import asyncio
import json
from pathlib import Path
from typing import Any

from pydantic import BaseModel

from vsp.app.main import VspDataEnrichment
from vsp.app.model.linkedin.linkedin_models import LinkedinProfile
from vsp.app.model.vsp.vsp_models import VSPProfile
from vsp.shared import logger_factory

logger = logger_factory.get_logger(__name__)


class ComparisonResult(BaseModel):
    profile_name: str
    correct_enums: int
    total_enums: int
    accuracy: float
    additional_enums_in_actual: int
    comparisons: dict[str, Any]


def load_profiles() -> dict[str, tuple[LinkedinProfile, VSPProfile]]:
    sample_profiles_dir = Path("tests/test_data/sample_profiles")
    classified_profiles_dir = Path("tests/test_data/sample_profiles_classified")
    profiles = {}

    for profile_file in sample_profiles_dir.glob("*.json"):
        name = profile_file.stem
        with profile_file.open() as f:
            linkedin_data = json.load(f)
            linkedin_profile = LinkedinProfile.model_validate(linkedin_data)

        classified_file = classified_profiles_dir / f"{name}.json"
        if classified_file.exists():
            with classified_file.open() as f:
                classified_data = json.load(f)
                classified_profile = VSPProfile.model_validate(classified_data)

            profiles[name] = (linkedin_profile, classified_profile)

    return profiles


async def compare_profiles(linkedin_profile: LinkedinProfile, classified_profile: VSPProfile) -> ComparisonResult:
    vsp_enrichment = VspDataEnrichment()
    result = await vsp_enrichment.process_linkedin_profile(linkedin_profile)
    comparisons = {}
    correct_enums = 0
    total_enums = 0
    additional_enums_in_actual = 0

    # Compare educations
    for classified_edu, result_edu in zip(classified_profile.education.education_history, result.classified_educations):
        comparisons[f"Education: {classified_edu.school}"] = {
            "expected": classified_edu.degree_characterization,
            "actual": result_edu.classification.output.value,
            "confidence": result_edu.classification.confidence,
            "reasoning": result_edu.classification.reasoning,
        }
        if classified_edu.degree_characterization is not None:
            total_enums += 1
            if classified_edu.degree_characterization == result_edu.classification.output.value:
                correct_enums += 1
        elif result_edu.classification.output.value is not None:
            additional_enums_in_actual += 1

    # Compare work experiences
    for classified_exp, result_exp in zip(
        classified_profile.professional_experience.experience_history, result.classified_work_experiences
    ):
        exp_key = f"Job: {classified_exp.title} at {classified_exp.company}"
        comparisons[exp_key] = {
            "primary_job_type": {
                "expected": classified_exp.primary_job_type,
                "actual": result_exp.work_experience_classification.primary_job_type.value,
                "confidence": result_exp.work_experience_classification.confidence,
                "reasoning": result_exp.work_experience_classification.reasoning,
            },
            "secondary_job_type": {
                "expected": classified_exp.secondary_job_type,
                "actual": result_exp.work_experience_classification.secondary_job_type.value,
                "confidence": result_exp.work_experience_classification.confidence,
                "reasoning": result_exp.work_experience_classification.reasoning,
            },
        }
        if classified_exp.primary_job_type is not None:
            total_enums += 1
            if classified_exp.primary_job_type == result_exp.work_experience_classification.primary_job_type.value:
                correct_enums += 1
        elif result_exp.work_experience_classification.primary_job_type.value is not None:
            additional_enums_in_actual += 1

        if classified_exp.secondary_job_type is not None:
            total_enums += 1
            if classified_exp.secondary_job_type == result_exp.work_experience_classification.secondary_job_type.value:
                correct_enums += 1
        elif result_exp.work_experience_classification.secondary_job_type.value is not None:
            additional_enums_in_actual += 1

        # Add comparisons for investment banking, investing focus, etc. if available
        if result_exp.investment_banking_classification:
            comparisons[exp_key]["investment_banking_group"] = {
                "expected": classified_exp.investment_banking_focus,
                "actual": result_exp.investment_banking_classification.investment_banking_group.value,
                "confidence": result_exp.investment_banking_classification.confidence,
                "reasoning": result_exp.investment_banking_classification.reasoning,
            }
            if classified_exp.investment_banking_focus is not None:
                total_enums += 1
                if (
                    classified_exp.investment_banking_focus
                    == result_exp.investment_banking_classification.investment_banking_group.value
                ):
                    correct_enums += 1
            elif result_exp.investment_banking_classification.investment_banking_group.value is not None:
                additional_enums_in_actual += 1

        if result_exp.investing_focus_asset_class_classification:
            comparisons[exp_key]["investing_focus_asset_class"] = {
                "expected": classified_exp.investing_focus_stage,
                "actual": result_exp.investing_focus_asset_class_classification.investing_focus_asset_class.value,
                "confidence": result_exp.investing_focus_asset_class_classification.confidence,
                "reasoning": result_exp.investing_focus_asset_class_classification.reasoning,
            }
            if classified_exp.investing_focus_stage is not None:
                total_enums += 1
                if (
                    classified_exp.investing_focus_stage
                    == result_exp.investing_focus_asset_class_classification.investing_focus_asset_class.value
                ):
                    correct_enums += 1
            elif result_exp.investing_focus_asset_class_classification.investing_focus_asset_class.value is not None:
                additional_enums_in_actual += 1

        if result_exp.investing_focus_sector_classification:
            comparisons[exp_key]["investing_focus_sector"] = {
                "expected": classified_exp.investing_focus_sector,
                "actual": result_exp.investing_focus_sector_classification.investing_focus_sector.value,
                "confidence": result_exp.investing_focus_sector_classification.confidence,
                "reasoning": result_exp.investing_focus_sector_classification.reasoning,
            }
            if classified_exp.investing_focus_sector is not None:
                total_enums += 1
                if (
                    classified_exp.investing_focus_sector
                    == result_exp.investing_focus_sector_classification.investing_focus_sector.value
                ):
                    correct_enums += 1
            elif result_exp.investing_focus_sector_classification.investing_focus_sector.value is not None:
                additional_enums_in_actual += 1

    accuracy = correct_enums / total_enums if total_enums > 0 else 0
    return ComparisonResult(
        profile_name=f"{linkedin_profile.first_name} {linkedin_profile.last_name}",
        correct_enums=correct_enums,
        total_enums=total_enums,
        accuracy=accuracy,
        additional_enums_in_actual=additional_enums_in_actual,
        comparisons=comparisons,
    )


async def run_tests() -> None:
    profiles = load_profiles()
    results = []

    for _, (linkedin_profile, classified_profile) in profiles.items():
        result = await compare_profiles(linkedin_profile, classified_profile)
        results.append(result)
        logger.info(
            f"Processed {result.profile_name}: Accuracy {result.accuracy:.2%}, "
            f"Additional enums in actual: {result.additional_enums_in_actual}"
        )

    overall_accuracy = sum(r.accuracy for r in results) / len(results)
    total_additional_enums = sum(r.additional_enums_in_actual for r in results)
    logger.info(f"Overall accuracy: {overall_accuracy:.2%}")
    logger.info(f"Total additional enums in actual: {total_additional_enums}")

    # Save detailed results to a JSON file
    with open("enum_classifier_results.json", "w") as f:
        json.dump([r.model_dump() for r in results], f, indent=2)

    logger.info("Detailed results saved to enum_classifier_results.json")


if __name__ == "__main__":
    asyncio.run(run_tests())