Merge branch 'main' of bitbucket.org:zlalani/gmal-scope-builder
This commit is contained in:
DJP
commit
0aacb2bb4c
7 changed files with 269 additions and 128 deletions
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@ -2,11 +2,11 @@
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import logging
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from fastapi import APIRouter, Depends, HTTPException, UploadFile, File
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from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, UploadFile, File
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.database import get_db
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from app.database import get_db, async_session
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from app.models.gmal import GmalAsset
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from app.models.project import Project, ClientAsset, Match, ProjectStatus, MatchConfidence
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from app.schemas.project import ClientAssetOut, ClientAssetUpdate, MatchOut, MatchSelectRequest, ManualMatchRequest
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@ -17,23 +17,85 @@ router = APIRouter()
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logger = logging.getLogger(__name__)
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async def _background_parse(project_id: int, filename: str, text: str, metadata: dict):
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"""Run AI parsing and save results in the background (own DB session)."""
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async with async_session() as db:
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try:
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result = await db.execute(select(Project).where(Project.id == project_id))
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project = result.scalar_one_or_none()
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if not project:
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return
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# Stage 3: AI parsing
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try:
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extracted, usage_info = parse_text_with_ai(text)
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except Exception as e:
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logger.error(f"AI parsing failed for project {project_id}: {e}")
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project.status = ProjectStatus.DRAFT
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project.parse_stage = f"AI parsing failed: {str(e)}"
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await db.commit()
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return
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# Save AI costs
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project.ai_input_tokens = (project.ai_input_tokens or 0) + usage_info.get("input_tokens", 0)
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project.ai_output_tokens = (project.ai_output_tokens or 0) + usage_info.get("output_tokens", 0)
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project.ai_cost_usd = float(project.ai_cost_usd or 0) + usage_info.get("cost_usd", 0)
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project.ai_call_count = (project.ai_call_count or 0) + 1
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# Stage 4: Saving results
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project.parse_stage = f"AI found {len(extracted)} assets. Saving..."
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await db.commit()
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# Clear existing client assets
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existing = await db.execute(
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select(ClientAsset).where(ClientAsset.project_id == project_id)
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)
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for ca in existing.scalars().all():
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await db.delete(ca)
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# Create client asset records (skip zero-quantity assets)
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assets = []
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for idx, item in enumerate(extracted):
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volume = item.get("volume", 1)
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if volume <= 0:
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continue
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ca = ClientAsset(
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project_id=project_id,
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raw_name=item.get("name", "Unknown"),
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raw_description=item.get("description", ""),
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volume=volume,
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sort_order=idx + 1,
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)
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db.add(ca)
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assets.append(ca)
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project.status = ProjectStatus.REVIEW
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project.parse_stage = f"Done! {len(assets)} assets extracted."
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await db.commit()
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logger.info(f"Background parse complete for project {project_id}: {len(assets)} assets")
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except Exception as e:
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logger.error(f"Background parse error for project {project_id}: {e}")
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@router.post("/{project_id}/upload")
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async def upload_client_document(
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project_id: int,
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background_tasks: BackgroundTasks,
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file: UploadFile = File(...),
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db: AsyncSession = Depends(get_db),
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):
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"""Upload a client document and extract assets using AI."""
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project = await _get_project(project_id, db)
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# Stage 1: Uploading
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# Stage 1: Read file
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content = await file.read()
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project.source_filename = file.filename
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project.status = ProjectStatus.PARSING
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project.parse_stage = f"Uploading {file.filename}..."
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await db.commit()
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# Stage 2: Extracting text
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# Stage 2: Extract text (fast, synchronous)
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project.parse_stage = "Extracting text from document..."
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await db.commit()
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@ -49,57 +111,12 @@ async def upload_client_document(
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project.parse_stage = f"Extracted {metadata['char_count']:,} characters{sheets_info}. Sending to AI..."
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await db.commit()
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# Stage 3: AI parsing
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try:
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extracted, usage_info = parse_text_with_ai(text)
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except Exception as e:
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logger.error(f"AI parsing failed: {e}")
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project.status = ProjectStatus.DRAFT
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project.parse_stage = None
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await db.commit()
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raise HTTPException(status_code=400, detail=f"Failed to parse document: {str(e)}")
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# Save AI costs
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project.ai_input_tokens = (project.ai_input_tokens or 0) + usage_info.get("input_tokens", 0)
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project.ai_output_tokens = (project.ai_output_tokens or 0) + usage_info.get("output_tokens", 0)
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project.ai_cost_usd = float(project.ai_cost_usd or 0) + usage_info.get("cost_usd", 0)
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project.ai_call_count = (project.ai_call_count or 0) + 1
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# Stage 4: Saving results
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project.parse_stage = f"AI found {len(extracted)} assets. Saving..."
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await db.commit()
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# Clear existing client assets
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existing = await db.execute(
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select(ClientAsset).where(ClientAsset.project_id == project_id)
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)
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for ca in existing.scalars().all():
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await db.delete(ca)
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# Create client asset records
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assets = []
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for idx, item in enumerate(extracted):
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ca = ClientAsset(
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project_id=project_id,
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raw_name=item.get("name", "Unknown"),
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raw_description=item.get("description", ""),
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volume=item.get("volume", 1),
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sort_order=idx + 1,
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)
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db.add(ca)
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assets.append(ca)
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project.status = ProjectStatus.REVIEW
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project.parse_stage = f"Done! {len(assets)} assets extracted."
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await db.commit()
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# Stage 3+4: AI parsing runs in background — return 202 immediately
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background_tasks.add_task(_background_parse, project_id, file.filename, text, metadata)
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return {
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"message": f"Extracted {len(assets)} assets from {file.filename}",
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"asset_count": len(assets),
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"assets": [
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{"name": a.raw_name, "description": a.raw_description, "volume": a.volume}
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for a in assets
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],
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"message": f"Document received. AI parsing started for {file.filename}.",
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"status": "parsing",
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}
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@ -139,8 +156,48 @@ async def update_client_asset(
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return ca
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async def _background_match(project_id: int, asset_snapshots: list):
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"""Run AI matching in the background (own DB session)."""
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async with async_session() as db:
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try:
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result = await db.execute(select(Project).where(Project.id == project_id))
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project = result.scalar_one_or_none()
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if not project:
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return
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# Reconstruct ClientAsset-like objects from snapshots for match_client_assets
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ca_result = await db.execute(
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select(ClientAsset).where(ClientAsset.id.in_([s["id"] for s in asset_snapshots]))
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.order_by(ClientAsset.sort_order)
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)
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client_assets = ca_result.scalars().all()
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matches = await match_client_assets(db, project_id, client_assets)
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await db.refresh(project)
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project.status = ProjectStatus.REVIEW
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await db.commit()
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logger.info(f"Background match complete for project {project_id}: {len(matches)} matches")
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except Exception as e:
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logger.error(f"Background match error for project {project_id}: {e}")
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try:
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async with async_session() as db2:
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result = await db2.execute(select(Project).where(Project.id == project_id))
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project = result.scalar_one_or_none()
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if project:
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project.status = ProjectStatus.REVIEW
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await db2.commit()
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except Exception:
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pass
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@router.post("/{project_id}/match")
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async def run_matching(project_id: int, db: AsyncSession = Depends(get_db)):
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async def run_matching(
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project_id: int,
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background_tasks: BackgroundTasks,
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db: AsyncSession = Depends(get_db),
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):
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"""Trigger AI matching for all client assets in this project."""
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project = await _get_project(project_id, db)
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@ -153,6 +210,9 @@ async def run_matching(project_id: int, db: AsyncSession = Depends(get_db)):
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if not client_assets:
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raise HTTPException(status_code=400, detail="No client assets to match. Upload a document first.")
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# Snapshot IDs before clearing (ORM objects expire after commit)
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asset_snapshots = [{"id": ca.id} for ca in client_assets]
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# Clear existing matches
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for ca in client_assets:
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matches_result = await db.execute(select(Match).where(Match.client_asset_id == ca.id))
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@ -162,17 +222,12 @@ async def run_matching(project_id: int, db: AsyncSession = Depends(get_db)):
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project.status = ProjectStatus.MATCHING
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await db.commit()
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# Run matching (batched, parallel, commits per batch)
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matches = await match_client_assets(db, project_id, client_assets)
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# Refresh project and set final status
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await db.refresh(project)
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project.status = ProjectStatus.REVIEW
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await db.commit()
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# Run matching in background — return 202 immediately
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background_tasks.add_task(_background_match, project_id, asset_snapshots)
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return {
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"message": f"Matched {len(client_assets)} client assets",
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"total_matches": len(matches),
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"message": f"Matching started for {len(client_assets)} client assets.",
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"status": "matching",
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}
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@ -1,6 +1,5 @@
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import os
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import httpx
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from functools import lru_cache
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from fastapi import Depends, HTTPException, status
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from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
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from jose import jwt, JWTError
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@ -13,22 +12,20 @@ ISSUER = f"https://login.microsoftonline.com/{TENANT_ID}/v2.0"
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bearer_scheme = HTTPBearer(auto_error=False)
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@lru_cache(maxsize=1)
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def _fetch_jwks() -> dict:
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"""Fetch JWKS from Azure. Cached in process memory; restart to refresh."""
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response = httpx.get(JWKS_URL, timeout=10)
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response.raise_for_status()
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return response.json()
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# Module-level cache — populated once per process, never blocks the event loop
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_jwks_cache: dict | None = None
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def _get_jwks() -> dict:
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try:
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return _fetch_jwks()
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except Exception:
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# Clear cache and retry once on failure
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_fetch_jwks.cache_clear()
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return _fetch_jwks()
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async def _get_jwks() -> dict:
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"""Fetch JWKS from Azure using async HTTP. Cached in process memory."""
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global _jwks_cache
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if _jwks_cache is not None:
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return _jwks_cache
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async with httpx.AsyncClient(timeout=10) as client:
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response = await client.get(JWKS_URL)
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response.raise_for_status()
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_jwks_cache = response.json()
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return _jwks_cache
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async def get_current_user(
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@ -42,12 +39,21 @@ async def get_current_user(
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token = credentials.credentials
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try:
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jwks = _get_jwks()
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jwks = await _get_jwks()
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header = jwt.get_unverified_header(token)
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key = next(
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(k for k in jwks["keys"] if k.get("kid") == header.get("kid")),
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None,
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)
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if key is None:
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# Key not in cache — fetch fresh JWKS once (keys can rotate)
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global _jwks_cache
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_jwks_cache = None
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jwks = await _get_jwks()
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key = next(
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(k for k in jwks["keys"] if k.get("kid") == header.get("kid")),
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None,
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)
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if key is None:
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raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Unknown signing key")
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@ -62,7 +68,11 @@ async def get_current_user(
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return {
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"oid": payload.get("oid"),
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"name": payload.get("name"),
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"email": payload.get("preferred_username") or payload.get("email"),
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"email": (
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payload.get("preferred_username")
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or payload.get("upn")
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or payload.get("email")
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),
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}
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except JWTError as e:
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raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail=f"Invalid token: {e}")
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|
|
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@ -64,10 +64,29 @@ async def export_ratecard_excel(db: AsyncSession, project: Project, efficiency_l
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gmals_result = await db.execute(select(GmalAsset).where(GmalAsset.id.in_(gmal_ids)))
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gmals = {g.id: g for g in gmals_result.scalars().all()}
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# Load selected matches for caveat lookup
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matches_result = await db.execute(
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select(Match).where(
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Match.client_asset_id.in_(asset_ids),
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Match.is_selected == True,
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)
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)
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selected_matches = matches_result.scalars().all()
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caveat_by_asset = {}
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for m in selected_matches:
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parts = []
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if m.caveat_text:
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parts.append(m.caveat_text)
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gmal = gmals.get(m.gmal_asset_id)
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if gmal and gmal.caveats:
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parts.append(f"GMAL Standard Caveats: {gmal.caveats}")
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caveat_by_asset[m.client_asset_id] = "\n\n".join(parts)
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# Sheet 1: Ratecard Summary (roles x assets matrix)
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ws1 = wb.active
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ws1.title = "Ratecard Summary"
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_build_ratecard_sheet(ws1, lines, roles, client_assets, gmals)
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_build_ratecard_sheet(ws1, lines, roles, client_assets, gmals, caveat_by_asset)
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# Sheet 2: Asset Detail
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ws2 = wb.create_sheet("Asset Detail")
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|
|
@ -86,8 +105,10 @@ async def export_ratecard_excel(db: AsyncSession, project: Project, efficiency_l
|
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return _workbook_to_bytes(wb)
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def _build_ratecard_sheet(ws, lines, roles, client_assets, gmals):
|
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def _build_ratecard_sheet(ws, lines, roles, client_assets, gmals, caveats: dict | None = None):
|
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"""Build the main ratecard matrix: rows=roles, cols=client assets."""
|
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if caveats is None:
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caveats = {}
|
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# Get unique sorted client assets and roles
|
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asset_ids_ordered = sorted(client_assets.keys())
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role_ids_ordered = sorted(roles.keys(), key=lambda rid: (roles[rid].discipline, roles[rid].sort_order or 0))
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|
|
@ -127,9 +148,24 @@ def _build_ratecard_sheet(ws, lines, roles, client_assets, gmals):
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ws.cell(row=1, column=total_col, value="Total Hours").font = HEADER_FONT
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ws.cell(row=1, column=total_col).fill = HEADER_FILL
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# Caveats row (row 2)
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CAVEAT_FONT = Font(italic=True, size=9, color="555555")
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CAVEAT_FILL = PatternFill(start_color="FFFBF0", end_color="FFFBF0", fill_type="solid")
|
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ws.cell(row=2, column=1, value="").fill = CAVEAT_FILL
|
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ws.cell(row=2, column=2, value="Assumptions / Caveats").font = Font(italic=True, bold=True, size=9, color="92400E")
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ws.cell(row=2, column=2).fill = CAVEAT_FILL
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for col_idx, asset_id in enumerate(asset_ids_ordered, 3):
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caveat = caveats.get(asset_id, "")
|
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cell = ws.cell(row=2, column=col_idx, value=caveat)
|
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cell.font = CAVEAT_FONT
|
||||
cell.fill = CAVEAT_FILL
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cell.alignment = Alignment(wrap_text=True, vertical="top")
|
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ws.cell(row=2, column=total_col).fill = CAVEAT_FILL
|
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ws.row_dimensions[2].height = 60
|
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|
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# Data rows
|
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current_discipline = None
|
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row_idx = 2
|
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row_idx = 3
|
||||
|
||||
for role_id in role_ids_ordered:
|
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role = roles[role_id]
|
||||
|
|
@ -181,7 +217,7 @@ def _build_ratecard_sheet(ws, lines, roles, client_assets, gmals):
|
|||
|
||||
async def _build_asset_detail_sheet(ws, db, project, client_assets, gmals):
|
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"""Build the asset detail sheet showing matches and caveats."""
|
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headers = ["Client Asset", "Volume", "Matched GMAL", "GMAL Name", "Confidence", "Score", "Caveats"]
|
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headers = ["Client Asset", "Volume", "Matched GMAL", "GMAL Name", "Confidence", "Score", "Match Caveats", "GMAL Standard Caveats"]
|
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for col_idx, header in enumerate(headers, 1):
|
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cell = ws.cell(row=1, column=col_idx, value=header)
|
||||
cell.font = HEADER_FONT
|
||||
|
|
@ -213,13 +249,17 @@ async def _build_asset_detail_sheet(ws, db, project, client_assets, gmals):
|
|||
ws.cell(row=row_idx, column=5, value=match.confidence.value)
|
||||
ws.cell(row=row_idx, column=6, value=float(match.confidence_score) if match.confidence_score else 0)
|
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ws.cell(row=row_idx, column=7, value=match.caveat_text or "")
|
||||
ws.cell(row=row_idx, column=7).alignment = Alignment(wrap_text=True, vertical="top")
|
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gmal_caveats = (gmal.caveats or "") if gmal else ""
|
||||
ws.cell(row=row_idx, column=8, value=gmal_caveats)
|
||||
ws.cell(row=row_idx, column=8).alignment = Alignment(wrap_text=True, vertical="top")
|
||||
else:
|
||||
ws.cell(row=row_idx, column=3, value="No match")
|
||||
|
||||
row_idx += 1
|
||||
|
||||
# Column widths
|
||||
widths = [30, 10, 15, 40, 12, 10, 60]
|
||||
widths = [30, 10, 15, 40, 12, 10, 60, 60]
|
||||
for i, w in enumerate(widths, 1):
|
||||
ws.column_dimensions[get_column_letter(i)].width = w
|
||||
|
||||
|
|
|
|||
|
|
@ -182,8 +182,10 @@ function NavBar() {
|
|||
const { instance, accounts } = useMsal();
|
||||
const user = accounts[0];
|
||||
|
||||
function handleLogout() {
|
||||
instance.logoutRedirect({ postLogoutRedirectUri: '/gsb' });
|
||||
async function handleLogout() {
|
||||
// Clear local MSAL cache only — does not sign out of the Microsoft account
|
||||
await instance.clearCache();
|
||||
window.location.href = '/gsb';
|
||||
}
|
||||
|
||||
return (
|
||||
|
|
|
|||
|
|
@ -13,10 +13,11 @@ api.interceptors.request.use(async (config) => {
|
|||
|
||||
try {
|
||||
const result = await msalInstance.acquireTokenSilent({
|
||||
...loginRequest,
|
||||
scopes: ['openid', 'profile', 'email'],
|
||||
account: accounts[0],
|
||||
});
|
||||
config.headers.Authorization = `Bearer ${result.accessToken}`;
|
||||
// ID token has audience=CLIENT_ID so the backend can validate it
|
||||
config.headers.Authorization = `Bearer ${result.idToken}`;
|
||||
} catch {
|
||||
// Token expired or failed — trigger interactive login
|
||||
await msalInstance.loginRedirect(loginRequest);
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@ export const msalConfig: Configuration = {
|
|||
auth: {
|
||||
clientId: '9079054c-9620-4757-a256-23413042f1ef',
|
||||
authority: 'https://login.microsoftonline.com/e519c2e6-bc6d-4fdf-8d9c-923c2f002385',
|
||||
redirectUri: 'https://optical-dev.oliver.solutions/gsb',
|
||||
postLogoutRedirectUri: 'https://optical-dev.oliver.solutions/gsb',
|
||||
redirectUri: 'https://optical-dev.oliver.solutions/gsb/',
|
||||
postLogoutRedirectUri: 'https://optical-dev.oliver.solutions/gsb/',
|
||||
},
|
||||
cache: {
|
||||
cacheLocation: 'localStorage',
|
||||
|
|
|
|||
|
|
@ -91,53 +91,64 @@ export default function ProjectView() {
|
|||
setUploading(true);
|
||||
setUploadStage(`Uploading ${file.name}...`);
|
||||
|
||||
// Poll project status for stage updates
|
||||
try {
|
||||
const form = new FormData();
|
||||
form.append('file', file);
|
||||
await api.post(`/projects/${id}/upload`, form);
|
||||
} catch (err: any) {
|
||||
alert(`Upload failed: ${err.response?.data?.detail || err.message}`);
|
||||
setUploading(false);
|
||||
setUploadStage('');
|
||||
return;
|
||||
}
|
||||
|
||||
// Poll until background parsing completes (status leaves 'parsing')
|
||||
const pollInterval = setInterval(async () => {
|
||||
try {
|
||||
const res = await api.get(`/projects/${id}`);
|
||||
if (res.data.parse_stage) {
|
||||
setUploadStage(res.data.parse_stage);
|
||||
}
|
||||
if (res.data.status !== 'parsing') {
|
||||
clearInterval(pollInterval);
|
||||
setUploading(false);
|
||||
setUploadStage('');
|
||||
await loadProject();
|
||||
setTab('matches');
|
||||
}
|
||||
} catch {}
|
||||
}, 1500);
|
||||
|
||||
try {
|
||||
const form = new FormData();
|
||||
form.append('file', file);
|
||||
await api.post(`/projects/${id}/upload`, form);
|
||||
await loadProject();
|
||||
setTab('matches');
|
||||
} catch (err: any) {
|
||||
alert(`Upload failed: ${err.response?.data?.detail || err.message}`);
|
||||
} finally {
|
||||
clearInterval(pollInterval);
|
||||
setUploading(false);
|
||||
setUploadStage('');
|
||||
}
|
||||
}
|
||||
|
||||
async function handleMatch() {
|
||||
setMatching(true);
|
||||
// Start polling for matches while the request runs
|
||||
const pollInterval = setInterval(async () => {
|
||||
try {
|
||||
const matchRes = await api.get(`/projects/${id}/matches`);
|
||||
setMatches(matchRes.data);
|
||||
} catch {}
|
||||
}, 3000);
|
||||
|
||||
try {
|
||||
await api.post(`/projects/${id}/match`);
|
||||
await loadProject();
|
||||
} catch (err: any) {
|
||||
if (!err.message?.includes('cancel')) {
|
||||
alert(`Matching failed: ${err.response?.data?.detail || err.message}`);
|
||||
}
|
||||
await loadProject();
|
||||
} finally {
|
||||
clearInterval(pollInterval);
|
||||
setMatching(false);
|
||||
await loadProject();
|
||||
return;
|
||||
}
|
||||
|
||||
// Poll until background matching completes (status leaves 'matching')
|
||||
const pollInterval = setInterval(async () => {
|
||||
try {
|
||||
const [matchRes, projRes] = await Promise.all([
|
||||
api.get(`/projects/${id}/matches`),
|
||||
api.get(`/projects/${id}`),
|
||||
]);
|
||||
setMatches(matchRes.data);
|
||||
if (projRes.data.status !== 'matching') {
|
||||
clearInterval(pollInterval);
|
||||
setMatching(false);
|
||||
await loadProject();
|
||||
}
|
||||
} catch {}
|
||||
}, 3000);
|
||||
}
|
||||
|
||||
async function handleCancelMatch() {
|
||||
|
|
@ -184,10 +195,32 @@ export default function ProjectView() {
|
|||
});
|
||||
}
|
||||
|
||||
function getExcelExportUrl() {
|
||||
async function downloadFile(url: string, filename: string) {
|
||||
try {
|
||||
const response = await api.get(url, { responseType: 'blob' });
|
||||
const blob = new Blob([response.data], { type: response.headers['content-type'] });
|
||||
const objectUrl = URL.createObjectURL(blob);
|
||||
const a = document.createElement('a');
|
||||
a.href = objectUrl;
|
||||
a.download = filename;
|
||||
document.body.appendChild(a);
|
||||
a.click();
|
||||
document.body.removeChild(a);
|
||||
URL.revokeObjectURL(objectUrl);
|
||||
} catch (err: any) {
|
||||
alert(`Export failed: ${err.response?.data?.detail || err.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
function handleExcelExport() {
|
||||
const levels = Array.from(selectedEfficiencyLevels).sort().join(',');
|
||||
const base = `/api/projects/${id}/ratecard/export/excel`;
|
||||
return levels ? `${base}?efficiency_levels=${levels}` : base;
|
||||
const base = `/projects/${id}/ratecard/export/excel`;
|
||||
const url = levels ? `${base}?efficiency_levels=${levels}` : base;
|
||||
downloadFile(url, `${project?.name || 'ratecard'}.xlsx`);
|
||||
}
|
||||
|
||||
function handlePdfExport() {
|
||||
downloadFile(`/projects/${id}/ratecard/export/pdf`, `${project?.name || 'caveats'}_caveats.pdf`);
|
||||
}
|
||||
|
||||
async function handleDelete() {
|
||||
|
|
@ -455,12 +488,12 @@ export default function ProjectView() {
|
|||
<span className="rc-assets">{ratecard.total_assets} assets</span>
|
||||
</div>
|
||||
<div className="rc-exports">
|
||||
<a href={getExcelExportUrl()} className="btn btn-secondary">
|
||||
<button onClick={handleExcelExport} className="btn btn-secondary">
|
||||
Export Excel
|
||||
</a>
|
||||
<a href={`/api/projects/${id}/ratecard/export/pdf`} className="btn btn-secondary">
|
||||
</button>
|
||||
<button onClick={handlePdfExport} className="btn btn-secondary">
|
||||
Export PDF Caveats
|
||||
</a>
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
|
@ -575,12 +608,12 @@ export default function ProjectView() {
|
|||
<div className="rc-header" style={{ marginBottom: 16 }}>
|
||||
<div />
|
||||
<div style={{ display: 'flex', gap: 8 }}>
|
||||
<a href={getExcelExportUrl()} className="btn btn-secondary">
|
||||
<button onClick={handleExcelExport} className="btn btn-secondary">
|
||||
Export Excel {selectedEfficiencyLevels.size > 0 ? `(+${selectedEfficiencyLevels.size} AI tabs)` : ''}
|
||||
</a>
|
||||
<a href={`/api/projects/${id}/ratecard/export/pdf`} className="btn btn-secondary">
|
||||
</button>
|
||||
<button onClick={handlePdfExport} className="btn btn-secondary">
|
||||
Export PDF Caveats
|
||||
</a>
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue