#!/usr/bin/env python3
"""
Multi-Agent Job Application Assistant - HuggingFace Spaces Deployment
Production-ready system with Gemini 2.5 Flash, A2A Protocol, and MCP Integration
Features: Resume/Cover Letter Generation, Job Matching, Document Export, Advanced AI Agents
"""

import os
import uuid
import time
import logging
import asyncio
from typing import List, Optional, Dict, Any
from dataclasses import dataclass, field
import webbrowser
from datetime import datetime, timedelta
import json
from pathlib import Path

import gradio as gr
from dotenv import load_dotenv
import nest_asyncio

# Apply nest_asyncio for async support in Gradio
try:
    nest_asyncio.apply()
except:
    pass

# Load environment variables
load_dotenv(override=True)

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# =======================
# Try to import from system, fall back to standalone mode if not available
# =======================

USE_SYSTEM_AGENTS = True
ADVANCED_FEATURES = False
LANGEXTRACT_AVAILABLE = False

try:
    from agents.orchestrator import OrchestratorAgent
    from models.schemas import JobPosting, OrchestrationResult
    logger.info("System agents loaded - full functionality available")
    
    # Try to import LangExtract service
    try:
        from services.langextract_service import (
            extract_job_info, 
            extract_ats_keywords,
            optimize_for_ats,
            create_extraction_summary,
            create_ats_report
        )
        LANGEXTRACT_AVAILABLE = True
        logger.info("📊 LangExtract service loaded for enhanced extraction")
    except ImportError:
        LANGEXTRACT_AVAILABLE = False
    
    # Try to import advanced AI agent features
    try:
        from agents.parallel_executor import ParallelAgentExecutor, ParallelJobProcessor, MetaAgent
        from agents.temporal_tracker import TemporalApplicationTracker, TemporalKnowledgeGraph
        from agents.observability import AgentTracer, AgentMonitor, TriageAgent, global_tracer
        from agents.context_engineer import ContextEngineer, DataFlywheel
        from agents.context_scaler import ContextScalingOrchestrator
        ADVANCED_FEATURES = True
        logger.info("✨ Advanced AI agent features loaded successfully!")
    except ImportError as e:
        logger.info(f"Advanced features not available: {e}")
    
    # Try to import knowledge graph service
    try:
        from services.knowledge_graph_service import get_knowledge_graph_service
        kg_service = get_knowledge_graph_service()
        KG_AVAILABLE = kg_service.is_enabled()
        if KG_AVAILABLE:
            logger.info("📊 Knowledge Graph service initialized - tracking enabled")
    except ImportError:
        KG_AVAILABLE = False
        kg_service = None
        logger.info("Knowledge graph service not available")
    
    USE_SYSTEM_AGENTS = True
        
except ImportError:
    logger.info("Running in standalone mode - using simplified agents")
    USE_SYSTEM_AGENTS = False
    
    # Define minimal data structures for standalone operation
    @dataclass
    class JobPosting:
        id: str
        title: str
        company: str
        description: str
        location: Optional[str] = None
        url: Optional[str] = None
        source: Optional[str] = None
        saved_by_user: bool = False

    @dataclass
    class ResumeDraft:
        job_id: str
        text: str
        keywords_used: List[str] = field(default_factory=list)

    @dataclass  
    class CoverLetterDraft:
        job_id: str
        text: str
        keywords_used: List[str] = field(default_factory=list)

    @dataclass
    class OrchestrationResult:
        job: JobPosting
        resume: ResumeDraft
        cover_letter: CoverLetterDraft
        metrics: Optional[Dict[str, Any]] = None

    # Simplified orchestrator for standalone operation
    class OrchestratorAgent:
        def __init__(self):
            self.mock_jobs = [
                JobPosting(
                    id="example_1",
                    title="Senior Software Engineer",
                    company="Tech Corp",
                    location="Remote",
                    description="We need a Senior Software Engineer with Python, AWS, Docker experience.",
                    saved_by_user=True
                )
            ]
        
        def get_saved_jobs(self):
            return self.mock_jobs
        
        def run_for_jobs(self, jobs, **kwargs):
            results = []
            for job in jobs:
                resume = ResumeDraft(
                    job_id=job.id,
                    text=f"Professional Resume for {job.title}\n\nExperienced professional with skills matching {job.company} requirements.",
                    keywords_used=["Python", "AWS", "Docker"]
                )
                cover = CoverLetterDraft(
                    job_id=job.id,
                    text=f"Dear Hiring Manager,\n\nI am excited to apply for the {job.title} position at {job.company}.",
                    keywords_used=["leadership", "innovation"]
                )
                results.append(OrchestrationResult(
                    job=job,
                    resume=resume,
                    cover_letter=cover,
                    metrics={
                        "salary": {"USD": {"low": 100000, "high": 150000}},
                        "p_resume": 0.75,
                        "p_cover": 0.80,
                        "overall_p": 0.60
                    }
                ))
            return results
        
        def regenerate_for_job(self, job, **kwargs):
            return self.run_for_jobs([job], **kwargs)[0]

# Initialize orchestrator and advanced features
try:
    orch = OrchestratorAgent()
    logger.info("Orchestrator initialized successfully")
    
    # Initialize advanced features if available
    if ADVANCED_FEATURES:
        # Initialize parallel executor
        parallel_executor = ParallelAgentExecutor(max_workers=4)
        parallel_processor = ParallelJobProcessor()
        meta_agent = MetaAgent()
        
        # Initialize temporal tracker
        temporal_tracker = TemporalApplicationTracker()
        
        # Initialize observability
        agent_tracer = AgentTracer()
        agent_monitor = AgentMonitor()
        triage_agent = TriageAgent(agent_tracer)
        
        # Initialize context engineering
        context_engineer = ContextEngineer()
        context_scaler = ContextScalingOrchestrator()
        
        logger.info("✅ All advanced AI agent features initialized")
    else:
        parallel_executor = None
        temporal_tracker = None
        agent_tracer = None
        context_engineer = None
        
except Exception as e:
    logger.error(f"Failed to initialize orchestrator: {e}")
    raise

# Session state
STATE = {
    "user_id": "default_user",
    "cv_seed": None,
    "cover_seed": None,
    "agent2_notes": "",
    "custom_jobs": [],
    "cv_chat": "",
    "cover_chat": "",
    "results": [],
    "inspiration_url": "https://www.careeraddict.com/7-funniest-cover-letters",
    "use_inspiration": False,
    "linkedin_authenticated": False,
    "linkedin_profile": None,
    "parallel_mode": False,
    "track_applications": True,
    "enable_observability": True,
    "use_context_engineering": True,
    "execution_timeline": None,
    "application_history": [],
}

# Check LinkedIn OAuth configuration
LINKEDIN_CLIENT_ID = os.getenv("LINKEDIN_CLIENT_ID")
LINKEDIN_CLIENT_SECRET = os.getenv("LINKEDIN_CLIENT_SECRET")
MOCK_MODE = os.getenv("MOCK_MODE", "true").lower() == "true"

# Check Adzuna configuration
ADZUNA_APP_ID = os.getenv("ADZUNA_APP_ID")
ADZUNA_APP_KEY = os.getenv("ADZUNA_APP_KEY")


def add_custom_job(title: str, company: str, location: str, url: str, desc: str):
    """Add a custom job with validation"""
    try:
        if not title or not company or not desc:
            return gr.update(value="❌ Title, Company, and Description are required"), None
        
        job = JobPosting(
            id=f"custom_{uuid.uuid4().hex[:8]}",
            title=title.strip(),
            company=company.strip(),
            location=location.strip() if location else None,
            description=desc.strip(),
            url=url.strip() if url else None,
            source="custom",
            saved_by_user=True,
        )
        STATE["custom_jobs"].append(job)
        logger.info(f"Added custom job: {job.title} at {job.company}")
        return gr.update(value=f"✅ Added: {job.title} at {job.company}"), ""
    except Exception as e:
        logger.error(f"Error adding job: {e}")
        return gr.update(value=f"❌ Error: {str(e)}"), None


def get_linkedin_auth_url():
    """Get LinkedIn OAuth URL"""
    if USE_SYSTEM_AGENTS and not MOCK_MODE and LINKEDIN_CLIENT_ID:
        try:
            from services.linkedin_client import LinkedInClient
            client = LinkedInClient()
            return client.get_authorize_url()
        except Exception as e:
            logger.error(f"LinkedIn OAuth error: {e}")
    return None


def linkedin_login():
    """Handle LinkedIn login"""
    auth_url = get_linkedin_auth_url()
    if auth_url:
        webbrowser.open(auth_url)
        return "✅ Opening LinkedIn login in browser...", True
    else:
        return "⚠️ LinkedIn OAuth not configured or in mock mode", False


def search_adzuna_jobs(query: str = "Software Engineer", location: str = "London"):
    """Search jobs using Adzuna API"""
    if ADZUNA_APP_ID and ADZUNA_APP_KEY:
        try:
            from services.job_aggregator import JobAggregator
            aggregator = JobAggregator()
            
            # Handle SSL issues for corporate networks
            import requests
            import urllib3
            old_get = requests.get
            def patched_get(*args, **kwargs):
                if 'adzuna' in str(args[0]):
                    kwargs['verify'] = False
                    urllib3.disable_warnings()
                return old_get(*args, **kwargs)
            requests.get = patched_get
            
            jobs = aggregator.search_adzuna(query, location)
            return jobs, f"✅ Found {len(jobs)} jobs from Adzuna"
        except Exception as e:
            logger.error(f"Adzuna search error: {e}")
            return [], f"❌ Adzuna search failed: {str(e)}"
    return [], "⚠️ Adzuna API not configured"


def list_jobs_options():
    """Get list of available jobs with enhanced sources"""
    try:
        all_jobs = []
        
        # Get LinkedIn/mock jobs
        saved_jobs = orch.get_saved_jobs()
        all_jobs.extend(saved_jobs)
        
        # Add custom jobs
        custom_jobs = STATE.get("custom_jobs", [])
        all_jobs.extend(custom_jobs)
        
        # Try to add Adzuna jobs if configured
        if ADZUNA_APP_ID and ADZUNA_APP_KEY:
            adzuna_jobs, _ = search_adzuna_jobs("Software Engineer", "Remote")
            all_jobs.extend(adzuna_jobs[:10])  # Add top 10 Adzuna jobs
        
        labels = [f"{j.title} — {j.company} ({j.location or 'N/A'}) [{j.source or 'custom'}]" for j in all_jobs]
        return labels
    except Exception as e:
        logger.error(f"Error listing jobs: {e}")
        return []


def generate(selected_labels: List[str]):
    """Generate documents with advanced AI features"""
    try:
        if not selected_labels:
            return "⚠️ Please select at least one job to process", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
        
        # Triage the request if observability is enabled
        if ADVANCED_FEATURES and STATE.get("enable_observability") and agent_tracer:
            routing = triage_agent.triage_request(f"Generate documents for {len(selected_labels)} jobs")
            logger.info(f"Triage routing: {routing}")
        
        # Map labels to job objects
        all_jobs = orch.get_saved_jobs() + STATE.get("custom_jobs", [])
        
        # Update label mapping to handle source tags
        label_to_job = {}
        for j in all_jobs:
            label = f"{j.title} — {j.company} ({j.location or 'N/A'})"
            label_with_source = f"{label} [{j.source or 'custom'}]"
            # Map both versions
            label_to_job[label] = j
            label_to_job[label_with_source] = j
        
        jobs = [label_to_job[l] for l in selected_labels if l in label_to_job]
        
        if not jobs:
            return "❌ No valid jobs found", None, None
        
        logger.info(f"Generating documents for {len(jobs)} jobs")
        
        # Use context engineering if enabled
        if ADVANCED_FEATURES and STATE.get("use_context_engineering") and context_engineer:
            for job in jobs:
                # Engineer optimal context for each job
                context = context_engineer.engineer_context(
                    query=f"Generate resume and cover letter for {job.title} at {job.company}",
                    raw_sources=[
                        ("job_description", job.description),
                        ("cv_seed", STATE.get("cv_seed") or ""),
                        ("notes", STATE.get("agent2_notes") or "")
                    ]
                )
                # Store engineered context
                job.metadata = job.metadata or {}
                job.metadata['engineered_context'] = context
        
        # Run generation (parallel or sequential)
        start = time.time()
        
        if ADVANCED_FEATURES and STATE.get("parallel_mode") and parallel_executor:
            # Use parallel processing
            logger.info("Using parallel processing for document generation")
            results = asyncio.run(parallel_processor.process_jobs_parallel(
                jobs=jobs,
                cv_agent_func=lambda j: orch.cv_agent.get_draft(j, STATE.get("cv_seed")),
                cover_agent_func=lambda j: orch.cover_letter_agent.get_draft(j, STATE.get("cover_seed"))
            ))
        else:
            # Standard sequential processing
            results = orch.run_for_jobs(
                jobs,
                user_id=STATE.get("user_id", "default_user"),
                cv_chat=STATE.get("cv_chat"),
                cover_chat=STATE.get("cover_chat"),
                cv_seed=STATE.get("cv_seed"),
                cover_seed=STATE.get("cover_seed"),
                agent2_notes=STATE.get("agent2_notes"),
                inspiration_url=(STATE.get("inspiration_url") if STATE.get("use_inspiration") else None),
            )
        
        total_time = time.time() - start
        STATE["results"] = results

        # Track applications temporally if enabled
        if ADVANCED_FEATURES and STATE.get("track_applications") and temporal_tracker:
            for result in results:
                temporal_tracker.track_application(result.job, "generated", {
                    'generation_time': total_time,
                    'parallel_mode': STATE.get("parallel_mode", False)
                })
        
        # Track in knowledge graph if available
        if 'kg_service' in globals() and kg_service and kg_service.is_enabled():
            for result in results:
                try:
                    # Extract skills from job description
                    skills = []
                    if hasattr(result, 'matched_keywords'):
                        skills = result.matched_keywords
                    elif hasattr(result.job, 'description'):
                        # Simple skill extraction from job description
                        common_skills = ['python', 'java', 'javascript', 'react', 'node', 
                                       'aws', 'azure', 'docker', 'kubernetes', 'sql',
                                       'machine learning', 'ai', 'data science']
                        job_desc_lower = result.job.description.lower()
                        skills = [s for s in common_skills if s in job_desc_lower]
                    
                    # Track the application
                    kg_service.track_application(
                        user_name=STATE.get("user_name", "User"),
                        company=result.job.company,
                        job_title=result.job.title,
                        job_description=result.job.description,
                        cv_text=result.resume.text,
                        cover_letter=result.cover_letter.text,
                        skills_matched=skills,
                        score=getattr(result, 'match_score', 0.0)
                    )
                    logger.info(f"Tracked application in knowledge graph: {result.job.title} @ {result.job.company}")
                except Exception as e:
                    logger.warning(f"Failed to track in knowledge graph: {e}")
        
        # Record to context engineering flywheel
        if ADVANCED_FEATURES and context_engineer:
            for result in results:
                if hasattr(result.job, 'metadata') and 'engineered_context' in result.job.metadata:
                    context_engineer.record_feedback(
                        result.job.metadata['engineered_context'],
                        result.resume.text[:500],  # Sample output
                        0.8  # Success score (could be calculated)
                    )
        
        # Build preview
        blocks = [f"✅ Generated {len(results)} documents in {total_time:.2f}s\n"]
        pptx_buttons = []
        
        for i, res in enumerate(results):
            blocks.append(f"### 📄 {res.job.title} — {res.job.company}")
            blocks.append("**Resume Preview:**")
            blocks.append("```")
            blocks.append(res.resume.text[:1500] + "...")
            blocks.append("```")
            blocks.append("\n**Cover Letter Preview:**")
            blocks.append("```")
            blocks.append(res.cover_letter.text[:1000] + "...")
            blocks.append("```")
            
            # Add PowerPoint export option
            blocks.append(f"\n**[📊 Export as PowerPoint CV - Job #{i+1}]**")
            pptx_buttons.append((res.resume, res.job))
        
        STATE["pptx_candidates"] = pptx_buttons
        return "\n".join(blocks), total_time, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
        
    except Exception as e:
        logger.error(f"Error generating documents: {e}")
        return f"❌ Error: {str(e)}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)


def regenerate_one(job_label: str):
    """Regenerate documents for a single job"""
    try:
        if not job_label:
            return "⚠️ Please select a job to regenerate", None
        
        all_jobs = orch.get_saved_jobs() + STATE.get("custom_jobs", [])
        label_to_job = {f"{j.title} — {j.company} ({j.location or 'N/A'})": j for j in all_jobs}
        job = label_to_job.get(job_label)
        
        if not job:
            return f"❌ Job not found: {job_label}", None
        
        start = time.time()
        result = orch.regenerate_for_job(
            job,
            user_id=STATE.get("user_id", "default_user"),
            cv_chat=STATE.get("cv_chat"),
            cover_chat=STATE.get("cover_chat"),
            cv_seed=STATE.get("cv_seed"),
            cover_seed=STATE.get("cover_seed"),
            agent2_notes=STATE.get("agent2_notes"),
            inspiration_url=(STATE.get("inspiration_url") if STATE.get("use_inspiration") else None),
        )
        elapsed = time.time() - start
        
        # Update state
        new_results = []
        for r in STATE.get("results", []):
            if r.job.id == job.id:
                new_results.append(result)
            else:
                new_results.append(r)
        STATE["results"] = new_results
        
        preview = f"### 🔄 Regenerated: {result.job.title} — {result.job.company}\n\n"
        preview += "**Resume:**\n```\n" + result.resume.text[:1500] + "\n...```\n\n"
        preview += "**Cover Letter:**\n```\n" + result.cover_letter.text[:1000] + "\n...```"
        
        return preview, elapsed
        
    except Exception as e:
        logger.error(f"Error regenerating: {e}")
        return f"❌ Error: {str(e)}", None


def export_to_powerpoint(job_index: int, template: str = "modern_blue"):
    """Export resume to PowerPoint CV"""
    try:
        candidates = STATE.get("pptx_candidates", [])
        if not candidates or job_index >= len(candidates):
            return "❌ No resume available for export", None
        
        resume, job = candidates[job_index]
        
        # Import the PowerPoint CV generator
        try:
            from services.powerpoint_cv import convert_resume_to_powerpoint
            pptx_path = convert_resume_to_powerpoint(resume, job, template)
            if pptx_path:
                return f"✅ PowerPoint CV created: {pptx_path}", pptx_path
        except ImportError:
            # Fallback to local generation
            from pptx import Presentation
            from pptx.util import Inches, Pt
            
            prs = Presentation()
            
            # Title slide
            slide = prs.slides.add_slide(prs.slide_layouts[0])
            slide.shapes.title.text = resume.sections.get("name", "Professional CV")
            slide.placeholders[1].text = f"{resume.sections.get('title', '')}\n{resume.sections.get('email', '')}"
            
            # Summary slide
            slide = prs.slides.add_slide(prs.slide_layouts[1])
            slide.shapes.title.text = "Professional Summary"
            slide.placeholders[1].text = resume.sections.get("summary", "")[:500]
            
            # Experience slide
            slide = prs.slides.add_slide(prs.slide_layouts[1])
            slide.shapes.title.text = "Professional Experience"
            exp_text = []
            for exp in resume.sections.get("experience", [])[:3]:
                exp_text.append(f"• {exp.get('title', '')} @ {exp.get('company', '')}")
                exp_text.append(f"  {exp.get('dates', '')}")
            slide.placeholders[1].text = "\n".join(exp_text)
            
            # Skills slide
            slide = prs.slides.add_slide(prs.slide_layouts[1])
            slide.shapes.title.text = "Core Skills"
            skills_text = []
            for category, items in resume.sections.get("skills", {}).items():
                if isinstance(items, list):
                    skills_text.append(f"{category}: {', '.join(items[:5])}")
            slide.placeholders[1].text = "\n".join(skills_text)
            
            # Save
            output_path = f"cv_{job.company.replace(' ', '_')}_{template}.pptx"
            prs.save(output_path)
            return f"✅ PowerPoint CV created: {output_path}", output_path
            
    except Exception as e:
        logger.error(f"PowerPoint export error: {e}")
        return f"❌ Export failed: {str(e)}", None


def extract_from_powerpoint(file_path: str):
    """Extract content from uploaded PowerPoint"""
    try:
        from pptx import Presentation
        
        prs = Presentation(file_path)
        extracted_text = []
        
        for slide in prs.slides:
            for shape in slide.shapes:
                if hasattr(shape, "text"):
                    text = shape.text.strip()
                    if text:
                        extracted_text.append(text)
        
        combined_text = "\n".join(extracted_text)
        
        # Use as CV seed
        STATE["cv_seed"] = combined_text
        
        return f"✅ Extracted {len(extracted_text)} text blocks from PowerPoint\n\nPreview:\n{combined_text[:500]}..."
        
    except Exception as e:
        logger.error(f"PowerPoint extraction error: {e}")
        return f"❌ Extraction failed: {str(e)}"


def summary_table():
    """Generate summary table"""
    try:
        import pandas as pd
        res = STATE.get("results", [])
        if not res:
            return pd.DataFrame({"Status": ["No results yet. Generate documents first."]})
        
        rows = []
        for r in res:
            m = r.metrics or {}
            sal = m.get("salary", {})
            
            # Handle different salary formats
            usd = sal.get("USD", {})
            gbp = sal.get("GBP", {})
            
            rows.append({
                "Job": f"{r.job.title} — {r.job.company}",
                "Location": r.job.location or "N/A",
                "USD": f"${usd.get('low', 0):,}-${usd.get('high', 0):,}" if usd else "N/A",
                "GBP": f"£{gbp.get('low', 0):,}-£{gbp.get('high', 0):,}" if gbp else "N/A",
                "Resume Score": f"{m.get('p_resume', 0):.1%}",
                "Cover Score": f"{m.get('p_cover', 0):.1%}",
                "Overall": f"{m.get('overall_p', 0):.1%}",
            })
        return pd.DataFrame(rows)
    except ImportError:
        # If pandas not available, return simple dict
        return {"Error": ["pandas not installed - table view unavailable"]}
    except Exception as e:
        logger.error(f"Error generating summary: {e}")
        return {"Error": [str(e)]}


def build_app():
    """Build the Gradio interface with LinkedIn OAuth and Adzuna integration"""
    with gr.Blocks(
        title="Job Application Assistant",
        theme=gr.themes.Soft(),
        css="""
        .gradio-container { max-width: 1400px; margin: auto; }
        """
    ) as demo:
        gr.Markdown("""
        # 🚀 Multi-Agent Job Application Assistant
        ### AI-Powered Resume & Cover Letter Generation with ATS Optimization
        ### Now with LinkedIn OAuth + Adzuna Job Search!
        """)
        
        # System Status
        status_items = []
        if USE_SYSTEM_AGENTS:
            status_items.append("✅ **Full System Mode**")
        else:
            status_items.append("⚠️ **Standalone Mode**")
        
        if ADVANCED_FEATURES:
            status_items.append("🚀 **Advanced AI Features**")
        
        if LANGEXTRACT_AVAILABLE:
            status_items.append("📊 **LangExtract Enhanced**")
        
        if not MOCK_MODE and LINKEDIN_CLIENT_ID:
            status_items.append("✅ **LinkedIn OAuth Ready**")
        else:
            status_items.append("⚠️ **LinkedIn in Mock Mode**")
        
        if ADZUNA_APP_ID and ADZUNA_APP_KEY:
            status_items.append("✅ **Adzuna API Active** (5000 jobs/month)")
        else:
            status_items.append("⚠️ **Adzuna Not Configured**")
        
        gr.Markdown(" | ".join(status_items))
        
        # Show advanced features if available
        if ADVANCED_FEATURES:
            advanced_features = []
            if 'parallel_executor' in locals():
                advanced_features.append("⚡ Parallel Processing")
            if 'temporal_tracker' in locals():
                advanced_features.append("📊 Temporal Tracking")
            if 'agent_tracer' in locals():
                advanced_features.append("🔍 Observability")
            if 'context_engineer' in locals():
                advanced_features.append("🧠 Context Engineering")
            
            if advanced_features:
                gr.Markdown(f"**Advanced Features Available:** {' | '.join(advanced_features)}")

        # Import enhanced UI components
        try:
            from services.enhanced_ui import (
                create_enhanced_ui_components,
                handle_resume_upload,
                handle_linkedin_import,
                handle_job_matching,
                handle_document_export,
                populate_ui_from_data,
                format_job_matches_for_display,
                generate_recommendations_markdown,
                generate_skills_gap_analysis
            )
            ENHANCED_UI_AVAILABLE = True
        except ImportError:
            ENHANCED_UI_AVAILABLE = False
            logger.warning("Enhanced UI components not available")
        
        with gr.Row():
            # Left column - Configuration
            with gr.Column(scale=2):
                gr.Markdown("## ⚙️ Configuration")
                
                # Enhanced Resume Upload Section (if available)
                if ENHANCED_UI_AVAILABLE:
                    ui_components = create_enhanced_ui_components()
                    
                    # Create a wrapper function that properly handles the response
                    def process_resume_and_populate(file_path):
                        """Process resume upload and return extracted data for UI fields"""
                        if not file_path:
                            return populate_ui_from_data({})
                        
                        try:
                            # Call handle_resume_upload to extract data
                            response = handle_resume_upload(file_path)
                            
                            # Extract the data from the response
                            if response and isinstance(response, dict):
                                data = response.get('data', {})
                                # Return the populated fields
                                return populate_ui_from_data(data)
                            else:
                                return populate_ui_from_data({})
                        except Exception as e:
                            logger.error(f"Error processing resume: {e}")
                            return populate_ui_from_data({})
                    
                    # Wire up the handlers - single function call
                    ui_components['extract_btn'].click(
                        fn=process_resume_and_populate,
                        inputs=[ui_components['resume_upload']],
                        outputs=[
                            ui_components['contact_name'],
                            ui_components['contact_email'],
                            ui_components['contact_phone'],
                            ui_components['contact_linkedin'],
                            ui_components['contact_location'],
                            ui_components['summary_text'],
                            ui_components['experience_data'],
                            ui_components['skills_list'],
                            ui_components['education_data']
                        ]
                    )
                    
                    ui_components['linkedin_auto_fill'].click(
                        fn=handle_linkedin_import,
                        inputs=[ui_components['linkedin_url'], gr.State()],
                        outputs=[gr.State()]
                    ).then(
                        fn=populate_ui_from_data,
                        inputs=[gr.State()],
                        outputs=[
                            ui_components['contact_name'],
                            ui_components['contact_email'],
                            ui_components['contact_phone'],
                            ui_components['contact_linkedin'],
                            ui_components['contact_location'],
                            ui_components['summary_text'],
                            ui_components['experience_data'],
                            ui_components['skills_list'],
                            ui_components['education_data']
                        ]
                    )
                
                # LinkedIn OAuth Section (keep existing)
                elif not MOCK_MODE and LINKEDIN_CLIENT_ID:
                    with gr.Accordion("🔐 LinkedIn Authentication", open=True):
                        linkedin_status = gr.Textbox(
                            label="Status",
                            value="Not authenticated",
                            interactive=False
                        )
                        linkedin_btn = gr.Button("🔗 Sign in with LinkedIn", variant="primary")
                        linkedin_btn.click(
                            fn=linkedin_login,
                            outputs=[linkedin_status, gr.State()]
                        )
                
                # Advanced AI Features Section
                if ADVANCED_FEATURES:
                    with gr.Accordion("🚀 Advanced AI Features", open=True):
                        gr.Markdown("### AI Agent Enhancements")
                        
                        with gr.Row():
                            parallel_mode = gr.Checkbox(
                                label="⚡ Parallel Processing (3-5x faster)",
                                value=STATE.get("parallel_mode", False)
                            )
                            track_apps = gr.Checkbox(
                                label="📊 Temporal Tracking",
                                value=STATE.get("track_applications", True)
                            )
                        
                        with gr.Row():
                            observability = gr.Checkbox(
                                label="🔍 Observability & Tracing",
                                value=STATE.get("enable_observability", True)
                            )
                            context_eng = gr.Checkbox(
                                label="🧠 Context Engineering",
                                value=STATE.get("use_context_engineering", True)
                            )
                        
                        def update_features(parallel, track, observe, context):
                            STATE["parallel_mode"] = parallel
                            STATE["track_applications"] = track
                            STATE["enable_observability"] = observe
                            STATE["use_context_engineering"] = context
                            
                            features = []
                            if parallel: features.append("Parallel")
                            if track: features.append("Tracking")
                            if observe: features.append("Observability")
                            if context: features.append("Context Engineering")
                            
                            return f"✅ Features enabled: {', '.join(features) if features else 'None'}"
                        
                        features_status = gr.Textbox(label="Features Status", interactive=False)
                        
                        parallel_mode.change(
                            fn=lambda p: update_features(p, track_apps.value, observability.value, context_eng.value),
                            inputs=[parallel_mode],
                            outputs=features_status
                        )
                        track_apps.change(
                            fn=lambda t: update_features(parallel_mode.value, t, observability.value, context_eng.value),
                            inputs=[track_apps],
                            outputs=features_status
                        )
                        observability.change(
                            fn=lambda o: update_features(parallel_mode.value, track_apps.value, o, context_eng.value),
                            inputs=[observability],
                            outputs=features_status
                        )
                        context_eng.change(
                            fn=lambda c: update_features(parallel_mode.value, track_apps.value, observability.value, c),
                            inputs=[context_eng],
                            outputs=features_status
                        )
                
                with gr.Accordion("📝 Profile & Notes", open=True):
                    agent2_notes = gr.Textbox(
                        label="Additional Context",
                        value=STATE["agent2_notes"],
                        lines=4,
                        placeholder="E.g., visa requirements, years of experience, preferred technologies..."
                    )
                    def set_notes(n):
                        STATE["agent2_notes"] = n or ""
                        return "✅ Notes saved"
                    notes_result = gr.Textbox(label="Status", interactive=False)
                    agent2_notes.change(set_notes, inputs=agent2_notes, outputs=notes_result)
                
                with gr.Accordion("📄 Resume Settings", open=False):
                    cv_chat = gr.Textbox(
                        label="Resume Instructions",
                        value=STATE["cv_chat"],
                        lines=3,
                        placeholder="E.g., Emphasize leadership experience..."
                    )
                    
                    # PowerPoint Upload
                    gr.Markdown("### 📊 Upload PowerPoint to Extract Content")
                    pptx_upload = gr.File(
                        label="Upload PowerPoint (.pptx)",
                        file_types=[".pptx"],
                        type="filepath"
                    )
                    pptx_extract_btn = gr.Button("📥 Extract from PowerPoint")
                    pptx_extract_status = gr.Textbox(label="Extraction Status", interactive=False)
                    
                    cv_seed = gr.Textbox(
                        label="Resume Template (optional)",
                        value=STATE["cv_seed"] or "",
                        lines=10,
                        placeholder="Paste your existing resume here or extract from PowerPoint..."
                    )
                    
                    def set_cv(c, s):
                        STATE["cv_chat"] = c or ""
                        STATE["cv_seed"] = s or None
                        return "✅ Resume settings updated"
                    
                    def handle_pptx_upload(file):
                        if file:
                            status = extract_from_powerpoint(file)
                            return status, STATE.get("cv_seed", "")
                        return "No file uploaded", STATE.get("cv_seed", "")
                    
                    pptx_extract_btn.click(
                        fn=handle_pptx_upload,
                        inputs=pptx_upload,
                        outputs=[pptx_extract_status, cv_seed]
                    )
                    
                    cv_info = gr.Textbox(label="Status", interactive=False)
                    cv_chat.change(lambda x: set_cv(x, cv_seed.value), inputs=cv_chat, outputs=cv_info)
                    cv_seed.change(lambda x: set_cv(cv_chat.value, x), inputs=cv_seed, outputs=cv_info)
                
                with gr.Accordion("✉️ Cover Letter Settings", open=False):
                    cover_chat = gr.Textbox(
                        label="Cover Letter Instructions",
                        value=STATE["cover_chat"],
                        lines=3,
                        placeholder="E.g., Professional tone, mention relocation..."
                    )
                    cover_seed = gr.Textbox(
                        label="Cover Letter Template (optional)",
                        value=STATE["cover_seed"] or "",
                        lines=10,
                        placeholder="Paste your existing cover letter here..."
                    )
                    def set_cover(c, s):
                        STATE["cover_chat"] = c or ""
                        STATE["cover_seed"] = s or None
                        return "✅ Cover letter settings updated"
                    cover_info = gr.Textbox(label="Status", interactive=False)
                    cover_chat.change(lambda x: set_cover(x, cover_seed.value), inputs=cover_chat, outputs=cover_info)
                    cover_seed.change(lambda x: set_cover(cover_chat.value, x), inputs=cover_seed, outputs=cover_info)
                
                gr.Markdown("## 💼 Jobs")
                
                # Adzuna Job Search
                if ADZUNA_APP_ID and ADZUNA_APP_KEY:
                    with gr.Accordion("🔍 Search Adzuna Jobs", open=True):
                        with gr.Row():
                            adzuna_query = gr.Textbox(
                                label="Job Title",
                                value="Software Engineer",
                                placeholder="e.g., Python Developer"
                            )
                            adzuna_location = gr.Textbox(
                                label="Location",
                                value="London",
                                placeholder="e.g., New York, Remote"
                            )
                        
                        adzuna_search_btn = gr.Button("🔍 Search Adzuna", variant="primary")
                        adzuna_results = gr.Textbox(
                            label="Search Results",
                            lines=3,
                            interactive=False
                        )
                        
                        def search_and_display(query, location):
                            jobs, message = search_adzuna_jobs(query, location)
                            # Add jobs to state
                            if jobs:
                                STATE["custom_jobs"].extend(jobs[:5])  # Add top 5 to available jobs
                            return message
                        
                        adzuna_search_btn.click(
                            fn=search_and_display,
                            inputs=[adzuna_query, adzuna_location],
                            outputs=adzuna_results
                        )
                
                with gr.Accordion("➕ Add Custom Job", open=True):
                    c_title = gr.Textbox(label="Job Title*", placeholder="e.g., Senior Software Engineer")
                    c_company = gr.Textbox(label="Company*", placeholder="e.g., Google")
                    c_loc = gr.Textbox(label="Location", placeholder="e.g., Remote, New York")
                    c_url = gr.Textbox(label="Job URL", placeholder="https://...")
                    c_desc = gr.Textbox(
                        label="Job Description*",
                        lines=8,
                        placeholder="Paste the complete job description here..."
                    )
                    
                    with gr.Row():
                        add_job_btn = gr.Button("➕ Add Job", variant="primary")
                        load_example_btn = gr.Button("📝 Load Example")
                    
                    add_job_info = gr.Textbox(label="Status", interactive=False)
                    
                    def load_example():
                        return (
                            "Senior Software Engineer",
                            "Tech Corp",
                            "Remote",
                            "",
                            "We are looking for a Senior Software Engineer with 5+ years of experience in Python, AWS, and Docker. You will lead technical initiatives and build scalable systems."
                        )
                    
                    load_example_btn.click(
                        fn=load_example,
                        outputs=[c_title, c_company, c_loc, c_url, c_desc]
                    )
                    
                    add_job_btn.click(
                        fn=add_custom_job,
                        inputs=[c_title, c_company, c_loc, c_url, c_desc],
                        outputs=[add_job_info, c_title]
                    )
                
                job_select = gr.CheckboxGroup(
                    choices=list_jobs_options(),
                    label="📋 Select Jobs to Process"
                )
                refresh_jobs = gr.Button("🔄 Refresh Job List")
                refresh_jobs.click(lambda: gr.update(choices=list_jobs_options()), outputs=job_select)

            # Right column - Generation
            with gr.Column(scale=3):
                gr.Markdown("## 📄 Document Generation")
                
                gen_btn = gr.Button("🚀 Generate Documents", variant="primary", size="lg")
                out_preview = gr.Markdown("Ready to generate documents...")
                out_time = gr.Number(label="Processing Time (seconds)")
                
                # PowerPoint Export Section
                with gr.Accordion("📊 Export to PowerPoint CV", open=False, visible=False) as pptx_section:
                    gr.Markdown("### Convert your resume to a professional PowerPoint presentation")
                    with gr.Row():
                        pptx_job_select = gr.Number(
                            label="Job Index (1, 2, 3...)",
                            value=1,
                            minimum=1,
                            step=1
                        )
                        pptx_template = gr.Dropdown(
                            choices=["modern_blue", "corporate_gray", "elegant_green", "warm_red"],
                            value="modern_blue",
                            label="Template Style"
                        )
                    
                    export_pptx_btn = gr.Button("📊 Create PowerPoint CV", variant="primary")
                    pptx_status = gr.Textbox(label="Export Status", interactive=False)
                    pptx_file = gr.File(label="Download PowerPoint", visible=False)
                    
                    def handle_pptx_export(job_idx, template):
                        status, file_path = export_to_powerpoint(int(job_idx) - 1, template)
                        if file_path:
                            return status, gr.update(visible=True, value=file_path)
                        return status, gr.update(visible=False)
                    
                    export_pptx_btn.click(
                        fn=handle_pptx_export,
                        inputs=[pptx_job_select, pptx_template],
                        outputs=[pptx_status, pptx_file]
                    )
                
                # Word Document Export Section
                with gr.Accordion("📝 Export to Word Documents", open=False, visible=False) as word_section:
                    gr.Markdown("### Generate professional Word documents")
                    with gr.Row():
                        word_job_select = gr.Number(
                            label="Job Index (1, 2, 3...)",
                            value=1,
                            minimum=1,
                            step=1
                        )
                        word_template = gr.Dropdown(
                            choices=["modern", "executive", "creative", "minimal", "academic"],
                            value="modern",
                            label="Document Style"
                        )
                    
                    with gr.Row():
                        export_word_resume_btn = gr.Button("📄 Export Resume as Word", variant="primary")
                        export_word_cover_btn = gr.Button("✉️ Export Cover Letter as Word", variant="primary")
                    
                    word_status = gr.Textbox(label="Export Status", interactive=False)
                    word_files = gr.File(label="Download Word Documents", visible=False, file_count="multiple")
                    
                    def handle_word_export(job_idx, template, doc_type="resume"):
                        try:
                            from services.word_cv import WordCVGenerator
                            generator = WordCVGenerator()
                            
                            candidates = STATE.get("pptx_candidates", [])
                            if not candidates or job_idx > len(candidates):
                                return "❌ No documents available", gr.update(visible=False)
                            
                            resume, job = candidates[int(job_idx) - 1]
                            
                            files = []
                            if doc_type == "resume" or doc_type == "both":
                                resume_path = generator.create_resume_document(resume, job, template)
                                if resume_path:
                                    files.append(resume_path)
                            
                            if doc_type == "cover" or doc_type == "both":
                                # Get cover letter from results
                                results = STATE.get("results", [])
                                cover_letter = None
                                for r in results:
                                    if r.job.id == job.id:
                                        cover_letter = r.cover_letter
                                        break
                                
                                if cover_letter:
                                    cover_path = generator.create_cover_letter_document(cover_letter, job, template)
                                    if cover_path:
                                        files.append(cover_path)
                            
                            if files:
                                return f"✅ Created {len(files)} Word document(s)", gr.update(visible=True, value=files)
                            return "❌ Failed to create documents", gr.update(visible=False)
                            
                        except Exception as e:
                            return f"❌ Error: {str(e)}", gr.update(visible=False)
                    
                    export_word_resume_btn.click(
                        fn=lambda idx, tmpl: handle_word_export(idx, tmpl, "resume"),
                        inputs=[word_job_select, word_template],
                        outputs=[word_status, word_files]
                    )
                    
                    export_word_cover_btn.click(
                        fn=lambda idx, tmpl: handle_word_export(idx, tmpl, "cover"),
                        inputs=[word_job_select, word_template],
                        outputs=[word_status, word_files]
                    )
                
                # Excel Tracker Export
                with gr.Accordion("📊 Export Excel Tracker", open=False, visible=False) as excel_section:
                    gr.Markdown("### Create comprehensive job application tracker")
                    
                    export_excel_btn = gr.Button("📈 Generate Excel Tracker", variant="primary")
                    excel_status = gr.Textbox(label="Export Status", interactive=False)
                    excel_file = gr.File(label="Download Excel Tracker", visible=False)
                    
                    def handle_excel_export():
                        try:
                            from services.excel_tracker import ExcelTracker
                            tracker = ExcelTracker()
                            
                            results = STATE.get("results", [])
                            if not results:
                                return "❌ No results to track", gr.update(visible=False)
                            
                            tracker_path = tracker.create_tracker(results)
                            if tracker_path:
                                return f"✅ Excel tracker created with {len(results)} applications", gr.update(visible=True, value=tracker_path)
                            return "❌ Failed to create tracker", gr.update(visible=False)
                            
                        except Exception as e:
                            return f"❌ Error: {str(e)}", gr.update(visible=False)
                    
                    export_excel_btn.click(
                        fn=handle_excel_export,
                        outputs=[excel_status, excel_file]
                    )
                
                gen_btn.click(fn=generate, inputs=[job_select], outputs=[out_preview, out_time, pptx_section, word_section, excel_section])
                
                gr.Markdown("## 🔄 Regenerate Individual Job")
                
                with gr.Row():
                    job_single = gr.Dropdown(choices=list_jobs_options(), label="Select Job")
                    refresh_single = gr.Button("🔄")
                
                refresh_single.click(lambda: gr.update(choices=list_jobs_options()), outputs=job_single)
                
                regen_btn = gr.Button("🔄 Regenerate Selected Job")
                regen_preview = gr.Markdown()
                regen_time = gr.Number(label="Regeneration Time (seconds)")
                regen_btn.click(fn=regenerate_one, inputs=[job_single], outputs=[regen_preview, regen_time])

                gr.Markdown("## 📊 Results Summary")
                
                update_summary = gr.Button("📊 Update Summary")
                table = gr.Dataframe(value=summary_table(), interactive=False)
                update_summary.click(fn=summary_table, outputs=table)
                
                # Knowledge Graph Section
                if 'kg_service' in globals() and kg_service and kg_service.is_enabled():
                    with gr.Accordion("📊 Knowledge Graph & Application Tracking", open=False):
                        gr.Markdown("""
                        ### 🧠 Application Knowledge Graph
                        Track your job applications, skills, and patterns over time.
                        """)
                        
                        with gr.Row():
                            with gr.Column(scale=1):
                                kg_user_name = gr.Textbox(
                                    label="Your Name",
                                    value=STATE.get("user_name", "User"),
                                    placeholder="Enter your name for tracking"
                                )
                                
                                def update_user_name(name):
                                    STATE["user_name"] = name
                                    return f"Tracking as: {name}"
                                
                                kg_user_status = gr.Markdown("Enter your name to start tracking")
                                kg_user_name.change(update_user_name, inputs=[kg_user_name], outputs=[kg_user_status])
                                
                                gr.Markdown("### 📈 Quick Actions")
                                
                                show_history_btn = gr.Button("📜 Show My History", variant="primary", size="sm")
                                show_trends_btn = gr.Button("📊 Show Skill Trends", variant="secondary", size="sm")
                                show_insights_btn = gr.Button("💡 Company Insights", variant="secondary", size="sm")
                            
                            with gr.Column(scale=2):
                                kg_output = gr.JSON(label="Knowledge Graph Data", visible=True)
                        
                        def show_user_history(user_name):
                            if kg_service and kg_service.is_enabled():
                                history = kg_service.get_user_history(user_name)
                                return history
                            return {"error": "Knowledge graph not available"}
                        
                        def show_skill_trends():
                            if kg_service and kg_service.is_enabled():
                                trends = kg_service.get_skill_trends()
                                return trends
                            return {"error": "Knowledge graph not available"}
                        
                        def show_company_insights():
                            if kg_service and kg_service.is_enabled():
                                # Get insights for all companies user applied to
                                history = kg_service.get_user_history(STATE.get("user_name", "User"))
                                companies = set()
                                for app in history.get("applications", []):
                                    if isinstance(app, dict) and "properties" in app:
                                        company = app["properties"].get("company")
                                        if company:
                                            companies.add(company)
                                
                                insights = {}
                                for company in list(companies)[:5]:  # Limit to 5 companies
                                    insights[company] = kg_service.get_company_insights(company)
                                return insights if insights else {"message": "No companies found in history"}
                            return {"error": "Knowledge graph not available"}
                        
                        show_history_btn.click(
                            show_user_history,
                            inputs=[kg_user_name],
                            outputs=[kg_output]
                        )
                        
                        show_trends_btn.click(
                            show_skill_trends,
                            inputs=[],
                            outputs=[kg_output]
                        )
                        
                        show_insights_btn.click(
                            show_company_insights,
                            inputs=[],
                            outputs=[kg_output]
                        )
                        
                        gr.Markdown("""
                        ### 📊 Features:
                        - **Application History**: Track all your job applications
                        - **Skill Analysis**: See which skills are in demand
                        - **Company Insights**: Learn about companies you've applied to
                        - **Pattern Recognition**: Identify successful application patterns
                        - All data stored locally in SQLite - no external dependencies!
                        """)
                
                # Enhanced Extraction with LangExtract
                if LANGEXTRACT_AVAILABLE:
                    with gr.Accordion("🔍 Enhanced Job Analysis (LangExtract)", open=False):
                        gr.Markdown("### AI-Powered Job & Resume Analysis")
                        
                        with gr.Tabs():
                            # Job Analysis Tab
                            with gr.TabItem("📋 Job Analysis"):
                                job_analysis_text = gr.Textbox(
                                    label="Paste Job Description",
                                    lines=10,
                                    placeholder="Paste the full job description here for analysis..."
                                )
                                analyze_job_btn = gr.Button("🔍 Analyze Job", variant="primary")
                                job_analysis_output = gr.Markdown()
                                
                                def analyze_job(text):
                                    if not text:
                                        return "Please paste a job description"
                                    
                                    job = extract_job_info(text)
                                    keywords = extract_ats_keywords(text)
                                    
                                    output = create_extraction_summary(job)
                                    output += "\n\n### 🎯 ATS Keywords\n"
                                    output += f"**High Priority:** {', '.join(keywords.high_priority[:10]) or 'None'}\n"
                                    output += f"**Medium Priority:** {', '.join(keywords.medium_priority[:10]) or 'None'}\n"
                                    
                                    return output
                                
                                analyze_job_btn.click(
                                    fn=analyze_job,
                                    inputs=job_analysis_text,
                                    outputs=job_analysis_output
                                )
                            
                            # ATS Optimization Tab
                            with gr.TabItem("🎯 ATS Optimizer"):
                                gr.Markdown("Compare your resume against job requirements")
                                with gr.Row():
                                    ats_resume = gr.Textbox(
                                        label="Your Resume",
                                        lines=10,
                                        placeholder="Paste your resume text..."
                                    )
                                    ats_job = gr.Textbox(
                                        label="Job Description",
                                        lines=10,
                                        placeholder="Paste the job description..."
                                    )
                                
                                optimize_btn = gr.Button("🎯 Optimize for ATS", variant="primary")
                                ats_report = gr.Markdown()
                                
                                def run_ats_optimization(resume, job):
                                    if not resume or not job:
                                        return "Please provide both resume and job description"
                                    
                                    result = optimize_for_ats(resume, job)
                                    return create_ats_report(result)
                                
                                optimize_btn.click(
                                    fn=run_ats_optimization,
                                    inputs=[ats_resume, ats_job],
                                    outputs=ats_report
                                )
                            
                            # Bulk Analysis Tab
                            with gr.TabItem("📊 Bulk Analysis"):
                                gr.Markdown("Analyze multiple jobs at once")
                                bulk_jobs_text = gr.Textbox(
                                    label="Paste Multiple Job Descriptions (separated by ---)",
                                    lines=15,
                                    placeholder="Job 1...\n---\nJob 2...\n---\nJob 3..."
                                )
                                bulk_analyze_btn = gr.Button("📊 Analyze All Jobs", variant="primary")
                                bulk_output = gr.Markdown()
                                
                                def analyze_bulk_jobs(text):
                                    if not text:
                                        return "Please paste job descriptions"
                                    
                                    jobs = text.split("---")
                                    results = []
                                    
                                    for i, job_text in enumerate(jobs, 1):
                                        if job_text.strip():
                                            job = extract_job_info(job_text)
                                            results.append(f"### Job {i}: {job.title or 'Unknown'}")
                                            results.append(f"**Company:** {job.company or 'Unknown'}")
                                            results.append(f"**Skills:** {', '.join(job.skills[:5]) or 'None detected'}")
                                            results.append("")
                                    
                                    return "\n".join(results) if results else "No valid jobs found"
                                
                                bulk_analyze_btn.click(
                                    fn=analyze_bulk_jobs,
                                    inputs=bulk_jobs_text,
                                    outputs=bulk_output
                                )
                
                # Advanced Features Results
                if ADVANCED_FEATURES:
                    with gr.Accordion("🎯 Advanced Analytics", open=False):
                        with gr.Tabs():
                            # Execution Timeline Tab
                            with gr.TabItem("⚡ Execution Timeline"):
                                show_timeline_btn = gr.Button("📊 Generate Timeline")
                                timeline_image = gr.Image(label="Parallel Execution Timeline", visible=False)
                                
                                def show_execution_timeline():
                                    if parallel_executor and hasattr(parallel_executor, 'execution_history'):
                                        try:
                                            import matplotlib.pyplot as plt
                                            fig = parallel_executor.plot_timeline()
                                            timeline_path = "execution_timeline.png"
                                            fig.savefig(timeline_path)
                                            plt.close()
                                            return gr.update(visible=True, value=timeline_path)
                                        except Exception as e:
                                            logger.error(f"Timeline generation error: {e}")
                                    return gr.update(visible=False)
                                
                                show_timeline_btn.click(fn=show_execution_timeline, outputs=timeline_image)
                            
                            # Application History Tab
                            with gr.TabItem("📜 Application History"):
                                history_btn = gr.Button("📋 Show History")
                                history_text = gr.Textbox(label="Application Timeline", lines=10, interactive=False)
                                
                                def show_application_history():
                                    if temporal_tracker:
                                        try:
                                            active = temporal_tracker.get_active_applications()
                                            patterns = temporal_tracker.analyze_patterns()
                                            
                                            history = "📊 Application Patterns:\n"
                                            history += f"• Total applications: {patterns.get('total_applications', 0)}\n"
                                            history += f"• This week: {patterns.get('applications_this_week', 0)}\n"
                                            history += f"• Response rate: {patterns.get('response_rate', '0%')}\n\n"
                                            
                                            history += "📋 Active Applications:\n"
                                            for app in active[:5]:
                                                history += f"• {app['company']} - {app['position']} ({app['status']})\n"
                                            
                                            return history
                                        except Exception as e:
                                            return f"Error retrieving history: {e}"
                                    return "Temporal tracking not available"
                                
                                history_btn.click(fn=show_application_history, outputs=history_text)
                            
                            # Observability Tab
                            with gr.TabItem("🔍 Agent Tracing"):
                                trace_btn = gr.Button("📝 Show Agent Trace")
                                trace_text = gr.Textbox(label="Agent Interaction Flow", lines=15, interactive=False)
                                
                                def show_agent_trace():
                                    if agent_tracer:
                                        try:
                                            import io
                                            from contextlib import redirect_stdout
                                            
                                            f = io.StringIO()
                                            with redirect_stdout(f):
                                                agent_tracer.print_interaction_flow()
                                            
                                            trace_output = f.getvalue()
                                            
                                            # Also get metrics
                                            metrics = agent_tracer.get_metrics()
                                            trace_output += f"\n\n📊 Metrics:\n"
                                            trace_output += f"• Total events: {metrics['total_events']}\n"
                                            trace_output += f"• Agents involved: {metrics['agents_involved']}\n"
                                            trace_output += f"• Tool calls: {metrics['tool_calls']}\n"
                                            trace_output += f"• Errors: {metrics['errors']}\n"
                                            
                                            return trace_output
                                        except Exception as e:
                                            return f"Error generating trace: {e}"
                                    return "Observability not available"
                                
                                trace_btn.click(fn=show_agent_trace, outputs=trace_text)
                            
                            # Context Engineering Tab
                            with gr.TabItem("🧠 Context Insights"):
                                context_btn = gr.Button("📊 Show Context Stats")
                                context_text = gr.Textbox(label="Context Engineering Insights", lines=10, interactive=False)
                                
                                def show_context_insights():
                                    if context_engineer:
                                        try:
                                            # Get flywheel recommendations
                                            sample_query = "Generate resume for software engineer"
                                            recommended = context_engineer.flywheel.get_recommended_sources(sample_query)
                                            
                                            insights = "🧠 Context Engineering Insights:\n\n"
                                            insights += f"📊 Flywheel Learning:\n"
                                            insights += f"• Successful contexts: {len(context_engineer.flywheel.successful_contexts)}\n"
                                            insights += f"• Pattern cache size: {len(context_engineer.flywheel.pattern_cache)}\n\n"
                                            
                                            if recommended:
                                                insights += f"💡 Recommended sources for '{sample_query}':\n"
                                                for source in recommended:
                                                    insights += f"  • {source}\n"
                                            
                                            # Memory hierarchy stats
                                            insights += f"\n📚 Memory Hierarchy:\n"
                                            insights += f"• L1 Cache: {len(context_engineer.memory.l1_cache)} items\n"
                                            insights += f"• L2 Memory: {len(context_engineer.memory.l2_memory)} items\n"
                                            insights += f"• L3 Storage: {len(context_engineer.memory.l3_index)} indexed\n"
                                            
                                            return insights
                                        except Exception as e:
                                            return f"Error getting insights: {e}"
                                    return "Context engineering not available"
                                
                                context_btn.click(fn=show_context_insights, outputs=context_text)
        
        # Configuration status
        config_status = []
        
        # LinkedIn OAuth
        if not MOCK_MODE and LINKEDIN_CLIENT_ID:
            config_status.append(f"✅ LinkedIn OAuth ({LINKEDIN_CLIENT_ID[:8]}...)")
        
        # Adzuna
        if ADZUNA_APP_ID and ADZUNA_APP_KEY:
            config_status.append(f"✅ Adzuna API ({ADZUNA_APP_ID})")
        
        # Gemini
        if os.getenv("GEMINI_API_KEY"):
            config_status.append("✅ Gemini AI")
        
        # Tavily
        if os.getenv("TAVILY_API_KEY"):
            config_status.append("✅ Tavily Research")
        
        if not config_status:
            config_status.append("ℹ️ Add API keys to .env for full functionality")
        
        gr.Markdown(f"""
        ---
        ### 🔧 Active Services: {' | '.join(config_status)}
        
        ### 💡 Quick Start:
        1. **Sign in** with LinkedIn (if configured)
        2. **Search** for jobs on Adzuna or add custom jobs
        3. **Configure** advanced features (if available)
        4. **Select** jobs and click "Generate Documents"
        5. **Review** AI-generated resume and cover letter
        6. **Export** to Word/PowerPoint/Excel
        7. **Analyze** with advanced analytics (if enabled)
        
        ### 📊 Current Capabilities:
        - **Job Sources**: {
            'Adzuna (5000/month)' if ADZUNA_APP_ID else 'Mock Data'
        }
        - **Authentication**: {
            'LinkedIn OAuth' if not MOCK_MODE and LINKEDIN_CLIENT_ID else 'Mock Mode'
        }
        - **AI Generation**: {
            'Gemini' if os.getenv("GEMINI_API_KEY") else 'Template Mode'
        }
        - **Advanced AI**: {
            'Parallel + Temporal + Observability + Context' if ADVANCED_FEATURES else 'Not Available'
        }
        
        ### 🚀 Performance Enhancements:
        - **Parallel Processing**: 3-5x faster document generation
        - **Temporal Tracking**: Complete application history with versioning
        - **Observability**: Full agent tracing and debugging
        - **Context Engineering**: Continuous learning and optimization
        - **Memory Hierarchy**: L1/L2/L3 caching for instant retrieval
        - **Compression**: Handle 1M+ tokens with intelligent scaling
        """)

        return demo


if __name__ == "__main__":
    print("=" * 60)
    print("Job Application Assistant - Gradio Interface")
    print("=" * 60)
    
    # Check configuration
    if USE_SYSTEM_AGENTS:
        print("✅ Full system mode - all features available")
    else:
        print("⚠️ Standalone mode - basic features only")
        print("   Place this file in the project directory for full features")
    
    if ADVANCED_FEATURES:
        print("🚀 Advanced AI Agent Features Loaded:")
        print("  ⚡ Parallel Processing (3-5x faster)")
        print("  📊 Temporal Tracking (complete history)")
        print("  🔍 Observability (full tracing)")
        print("  🧠 Context Engineering (continuous learning)")
        print("  📈 Context Scaling (1M+ tokens)")
    
    if os.getenv("GEMINI_API_KEY"):
        print("✅ Gemini API configured")
    else:
        print("ℹ️ No Gemini API key - using fallback generation")
    
    if os.getenv("TAVILY_API_KEY"):
        print("✅ Tavily API configured for web research")
    
    if ADZUNA_APP_ID:
        print("✅ Adzuna API configured for job search")
    
    if LINKEDIN_CLIENT_ID:
        print("✅ LinkedIn OAuth configured")
    
    print("\nStarting Gradio app...")
    print("=" * 60)
    
    try:
        app = build_app()
        app.launch(
            server_name="0.0.0.0",
            server_port=int(os.getenv("PORT", 7860)),
            share=False,
            show_error=True
        )
    except Exception as e:
        logger.error(f"Failed to start app: {e}")
        print(f"\n❌ Error: {e}")
        print("\nTroubleshooting:")
        print("1. Install required packages: pip install gradio pandas python-dotenv")
        print("2. Check your .env file exists and is valid")
        print("3. Ensure port 7860 is not in use")
        raise