chore: untrack and ignore venv directory
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"""
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resume_parser.py
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----------------
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Parses a resume (text or PDF) into a structured JSON profile
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used by the matching engine. Run once, outputs profile.json.
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"""
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import json
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import re
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import sys
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import os
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import logging
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logger = logging.getLogger(__name__)
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# ---- Known skill taxonomy ----
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SKILL_TAXONOMY = {
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"bgp": ["bgp", "border gateway protocol", "bgp peering", "route maps"],
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"ospf": ["ospf", "open shortest path first"],
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"eigrp": ["eigrp"],
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"mpls": ["mpls"],
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"vxlan": ["vxlan"],
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"vrrp": ["vrrp"],
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"vlans": ["vlan", "vlans"],
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"stp": ["stp", "spanning tree"],
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"tcp/ip": ["tcp/ip", "tcp", "ip", "tcpip"],
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"dns": ["dns", "domain name"],
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"dhcp": ["dhcp"],
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"qos": ["qos", "quality of service"],
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"ipv6": ["ipv6"],
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"multicast": ["multicast", "pim", "igmp"],
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"nat": ["nat", "network address translation"],
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"acls": ["acl", "acls", "access control list"],
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"load balancing": ["load balanc"],
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"cisco": ["cisco", "catalyst", "nexus"],
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"arista": ["arista"],
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"juniper": ["juniper", "junos", "srx"],
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"mikrotik": ["mikrotik"],
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"brocade": ["brocade"],
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"fortinet": ["fortinet", "fortigate"],
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"pfsense": ["pfsense"],
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"barracuda": ["barracuda"],
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"vyos": ["vyos"],
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"firewall": ["firewall", "ngfw"],
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"vpn": ["vpn", "ipsec", "site-to-site"],
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"802.1x": ["802.1x", "nac", "network access control"],
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"2fa": ["2fa", "mfa", "two-factor", "multi-factor"],
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"network segmentation": ["network segmentation", "microsegmentation"],
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"aws": ["aws", "amazon web services", "vpc"],
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"azure": ["azure"],
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"gcp": ["gcp", "google cloud"],
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"ibm cloud": ["ibm cloud"],
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"vmware": ["vmware", "esxi", "vsphere"],
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"proxmox": ["proxmox"],
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"lxc": ["lxc", "linux container"],
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"docker": ["docker", "container"],
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"kubernetes": ["kubernetes", "k8s"],
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"terraform": ["terraform"],
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"hybrid cloud": ["hybrid cloud"],
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"python": ["python"],
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"bash": ["bash", "shell script"],
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"ansible": ["ansible"],
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"git": ["git", "github", "gitlab"],
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"iac": ["infrastructure as code", "infrastructure-as-code", "iac"],
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"ci/cd": ["ci/cd", "cicd", "jenkins", "pipeline"],
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"zabbix": ["zabbix"],
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"librenms": ["librenms"],
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"nagios": ["nagios"],
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"snmp": ["snmp"],
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"syslog": ["syslog"],
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"datadog": ["datadog"],
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"grafana": ["grafana"],
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"prometheus": ["prometheus"],
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"splunk": ["splunk"],
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"solarwinds": ["solarwinds"],
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"active directory": ["active directory", "ad"],
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"linux": ["linux", "unix", "rhel", "ubuntu", "centos", "debian"],
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"windows server": ["windows server", "windows ad"],
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"voip": ["voip", "sip"],
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"sql": ["sql", "mysql", "postgres", "mssql"],
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"certificate management": ["certificate", "ssl", "tls", "pki"],
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}
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CERT_PATTERNS = [
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(r"ccnp", "CCNP"),
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(r"ccna", "CCNA"),
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(r"ccie", "CCIE"),
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(r"aws\s+certified\s+cloud\s+practitioner", "AWS Cloud Practitioner"),
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(r"aws\s+certified\s+solutions?\s+architect", "AWS Solutions Architect"),
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(r"aws\s+certified", "AWS Certified"),
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(r"comptia\s+security\+", "CompTIA Security+"),
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(r"comptia\s+network\+", "CompTIA Network+"),
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(r"jncia", "JNCIA"),
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(r"jncis", "JNCIS"),
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(r"fortinet\s+nse", "Fortinet NSE"),
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(r"palo\s+alto\s+pcnse", "PCNSE"),
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]
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TITLE_VARIANTS = {
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"network engineer": ["network engineer", "network eng", "noc engineer"],
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"infrastructure engineer": ["infrastructure engineer", "infra engineer"],
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"network administrator": ["network administrator", "network admin"],
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"system administrator": ["system administrator", "sysadmin", "systems administrator"],
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"cloud engineer": ["cloud engineer", "cloud network engineer"],
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"devops engineer": ["devops", "site reliability", "sre"],
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"security engineer": ["security engineer", "network security"],
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}
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def extract_skills(text):
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text_lower = text.lower()
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found = {}
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for skill_name, patterns in SKILL_TAXONOMY.items():
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for pattern in patterns:
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if pattern.lower() in text_lower:
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count = text_lower.count(pattern.lower())
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found[skill_name] = min(count, 5)
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break
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return found
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def extract_titles(text):
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titles = []
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title_patterns = [
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r"(?:^|\n)\s*([\w\s&/]+(?:Engineer|Administrator|Analyst|Manager|Architect|Specialist|Technician|Director))\s*(?:\(|–|-|\d|$)",
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]
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for pattern in title_patterns:
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matches = re.findall(pattern, text, re.IGNORECASE | re.MULTILINE)
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for m in matches:
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clean = m.strip()
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if 5 < len(clean) < 60:
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titles.append(clean)
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return list(set(titles))
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def extract_experience_years(text):
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explicit = re.search(r"(\d+)\+?\s*years?\s*(?:of\s+)?experience", text, re.IGNORECASE)
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if explicit:
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return int(explicit.group(1))
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date_ranges = re.findall(r"(\d{2}/\d{4})\s*[–-]\s*(\d{2}/\d{4}|present|current)", text, re.IGNORECASE)
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if date_ranges:
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from datetime import datetime
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total_months = 0
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for start_str, end_str in date_ranges:
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try:
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start = datetime.strptime(start_str, "%m/%Y")
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if end_str.lower() in ("present", "current"):
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end = datetime.now()
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else:
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end = datetime.strptime(end_str, "%m/%Y")
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months = (end.year - start.year) * 12 + (end.month - start.month)
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total_months += max(months, 0)
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except ValueError:
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continue
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if total_months > 0:
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return total_months // 12
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return 0
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def extract_certifications(text):
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text_lower = text.lower()
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found = []
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for pattern, cert_name in CERT_PATTERNS:
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if re.search(pattern, text_lower):
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found.append(cert_name)
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return list(set(found))
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def extract_education(text):
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education = []
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edu_patterns = [
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r"(bachelor|master|diploma|degree|b\.?s\.?|m\.?s\.?|ph\.?d|associate)[\w\s,]*(?:in\s+)?([\w\s&]+?)(?:\d{4}|\n|$)",
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]
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for pattern in edu_patterns:
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matches = re.findall(pattern, text, re.IGNORECASE)
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for level, field in matches:
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education.append({"level": level.strip(), "field": field.strip()[:50]})
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return education
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def parse_resume_text(text):
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skills = extract_skills(text)
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titles = extract_titles(text)
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years = extract_experience_years(text)
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certs = extract_certifications(text)
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education = extract_education(text)
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normalized_titles = set()
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for title in titles:
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title_lower = title.lower()
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for norm, variants in TITLE_VARIANTS.items():
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if any(v in title_lower for v in variants):
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normalized_titles.add(norm)
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break
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else:
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normalized_titles.add(title_lower)
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return {
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"skills": skills,
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"titles": list(normalized_titles),
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"raw_titles": titles,
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"years_experience": years,
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"certifications": certs,
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"education": education,
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"full_text": text,
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}
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def parse_resume_file(filepath):
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ext = os.path.splitext(filepath)[1].lower()
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if ext == ".txt":
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with open(filepath, "r", encoding="utf-8") as f:
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text = f.read()
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elif ext == ".pdf":
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try:
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import pdfplumber
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with pdfplumber.open(filepath) as pdf:
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text = "\n".join(page.extract_text() or "" for page in pdf.pages)
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except ImportError:
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logger.error("Install pdfplumber: pip install pdfplumber")
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sys.exit(1)
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else:
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raise ValueError(f"Unsupported file type: {ext}")
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return parse_resume_text(text)
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def save_profile(profile, output_path="profile.json"):
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save_data = {k: v for k, v in profile.items() if k != "full_text"}
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with open(output_path, "w") as f:
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json.dump(save_data, f, indent=2)
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logger.info(f"Profile saved to {output_path}")
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return save_data
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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if len(sys.argv) < 2:
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print("Usage: python resume_parser.py <resume.txt|resume.pdf>")
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sys.exit(1)
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filepath = sys.argv[1]
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profile = parse_resume_file(filepath)
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saved = save_profile(profile)
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print(json.dumps(saved, indent=2))
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