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