app.py

# -*- coding: utf-8 -*-
"""
Complete Standalone Resume Matcher for Google Colab
No external dependencies required - just paste and run!
UPDATED with new roles as requested.
"""

import gradio as gr
import json
import re
from datetime import datetime
from typing import Tuple, List, Dict, Any
from collections import Counter
import math

class TextProfileParser:
    """Simple text profile parser."""

    def parse(self, profile_text: str) -> Dict:
        """Parse profile text into structured data."""
        sections = {
            "education": [],
            "experience": [],
            "skills": [],
            "projects": [],
            "achievements": [],
            "certificates": []
        }

        lines = profile_text.split('\n')
        current_section = None

        for line in lines:
            line = line.strip()
            if not line:
                continue

            # Detect section headers
            line_lower = line.lower()
            if any(keyword in line_lower for keyword in ['education', 'academic']):
                current_section = 'education'
            elif any(keyword in line_lower for keyword in ['experience', 'work', 'employment']):
                current_section = 'experience'
            elif any(keyword in line_lower for keyword in ['skill', 'technical', 'competenc']):
                current_section = 'skills'
            elif any(keyword in line_lower for keyword in ['project', 'portfolio']):
                current_section = 'projects'
            elif any(keyword in line_lower for keyword in ['achievement', 'award', 'honor', 'certificate']):
                current_section = 'achievements'
            elif current_section and line:
                sections[current_section].append(line)

        return sections

class StandaloneResumeMatcherApp:
    """Complete standalone resume matcher with no external dependencies."""

    def __init__(self):
        print("Initializing Standalone Resume Matcher for Google Colab...")

        # Initialize basic components
        self.text_profile_parser = TextProfileParser()

        # Create comprehensive role database
        self.role_database = self._create_comprehensive_role_database()

        # Create skill database for better matching
        self.skill_database = self._create_skill_database()

        print("✅ Standalone application initialized successfully!")

    def _create_comprehensive_role_database(self):
        """Create a comprehensive role database with detailed information."""
        return {
            # Analytics Category
            "Business Analytics": {
                "category": "Analytics",
                "domain": "analytics",
                "required_skills": ["SQL", "Excel", "Python", "Tableau", "Power BI", "Statistics", "Data Analysis", "Business Intelligence", "KPI", "Dashboard"],
                "description": "Analyze business data to identify trends, patterns, and insights that drive strategic decision-making.",
                "experience_level": "Mid-level",
                "keywords": ["analytics", "business intelligence", "dashboard", "kpi", "metrics", "reporting", "data analysis", "insights", "visualization"],
                "responsibilities": ["Create dashboards", "Analyze KPIs", "Generate reports", "Data visualization", "Business insights"],
                "salary_range": "$70,000 - $120,000"
            },
            "Data Visualisation": {
                "category": "Analytics",
                "domain": "analytics",
                "required_skills": ["Tableau", "Power BI", "D3.js", "Python", "R", "SQL", "Data Storytelling", "Dashboard Design", "Looker"],
                "description": "Create compelling visual representations of data to communicate insights effectively to stakeholders.",
                "experience_level": "Mid-level",
                "keywords": ["visualization", "dashboard", "charts", "graphs", "storytelling", "tableau", "power bi", "design", "infographics"],
                "responsibilities": ["Design dashboards", "Create visualizations", "Data storytelling", "User experience design"],
                "salary_range": "$65,000 - $110,000"
            },
            # Retained Data Scientist as it's a key role in Analytics
            "Data Scientist": {
                "category": "Analytics",
                "domain": "data_science",
                "required_skills": ["Python", "Machine Learning", "Statistics", "SQL", "Pandas", "Scikit-learn", "TensorFlow", "PyTorch", "Predictive Modeling"],
                "description": "Build predictive models and analyze complex datasets to extract actionable insights.",
                "experience_level": "Senior-level",
                "keywords": ["machine learning", "data science", "predictive modeling", "algorithms", "statistical analysis", "deep learning"],
                "responsibilities": ["Model development", "Data analysis", "Algorithm design", "Statistical modeling", "Research"],
                "salary_range": "$90,000 - $160,000"
            },

            # Capital Markets Category
            "Equity Research": {
                "category": "Capital Markets",
                "domain": "finance",
                "required_skills": ["Financial Modeling", "Valuation", "Excel", "Bloomberg Terminal", "Equity Analysis", "DCF", "Comparable Analysis", "Financial Statements"],
                "description": "Analyze equity securities and provide investment recommendations based on fundamental analysis.",
                "experience_level": "Mid-level",
                "keywords": ["equity", "research", "valuation", "stocks", "investment", "analysis", "financial modeling"],
                "responsibilities": ["Company analysis", "Financial modeling", "Investment recommendations", "Research reports"],
                "salary_range": "$80,000 - $150,000"
            },
            "Investment Banking": {
                "category": "Capital Markets",
                "domain": "finance",
                "required_skills": ["Financial Modeling", "Valuation", "M&A", "Excel", "PowerPoint", "Bloomberg", "Due Diligence", "Pitch Books", "LBO Modeling"],
                "description": "Provide financial advisory services for mergers, acquisitions, and capital raising.",
                "experience_level": "Senior-level",
                "keywords": ["investment banking", "m&a", "ipo", "capital markets", "advisory", "valuation", "financing"],
                "responsibilities": ["M&A advisory", "Capital raising", "Financial modeling", "Pitch preparation", "Due diligence"],
                "salary_range": "$100,000 - $200,000"
            },
            "Risk Management": {
                "category": "Capital Markets",
                "domain": "finance",
                "required_skills": ["Risk Analysis", "VaR", "Monte Carlo", "Python", "R", "Excel", "Derivatives", "Credit Risk", "Market Risk"],
                "description": "Identify, assess, and mitigate financial risks across trading, credit, and operational activities.",
                "experience_level": "Senior-level",
                "keywords": ["risk", "var", "credit risk", "market risk", "compliance", "derivatives", "hedging"],
                "responsibilities": ["Risk assessment", "Model development", "Regulatory reporting", "Portfolio monitoring", "Stress testing"],
                "salary_range": "$90,000 - $160,000"
            },

            # Corporate Finance Category (New Category from Image)
            "Business Finance": {
                "category": "Corporate Finance",
                "domain": "finance",
                "required_skills": ["FP&A", "Budgeting", "Forecasting", "Financial Modeling", "Variance Analysis", "Excel", "PowerPoint", "SAP"],
                "description": "Act as a financial partner to business units, providing insights, analysis, and guidance to support strategic goals.",
                "experience_level": "Mid-level",
                "keywords": ["fp&a", "financial planning", "business partner", "budget", "forecast", "analysis"],
                "responsibilities": ["Financial planning and analysis", "Budget management", "Performance reporting", "Strategic support"],
                "salary_range": "$75,000 - $125,000"
            },
            "Corporate Finance": {
                "category": "Corporate Finance",
                "domain": "finance",
                "required_skills": ["Financial Modeling", "Valuation", "M&A", "Excel", "Capital Structure", "Treasury", "Financial Planning", "Investment Analysis"],
                "description": "Manage corporate financial strategy, capital structure, and major financial transactions like M&A.",
                "experience_level": "Senior-level",
                "keywords": ["corporate finance", "capital structure", "treasury", "m&a", "valuation", "strategy", "financing"],
                "responsibilities": ["Capital planning", "M&A analysis", "Treasury management", "Financial strategy", "Investor relations"],
                "salary_range": "$85,000 - $150,000"
            },
            "Financial Operations": {
                "category": "Corporate Finance",
                "domain": "finance",
                "required_skills": ["Accounts Payable", "Accounts Receivable", "Reconciliation", "ERP Systems", "Process Improvement", "Internal Controls", "Month-End Close"],
                "description": "Oversee and manage the daily financial operations of a company, including accounting, billing, and payments.",
                "experience_level": "Mid-level",
                "keywords": ["finops", "accounts payable", "receivable", "reconciliation", "erp", "sap", "oracle"],
                "responsibilities": ["Manage AP/AR", "Perform bank reconciliations", "Improve financial processes", "Ensure accurate transactions"],
                "salary_range": "$65,000 - $100,000"
            },
            "Tax and Accounting": {
                "category": "Corporate Finance",
                "domain": "finance",
                "required_skills": ["GAAP", "IFRS", "Tax Compliance", "Auditing", "Financial Reporting", "Corporate Tax", "Excel", "CPA"],
                "description": "Manage all aspects of accounting and tax compliance, ensuring accurate financial reporting and adherence to regulations.",
                "experience_level": "Senior-level",
                "keywords": ["tax", "accounting", "audit", "compliance", "gaap", "ifrs", "cpa", "financial statements"],
                "responsibilities": ["Tax planning and filing", "Financial statement preparation", "Internal and external audits", "Regulatory compliance"],
                "salary_range": "$80,000 - $140,000"
            },

            # Human Resources Category
            "HR Generalist": {
                "category": "Human Resources",
                "domain": "hr",
                "required_skills": ["Recruitment", "Employee Relations", "HRIS", "Training", "Compliance", "Performance Management", "Benefits Administration"],
                "description": "Manage various HR functions including recruitment, employee relations, and policy implementation.",
                "experience_level": "Mid-level",
                "keywords": ["human resources", "recruitment", "employee relations", "training", "compliance", "hr", "hiring"],
                "responsibilities": ["Recruitment", "Employee relations", "Training coordination", "Policy implementation", "Compliance"],
                "salary_range": "$55,000 - $85,000"
            },
            "Talent Acquisition": {
                "category": "Human Resources",
                "domain": "hr",
                "required_skills": ["Recruiting", "Sourcing", "Interviewing", "ATS", "LinkedIn Recruiter", "Boolean Search", "Employer Branding", "Candidate Experience"],
                "description": "Source, attract, and hire top talent through strategic recruitment and talent acquisition strategies.",
                "experience_level": "Mid-level",
                "keywords": ["recruiting", "talent acquisition", "sourcing", "hiring", "candidates", "interviews", "ats", "linkedin"],
                "responsibilities": ["Candidate sourcing", "Interview coordination", "Talent pipeline management", "Employer branding"],
                "salary_range": "$60,000 - $95,000"
            },

            # Marketing Category
            "Category Management": {
                "category": "Marketing",
                "domain": "marketing",
                "required_skills": ["Market Analysis", "Product Assortment", "Pricing Strategy", "Vendor Management", "P&L Management", "Consumer Insights", "Nielsen/IRI"],
                "description": "Manage a product category as a strategic business unit, responsible for its pricing, promotion, and profitability.",
                "experience_level": "Senior-level",
                "keywords": ["category management", "product assortment", "pricing", "vendor relations", "p&l", "merchandising"],
                "responsibilities": ["Develop category strategy", "Manage vendor relationships", "Optimize product mix", "Analyze sales data"],
                "salary_range": "$85,000 - $140,000"
            },
            "Digital Marketing": {
                "category": "Marketing",
                "domain": "marketing",
                "required_skills": ["SEO", "SEM", "Google Analytics", "Social Media", "Content Marketing", "PPC", "Email Marketing", "Google Ads"],
                "description": "Develop and execute digital marketing campaigns across online channels to drive brand awareness and conversions.",
                "experience_level": "Mid-level",
                "keywords": ["digital marketing", "seo", "sem", "social media", "google ads", "content marketing", "ppc", "email"],
                "responsibilities": ["Campaign management", "SEO optimization", "Social media strategy", "Content creation", "Analytics"],
                "salary_range": "$55,000 - $95,000"
            },
            "Market Research": {
                "category": "Marketing",
                "domain": "marketing",
                "required_skills": ["Survey Design", "Statistical Analysis", "SPSS", "R", "Excel", "Focus Groups", "Consumer Insights", "Competitive Analysis"],
                "description": "Conduct market research to understand consumer behavior, market trends, and competitive landscape.",
                "experience_level": "Mid-level",
                "keywords": ["market research", "surveys", "consumer insights", "analysis", "trends", "competitive intelligence"],
                "responsibilities": ["Research design", "Data collection", "Statistical analysis", "Insights generation", "Report presentation"],
                "salary_range": "$60,000 - $100,000"
            },
            "Marketing Management": {
                "category": "Marketing",
                "domain": "marketing",
                "required_skills": ["Marketing Strategy", "Brand Management", "Campaign Management", "Budgeting", "Team Leadership", "Market Analysis", "Go-to-Market Strategy"],
                "description": "Lead the marketing department by developing and implementing comprehensive marketing strategies to increase brand awareness and drive sales.",
                "experience_level": "Senior-level",
                "keywords": ["marketing manager", "brand management", "strategy", "campaigns", "leadership", "gtm"],
                "responsibilities": ["Develop marketing plans", "Manage marketing budget", "Lead marketing team", "Oversee brand strategy"],
                "salary_range": "$90,000 - $160,000"
            },
            "Performance Marketing": {
                "category": "Marketing",
                "domain": "marketing",
                "required_skills": ["Paid Advertising", "Google Ads", "Facebook Ads", "Analytics", "Conversion Tracking", "A/B Testing", "ROI Analysis", "Attribution Modeling"],
                "description": "Drive measurable marketing results through data-driven paid advertising and performance optimization.",
                "experience_level": "Mid-level",
                "keywords": ["performance marketing", "paid ads", "roi", "conversion", "optimization", "attribution", "programmatic"],
                "responsibilities": ["Campaign optimization", "Performance analysis", "A/B testing", "Budget allocation", "ROI maximization"],
                "salary_range": "$65,000 - $110,000"
            },

            # Operations Category
            "Customer Success": {
                "category": "Operations",
                "domain": "operations",
                "required_skills": ["Customer Relationship Management", "CRM", "Account Management", "Communication", "Problem Solving", "Retention Strategy", "Onboarding"],
                "description": "Ensure customer satisfaction, retention, and growth through proactive relationship management and support.",
                "experience_level": "Mid-level",
                "keywords": ["customer success", "retention", "account management", "onboarding", "satisfaction", "growth", "relationships"],
                "responsibilities": ["Customer onboarding", "Relationship management", "Retention strategies", "Success metrics", "Account growth"],
                "salary_range": "$60,000 - $100,000"
            },
            "Service Operations": {
                "category": "Operations",
                "domain": "operations",
                "required_skills": ["ITIL", "Service Delivery", "Incident Management", "Problem Management", "SLA Management", "ServiceNow", "Process Improvement"],
                "description": "Manage the end-to-end delivery of IT services to business users, ensuring stability, quality, and adherence to SLAs.",
                "experience_level": "Mid-level",
                "keywords": ["service delivery", "itil", "sla", "incident management", "operations", "servicenow"],
                "responsibilities": ["Oversee incident resolution", "Manage service level agreements", "Improve operational processes", "Coordinate support teams"],
                "salary_range": "$75,000 - $120,000"
            },
            "Supply Chain Management": {
                "category": "Operations",
                "domain": "operations",
                "required_skills": ["Supply Chain", "Logistics", "Procurement", "Inventory Management", "ERP", "SAP", "Vendor Management", "Forecasting"],
                "description": "Optimize supply chain operations including procurement, logistics, and inventory management.",
                "experience_level": "Senior-level",
                "keywords": ["supply chain", "logistics", "procurement", "inventory", "vendor management", "optimization"],
                "responsibilities": ["Supply planning", "Vendor management", "Inventory optimization", "Cost reduction", "Process improvement"],
                "salary_range": "$75,000 - $130,000"
            },

            # Sales Category
            "B2B Sales": {
                "category": "Sales",
                "domain": "sales",
                "required_skills": ["B2B Sales", "CRM", "Salesforce", "Lead Generation", "Negotiation", "Account Management", "Pipeline Management", "Presentation"],
                "description": "Drive business-to-business sales through relationship building, lead generation, and strategic account management.",
                "experience_level": "Mid-level",
                "keywords": ["b2b sales", "enterprise sales", "account management", "lead generation", "crm", "pipeline", "negotiation"],
                "responsibilities": ["Lead generation", "Account management", "Sales presentations", "Contract negotiation", "Relationship building"],
                "salary_range": "$60,000 - $120,000"
            },
            "B2C Sales": {
                "category": "Sales",
                "domain": "sales",
                "required_skills": ["Retail Sales", "Customer Engagement", "Product Knowledge", "POS Systems", "Closing Techniques", "Communication", "Upselling"],
                "description": "Sell products and services directly to individual consumers, focusing on customer experience and achieving sales targets.",
                "experience_level": "Entry-level",
                "keywords": ["b2c", "retail", "consumer sales", "customer service", "sales associate"],
                "responsibilities": ["Assist customers", "Process transactions", "Meet sales goals", "Maintain product knowledge"],
                "salary_range": "$40,000 - $75,000"
            },
            "BFSI Sales": {
                "category": "Sales",
                "domain": "sales",
                "required_skills": ["Financial Products", "Insurance", "Investment Advisory", "Relationship Management", "Regulatory Knowledge", "Wealth Management", "CRM"],
                "description": "Specialize in selling banking, financial services, and insurance (BFSI) products to clients.",
                "experience_level": "Mid-level",
                "keywords": ["bfsi", "banking sales", "insurance", "wealth management", "financial advisor", "relationship manager"],
                "responsibilities": ["Sell financial products", "Advise clients on investments", "Build client relationships", "Ensure compliance"],
                "salary_range": "$65,000 - $130,000"
            },
            "Channel Sales": {
                "category": "Sales",
                "domain": "sales",
                "required_skills": ["Partner Management", "Channel Strategy", "Co-marketing", "Sales Enablement", "Alliance Management", "Business Development", "Negotiation"],
                "description": "Develop and manage a network of partners, resellers, and distributors to sell a company's products and services.",
                "experience_level": "Senior-level",
                "keywords": ["channel sales", "partner management", "alliances", "resellers", "distributors", "business development"],
                "responsibilities": ["Recruit and onboard partners", "Develop channel strategy", "Enable partner sales", "Manage partner relationships"],
                "salary_range": "$80,000 - $150,000"
            },
            "Technology Sales": {
                "category": "Sales",
                "domain": "sales",
                "required_skills": ["Technology Products", "SaaS", "Software Sales", "Technical Knowledge", "Solution Selling", "CRM", "Salesforce", "Presentation"],
                "description": "Sell technology products and solutions by understanding customer technical requirements and demonstrating value.",
                "experience_level": "Mid-level",
                "keywords": ["technology sales", "saas", "software", "solution selling", "technical sales", "enterprise software"],
                "responsibilities": ["Technical sales", "Solution design", "Product demos", "ROI analysis", "Customer consultation"],
                "salary_range": "$70,000 - $140,000"
            },

            # Strategy Category
            "Business Consulting": {
                "category": "Strategy",
                "domain": "consulting",
                "required_skills": ["Strategy", "Business Analysis", "Problem Solving", "Presentation", "Excel", "PowerPoint", "Project Management", "Stakeholder Management"],
                "description": "Provide strategic business advice and solutions to help organizations improve performance and achieve goals.",
                "experience_level": "Senior-level",
                "keywords": ["consulting", "strategy", "business analysis", "problem solving", "advisory", "transformation"],
                "responsibilities": ["Strategy development", "Business analysis", "Client advisory", "Project management", "Solution implementation"],
                "salary_range": "$80,000 - $150,000"
            },
            "Business Research": {
                "category": "Strategy",
                "domain": "strategy",
                "required_skills": ["Primary Research", "Secondary Research", "Data Analysis", "Report Writing", "Competitive Intelligence", "Market Sizing", "Qualitative Analysis"],
                "description": "Conduct in-depth research and analysis on markets, competitors, and customers to support strategic business decisions.",
                "experience_level": "Mid-level",
                "keywords": ["business research", "market intelligence", "competitive analysis", "analyst", "insights", "secondary research"],
                "responsibilities": ["Gather market data", "Analyze competitive landscape", "Author research reports", "Provide strategic insights"],
                "salary_range": "$70,000 - $115,000"
            },
            "Corporate Strategy": {
                "category": "Strategy",
                "domain": "strategy",
                "required_skills": ["Strategic Planning", "Business Analysis", "Financial Modeling", "M&A", "Market Analysis", "Competitive Intelligence", "Executive Presentation"],
                "description": "Develop and execute corporate strategy including growth initiatives, M&A, and strategic planning.",
                "experience_level": "Senior-level",
                "keywords": ["corporate strategy", "strategic planning", "m&a", "growth", "competitive analysis", "business development"],
                "responsibilities": ["Strategy formulation", "M&A analysis", "Strategic planning", "Competitive analysis", "Executive reporting"],
                "salary_range": "$90,000 - $160,000"
            },

            # Technology Category
            "IT Business Analyst": {
                "category": "Technology",
                "domain": "technology",
                "required_skills": ["Business Analysis", "Requirements Gathering", "Process Mapping", "SQL", "Agile", "JIRA", "Documentation", "Stakeholder Management"],
                "description": "Bridge business and technology teams by analyzing requirements and ensuring IT solutions meet business needs.",
                "experience_level": "Mid-level",
                "keywords": ["business analyst", "requirements", "process analysis", "agile", "documentation", "stakeholder management"],
                "responsibilities": ["Requirements analysis", "Process documentation", "Stakeholder coordination", "Solution design", "Testing support"],
                "salary_range": "$70,000 - $110,000"
            },
            "IT PreSales": {
                "category": "Technology",
                "domain": "technology",
                "required_skills": ["Solution Architecture", "Technical Presentations", "RFP/RFI Response", "Proof of Concept", "Product Demonstration", "Client Engagement", "Requirement Analysis"],
                "description": "Provide critical technical expertise during the sales cycle, designing solutions and demonstrating product capabilities to prospective clients.",
                "experience_level": "Senior-level",
                "keywords": ["presales", "solution architect", "solution consultant", "technical sales", "rfp", "poc"],
                "responsibilities": ["Design technical solutions", "Deliver product demos", "Respond to RFPs", "Articulate business value"],
                "salary_range": "$95,000 - $160,000"
            },
            "IT Project Management": {
                "category": "Technology",
                "domain": "technology",
                "required_skills": ["Agile", "Scrum", "PMP", "Project Planning", "Budget Management", "Risk Management", "Stakeholder Management", "JIRA", "SDLC"],
                "description": "Plan, execute, and oversee information technology projects, ensuring they are completed on time, within budget, and to scope.",
                "experience_level": "Senior-level",
                "keywords": ["it project manager", "pmp", "agile", "scrum", "project delivery", "sdlc", "jira"],
                "responsibilities": ["Project planning", "Resource allocation", "Budget tracking", "Stakeholder communication", "Risk mitigation"],
                "salary_range": "$85,000 - $145,000"
            },
            "Product Management": {
                "category": "Technology",
                "domain": "product",
                "required_skills": ["Product Strategy", "Roadmapping", "User Research", "Analytics", "Agile", "A/B Testing", "Stakeholder Management", "Market Research"],
                "description": "Define product strategy, roadmap, and features based on market research and user needs.",
                "experience_level": "Senior-level",
                "keywords": ["product management", "strategy", "roadmap", "user research", "agile", "features", "analytics"],
                "responsibilities": ["Product strategy", "Roadmap planning", "Feature definition", "User research", "Stakeholder management"],
                "salary_range": "$90,000 - $160,000"
            },
             "Tech Consulting": {
                "category": "Technology",
                "domain": "consulting",
                "required_skills": ["Digital Transformation", "Cloud Strategy", "AWS", "Azure", "System Implementation", "Business Process Re-engineering", "Client Advisory", "Agile"],
                "description": "Advise clients on leveraging technology to solve business problems, improve efficiency, and drive innovation.",
                "experience_level": "Senior-level",
                "keywords": ["technology consulting", "advisory", "digital transformation", "cloud", "aws", "azure", "erp", "implementation"],
                "responsibilities": ["Client advisory", "Solution design", "Technology implementation", "Change management", "Strategy development"],
                "salary_range": "$90,000 - $170,000"
            },
            # Retained Software Engineer as it's a key role in Technology
            "Software Engineer": {
                "category": "Technology",
                "domain": "technology",
                "required_skills": ["Python", "JavaScript", "SQL", "Git", "APIs", "Web Development", "React", "Node.js", "Database Design", "Agile"],
                "description": "Develop and maintain software applications using modern programming languages and frameworks.",
                "experience_level": "Mid-level",
                "keywords": ["programming", "software development", "coding", "web development", "full stack", "backend", "frontend"],
                "responsibilities": ["Code development", "Software design", "Testing", "Debugging", "Code reviews"],
                "salary_range": "$75,000 - $140,000"
            },
        }

    def _create_skill_database(self):
        """Create a comprehensive skill database with categories."""
        return {
            "programming": ["Python", "Java", "JavaScript", "C++", "C#", "R", "SQL", "HTML", "CSS", "PHP", "Ruby", "Go", "Rust", "Swift", "Kotlin", "Scala", "MATLAB"],
            "data_science": ["Machine Learning", "Statistics", "Data Analysis", "Pandas", "NumPy", "Scikit-learn", "TensorFlow", "PyTorch", "Jupyter", "Data Mining", "Predictive Modeling", "Statistical Analysis"],
            "web_development": ["React", "Angular", "Vue.js", "Node.js", "Django", "Flask", "Spring", "Express", "Bootstrap", "jQuery", "REST APIs", "GraphQL"],
            "databases": ["MySQL", "PostgreSQL", "MongoDB", "Redis", "Oracle", "SQL Server", "Cassandra", "DynamoDB", "NoSQL", "Database Design"],
            "cloud": ["AWS", "Azure", "Google Cloud", "Docker", "Kubernetes", "Terraform", "Jenkins", "CI/CD", "DevOps", "Microservices"],
            "analytics": ["Tableau", "Power BI", "Google Analytics", "Excel", "SPSS", "SAS", "Looker", "Qlik", "D3.js", "Data Visualization", "Dashboard Design", "Nielsen/IRI", "KPI"],
            "marketing": ["SEO", "SEM", "Google Ads", "Facebook Ads", "Content Marketing", "Email Marketing", "Social Media", "PPC", "Marketing Automation", "Conversion Optimization", "A/B Testing", "Brand Management", "Go-to-Market Strategy", "Category Management"],
            "design": ["Figma", "Adobe Creative Suite", "Sketch", "InVision", "Photoshop", "Illustrator", "UI/UX Design", "Prototyping", "Wireframing", "User Research"],
            "project_management": ["Agile", "Scrum", "Kanban", "JIRA", "Trello", "Asana", "Project Planning", "Risk Management", "PMP", "Waterfall", "Stakeholder Management", "SDLC", "Budget Management"],
            "finance": ["Financial Modeling", "Valuation", "Accounting", "Budgeting", "Forecasting", "Excel", "Bloomberg", "Financial Reporting", "GAAP", "IFRS", "DCF", "LBO", "FP&A", "Variance Analysis", "Accounts Payable", "Accounts Receivable", "Reconciliation", "Internal Controls", "Tax Compliance", "CPA"],
            "capital_markets": ["Bloomberg Terminal", "Capital IQ", "Equity Research", "Fixed Income", "Derivatives", "Trading", "Portfolio Management", "Risk Management", "VaR", "Credit Analysis"],
            "sales": ["CRM", "Salesforce", "Lead Generation", "Account Management", "Pipeline Management", "Negotiation", "B2B Sales", "B2C Sales", "Channel Sales", "Sales Process", "Solution Selling", "Upselling", "Closing Techniques", "Partner Management"],
            "hr": ["Recruitment", "Talent Acquisition", "HRIS", "Employee Relations", "Performance Management", "Benefits Administration", "Training", "Compliance", "ATS", "LinkedIn Recruiter", "Sourcing"],
            "operations": ["Supply Chain", "Logistics", "Process Improvement", "Quality Management", "Vendor Management", "Inventory Management", "ERP", "SAP", "Lean Six Sigma", "ITIL", "Service Delivery", "SLA Management", "ServiceNow"],
            "consulting": ["Strategy", "Business Analysis", "Problem Solving", "Client Management", "Change Management", "Process Optimization", "Digital Transformation", "Management Consulting", "Client Advisory"],
            "technology": ["Software Development", "System Architecture", "Technical Leadership", "Cybersecurity", "Network Administration", "IT Support", "Database Administration", "Solution Architecture", "RFP/RFI Response", "Proof of Concept"],
            "research": ["Market Research", "Competitive Analysis", "Survey Design", "Focus Groups", "Statistical Analysis", "Research Methodology", "Data Collection", "Consumer Insights", "Primary Research", "Secondary Research"],
            "soft_skills": ["Leadership", "Communication", "Problem Solving", "Team Management", "Critical Thinking", "Negotiation", "Presentation", "Analytical Thinking", "Strategic Thinking", "Relationship Building"]
        }

    def _extract_skills_from_text(self, text: str) -> List[str]:
        """Extract skills from text using keyword matching."""
        text_lower = text.lower()
        found_skills = []

        # Check all skill categories
        for category, skills in self.skill_database.items():
            for skill in skills:
                # Check for exact match or partial match
                skill_lower = skill.lower()
                if skill_lower in text_lower or any(word in text_lower for word in skill_lower.split()):
                    if skill not in found_skills:
                        found_skills.append(skill)

        return found_skills

    def _calculate_text_similarity(self, text1: str, text2: str) -> float:
        """Calculate similarity between two texts using TF-IDF-like approach."""
        # Simple tokenization
        def tokenize(text):
            return re.findall(r'\b\w+\b', text.lower())

        tokens1 = tokenize(text1)
        tokens2 = tokenize(text2)

        # Calculate term frequencies
        tf1 = Counter(tokens1)
        tf2 = Counter(tokens2)

        # Get all unique terms
        all_terms = set(tokens1 + tokens2)

        # Calculate cosine similarity
        dot_product = sum(tf1[term] * tf2[term] for term in all_terms)
        magnitude1 = math.sqrt(sum(tf1[term]**2 for term in all_terms))
        magnitude2 = math.sqrt(sum(tf2[term]**2 for term in all_terms))

        if magnitude1 == 0 or magnitude2 == 0:
            return 0.0

        return dot_product / (magnitude1 * magnitude2)

    def _classify_domain(self, text: str) -> Tuple[str, float]:
        """Simple domain classification based on keywords."""
        domain_keywords = {
            "analytics": ["analytics", "data analysis", "business intelligence", "dashboard", "visualization", "tableau", "power bi", "insights"],
            "finance": ["financial", "accounting", "budget", "investment", "revenue", "profit", "banking", "capital markets", "equity", "valuation"],
            "technology": ["programming", "software", "development", "coding", "web", "api", "database", "it", "technical", "system"],
            "marketing": ["marketing", "seo", "social media", "advertising", "campaign", "brand", "digital marketing", "content"],
            "hr": ["human resources", "recruitment", "hiring", "employee", "training", "hr", "talent acquisition", "benefits"],
            "sales": ["sales", "revenue", "customer", "client", "negotiation", "crm", "b2b", "b2c", "channel"],
            "operations": ["operations", "process", "supply chain", "logistics", "quality", "service", "customer success"],
            "consulting": ["consulting", "strategy", "advisory", "business", "transformation", "research"],
            "product": ["product", "roadmap", "user", "feature", "requirements", "product management"],
            "data_science": ["machine learning", "data science", "predictive modeling", "algorithms", "statistical analysis"]
        }

        text_lower = text.lower()
        domain_scores = {}

        for domain, keywords in domain_keywords.items():
            score = sum(1 for keyword in keywords if keyword in text_lower)
            if score > 0:
                domain_scores[domain] = score / len(keywords)

        if domain_scores:
            best_domain = max(domain_scores, key=domain_scores.get)
            confidence = domain_scores[best_domain]
            return best_domain, confidence

        return "technology", 0.1  # Default

    def enhanced_profile_analysis(self, profile_text: str) -> Tuple[str, str, str]:
        """Enhanced profile analysis using standalone algorithms."""
        if not profile_text.strip():
            return "Please enter a profile text.", "", ""

        try:
            # 1. Basic profile parsing
            parsed_profile = self.text_profile_parser.parse(profile_text)

            # 2. Extract skills from profile
            profile_skills = self._extract_skills_from_text(profile_text)

            # 3. Domain classification
            domain, domain_conf = self._classify_domain(profile_text)

            # 4. Role matching
            role_matches = self._find_role_matches(profile_text, profile_skills)

            # 5. Get best role prediction
            if role_matches:
                predicted_role = role_matches[0]["role"]
                predicted_category = role_matches[0]["category"]
                predicted_domain = role_matches[0]["domain"]
                confidence = role_matches[0]["similarity_score"]
            else:
                predicted_role = "Software Engineer"
                predicted_category = "Technology"
                predicted_domain = "technology"
                confidence = 0.1

            # 6. Skill gap analysis
            gap_analysis = self._analyze_skill_gaps(profile_text, profile_skills, predicted_role)

            # Prepare results
            role_analysis = {
                "predicted_role": predicted_role,
                "predicted_category": predicted_category,
                "predicted_domain": predicted_domain,
                "confidence_scores": {"overall": confidence, "domain": domain_conf},
                "top_role_matches": [
                    {
                        "role": match["role"],
                        "category": match["category"],
                        "similarity": f"{match['similarity_score']:.2%}",
                        "confidence": f"{match['confidence']:.2%}",
                        "matched_skills": match["matched_skills"],
                        "reasoning": match["reasoning"]
                    }
                    for match in role_matches
                ],
                "domain_analysis": {
                    "detected_domain": domain,
                    "confidence": f"{domain_conf:.2%}",
                    "profile_skills": profile_skills[:15]  # Top 15 skills
                },
                "profile_sections": {
                    "education_count": len(parsed_profile.get("education", [])),
                    "experience_count": len(parsed_profile.get("experience", [])),
                    "projects_count": len(parsed_profile.get("projects", [])),
                    "achievements_count": len(parsed_profile.get("achievements", [])),
                    "certificates_count": len(parsed_profile.get("certificates", []))
                },
                "system_status": {
                    "mode": "Google Colab Standalone Mode",
                    "dependencies": "Zero external dependencies"
                }
            }

            # Career recommendations HTML
            career_recommendations = self._generate_career_recommendations_html(
                predicted_role, predicted_category, predicted_domain, role_matches, gap_analysis
            )

            return (
                json.dumps(role_analysis, indent=2),
                json.dumps(gap_analysis, indent=2),
                career_recommendations
            )

        except Exception as e:
            error_msg = f"Error in profile analysis: {str(e)}"
            return error_msg, "", ""

    def _find_role_matches(self, profile_text: str, profile_skills: List[str]) -> List[Dict]:
        """Find role matches using multiple scoring methods."""
        matches = []

        for role, data in self.role_database.items():
            # 1. Skill-based scoring
            matched_skills = []
            for skill in data["required_skills"]:
                if skill in profile_skills:
                    matched_skills.append(skill)

            skill_score = len(matched_skills) / len(data["required_skills"]) if data["required_skills"] else 0

            # 2. Keyword-based scoring
            keyword_score = 0
            for keyword in data["keywords"]:
                if keyword.lower() in profile_text.lower():
                    keyword_score += 1
            keyword_score = keyword_score / len(data["keywords"]) if data["keywords"] else 0

            # 3. Text similarity scoring
            role_text = f"{data['description']} {' '.join(data['required_skills'])} {' '.join(data['keywords'])}"
            text_similarity = self._calculate_text_similarity(profile_text, role_text)

            # 4. Combined scoring
            combined_score = (skill_score * 0.4) + (keyword_score * 0.3) + (text_similarity * 0.3)

            # 5. Confidence calculation
            confidence = min(combined_score + (len(matched_skills) * 0.05), 1.0)

            # 6. Reasoning
            reasoning = f"Skills: {len(matched_skills)}/{len(data['required_skills'])}, Keywords: {int(keyword_score * len(data['keywords']))}/{len(data['keywords'])}, Text similarity: {text_similarity:.2%}"

            matches.append({
                "role": role,
                "category": data["category"],
                "domain": data["domain"],
                "similarity_score": combined_score,
                "confidence": confidence,
                "matched_skills": matched_skills,
                "reasoning": reasoning,
                "salary_range": data.get("salary_range", "Not specified")
            })

        # Sort by combined score
        matches.sort(key=lambda x: x["similarity_score"], reverse=True)
        return matches

    def _analyze_skill_gaps(self, profile_text: str, profile_skills: List[str], target_role: str) -> Dict:
        """Analyze skill gaps for the target role."""
        if target_role not in self.role_database:
            return {"error": f"Role '{target_role}' not found"}

        role_data = self.role_database[target_role]
        required_skills = set(role_data["required_skills"])
        current_skills = set(profile_skills)

        matching_skills = list(required_skills & current_skills)
        missing_skills = list(required_skills - current_skills)

        # Generate recommendations
        recommendations = []
        skill_learning_map = {
            "python": "Complete Python programming courses on Coursera or edX",
            "sql": "Practice SQL queries on HackerRank or LeetCode",
            "tableau": "Get Tableau certification through official Tableau training",
            "power bi": "Complete Microsoft Power BI certification path",
            "machine learning": "Take Andrew Ng's Machine Learning course on Coursera",
            "aws": "Pursue AWS certification starting with Cloud Practitioner",
            "javascript": "Complete JavaScript fundamentals on freeCodeCamp",
            "react": "Build projects using React through official React tutorial",
            "excel": "Complete advanced Excel courses focusing on data analysis",
            "agile": "Consider Scrum Master or Product Owner certification",
            "google analytics": "Complete Google Analytics certification",
            "seo": "Take SEO courses on Moz Academy or SEMrush",
            "financial modeling": "Complete financial modeling courses on Wall Street Prep",
            "bloomberg terminal": "Get Bloomberg Market Concepts (BMC) certification",
            "salesforce": "Pursue Salesforce Administrator certification",
            "crm": "Learn CRM best practices through HubSpot Academy"
        }

        for skill in missing_skills[:8]:  # Top 8 missing skills
            skill_lower = skill.lower()
            recommendation = skill_learning_map.get(skill_lower, f"Seek online courses or tutorials for {skill}")

            # Determine priority
            if skill_lower in ["python", "sql", "excel", "javascript"]:
                priority = "High"
            elif skill_lower in ["tableau", "power bi", "aws", "react"]:
                priority = "Medium"
            else:
                priority = "Low"

            recommendations.append({
                "skill": skill,
                "recommendation": recommendation,
                "priority": priority,
                "estimated_time": "2-4 weeks" if priority == "High" else "1-2 weeks"
            })

        # Calculate match percentage
        match_percentage = len(matching_skills) / len(required_skills) if required_skills else 0

        return {
            "target_role": target_role,
            "current_match": f"{match_percentage:.1%}",
            "matching_skills": matching_skills,
            "skill_gaps": missing_skills,
            "recommendations": recommendations,
            "role_info": {
                "description": role_data["description"],
                "experience_level": role_data["experience_level"],
                "salary_range": role_data.get("salary_range", "Not specified"),
                "key_responsibilities": role_data.get("responsibilities", [])
            }
        }

    def _generate_career_recommendations_html(self, predicted_role: str, predicted_category: str,
                                              predicted_domain: str, role_matches: List[Dict],
                                              gap_analysis: Dict) -> str:
        """Generate comprehensive HTML for career recommendations."""

        best_match = role_matches[0] if role_matches else None

        html = f"""
        <div style="font-family: Arial, sans-serif; max-width: 900px; color: white;">
            <h2 style="color: white; border-bottom: 2px solid #D1C4E9; padding-bottom: 10px;">
                🎯 Career Recommendations & Analysis
            </h2>

            <div style="background: #7E57C2; padding: 20px; border-radius: 10px; margin: 20px 0;">
                <h3 style="color: white; margin-top: 0;">
                    🏆 Best Role Match: {predicted_role}
                </h3>
                <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px; color: #F3E5F5;">
                    <div>
                        <p><strong>Category:</strong> {predicted_category}</p>
                        <p><strong>Domain:</strong> {predicted_domain}</p>
                    </div>
                    <div>
                        <p><strong>Match Score:</strong> {best_match['similarity_score']:.1%}</p>
                        <p><strong>Salary Range:</strong> {best_match.get('salary_range', 'Not specified')}</p>
                    </div>
                </div>
            </div>

            <div style="background: #5E35B1; padding: 20px; border-radius: 10px; margin: 20px 0;">
                <h3 style="color: white; margin-top: 0;">📈 Top Alternative Roles</h3>
                <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 10px;">
        """

        for i, match in enumerate(role_matches[1:5], 1):  # Top 4 alternatives
            html += f"""
                    <div style="background: #7E57C2; color: white; padding: 10px; border-radius: 5px; border-left: 3px solid #D1C4E9;">
                        <strong>{match['role']}</strong><br>
                        <small style="color: #F3E5F5;">Category: {match['category']} | Score: {match['similarity_score']:.1%}</small>
                    </div>
            """

        html += """
                </div>
            </div>
        """

        # Skill recommendations
        if gap_analysis.get("recommendations"):
            html += """
            <div style="background: #5E35B1; padding: 20px; border-radius: 10px; margin: 20px 0;">
                <h3 style="color: white; margin-top: 0;">🎓 Skill Development Roadmap</h3>
                <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;">
            """

            high_priority = [rec for rec in gap_analysis["recommendations"] if rec["priority"] == "High"]
            medium_priority = [rec for rec in gap_analysis["recommendations"] if rec["priority"] == "Medium"]

            html += """
                <div>
                    <h4 style="color: white; margin-top: 0;">🔥 High Priority Skills</h4>
            """

            for rec in high_priority[:3]:
                html += f"""
                    <div style="margin: 10px 0; padding: 10px; background: #7E57C2; color: white; border-radius: 5px; border-left: 3px solid #D1C4E9;">
                        <strong>{rec['skill']}</strong>
                        <br><small style="color: #F3E5F5;">⏱️ {rec.get('estimated_time', '2-4 weeks')}</small>
                        <br><small style="color: #F3E5F5;">💡 {rec['recommendation']}</small>
                    </div>
                """

            html += """
                </div>
                <div>
                    <h4 style="color: white; margin-top: 0;">⭐ Medium Priority Skills</h4>
            """

            for rec in medium_priority[:3]:
                html += f"""
                    <div style="margin: 10px 0; padding: 10px; background: #7E57C2; color: white; border-radius: 5px; border-left: 3px solid #D1C4E9;">
                        <strong>{rec['skill']}</strong>
                        <br><small style="color: #F3E5F5;">⏱️ {rec.get('estimated_time', '1-2 weeks')}</small>
                        <br><small style="color: #F3E5F5;">💡 {rec['recommendation']}</small>
                    </div>
                """

            html += """
                </div>
            </div>
            </div>
            """

        # Career path suggestions
        html += f"""
            <div style="background: #512DA8; padding: 20px; border-radius: 10px; margin: 20px 0;">
                <h3 style="color: white; margin-top: 0;">🚀 Career Development Path</h3>
                <div style="display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 15px;">
                    <div style="background: #673AB7; color: white; padding: 15px; border-radius: 8px;">
                        <h4 style="color: white; margin-top: 0;">📚 Immediate (1-3 months)</h4>
                        <ul style="margin: 0; padding-left: 20px; font-size: 14px; color: #F3E5F5;">
                            <li>Focus on high-priority skills</li>
                            <li>Complete online certifications</li>
                            <li>Build portfolio projects</li>
                            <li>Network in {predicted_domain} domain</li>
                        </ul>
                    </div>
                    <div style="background: #673AB7; color: white; padding: 15px; border-radius: 8px;">
                        <h4 style="color: white; margin-top: 0;">🎯 Short-term (3-12 months)</h4>
                        <ul style="margin: 0; padding-left: 20px; font-size: 14px; color: #F3E5F5;">
                            <li>Apply for {predicted_role} positions</li>
                            <li>Gain hands-on experience</li>
                            <li>Develop medium-priority skills</li>
                            <li>Seek mentorship opportunities</li>
                        </ul>
                    </div>
                    <div style="background: #673AB7; color: white; padding: 15px; border-radius: 8px;">
                        <h4 style="color: white; margin-top: 0;">🌟 Long-term (1-2 years)</h4>
                        <ul style="margin: 0; padding-left: 20px; font-size: 14px; color: #F3E5F5;">
                            <li>Target senior roles in {predicted_category}</li>
                            <li>Develop leadership skills</li>
                            <li>Consider specialization</li>
                            <li>Explore management tracks</li>
                        </ul>
                    </div>
                </div>
            </div>
        </div>
        """

        return html

    def compare_profile_with_jd(self, profile_text: str, job_description: str) -> str:
        """Compare a profile with a job description and provide detailed analysis."""
        if not profile_text.strip() or not job_description.strip():
            return "Please provide both profile and job description for comparison."

        try:
            # Analyze the profile
            profile_analysis = self.enhanced_profile_analysis(profile_text)
            role_analysis = json.loads(profile_analysis[0])

            # Extract skills from profile and JD
            profile_skills = set(role_analysis["domain_analysis"]["profile_skills"])
            jd_skills = self._extract_skills_from_text(job_description)
            jd_skills_set = set(jd_skills)

            # Calculate text similarity between profile and JD
            text_similarity = self._calculate_text_similarity(profile_text, job_description)

            # Find matching and missing skills
            matching_skills = list(profile_skills & jd_skills_set)
            missing_skills = list(jd_skills_set - profile_skills)
            extra_skills = list(profile_skills - jd_skills_set)

            # Calculate overall match score
            skill_match_score = len(matching_skills) / len(jd_skills_set) if jd_skills_set else 0
            overall_score = (skill_match_score * 0.6) + (text_similarity * 0.4)

            # Determine match level
            if overall_score >= 0.8:
                match_level = "Excellent Match"
            elif overall_score >= 0.6:
                match_level = "Good Match"
            elif overall_score >= 0.4:
                match_level = "Fair Match"
            else:
                match_level = "Poor Match"

            # Extract key requirements from JD
            jd_domain, jd_domain_conf = self._classify_domain(job_description)

            comparison = {
                "overall_match_score": f"{overall_score:.1%}",
                "match_level": match_level,
                "profile_analysis": {
                    "predicted_role": role_analysis["predicted_role"],
                    "category": role_analysis["predicted_category"],
                    "domain": role_analysis["predicted_domain"],
                    "confidence": role_analysis["confidence_scores"]["overall"],
                    "total_skills": len(profile_skills)
                },
                "job_description_analysis": {
                    "detected_domain": jd_domain,
                    "domain_confidence": f"{jd_domain_conf:.1%}",
                    "required_skills": jd_skills,
                    "total_required_skills": len(jd_skills_set)
                },
                "skill_analysis": {
                    "matching_skills": matching_skills,
                    "matching_skills_count": len(matching_skills),
                    "missing_skills": missing_skills[:10],  # Top 10 missing skills
                    "missing_skills_count": len(missing_skills),
                    "extra_skills": extra_skills[:5],  # Top 5 extra skills
                    "skill_match_percentage": f"{skill_match_score:.1%}"
                },
                "compatibility_scores": {
                    "skill_compatibility": f"{skill_match_score:.1%}",
                    "text_similarity": f"{text_similarity:.1%}",
                    "overall_score": f"{overall_score:.1%}"
                },
                "recommendations": {
                    "should_apply": overall_score >= 0.5,
                    "key_improvements": missing_skills[:5],
                    "strengths": matching_skills[:5],
                    "interview_readiness": "High" if overall_score >= 0.7 else "Medium" if overall_score >= 0.5 else "Low"
                }
            }

            return json.dumps(comparison, indent=2)

        except Exception as e:
            return f"Error in profile vs JD comparison: {str(e)}"

    def compare_profile_with_jd_detailed(self, profile_text: str, job_description: str) -> tuple:
        """Compare profile with JD and return both JSON and detailed HTML analysis."""
        if not profile_text.strip() or not job_description.strip():
            return "Please provide both profile and job description for comparison.", ""

        try:
            # Get the basic comparison
            comparison_json = self.compare_profile_with_jd(profile_text, job_description)
            comparison_data = json.loads(comparison_json)

            # Generate detailed HTML analysis
            detailed_html = self._generate_jd_comparison_html(comparison_data)

            return comparison_json, detailed_html

        except Exception as e:
            error_msg = f"Error in detailed profile vs JD comparison: {str(e)}"
            return error_msg, ""

    def _generate_jd_comparison_html(self, comparison_data: dict) -> str:
        """Generate detailed HTML for profile vs JD comparison."""
        overall_score = float(comparison_data["overall_match_score"].replace('%', '')) / 100
        match_level = comparison_data["match_level"]

        # Determine color based on match level - NEW PURPLE THEME
        if overall_score >= 0.8:
            score_color = "#673AB7"  # Bright Purple for Excellent
        elif overall_score >= 0.6:
            score_color = "#7E57C2"  # Medium Purple for Good
        elif overall_score >= 0.4:
            score_color = "#9575CD"  # Lighter Purple for Fair
        else:
            score_color = "#5E35B1"  # Darker Purple for Poor

        html = f"""
        <div style="font-family: Arial, sans-serif; max-width: 900px; color: white;">
            <h2 style="color: white; border-bottom: 2px solid #D1C4E9; padding-bottom: 10px;">
                📊 Profile vs Job Description Analysis
            </h2>

            <div style="background: {score_color}; padding: 20px; border-radius: 10px; margin: 20px 0; text-align: center;">
                <h3 style="margin: 0; font-size: 24px; color: white;">
                    {match_level}: {comparison_data['overall_match_score']}
                </h3>
                <p style="margin: 10px 0 0 0; font-size: 16px; color: #F3E5F5;">
                    Interview Readiness: {comparison_data['recommendations']['interview_readiness']}
                </p>
            </div>

            <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0;">
                <div style="background: #5E35B1; padding: 20px; border-radius: 10px;">
                    <h3 style="color: white; margin-top: 0;">👤 Profile Analysis</h3>
                    <p><strong>Predicted Role:</strong> {comparison_data['profile_analysis']['predicted_role']}</p>
                    <p><strong>Category:</strong> {comparison_data['profile_analysis']['category']}</p>
                    <p><strong>Domain:</strong> {comparison_data['profile_analysis']['domain']}</p>
                    <p><strong>Total Skills:</strong> {comparison_data['profile_analysis']['total_skills']}</p>
                </div>

                <div style="background: #5E35B1; padding: 20px; border-radius: 10px;">
                    <h3 style="color: white; margin-top: 0;">📄 Job Requirements</h3>
                    <p><strong>Detected Domain:</strong> {comparison_data['job_description_analysis']['detected_domain']}</p>
                    <p><strong>Domain Confidence:</strong> {comparison_data['job_description_analysis']['domain_confidence']}</p>
                    <p><strong>Required Skills:</strong> {comparison_data['job_description_analysis']['total_required_skills']}</p>
                </div>
            </div>

            <div style="background: #673AB7; padding: 20px; border-radius: 10px; margin: 20px 0;">
                <h3 style="color: white; margin-top: 0;">✅ Matching Skills ({comparison_data['skill_analysis']['matching_skills_count']} skills)</h3>
                <div style="display: flex; flex-wrap: wrap; gap: 8px;">
        """

        for skill in comparison_data['skill_analysis']['matching_skills']:
            html += f'<span style="background: #512DA8; color: white; padding: 4px 8px; border-radius: 4px; font-size: 12px;">{skill}</span>'

        html += """
                </div>
            </div>
        """

        if comparison_data['skill_analysis']['missing_skills']:
            html += f"""
            <div style="background: #7E57C2; padding: 20px; border-radius: 10px; margin: 20px 0;">
                <h3 style="color: white; margin-top: 0;">❌ Missing Skills ({comparison_data['skill_analysis']['missing_skills_count']} skills)</h3>
                <div style="display: flex; flex-wrap: wrap; gap: 8px;">
            """

            for skill in comparison_data['skill_analysis']['missing_skills']:
                html += f'<span style="background: #9575CD; color: white; padding: 4px 8px; border-radius: 4px; font-size: 12px;">{skill}</span>'

            html += """
                </div>
            </div>
            """

        html += f"""
            <div style="background: #512DA8; padding: 20px; border-radius: 10px; margin: 20px 0;">
                <h3 style="color: white; margin-top: 0;">📈 Compatibility Scores</h3>
                <div style="display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 15px; text-align: center;">
                    <div>
                        <h4 style="margin: 0;">Skill Match</h4>
                        <p style="font-size: 18px; font-weight: bold; margin: 5px 0;">{comparison_data['compatibility_scores']['skill_compatibility']}</p>
                    </div>
                    <div>
                        <h4 style="margin: 0;">Text Similarity</h4>
                        <p style="font-size: 18px; font-weight: bold; margin: 5px 0;">{comparison_data['compatibility_scores']['text_similarity']}</p>
                    </div>
                    <div>
                        <h4 style="margin: 0;">Overall Score</h4>
                        <p style="font-size: 18px; font-weight: bold; margin: 5px 0;">{comparison_data['compatibility_scores']['overall_score']}</p>
                    </div>
                </div>
            </div>

            <div style="background: #4527A0; padding: 20px; border-radius: 10px; margin: 20px 0;">
                <h3 style="color: white; margin-top: 0;">💡 Recommendations</h3>
                <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px; color: #F3E5F5;">
                    <div>
                        <h4>Should Apply?</h4>
                        <p style="font-weight: bold; color: white;">
                            {"✅ Yes, good fit!" if comparison_data['recommendations']['should_apply'] else "❌ Consider improving skills first"}
                        </p>

                        <h4>Key Strengths:</h4>
                        <ul>
        """

        for strength in comparison_data['recommendations']['strengths']:
            html += f"<li>{strength}</li>"

        html += f"""
                        </ul>
                    </div>
                    <div>
                        <h4>Priority Improvements:</h4>
                        <ul>
        """

        for improvement in comparison_data['recommendations']['key_improvements']:
            html += f"<li>{improvement}</li>"

        html += """
                        </ul>
                    </div>
                </div>
            </div>
        </div>
        """

        return html


def create_colab_interface():
    """Create Gradio interface optimized for Google Colab."""
    app = StandaloneResumeMatcherApp()

    with gr.Blocks(title="Resume Matcher", theme=gr.themes.Soft()) as interface:
        gr.Markdown("# 🚀 Resume Matcher - Standalone Snappy Edition")
        gr.Markdown("""
        **✅ Zero Dependencies | ⚡ Instant Results | 🎯 Advanced AI Analytics**

        **Perfect for Quick Scans - Just paste and run!**

        **Key Features:**
        - 🎯 **Smart Role Matching**: AI-powered role classification
        - 📊 **Skill Gap Analysis**: Identifies missing skills with learning roadmaps
        - 📋 **Profile vs Job Matching**: Detailed compatibility scoring
        - 💰 **Salary Insights**: Market salary ranges for different roles
        - 🎓 **Learning Recommendations**: Personalized skill development plans
        - 🚀 **Career Roadmaps**: Short-term and long-term career guidance
        """)

        with gr.Tabs():
            # Tab 1: Profile Analysis
            with gr.TabItem("🎯 Profile Analysis"):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("## 📝 Candidate Profile Input")
                        profile_input = gr.Textbox(
                            label="Paste Candidate Profile",
                            placeholder="""Example format:

Education:
Master's in Computer Science - Stanford University - 3.8 GPA

Experience:
Google - Software Engineer - 2020-2023 - Developed web applications using React and Python
Microsoft - Intern - 2019 - Built machine learning models for data analysis

Skills:
Python, JavaScript, React, SQL, Machine Learning, AWS, Git, Agile, Problem Solving

Projects:
E-commerce Platform - Built full-stack web application with React frontend and Django backend
Data Analysis Tool - Created Python-based tool for analyzing customer behavior data

Achievements:
AWS Certified Developer, Published research paper on ML algorithms, Led team of 5 developers""",
                            lines=20,
                            max_lines=30
                        )

                        analyze_btn = gr.Button("🔍 Analyze Profile", variant="primary", size="lg")

                    with gr.Column(scale=1):
                        gr.Markdown("## 📊 Analysis Results")

                        with gr.Tabs():
                            with gr.TabItem("🎯 Role Classification"):
                                role_analysis_output = gr.JSON(label="Detailed Role Analysis")

                            with gr.TabItem("📈 Skill Gap Analysis"):
                                skill_gap_output = gr.JSON(label="Skill Gap & Recommendations")

                            with gr.TabItem("🚀 Career Roadmap"):
                                career_recommendations_output = gr.HTML(label="Career Development Plan")

                analyze_btn.click(
                    fn=app.enhanced_profile_analysis,
                    inputs=[profile_input],
                    outputs=[role_analysis_output, skill_gap_output, career_recommendations_output]
                )

            # Tab 2: Profile vs Job Description
            with gr.TabItem("📋 Profile vs Job Description"):
                gr.Markdown("## 🎯 Compare Profile Against Job Description")

                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### 👤 Candidate Profile")
                        profile_jd_input = gr.Textbox(
                            label="Candidate Profile",
                            lines=12,
                            placeholder="Paste candidate profile here..."
                        )

                    with gr.Column():
                        gr.Markdown("### 📄 Job Description")
                        jd_input = gr.Textbox(
                            label="Job Description",
                            lines=12,
                            placeholder="""Paste job description here...

Example:
We are looking for a Software Engineer to join our team.

Requirements:
- 3+ years of experience in Python or Java
- Experience with web frameworks (Django, Flask, Spring)
- Knowledge of databases (SQL, NoSQL)
- Experience with cloud platforms (AWS, GCP, Azure)
- Strong problem-solving skills
- Bachelor's degree in Computer Science or related field

Responsibilities:
- Develop and maintain web applications
- Collaborate with cross-functional teams
- Write clean, maintainable code
- Participate in code reviews
- Debug and resolve technical issues"""
                        )

                compare_jd_btn = gr.Button("🔍 Analyze Match Score", variant="primary", size="lg")

                with gr.Tabs():
                    with gr.TabItem("📊 Match Score & Analysis"):
                        jd_comparison_output = gr.JSON(label="Profile vs JD Analysis")

                    with gr.TabItem("📈 Detailed Breakdown"):
                        jd_detailed_output = gr.HTML(label="Detailed Match Analysis")

                compare_jd_btn.click(
                    fn=app.compare_profile_with_jd_detailed,
                    inputs=[profile_jd_input, jd_input],
                    outputs=[jd_comparison_output, jd_detailed_output]
                )

        with gr.Accordion("ℹ️ How to Use & Features", open=False):
            gr.Markdown("""
            ### 🚀 Quick Start Guide:

            1. **Profile Analysis**: Paste a candidate's resume/profile in the first tab to get:
               - AI-powered role predictions with confidence scores
               - Skill gap analysis with priority levels
               - Personalized learning recommendations
               - Career development roadmap

            2. **Profile vs Job Matching**: Compare a profile against a job description to get:
               - Overall compatibility score
               - Detailed skill matching analysis
               - Interview readiness assessment
               - Specific improvement recommendations

            ### 🎯 What Makes This Special:

            - **Zero Setup**: No installations, dependencies, or API keys needed
            - **Instant Results**: Fast processing with immediate feedback
            - **Comprehensive Analysis**: Expanded role library across multiple categories
            - **Smart Algorithms**: Multi-factor scoring (skills + keywords + text similarity)
            - **Actionable Insights**: Specific learning paths and career guidance
            """)

    return interface


# Main execution
if __name__ == "__main__":
    print("🚀 Starting Resume Matcher...")
    print("✅ No external dependencies required!")
    print("📊 Loading comprehensive role database...")

    interface = create_colab_interface()

    print("🎯 Ready to analyze resumes and match profiles!")
    print("💡 Features: Role Classification | Skill Gap Analysis | Job Matching | Career Recommendations")

    # Launch with Colab-optimized settings
    interface.launch(
        share=True,  # Creates public link for sharing
        debug=True,  # Shows detailed error messages
        show_error=True,  # Display errors in interface
        quiet=False  # Show startup logs
    )