Create resume_parser.py

resume_parser.py

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+import fitz  # PyMuPDF for PDF text extraction
+import re
+import spacy
+from transformers import pipeline
+from docx import Document
+import dateparser
+from datetime import datetime
+from nltk.corpus import words
+from models.load_models import nlp_spacy, nlp_ner
+
+# NLTK words
+english_words = set(words.words())
+
+# Function to refine ORG entities
+def refine_org_entities(entities):
+    refined_entities = set()
+    company_suffixes = ['Inc', 'LLC', 'Corporation', 'Corp', 'Ltd', 'Co', 'GmbH', 'S.A.']
+
+    for entity in entities:
+        if any(entity.endswith(suffix) for suffix in company_suffixes):
+            refined_entities.add(entity)
+        elif re.match(r'([A-Z][a-z]+)\s([A-Z][a-z]+)', entity):
+            refined_entities.add(entity)
+    return list(refined_entities)
+
+# Function to extract ORG entities using NER
+def extract_orgs(text):
+    ner_results = nlp_ner(text)
+    orgs = set()
+    for entity in ner_results:
+        if entity['entity_group'] == 'ORG':
+            orgs.add(entity['word'])
+    return refine_org_entities(orgs)
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+    text = ""
+    for page_num in range(doc.page_count):
+        page = doc.load_page(page_num)
+        text += page.get_text()
+    return text
+
+# Function to extract text from DOCX
+def extract_text_from_doc(doc_file):
+    doc = Document(doc_file)
+    text = '\n'.join([para.text for para in doc.paragraphs])
+    return text
+
+# Function to extract experience
+def extract_experience(doc):
+    experience = 0
+    for ent in doc.ents:
+        if ent.label_ == "DATE":
+            date = dateparser.parse(ent.text)
+            if date:
+                experience = max(experience, datetime.now().year - date.year)
+    return experience
+
+# Function to extract phone numbers
+def extract_phone(text):
+    phone_patterns = [
+        r'\b(?:\+?1[-.\s]?)?(?:\(\d{3}\)|\d{3})[-.\s]?\d{3}[-.\s]?\d{4}\b',
+        r'\b\d{3}[-.\s]?\d{3}[-.\s]?\d{4}\b'
+    ]
+    for pattern in phone_patterns:
+        match = re.search(pattern, text)
+        if match:
+            return match.group()
+    return "Not found"
+
+# Function to extract email addresses
+def extract_email(text):
+    email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
+    match = re.search(email_pattern, text)
+    return match.group() if match else "Not found"
+
+# Function to extract colleges
+def extract_colleges(doc):
+    colleges = set()
+    edu_keywords = ["university", "college", "institute", "school"]
+    for ent in doc.ents:
+        if ent.label_ == "ORG" and any(keyword in ent.text.lower() for keyword in edu_keywords):
+            colleges.add(ent.text)
+    return list(colleges)
+
+# Function to extract LinkedIn profile
+def extract_linkedin(text):
+    linkedin_pattern = r'(?:https?:)?\/\/(?:[\w]+\.)?linkedin\.com\/in\/[A-z0-9_-]+\/?'
+    match = re.search(linkedin_pattern, text)
+    return match.group() if match else "Not found"
+
+# Main function to extract resume data
+def extract_resume_data(uploaded_file):
+    file_ext = uploaded_file.name.split('.')[-1].lower()
+    
+    # Extract text based on file type
+    if file_ext == 'pdf':
+        resume_text = extract_text_from_pdf(uploaded_file)
+    elif file_ext in ['docx', 'doc']:
+        resume_text = extract_text_from_doc(uploaded_file)
+    else:
+        raise ValueError("Unsupported file format.")
+
+    if not resume_text.strip():
+        raise ValueError("The resume appears to be empty.")
+
+    # Process the resume text using SpaCy
+    doc = nlp_spacy(resume_text)
+
+    # Extract required information
+    companies = extract_orgs(resume_text)
+    experience = extract_experience(doc)
+    phone = extract_phone(resume_text)
+    email = extract_email(resume_text)
+    colleges = extract_colleges(doc)
+    linkedin = extract_linkedin(resume_text)
+
+    return {
+        "Years of Experience": experience,
+        "Companies Worked For": ", ".join(companies),
+        "Phone Number": phone,
+        "Email ID": email,
+        "Colleges Attended": ", ".join(colleges),
+        "LinkedIn ID": linkedin
+    }, resume_text