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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)
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