#!/usr/bin/env python
"""
Test script to verify CSV export functionality with updated JSON structure
"""
import os
import sys
import django

# Setup Django environment
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'NorahUniversity.settings')
django.setup()

from recruitment.models import Candidate, JobPosting
from recruitment.views_frontend import export_candidates_csv
from django.test import RequestFactory
from django.contrib.auth.models import User

def test_csv_export():
    """Test the CSV export function with sample data"""

    print("🧪 Testing CSV Export Functionality")
    print("=" * 50)

    # Create a test request factory
    factory = RequestFactory()

    # Get or create a test user
    user, created = User.objects.get_or_create(
        username='testuser',
        defaults={'email': 'test@example.com', 'is_staff': True}
    )

    # Get a sample job
    job = JobPosting.objects.first()
    if not job:
        print("❌ No jobs found in database. Please create a job first.")
        return False

    print(f"📋 Using job: {job.title}")

    # Test different stages
    stages = ['screening', 'exam', 'interview', 'offer', 'hired']

    for stage in stages:
        print(f"\n🔍 Testing stage: {stage}")

        # Create a mock request
        request = factory.get(f'/export/{job.slug}/{stage}/')
        request.user = user
        request.GET = {'search': ''}

        try:
            # Call the export function
            response = export_candidates_csv(request, job.slug, stage)

            # Check if response is successful
            if response.status_code == 200:
                print(f"✅ {stage} export successful")

                # Read and analyze the CSV content
                content = response.content.decode('utf-8-sig')
                lines = content.split('\n')

                if len(lines) > 1:
                    headers = lines[0].split(',')
                    print(f"📊 Headers: {len(headers)} columns")
                    print(f"📊 Data rows: {len(lines) - 1}")

                    # Check for AI score column
                    if 'Match Score' in headers:
                        print("✅ Match Score column found")
                    else:
                        print("⚠️  Match Score column not found")

                    # Check for other AI columns
                    ai_columns = ['Years Experience', 'Screening Rating', 'Professional Category', 'Top 3 Skills']
                    found_ai_columns = [col for col in ai_columns if col in headers]
                    print(f"🤖 AI columns found: {found_ai_columns}")

                else:
                    print("⚠️  No data rows found")

            else:
                print(f"❌ {stage} export failed with status: {response.status_code}")

        except Exception as e:
            print(f"❌ {stage} export error: {str(e)}")
            import traceback
            traceback.print_exc()

    # Test with actual candidate data
    print(f"\n🔍 Testing with actual candidate data")
    candidates = Candidate.objects.filter(job=job)
    print(f"📊 Total candidates for job: {candidates.count()}")

    if candidates.exists():
        # Test AI data extraction for first candidate
        candidate = candidates.first()
        print(f"\n🧪 Testing AI data extraction for: {candidate.name}")

        try:
            # Test the model properties
            print(f"📊 Match Score: {candidate.match_score}")
            print(f"📊 Years Experience: {candidate.years_of_experience}")
            print(f"📊 Screening Rating: {candidate.screening_stage_rating}")
            print(f"📊 Professional Category: {candidate.professional_category}")
            print(f"📊 Top 3 Skills: {candidate.top_3_keywords}")
            print(f"📊 Strengths: {candidate.strengths}")
            print(f"📊 Weaknesses: {candidate.weaknesses}")

            # Test AI analysis data structure
            if candidate.ai_analysis_data:
                print(f"📊 AI Analysis Data keys: {list(candidate.ai_analysis_data.keys())}")
                if 'analysis_data' in candidate.ai_analysis_data:
                    analysis_keys = list(candidate.ai_analysis_data['analysis_data'].keys())
                    print(f"📊 Analysis Data keys: {analysis_keys}")
                else:
                    print("⚠️  'analysis_data' key not found in ai_analysis_data")
            else:
                print("⚠️  No AI analysis data found")

        except Exception as e:
            print(f"❌ Error extracting AI data: {str(e)}")
            import traceback
            traceback.print_exc()

    print("\n🎉 CSV Export Test Complete!")
    return True

if __name__ == '__main__':
    test_csv_export()