364d2c69ff
New standalone Python/FastAPI service for automatic compliance document scanning, LLM-based classification, IPFS archival, and gap analysis. Includes extractors (PDF, DOCX, XLSX, PPTX), keyword fallback classifier, compliance matrix, and full REST API on port 8098. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
"""Onboarding report + gap analysis endpoints."""
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import json
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import uuid
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from fastapi import APIRouter, HTTPException, Header
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from pydantic import BaseModel
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from db import get_pool
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from gap_analysis.analyzer import generate_gap_analysis
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router = APIRouter(tags=["reports"])
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class ReportGenerate(BaseModel):
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job_id: str | None = None
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company_profiles: list[str] = ["universal", "data_processor", "ai_user"]
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@router.post("/reports/generate", status_code=201)
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async def generate_report(body: ReportGenerate, x_tenant_id: str = Header(...)):
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pool = await get_pool()
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tid = uuid.UUID(x_tenant_id)
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async with pool.acquire() as conn:
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# Count documents by classification for this tenant
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rows = await conn.fetch(
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"""SELECT classification, COUNT(*) as cnt
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FROM crawler_documents
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WHERE tenant_id = $1 AND classification IS NOT NULL
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GROUP BY classification""",
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tid,
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)
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classification_counts = {r["classification"]: r["cnt"] for r in rows}
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total_docs = await conn.fetchval(
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"SELECT COUNT(*) FROM crawler_documents WHERE tenant_id = $1", tid
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)
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# Run gap analysis
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analysis = generate_gap_analysis(classification_counts, body.company_profiles)
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# Store report
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async with pool.acquire() as conn:
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jid = uuid.UUID(body.job_id) if body.job_id else None
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row = await conn.fetchrow(
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"""INSERT INTO crawler_onboarding_reports
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(tenant_id, job_id, total_documents_found, classification_breakdown, gaps, compliance_score)
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VALUES ($1, $2, $3, $4, $5, $6)
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RETURNING *""",
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tid, jid, total_docs,
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json.dumps(classification_counts),
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json.dumps(analysis["gaps"]),
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analysis["compliance_score"],
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)
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result = dict(row)
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result["gap_summary"] = analysis["gap_summary"]
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result["covered"] = analysis["covered"]
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result["total_required"] = analysis["total_required"]
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return result
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@router.get("/reports")
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async def list_reports(x_tenant_id: str = Header(...)):
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pool = await get_pool()
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async with pool.acquire() as conn:
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rows = await conn.fetch(
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"SELECT * FROM crawler_onboarding_reports WHERE tenant_id = $1 ORDER BY created_at DESC LIMIT 20",
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uuid.UUID(x_tenant_id),
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)
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return [dict(r) for r in rows]
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@router.get("/reports/{report_id}")
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async def get_report(report_id: str, x_tenant_id: str = Header(...)):
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pool = await get_pool()
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async with pool.acquire() as conn:
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row = await conn.fetchrow(
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"SELECT * FROM crawler_onboarding_reports WHERE id = $1 AND tenant_id = $2",
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uuid.UUID(report_id), uuid.UUID(x_tenant_id),
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)
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if not row:
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raise HTTPException(404, "Report not found")
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result = dict(row)
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# Parse stored JSON
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if isinstance(result.get("gaps"), str):
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result["gaps"] = json.loads(result["gaps"])
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if isinstance(result.get("classification_breakdown"), str):
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result["classification_breakdown"] = json.loads(result["classification_breakdown"])
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# Add computed summary
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gaps = result.get("gaps", [])
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result["gap_summary"] = {
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"critical": sum(1 for g in gaps if g.get("severity") == "CRITICAL"),
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"high": sum(1 for g in gaps if g.get("severity") == "HIGH"),
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"medium": sum(1 for g in gaps if g.get("severity") == "MEDIUM"),
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}
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return result
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