from uuid import UUID
import uuid

from sqlalchemy import select
from src.repositories import BaseRepository

from src.models import (
    AnalysisType,
    Analysis,
    Proposal,
    ComparativeWeights,
    Weights,
    Evaluations,
    EvaluationType,
    AnalysisStatus,
)


class ScoreClient:
    def __init__(self):
        self.repository = BaseRepository
        self.weighted_scores = {"NONE": 0, "LOW": 1, "MEDIUM": 3, "HIGH": 7}

    async def __aenter__(self):
        return self

    async def __aexit__(self, exc_type, exc_value, traceback):
        pass

    async def update_ai_score(
        self, evaluation_id: str, analysis_score_type: str = None
    ):
        async with self.repository(model=Evaluations).get_session() as session:
            query = select(Evaluations).where(Evaluations.id == evaluation_id)
            result = await session.execute(query)
            evaluation = result.scalars().first()
            proposal_id = evaluation.proposal_id

        async with self.repository(model=Proposal).get_session() as session:
            query = select(Proposal).where(Proposal.id == proposal_id)
            result = await session.execute(query)
            proposal = result.scalars().first()
            rfp_id = proposal.rfp_id

        async with self.repository(model=Analysis).get_session() as session:
            query = select(Analysis).where(Analysis.evaluation_id == evaluation_id)
            result = await session.execute(query)
            results = result.scalars().all()
            deficiency = [
                result.insights
                for result in results
                if result.analysis_type == AnalysisType.DEFICIENCIES
            ]
            weaknesses = [
                result.insights
                for result in results
                if result.analysis_type == AnalysisType.WEAKNESSES
            ]
            strengths = [
                result.insights
                for result in results
                if result.analysis_type == AnalysisType.STRENGTHS
            ]
            if analysis_score_type != "AI":
                deficiency = [
                    result.insights
                    for result in results
                    if result.analysis_type == AnalysisType.DEFICIENCIES
                    and result.status != AnalysisStatus.REJECTED
                ]
                weaknesses = [
                    result.insights
                    for result in results
                    if result.analysis_type == AnalysisType.WEAKNESSES
                    and result.status != AnalysisStatus.REJECTED
                ]
                strengths = [
                    result.insights
                    for result in results
                    if result.analysis_type == AnalysisType.STRENGTHS
                    and result.status != AnalysisStatus.REJECTED
                ]
            start_score = 0 if deficiency else 100

        async with self.repository(model=ComparativeWeights).get_session() as session:
            query = select(ComparativeWeights).where(
                ComparativeWeights.rfp_id == rfp_id
            )
            result = await session.execute(query)
            comparative_weights = result.scalars().first()

        start_score += len(strengths) * comparative_weights.strengths_weight
        start_score -= len(weaknesses) * comparative_weights.weaknesses_weight

        async with self.repository(model=Evaluations).get_session() as session:
            query = select(Evaluations).where(Evaluations.id == evaluation_id)
            result = await session.execute(query)
            evaluation = result.scalars().first()
            if analysis_score_type == "AI":
                evaluation.ai_score = start_score
                evaluation.adjusted_score = start_score
            else:
                evaluation.adjusted_score = start_score
            await session.commit()
            await session.refresh(evaluation)
            query = select(Evaluations).where(Evaluations.proposal_id == proposal_id)
            result = await session.execute(query)
            evaluations = result.scalars().all()

        technical_ai_score = [
            evaluation.ai_score
            for evaluation in evaluations
            if evaluation.evaluation_type == EvaluationType.TECHNICAL
            and evaluation.ai_score is not None
        ]
        management_ai_score = [
            evaluation.ai_score
            for evaluation in evaluations
            if evaluation.evaluation_type == EvaluationType.MANAGEMENT
            and evaluation.ai_score is not None
        ]
        past_performance_ai_score = [
            evaluation.ai_score
            for evaluation in evaluations
            if evaluation.evaluation_type == EvaluationType.PAST_PERFORMANCE
            and evaluation.ai_score is not None
        ]
        price_ai_score = [
            evaluation.ai_score
            for evaluation in evaluations
            if evaluation.evaluation_type == EvaluationType.PRICE
            and evaluation.ai_score is not None
        ]

        if analysis_score_type != "AI":
            technical_ai_score = [
                evaluation.adjusted_score
                for evaluation in evaluations
                if evaluation.evaluation_type == EvaluationType.TECHNICAL
                and evaluation.adjusted_score is not None
            ]
            management_ai_score = [
                evaluation.adjusted_score
                for evaluation in evaluations
                if evaluation.evaluation_type == EvaluationType.MANAGEMENT
                and evaluation.adjusted_score is not None
            ]
            past_performance_ai_score = [
                evaluation.adjusted_score
                for evaluation in evaluations
                if evaluation.evaluation_type == EvaluationType.PAST_PERFORMANCE
                and evaluation.adjusted_score is not None
            ]
            price_ai_score = [
                evaluation.adjusted_score
                for evaluation in evaluations
                if evaluation.evaluation_type == EvaluationType.PRICE
                and evaluation.adjusted_score is not None
            ]

        technical_ai_score = technical_ai_score[0] if technical_ai_score else 0
        management_ai_score = management_ai_score[0] if management_ai_score else 0
        past_performance_ai_score = (
            past_performance_ai_score[0] if past_performance_ai_score else 0
        )
        price_ai_score = price_ai_score[0] if price_ai_score else 0

        technical_weight = self.weighted_scores[
            comparative_weights.technical_weight.name
        ]
        management_weight = self.weighted_scores[
            comparative_weights.management_weight.name
        ]
        past_performance_weight = self.weighted_scores[
            comparative_weights.past_performance_weight.name
        ]
        price_weight = self.weighted_scores[comparative_weights.price_weight.name]

        ai_score = (
            technical_ai_score * technical_weight
            + management_ai_score * management_weight
            + past_performance_ai_score * past_performance_weight
            + price_ai_score * price_weight
        )
        ai_score /= (
            technical_weight
            + management_weight
            + past_performance_weight
            + price_weight
        )

        async with self.repository(model=Proposal).get_session() as session:
            query = select(Proposal).where(Proposal.id == proposal_id)
            result = await session.execute(query)
            evaluation = result.scalars().first()
            if analysis_score_type == "AI":
                evaluation.ai_score = ai_score
                evaluation.final_score = ai_score
            else:
                evaluation.final_score = ai_score
            await session.commit()
            await session.refresh(evaluation)