| import pickle
|
| import numpy as np
|
| import spacy
|
| import re
|
| import string
|
| import streamlit as st
|
| from tensorflow.keras.models import load_model
|
| from tensorflow.keras.preprocessing.sequence import pad_sequences
|
| import os
|
|
|
| abbreviations = {
|
| "mgr": "manager",
|
| "sr": "senior",
|
| "jr": "junior",
|
| "asst": "assistant",
|
| "assoc": "associate",
|
| "dept": "department",
|
| "exp": "experience",
|
| "hr": "human resources",
|
| "acct": "account",
|
| "acctg": "accounting",
|
| "fin": "finance",
|
| "eng": "engineer",
|
| "engg": "engineering",
|
| "it": "information technology",
|
| "qa": "quality assurance",
|
| "dev": "development",
|
| "devops": "development operations",
|
| "proj": "project",
|
| "mktg": "marketing",
|
| "biz": "business",
|
| "comm": "communication",
|
| "adm": "administration",
|
| "sec": "secretary",
|
| "exec": "executive",
|
| "corp": "corporation",
|
| "intl": "international",
|
| "rep": "representative",
|
| "mfg": "manufacturing",
|
| "prod": "production",
|
| "purch": "purchasing",
|
| "sales": "sales",
|
| "cust": "customer",
|
| "svc": "service",
|
| "tech": "technical",
|
| "sup": "supervisor",
|
| "supv": "supervision",
|
| "log": "logistics",
|
| "inv": "inventory",
|
| "sch": "schedule",
|
| "edu": "education",
|
| "lang": "language",
|
| "pr": "public relations",
|
| "hrd": "human resources development",
|
| "cfo": "chief financial officer",
|
| "ceo": "chief executive officer",
|
| "coo": "chief operating officer",
|
| "cmo": "chief marketing officer",
|
| "cto": "chief technology officer",
|
| "cio": "chief information officer",
|
| "pmo": "project management office",
|
| "pmp": "project management professional",
|
| "ba": "business analyst",
|
| "bpm": "business process management",
|
| "ui": "user interface",
|
| "ux": "user experience",
|
| "svp": "senior vice president",
|
| "vp": "vice president",
|
| "gm": "general manager",
|
| "doe": "depends on experience",
|
| "r&d": "research and development",
|
| "seo": "search engine optimization",
|
| "sem": "search engine marketing",
|
| "smm": "social media marketing",
|
| "b2b": "business to business",
|
| "b2c": "business to consumer",
|
| "kpi": "key performance indicator",
|
| "roi": "return on investment",
|
| "saas": "software as a service",
|
| "paas": "platform as a service",
|
| "iaas": "infrastructure as a service",
|
| "crm": "customer relationship management",
|
| "erp": "enterprise resource planning",
|
| "sd": "software development",
|
| "pm": "project manager",
|
| "pa": "personal assistant",
|
| "exec": "executive",
|
| "fin": "finance",
|
| "hrm": "human resources management",
|
| "it": "information technology",
|
| "pr": "public relations",
|
| "qa": "quality assurance",
|
| "r&d": "research and development",
|
| "scm": "supply chain management",
|
| "seo": "search engine optimization",
|
| "smm": "social media marketing",
|
| "ux": "user experience",
|
| "ui": "user interface",
|
| "bi": "business intelligence",
|
| "dev": "development",
|
| "ops": "operations"
|
| }
|
|
|
| def ensure_model_installed():
|
| try:
|
| spacy.load('en_core_web_sm')
|
| except OSError:
|
| from spacy.cli import download
|
| download('en_core_web_sm')
|
| spacy.load('en_core_web_sm')
|
|
|
|
|
| ensure_model_installed()
|
|
|
| nlp = spacy.load("en_core_web_sm")
|
|
|
| def expand_abbreviations(text, abbreviations):
|
| for abbr, expanded in abbreviations.items():
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| text = re.sub(r'\b{}\b'.format(abbr), expanded, text, flags=re.IGNORECASE)
|
| return text
|
|
|
| def clean_and_preprocess(text):
|
| text = expand_abbreviations(text, abbreviations)
|
| text = text.lower()
|
| text = re.sub(r'\d+', '', text)
|
| text = text.translate(str.maketrans('', '', string.punctuation))
|
| text = re.sub(r'\s+', ' ', text).strip()
|
| doc = nlp(text)
|
| tokens = [token.lemma_ for token in doc if token.is_alpha and not token.is_stop]
|
| return ' '.join(tokens)
|
|
|
| def extract_nouns(text):
|
| doc = nlp(text)
|
| nouns = [token.lemma_ for token in doc if token.pos_ == "NOUN"]
|
| return nouns
|
|
|
| def load_model_and_tokenizers_for_hr():
|
| model_path = 'modelfile/bighr2.keras'
|
| tokenizer_path = 'tokernizer/tokenizershr.pkl'
|
| if not os.path.isfile(model_path):
|
| raise FileNotFoundError(f"Model file not found: {model_path}")
|
| if not os.path.isfile(tokenizer_path):
|
| raise FileNotFoundError(f"Tokenizer file not found: {tokenizer_path}")
|
| model = load_model(model_path)
|
| with open(tokenizer_path, 'rb') as f:
|
| tokenizers = pickle.load(f)
|
| resume_tokenizer = tokenizers.get('resume_tokenizer')
|
| description_tokenizer = tokenizers.get('description_tokenizer')
|
| common_nouns_tokenizer = tokenizers.get('common_nouns_tokenizer')
|
| if not (resume_tokenizer and description_tokenizer and common_nouns_tokenizer):
|
| raise ValueError("Tokenizer components are missing from the file.")
|
| return model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer
|
|
|
| def load_model_and_tokenizers_for_it():
|
| model_path = 'modelfile/bigit2.keras'
|
| tokenizer_path = 'tokernizer/tokenizersit.pkl'
|
| if not os.path.isfile(model_path):
|
| raise FileNotFoundError(f"Model file not found: {model_path}")
|
| if not os.path.isfile(tokenizer_path):
|
| raise FileNotFoundError(f"Tokenizer file not found: {tokenizer_path}")
|
| model = load_model(model_path)
|
| with open(tokenizer_path, 'rb') as f:
|
| tokenizers = pickle.load(f)
|
| resume_tokenizer = tokenizers.get('resume_tokenizer')
|
| description_tokenizer = tokenizers.get('description_tokenizer')
|
| common_nouns_tokenizer = tokenizers.get('common_nouns_tokenizer')
|
| if not (resume_tokenizer and description_tokenizer and common_nouns_tokenizer):
|
| raise ValueError("Tokenizer components are missing from the file.")
|
| return model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer
|
|
|
| def load_model_and_tokenizers_for_sales():
|
| model_path = 'modelfile/bigrsales2.keras'
|
| tokenizer_path = 'tokernizer/tokenizerssales.pkl'
|
| if not os.path.isfile(model_path):
|
| raise FileNotFoundError(f"Model file not found: {model_path}")
|
| if not os.path.isfile(tokenizer_path):
|
| raise FileNotFoundError(f"Tokenizer file not found: {tokenizer_path}")
|
| model = load_model(model_path)
|
| with open(tokenizer_path, 'rb') as f:
|
| tokenizers = pickle.load(f)
|
| resume_tokenizer = tokenizers.get('resume_tokenizer')
|
| description_tokenizer = tokenizers.get('description_tokenizer')
|
| common_nouns_tokenizer = tokenizers.get('common_nouns_tokenizer')
|
| if not (resume_tokenizer and description_tokenizer and common_nouns_tokenizer):
|
| raise ValueError("Tokenizer components are missing from the file.")
|
| return model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer
|
|
|
| def load_model_and_tokenizers_for_health():
|
| model_path = 'modelfile/bighealth2.keras'
|
| tokenizer_path = 'tokernizer/tokenizershealth.pkl'
|
| if not os.path.isfile(model_path):
|
| raise FileNotFoundError(f"Model file not found: {model_path}")
|
| if not os.path.isfile(tokenizer_path):
|
| raise FileNotFoundError(f"Tokenizer file not found: {tokenizer_path}")
|
| model = load_model(model_path)
|
| with open(tokenizer_path, 'rb') as f:
|
| tokenizers = pickle.load(f)
|
| resume_tokenizer = tokenizers.get('resume_tokenizer')
|
| description_tokenizer = tokenizers.get('description_tokenizer')
|
| common_nouns_tokenizer = tokenizers.get('common_nouns_tokenizer')
|
| if not (resume_tokenizer and description_tokenizer and common_nouns_tokenizer):
|
| raise ValueError("Tokenizer components are missing from the file.")
|
| return model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer
|
|
|
| def load_model_and_tokenizers_for_other():
|
| model_path = 'modelfile/bigothers2.keras'
|
| tokenizer_path = 'tokernizer/tokenizersothers.pkl'
|
| if not os.path.isfile(model_path):
|
| raise FileNotFoundError(f"Model file not found: {model_path}")
|
| if not os.path.isfile(tokenizer_path):
|
| raise FileNotFoundError(f"Tokenizer file not found: {tokenizer_path}")
|
| model = load_model(model_path)
|
| with open(tokenizer_path, 'rb') as f:
|
| tokenizers = pickle.load(f)
|
| resume_tokenizer = tokenizers.get('resume_tokenizer')
|
| description_tokenizer = tokenizers.get('description_tokenizer')
|
| common_nouns_tokenizer = tokenizers.get('common_nouns_tokenizer')
|
| if not (resume_tokenizer and description_tokenizer and common_nouns_tokenizer):
|
| raise ValueError("Tokenizer components are missing from the file.")
|
| return model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer
|
|
|
|
|
| st.title("ATS")
|
|
|
| st.write("Upload your resume and job description, then select the job sector to analyze how well the resume fits the job description.")
|
|
|
|
|
| resume = st.text_area("Paste your Resume:", height=150)
|
|
|
|
|
| job_description = st.text_area("Paste Job Description:", height=150)
|
|
|
|
|
| sector = st.selectbox("Select Sector:", ['HR', 'IT', 'Sales', 'Health', 'Other'])
|
|
|
| if st.button("Calculate ATS Score"):
|
| if resume and job_description:
|
| try:
|
| if sector == 'HR':
|
| model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer = load_model_and_tokenizers_for_hr()
|
| elif sector == 'IT':
|
| model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer = load_model_and_tokenizers_for_it()
|
| elif sector == 'Sales':
|
| model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer = load_model_and_tokenizers_for_sales()
|
| elif sector == 'Health':
|
| model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer = load_model_and_tokenizers_for_health()
|
| elif sector == 'Other':
|
| model, resume_tokenizer, description_tokenizer, common_nouns_tokenizer = load_model_and_tokenizers_for_other()
|
|
|
| processed_resume = clean_and_preprocess(resume)
|
| processed_description = clean_and_preprocess(job_description)
|
|
|
| resume_sequence = resume_tokenizer.texts_to_sequences([processed_resume])
|
| resume_data_padded = pad_sequences(resume_sequence, maxlen=1500)
|
|
|
| description_sequence = description_tokenizer.texts_to_sequences([processed_description])
|
| description_data_padded = pad_sequences(description_sequence, maxlen=1500)
|
|
|
| common_nouns = set(extract_nouns(processed_resume))
|
| common_nouns_str = ' '.join(common_nouns)
|
|
|
| common_nouns_sequence = common_nouns_tokenizer.texts_to_sequences([common_nouns_str])
|
| common_nouns_data = pad_sequences(common_nouns_sequence, maxlen=10)
|
|
|
| prediction = model.predict([resume_data_padded, description_data_padded, common_nouns_data])
|
| st.success(f"Your predicted ATS Score is: {prediction[0][0]:.2f}")
|
|
|
| except FileNotFoundError as fnf_error:
|
| st.error(str(fnf_error))
|
| except ValueError as val_error:
|
| st.error(str(val_error))
|
| except Exception as e:
|
| st.error(f"An error occurred: {e}")
|
| else:
|
| st.error("Please paste both your resume and job description before analyzing.")
|
|
|
