def res(jobfile,skillset,jd_exp):
Resume_Vector = []
Resume_skill_vector = []
Resume_email_vector = []
Resume_phoneNo_vector = []
Resume_name_vector = []
Resume_nonTechSkills_vector = []
Resume_exp_vector = []
Ordered_list_Resume = []
LIST_OF_FILES = []
LIST_OF_FILES_PDF = []
LIST_OF_FILES_DOC = []
LIST_OF_FILES_DOCX = []
Resumes = []
Temp_pdf = []
os.chdir("..")
print(os.getcwd())
os.chdir('Upload-Resume')
jd_weightage = 15
not_found = 'Not Found'
extract_exp = ExtractExp()
for file in glob.glob('**/*.pdf', recursive=True):
LIST_OF_FILES_PDF.append(file)
for file in glob.glob('**/*.doc', recursive=True):
LIST_OF_FILES_DOC.append(file)
for file in glob.glob('**/*.docx', recursive=True):
LIST_OF_FILES_DOCX.append(file)
for file in glob.glob('**/*.rtf', recursive=True):
LIST_OF_FILES_DOCX.append(file)
for file in glob.glob('**/*.txt', recursive=True):
LIST_OF_FILES_DOCX.append(file)
LIST_OF_FILES = LIST_OF_FILES_DOC + LIST_OF_FILES_DOCX + LIST_OF_FILES_PDF
# LIST_OF_FILES.remove("antiword.exe")
print("This is LIST OF FILES")
print(LIST_OF_FILES)
print("####### PARSING ########")
pythoncom.CoInitialize()
for count,i in enumerate(LIST_OF_FILES):
Temp = i.rsplit('.', 1)
if Temp[1] == "pdf" or Temp[1] == "Pdf" or Temp[1] == "PDF":
try:
print(count," This is PDF" , i)
with open(i,'rb') as pdf_file:
read_pdf = PyPDF2.PdfFileReader(pdf_file)
# page = read_pdf.getPage(0)
# page_content = page.extractText()
# Resumes.append(Temp_pdf)
number_of_pages = read_pdf.getNumPages()
for page_number in range(number_of_pages):
page = read_pdf.getPage(page_number)
page_content = page.extractText()
page_content = page_content.replace('\n', ' ')
# page_content.replace("\r", "")
Temp_pdf = str(Temp_pdf) + str(page_content)
# Temp_pdf.append(page_content)
# print(Temp_pdf)
Resumes.extend([Temp_pdf])
Temp_pdf = ''
Ordered_list_Resume.append(i)
# f = open(str(i)+str("+") , 'w')
# f.write(page_content)
# f.close()
except Exception as e:
print(e)
print(traceback.format_exc())
elif Temp[1] == "doc" or Temp[1] == "Doc" or Temp[1] == "DOC":
print(count," This is DOC" , i)
parse_docfile(i)
elif Temp[1] == "rtf" or Temp[1] == "Rtf" or Temp[1] == "RTF":
print(count," This is Rtf" , i)
try:
rtf_path = Path(i)
with rtf_path.open() as source:
docText = rtf_to_text(source.read())
c = [docText]
Resumes.extend(c)
Ordered_list_Resume.append(i)
except Exception as e: print(e)
elif Temp[1] == "docx" or Temp[1] == "Docx" or Temp[1] == "DOCX":
print(count," This is DOCX" , i)
try:
a = textract.process(i)
a = a.replace(b'\n', b' ')
a = a.replace(b'\r', b' ')
b = str(a)
c = [b]
Resumes.extend(c)
Ordered_list_Resume.append(i)
except Exception as e: print(e)
elif Temp[1] == "txt" or Temp[1] == "Txt" or Temp[1] == "TXT":
print(count," This is txt" , i)
try:
f = open(file,'r')
lines = f.readlines()
a = "\n".join(lines)
c = [str(a)]
Resumes.extend(c)
Ordered_list_Resume.append(i)
f.close()
except Exception as e: print(e)
elif Temp[1] == "ex" or Temp[1] == "Exe" or Temp[1] == "EXE":
print("This is EXE" , i)
pass
print("Done Parsing.")
print("Please wait we are preparing ranking.")
Job_Desc = 0
try:
tttt = str(jobfile)
tttt = summarize(tttt, word_count=100)
text = [tttt]
except:
text = 'None'
vectorizer = TfidfVectorizer(stop_words='english')
# print(text)
vectorizer.fit(text)
vector = vectorizer.transform(text)
Job_Desc = vector.toarray()
# print("\n\n")
# print("This is job desc : " , Job_Desc)
tempList = Ordered_list_Resume
os.chdir('../')
flask_return = []
for index,i in enumerate(Resumes):
text = i
temptext = str(text)
tttt = str(text)
try:
tttt = summarize(tttt, word_count=100)
text = [tttt]
vector = vectorizer.transform(text)
Resume_Vector.append(vector.toarray())
Resume_skill_vector.append(skills.programmingScore(temptext,jobfile+skillset))
experience = extract_exp.get_features(temptext)
Resume_name_vector.append(experience)
temp_phone = entity.extract_phone_numbers(temptext)
if(len(temp_phone) == 0):
Resume_phoneNo_vector.append(not_found)
else:
Resume_phoneNo_vector.append(temp_phone)
temp_email = entity.extract_email_addresses(temptext)
if(len(temp_email) == 0):
Resume_email_vector.append(not_found)
else:
Resume_email_vector.append(temp_email)
Resume_exp_vector.append(extract_exp.get_exp_weightage(jd_exp,experience))
Resume_nonTechSkills_vector.append(skills.NonTechnicalSkillScore(temptext,jobfile+skillset))
print("Rank prepared for ",Ordered_list_Resume.__getitem__(index))
except Exception:
print(traceback.format_exc())
tempList.__delitem__(index)
for index,i in enumerate(Resume_Vector):
samples = i
similarity = cosine_similarity(samples,Job_Desc)[0][0]
"""Ordered_list_Resume_Score.extend(similarity)"""
#print(Resume_skill_vector)
#print(Resume_nonTechSkills_vector)
#print(Resume_exp_vector)
final_rating = round(similarity*jd_weightage,2)+Resume_skill_vector.__getitem__(index)+Resume_nonTechSkills_vector.__getitem__(index)+Resume_exp_vector.__getitem__(index)
res = ResultElement(round(similarity*jd_weightage,2), tempList.__getitem__(index),round(Resume_skill_vector.__getitem__(index),2),
Resume_name_vector.__getitem__(index),Resume_phoneNo_vector.__getitem__(index),Resume_email_vector.__getitem__(index),
Resume_nonTechSkills_vector.__getitem__(index),Resume_exp_vector.__getitem__(index),round(final_rating,2))
flask_return.append(res)
flask_return.sort(key=lambda x: x.finalRank, reverse=True)
return flask_return
def res(jobfile,skillset,jd_exp,min_qual):
Resume_Vector = []
Resume_skill_vector = []
min_qual_vector = []
is_min_qual = []
Resume_skill_list = []
Resume_non_skill_list = []
Resume_email_vector = []
Resume_phoneNo_vector = []
Resume_ApplicantName_vector = []
Resume_name_vector = []
Resume_nonTechSkills_vector = []
Resume_exp_vector = []
Ordered_list_Resume = []
LIST_OF_FILES = []
LIST_OF_FILES_PDF = []
LIST_OF_FILES_DOC = []
LIST_OF_FILES_DOCX = []
Resumes = []
Temp_pdf = []
Resume_title = []
#if count == 0:
# os.chdir("..")
# print(os.getcwd())
# os.chdir('Upload-Resume')
jd_weightage = 15
not_found = 'Not Found'
extract_exp = ExtractExp()
"""
resumePath = r'Upload-Resume'
for file in glob.glob(resumePath+'/*.pdf'):
LIST_OF_FILES_PDF.append(file)
for file in glob.glob(resumePath+'/*.doc'):
LIST_OF_FILES_DOC.append(file)
for file in glob.glob(resumePath+'/*.docx'):
LIST_OF_FILES_DOCX.append(file)
for file in glob.glob(resumePath+'/*.rtf'):
LIST_OF_FILES_DOCX.append(file)
for file in glob.glob(resumePath+'/*.txt'):
LIST_OF_FILES_DOCX.append(file)
LIST_OF_FILES = LIST_OF_FILES_DOC + LIST_OF_FILES_DOCX + LIST_OF_FILES_PDF
# LIST_OF_FILES.remove("antiword.exe")
print("This is LIST OF FILES")
print(LIST_OF_FILES)
"""
LIST_OF_FILES=['teja.rtf']
print("####### PARSING ########")
#pythoncom.CoInitialize()
print(" Testing")
print("Completed")
for count,i in enumerate(LIST_OF_FILES):
Temp = i.rsplit('.', 1)
Resume_title.extend(str(i))
if Temp[1] == "pdf" or Temp[1] == "Pdf" or Temp[1] == "PDF":
try:
print(count," This is PDF" , i)
with open(i,'rb') as pdf_file:
for page in p.extract_text_by_page(i):
page = page.replace('\n', ' ')
Temp_pdf = str(page)
Resumes.extend([Temp_pdf])
Temp_pdf = ''
Ordered_list_Resume.append(i)
"""
read_pdf = PyPDF2.PdfFileReader(pdf_file,strict=False)
# page = read_pdf.getPage(0)
# page_content = page.extractText()
# Resumes.append(Temp_pdf)
number_of_pages = read_pdf.getNumPages()
for page_number in range(number_of_pages):
page = read_pdf.getPage(page_number)
page_content = page.extractText()
page_content = page_content.replace('\n', ' ')
#print("hello")
#teyw=page_content.read()
#print(teyw)
# page_content.replace("\r", "")
Temp_pdf = str(Temp_pdf) + str(page_content)
# Temp_pdf.append(page_content)
print(Temp_pdf)
Resumes.extend([Temp_pdf])
Temp_pdf = ''
Ordered_list_Resume.append(i)
f = open(str(i)+str("+") , 'w')
f.write(page_content)
f.close()
"""
except Exception as e:
print(e)
print(traceback.format_exc())
elif Temp[1] == "doc" or Temp[1] == "Doc" or Temp[1] == "DOC":
print(count," This is DOC" , i)
#parse_docfile(i)
elif Temp[1] == "rtf" or Temp[1] == "Rtf" or Temp[1] == "RTF":
print(count," This is Rtf" , i)
try:
rtf_path = Path(i)
with rtf_path.open() as source:
docText = rtf_to_text(source.read())
c = [docText]
Resumes.extend(c)
Ordered_list_Resume.append(i)
except Exception as e: print(e)
elif Temp[1] == "docx" or Temp[1] == "Docx" or Temp[1] == "DOCX":
print(count," This is DOCX" , i)
try:
a = textract.process(i)
a = a.replace(b'\n', b' ')
a = a.replace(b'\r', b' ')
b = str(a)
c = [b]
Resumes.extend(c)
Ordered_list_Resume.append(i)
except Exception as e: print(e)
elif Temp[1] == "txt" or Temp[1] == "Txt" or Temp[1] == "TXT":
print(count," This is txt" , i)
try:
f = open(file,'r')
lines = f.readlines()
a = "\n".join(lines)
c = [str(a)]
Resumes.extend(c)
Ordered_list_Resume.append(i)
f.close()
except Exception as e: print(e)
elif Temp[1] == "ex" or Temp[1] == "Exe" or Temp[1] == "EXE":
print("This is EXE" , i)
pass
print("Done Parsing.")
print("Please wait we are preparing ranking.")
Job_Desc = 0
try:
tttt = str(jobfile)
tttt = summarize(tttt, word_count=100)
text = [tttt]
except:
text = 'None'
#vectorizer = TfidfVectorizer(stop_words='english')
vectorizer = TfidfVectorizer()
# print(text)
vectorizer.fit(text)
vector = vectorizer.transform(text)
Job_Desc = vector.toarray()
print("This is jobbbbbb")
print(Job_Desc)
# print("\n\n")
# print("This is job desc : " , Job_Desc)
tempList = Ordered_list_Resume
#os.chdir('../')
flask_return = []
for index,i in enumerate(Resumes):
text = i
temptext = str(text)
tttt = str(text)
print("This is resume text")
try:
#tttt = summarize(tttt, word_count=1000)
text = [tttt]
print("mark")
print(text)
vector = vectorizer.transform(text)
print("puka")
print(vector.toarray())
Resume_Vector.append(vector.toarray())
#min_qual_score = skills.minQualificationScore(temptext,min_qual)
min_qual_score = skills.programmingScore(temptext,min_qual,progWords = None)
print("Score")
print(min_qual_score)
print("end")
min_qual_vector.append(min_qual_score)
confidence = {}
#score = int((min_qual_score/globals.min_qual_weightage)*100)
score = int((min_qual_score/10)*100)
confidence['confidence'] = score
if score >= 60:
confidence['min qual'] = 'Yes'
elif score < 60 and score > 0:
confidence['min qual'] = 'May Be'
else:
confidence['min qual'] = 'Yes'
is_min_qual.append(confidence)
Resume_skill_vector.append(skills.programmingScore(temptext,jobfile+skillset,progWords = None))
Resume_skill_list.append(skills.NonTechnicalSkillScore(temptext,jobfile+skillset,progWords = None))
Resume_non_skill_list.append(skills.NonTechnicalSkillScore(temptext,jobfile+skillset,progWords = None))
experience = extract_exp.get_features(temptext)
Resume_name_vector.append(experience)
#temp_applicantName = entity.extractPersonName(temptext, Resume_title[index])
#Resume_ApplicantName_vector.append(temp_applicantName)
temp_phone = entity.extract_phone_numbers(temptext)
if(len(temp_phone) == 0):
Resume_phoneNo_vector.append(not_found)
else:
Resume_phoneNo_vector.append(temp_phone)
temp_email = entity.extract_email_addresses(temptext)
if(len(temp_email) == 0):
Resume_email_vector.append(not_found)
else:
Resume_email_vector.append(temp_email)
Resume_exp_vector.append(extract_exp.get_exp_weightage(str(jd_exp),experience))
Resume_nonTechSkills_vector.append(skills.NonTechnicalSkillScore(temptext,jobfile+skillset))
print("Rank prepared for ",Ordered_list_Resume.__getitem__(index))
except Exception:
print(traceback.format_exc())
tempList.__delitem__(index)
for index,i in enumerate(Resume_Vector):
samples = i
print("This is sampleees")
print(samples)
similarity = cosine_similarity(samples,Job_Desc)[0][0]
"""Ordered_list_Resume_Score.extend(similarity)"""
#print(Resume_skill_vector)
#print(Resume_nonTechSkills_vector)
#print(Resume_exp_vector)
final_rating = round((similarity*jd_weightage)*3.33,2)+(Resume_skill_vector.__getitem__(index))*3.33
res = ResultElement(round(similarity*jd_weightage,2)*3.33, os.path.basename(tempList.__getitem__(index)),round(Resume_skill_vector.__getitem__(index)*3.33,2),
Resume_name_vector.__getitem__(index), Resume_phoneNo_vector.__getitem__(index),Resume_email_vector.__getitem__(index),
Resume_nonTechSkills_vector.__getitem__(index),Resume_exp_vector.__getitem__(index),round(final_rating,2),Resume_skill_list.__getitem__(index),
Resume_non_skill_list.__getitem__(index),min_qual_vector.__getitem__(index),is_min_qual.__getitem__(index))
flask_return.append(res)
flask_return.sort(key=lambda x: x.finalRank, reverse=True)
return flask_return