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[WIP] Diagnose issues with summary page launch #32

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9d0abce
Initial plan
Copilot Dec 7, 2025
6aac6a9
Fix summary endpoint to include findings in response
Copilot Dec 7, 2025
aa884b4
Add comprehensive fix summary documentation
Copilot Dec 7, 2025
ecf071e
Add manual testing guide for summary page
Copilot Dec 7, 2025
95a3a32
Changes before error encountered
Copilot Dec 7, 2025
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247 changes: 131 additions & 116 deletions backend/app/api/v1/endpoints/summary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,16 @@
from app.api import deps
from app.core.cache import apply_cache_preset
from fastapi import Response
from app.schemas.summary import (
Finding,
FindingTypeEnum,
SeverityEnum,
StatusEnum,
CaseSummaryResponse,
IngestionMetrics,
ReconciliationMetrics,
AdjudicationMetrics,
)

router = APIRouter(prefix="/summary", tags=["summary"])

Expand Down Expand Up @@ -44,22 +54,99 @@ class ArchiveRequest(BaseModel):
archive_location: str = ""


class Finding(BaseModel):
id: str
type: str # pattern, amount, confirmation, false_positive, recommendation
severity: str # high, medium, low
description: str
evidence: List[str] = []
# ============================================
# Helper Functions
# ============================================


async def _generate_findings(
case_uuid: uuid.UUID,
db: AsyncSession
) -> List[Finding]:
"""
Generate findings for a case based on analysis results.
Shared logic used by both get_case_summary and get_findings endpoints.
"""
# Get analysis results with high risk scores
analysis_results = await db.execute(
select(models.AnalysisResult)
.where(
models.AnalysisResult.subject_id == case_uuid,
models.AnalysisResult.risk_score > 60,
)
.order_by(models.AnalysisResult.risk_score.desc())
.limit(10)
)
results = list(analysis_results.scalars().all())

# Generate findings
findings = []

if results:
# Pattern finding
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type=FindingTypeEnum.pattern,
severity=SeverityEnum.high,
description=f"Identified {len(results)} high-risk patterns involving multiple entities",
evidence=[str(r.id) for r in results[:3]],
)
)

# Amount summary
total_amount = sum(
float(r.metadata_.get("total_amount", 0)) if r.metadata_ else 0
for r in results
)
if total_amount > 0:
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type=FindingTypeEnum.amount,
severity=SeverityEnum.high,
description=f"Total flagged amount: ${total_amount:,.2f}",
evidence=[],
)
)

# Confirmation
confirmed = [r for r in results if r.decision == "confirmed_fraud"]
if confirmed:
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type=FindingTypeEnum.confirmation,
severity=SeverityEnum.high,
description=f"{len(confirmed)} confirmed fraudulent transactions referred to authorities",
evidence=[str(r.id) for r in confirmed],
)
)

class CaseSummaryResponse(BaseModel):
case_id: str
status: str # success, partial, failed
data_quality: float
days_to_resolution: int
ingestion: Dict[str, Any]
reconciliation: Dict[str, Any]
adjudication: Dict[str, Any]
# False positives
false_positives = [r for r in results if r.decision == "false_positive"]
if false_positives:
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type=FindingTypeEnum.false_positive,
severity=SeverityEnum.low,
description=f"{len(false_positives)} false positives correctly ruled out",
evidence=[],
)
)
else:
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type=FindingTypeEnum.recommendation,
severity=SeverityEnum.low,
description="No high-risk patterns detected. Case appears clean.",
evidence=[],
)
)

return findings


# ============================================
Expand Down Expand Up @@ -130,38 +217,42 @@ async def get_case_summary(

# Determine overall status
if resolved_count == total_alerts and total_alerts > 0:
status = "success"
status = StatusEnum.success
elif resolved_count > 0:
status = "partial"
status = StatusEnum.partial
else:
status = "failed" if total_alerts > 0 else "success"
status = StatusEnum.failed if total_alerts > 0 else StatusEnum.success

# Generate findings
findings = await _generate_findings(case_uuid, db)

# Cache for 5 minutes
apply_cache_preset(response, "short")

return CaseSummaryResponse(
case_id=case_id,
id=case_id,
status=status,
data_quality=data_quality,
days_to_resolution=days_to_resolution,
ingestion={
"records": transaction_count,
"files": 8, # Mock - could track in future
"completion": 100,
"status": "complete",
},
reconciliation={
"matchRate": 94.2, # Mock - calculate from actual matches
"newRecords": 890,
"rejected": 45,
"status": "complete",
},
adjudication={
"resolved": resolved_count,
"avgTime": f"{avg_time_minutes} min",
"totalAlerts": total_alerts,
"status": "complete" if resolved_count == total_alerts else "partial",
},
dataQuality=data_quality,
daysToResolution=days_to_resolution,
ingestion=IngestionMetrics(
records=transaction_count,
files=8, # Mock - could track in future
completion=100.0,
status=StatusEnum.complete,
),
reconciliation=ReconciliationMetrics(
matchRate=94.2, # Mock - calculate from actual matches
newRecords=890,
rejected=45,
status=StatusEnum.complete,
),
adjudication=AdjudicationMetrics(
resolved=resolved_count,
avgTime=f"{avg_time_minutes} min",
totalAlerts=total_alerts,
status=StatusEnum.complete if resolved_count == total_alerts else StatusEnum.partial,
),
findings=findings,
)


Expand All @@ -180,84 +271,8 @@ async def get_findings(
except ValueError:
raise HTTPException(status_code=400, detail="Invalid case ID format")

# Get analysis results with high risk scores
analysis_results = await db.execute(
select(models.AnalysisResult)
.where(
models.AnalysisResult.subject_id == case_uuid,
models.AnalysisResult.risk_score > 60,
)
.order_by(models.AnalysisResult.risk_score.desc())
.limit(10)
)
results = list(analysis_results.scalars().all())

# Generate findings
findings = []

if results:
# Pattern finding
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type="pattern",
severity="high",
description=f"Identified {len(results)} high-risk patterns involving multiple entities",
evidence=[str(r.id) for r in results[:3]],
)
)

# Amount summary
total_amount = sum(
float(r.metadata_.get("total_amount", 0)) if r.metadata_ else 0
for r in results
)
if total_amount > 0:
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type="amount",
severity="high",
description=f"Total flagged amount: ${total_amount:,.2f}",
evidence=[],
)
)

# Confirmation
confirmed = [r for r in results if r.decision == "confirmed_fraud"]
if confirmed:
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type="confirmation",
severity="high",
description=f"{len(confirmed)} confirmed fraudulent transactions referred to authorities",
evidence=[str(r.id) for r in confirmed],
)
)

# False positives
false_positives = [r for r in results if r.decision == "false_positive"]
if false_positives:
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type="false_positive",
severity="low",
description=f"{len(false_positives)} false positives correctly ruled out",
evidence=[],
)
)
else:
findings.append(
Finding(
id=f"finding_{uuid.uuid4().hex[:8]}",
type="recommendation",
severity="low",
description="No high-risk patterns detected. Case appears clean.",
evidence=[],
)
)
# Generate findings using shared helper
findings = await _generate_findings(case_uuid, db)

# Cache for 5 minutes
apply_cache_preset(response, "short")
Expand Down
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