feat: NLP patch insights + standalone demo mode

- dlib/ai/insights.py: pure-Python NLP analysis that correlates accepted
  AI suggestion operations/keywords/sections with submission outcomes
  (pending_review / published = positive, archived = negative)
- Backend: GET /api/v1/insights route + service + Pydantic schema
- Frontend: InsightsPanel component with bar charts for operation impact,
  section impact, and keyword signal lift scores
- Insights tab added to the version panel; compact preview on doc overview
- NEXT_PUBLIC_DEMO=true makes the webapp fully standalone: loads
  DEMO_DOCUMENTS / DEMO_SUBMISSIONS / DEMO_INSIGHTS from demo-data.ts,
  disables all mutating actions, shows a DEMO badge in the top bar
- apps/webapp/public/demo-cv.docx: static dummy CV (Alex Rivera) for demo
- scripts/gen_demo_cv.py: script to regenerate the demo DOCX
- .env.example: document NEXT_PUBLIC_DEMO flag

https://claude.ai/code/session_01LWxu2qrwY6BRjUFXXn7NiM

apps/backend/fastapi/app/api/routes/insights.py

@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from fastapi import APIRouter, Depends
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.api.deps import get_current_user, get_db
+from app.schemas.insights import InsightsResponse
+from app.services.insights import get_insights
+from dlib.auth import AuthenticatedUser
+
+router = APIRouter(prefix="/insights", tags=["insights"])
+
+
+@router.get("", response_model=InsightsResponse)
+async def insights_endpoint(
+    session: AsyncSession = Depends(get_db),
+    user: AuthenticatedUser = Depends(get_current_user),
+):
+    result = await get_insights(session, owner_id=user.sub)
+    return InsightsResponse(
+        total_submissions=result.total_submissions,
+        positive_count=result.positive_count,
+        positive_rate=result.positive_rate,
+        operation_impact=[
+            {"operation": o.operation, "total": o.total, "positive": o.positive, "rate": o.rate}
+            for o in result.operation_impact
+        ],
+        top_positive_keywords=[
+            {"keyword": k.keyword, "positive_count": k.positive_count, "negative_count": k.negative_count, "lift": k.lift}
+            for k in result.top_positive_keywords
+        ],
+        top_negative_keywords=[
+            {"keyword": k.keyword, "positive_count": k.positive_count, "negative_count": k.negative_count, "lift": k.lift}
+            for k in result.top_negative_keywords
+        ],
+        section_impact=[
+            {"section": s.section, "positive_rate": s.positive_rate, "count": s.count}
+            for s in result.section_impact
+        ],
+        has_data=result.has_data,
+    )