edu']
df['Contact_Number'] = details['mobno']
df['Experience'] = details['expe']
df = df.to_dict()
return df
def handleResume(request):
if request.method == 'POST':
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print('post')
resume = request.FILES.get('resume', None)
print(resume)
if resume:
saving = Resume(resume=resume)
saving.save()
media_path = os.path.join(BASE_DIR, 'resumes')
lpart = str(saving.resume).split('/')
full_path = os.path.join(media_path, lpart[1])
data = ResumeParser(str(full_path)).get_extracted_data()
candidate = Candidate(
name=data.get('name'),
email=data.get('email'),
phone=data.get('mobile_number'),
experience=float(data.get('total_experience')),
total_skills=len(data.get('skills')),
designation=data.get('designation'),
company="N/A" if data.get('company_names') is None else
data.get('company_names'))
candidate.save()
return render(request, "app/home.html", {})
return render(request, "app/cvform.html", {})
def home(request):
msg = None
skills = None
allowed_file_type = ['application/pdf']
if request.method == 'POST':
resume_file = request.FILES.get('resume')
file_path = None
if resume_file:
if resume_file.content_type in allowed_file_type:
try:
fs = FileSystemStorage()
filename = fs.save(resume_file.name, resume_file)
file_path = os.path.join(fs.location, filename)
parsed_data = ResumeParser(file_path).get_extracted_data()
except Exception as e:
msg = f"Error occurred while parsing the CV. Detail error msg: {str(e)}"
else:
skills = parsed_data.get('skills')
finally:
if file_path and os.path.isfile(file_path):
os.remove(file_path)
else:
msg = "Please provide pdf and docx document"
else:
msg = "Please upload your resume"
return render(request, 'home.html', context={'msg': msg, 'skills': skills})
def handle_uploaded_file(cv):
"""
Extracts skills mentioned in a CV.
Saves uploaded CV in a temporary location and uses
pyresparser to extract all necessary informatiion from
that CV. After extraction, Uploaded file is deleted.
Parameters:
cv (file): CV uploaded form UI
Returns:
list of string: List of skills extracted from CV. Might be empty list
if it is unable to extract skills
"""
upload_path = os.path.join(settings.BASE_DIR, "uploads", cv.name)
with open(upload_path, "wb+") as destination:
for chunk in cv.chunks():
destination.write(chunk)
try:
data = ResumeParser(upload_path).get_extracted_data()
except Exception:
data = {"skills": []}
finally:
os.remove(upload_path)
return data.get("skills", [])
def index(request):
if request.method == 'POST':
#save the file
file = request.FILES['file']
file_name = default_storage.save(file.name, file)
# Reading file from storage
file = default_storage.open(file_name)
file_url = default_storage.url(file_name)
#Use resume Parser library to read the informationn
# Start the stopwatch / counter
t1_start = process_time()
data = ResumeParser(file_url).get_extracted_data()
#get only skills values from the data dictionary
dict_df=data.get("skills")
#convert dictionary objects to string
result = str(dict_df)
result = json.dumps(result)
#default_storage.delete(file_name)
# Stop the stopwatch / counter
t1_stop = process_time()
#print("Elapsed time:", t1_stop, t1_start)
#print("Elapsed time during the whole program in seconds:",t1_stop-t1_start)
#processing_time= t1_stop-t1_start
# response = {'Skills': result,}
# 'time': format(t1_stop-t1_startime, '.2f'),
# response = {
# 'upload_file_path': upload_file_path,
# 'Name': data['name'],
# 'Email': data['email'],
# 'Skills': data['skills'],
# 'time': format(stop_time - start_time, '.2f'),
# }
return render(request, 'skillreader/index.html', context=result)
else:
return render(request, 'skillreader/index.html')
def handleResume(request):
if request.method == 'POST':
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print('post')
resume = request.FILES.get('resume', None)
print(resume)
if resume:
saving = Resume(resume=resume)
saving.save()
media_path = os.path.join(base_dir, 'Resumes')
l_part = str(saving.resume).split('/')
full_path = os.path.join(media_path, l_part[1])
data = ResumeParser(str(full_path)).get_extracted_data()
print(data.get('total_experience'))
print(data.get('skills'))
skills = data.get('skills')
predefined_skill = Skill.objects.all()
skills_score = 0
for skill in skills:
skill = skill.casefold()
for pre_skill in predefined_skill:
if pre_skill.skill_name.casefold() == skill:
skills_score = skills_score + pre_skill.score
print(skills_score)
experiences_skills_combined_score = float(
data.get('total_experience')) + skills_score
print(experiences_skills_combined_score)
candidate = Candidate(
name=data.get('name'),
email=data.get('email'),
phone=data.get('mobile_number'),
total_experiences=float(data.get('total_experience')),
total_skills=len(data.get('skills')),
designation="N/A" if data.get('designation') is None else
data.get('designation'),
company="N/A" if data.get('company_names') is None else
data.get('company_names'),
skills_score=skills_score,
experiences_skills_combined_score=
experiences_skills_combined_score)
candidate.save()
return render(request, "Authority/sorted_list.html", {})
return render(request, "Candidate/cv_form.html", {})
def predict():
if request.method == 'POST':
if not request.form['fname'] or not request.form['message']:
print('Please enter all the fields', error)
else:
message = request.form['message']
if (len(message) > 10):
def eval_string(my_post):
c = cv.transform([tokeniser(my_post)])
x = idf_transformer.transform(c)
ie = lr_ie.predict_proba(x).flatten()
ns = lr_ns.predict_proba(x).flatten()
tf = lr_tf.predict_proba(x).flatten()
jp = lr_jp.predict_proba(x).flatten()
score = [0, 0, 0, 0, 0, 0, 0, 0, 0]
score[0] = ((ie[1] + ns[1] + tf[0] + jp[0]) / 4) * 100
#Calculated all the 8 personality types
#introvert
score[1] = ie[0] * 100
#extrovert
score[2] = ie[1] * 100
# intuitive
score[3] = ns[0] * 100
#sensor
score[4] = ns[1] * 100
# thinker
score[5] = tf[0] * 100
# feeler
score[6] = tf[1] * 100
# judger
score[7] = jp[0] * 100
# perciever
score[8] = jp[1] * 100
return score
scores_list = eval_string(message)
personality_score = scores_list[0]
personality_score = int(round(personality_score))
my_prediction = personality_score
f = request.files['upload']
f.save(
os.path.join(app.config['UPLOAD_FOLDER'],
secure_filename(f.filename)))
resume_data = ResumeParser(f.filename).get_extracted_data()
print(resume_data)
#extracting data from resume
college = (resume_data.get("college"))
email = (resume_data.get("email"))
mobilenumber = (resume_data.get("mobile_number"))
skills = (resume_data.get("skills"))
experience = resume_data.get("total_experience")
#Calculating SKills Score out of 40
skills_score = ((len((text & set(skills)))) / len(text)) * 40
#Calculating Experience Score out of 40
if (experience > req_experience[1]):
experience_score = 40
elif (experience < req_experience[0]):
experience_score = 0
else:
experience_score = (experience / req_experience[1]) * 40
candidate_total = experience_score + skills_score + (
personality_score * 0.2)
experience_score = experience_score * 2.5
skills_score = skills_score * 2.5
# candidates = database.query.order_by(database.total_score.desc()).all() #fetch them all in one query
# rank=candidates.id
#GETTING PERSONALITY TYPE SCORES
introvert = (int(round(scores_list[1])))
extrovert = (int(round(scores_list[2])))
intuitive = (int(round(scores_list[3])))
sensor = (int(round(scores_list[4])))
thinker = (int(round(scores_list[5])))
feeler = (int(round(scores_list[6])))
judger = (int(round(scores_list[7])))
perciever = (int(round(scores_list[8])))
data = database(request.form['fname'], request.form['message'],
personality_score, skills_score, experience_score,
candidate_total, college, email, mobilenumber,
introvert, extrovert, sensor, intuitive, thinker,
feeler, judger, perciever)
db.session.add(data)
db.session.commit()
print('Record was successfully submitted')
else:
my_prediction = 3
return "YOUR RECORD WAS SUCESSFULLY SUBMITTED"
def getTotalExperience(self, f):
data = ResumeParser(f).get_extracted_data()
total_experience = data.get('total_experience', 'No experience found')
return total_experience
def getExperience(self, f):
data = ResumeParser(f).get_extracted_data()
experience = data.get('experience', 'No experience found')
return experience
def getSkills(self, f):
data = ResumeParser(f).get_extracted_data()
candidate_skills = data.get('skills', 'No skills found')
return candidate_skills
def getName(self, f):
data = ResumeParser(f).get_extracted_data()
candidate_name = data.get('name', 'No Name Found')
return candidate_name