feat(dashboard): merge dev → main (US-06)

backend/main.py

@@ -9,7 +9,7 @@
 
 from core.rate_limit import limiter
 from database import get_db
-from routers import annotate, auth, clean, collect, data, review, seed, users
+from routers import annotate, auth, clean, collect, dashboard, data, review, seed, users
 
 # Em produção, definir CORS_ORIGINS no Vercel Dashboard:
 #   CORS_ORIGINS=https://seu-frontend.vercel.app
@@ -45,6 +45,7 @@ async def rate_limit_handler(request: Request, exc: RateLimitExceeded):
 app.include_router(collect.router)
 app.include_router(clean.router)
 app.include_router(data.router)
+app.include_router(dashboard.router)
 app.include_router(annotate.router)
 app.include_router(review.router)
 app.include_router(seed.router)

backend/requirements.txt

@@ -16,3 +16,4 @@ pytest-mock==3.14.0
 ruff==0.6.9
 bandit==1.9.4
 pip-audit==2.10.0
+plotly==5.24.1

backend/routers/dashboard.py

@@ -0,0 +1,96 @@
+"""Router da US-06 — Dashboard de Análise."""
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+
+from database import get_db
+from models.user import User
+from schemas.dashboard import (
+    BotCommentsResponse,
+    CriteriaEffectivenessItem,
+    GlobalDashboardResponse,
+    UserDashboardResponse,
+    VideoDashboardResponse,
+)
+from services.auth import get_current_user
+from services.dashboard import (
+    get_bot_comments,
+    get_criteria_effectiveness,
+    get_global_dashboard,
+    get_user_dashboard,
+    get_video_dashboard,
+)
+
+router = APIRouter(prefix="/dashboard", tags=["dashboard"])
+
+
+@router.get("/global", response_model=GlobalDashboardResponse)
+def global_endpoint(
+    criteria: str | None = Query(default=None),
+    db: Session = Depends(get_db),
+    _user: User = Depends(get_current_user),
+):
+    criteria_list = (
+        [c.strip() for c in criteria.split(",") if c.strip()] if criteria else None
+    )
+    return get_global_dashboard(db, criteria=criteria_list)
+
+
+@router.get(
+    "/criteria-effectiveness",
+    response_model=list[CriteriaEffectivenessItem],
+)
+def criteria_effectiveness_endpoint(
+    video_id: str | None = Query(default=None),
+    db: Session = Depends(get_db),
+    _user: User = Depends(get_current_user),
+):
+    return get_criteria_effectiveness(db, video_id=video_id)
+
+
+@router.get("/video", response_model=VideoDashboardResponse)
+def video_endpoint(
+    video_id: str = Query(),
+    criteria: str | None = Query(default=None),
+    db: Session = Depends(get_db),
+    _user: User = Depends(get_current_user),
+):
+    criteria_list = (
+        [c.strip() for c in criteria.split(",") if c.strip()] if criteria else None
+    )
+    return get_video_dashboard(db, video_id=video_id, criteria=criteria_list)
+
+
+@router.get("/user", response_model=UserDashboardResponse)
+def user_endpoint(
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    return get_user_dashboard(db, user_id=current_user.id)
+
+
+@router.get("/bots", response_model=BotCommentsResponse)
+def bots_endpoint(
+    dataset_id: str | None = Query(default=None),
+    video_id: str | None = Query(default=None),
+    author: str | None = Query(default=None),
+    search: str | None = Query(default=None),
+    criteria: str | None = Query(default=None),
+    page: int = Query(default=1, ge=1),
+    page_size: int = Query(default=20, ge=1, le=100),
+    db: Session = Depends(get_db),
+    _user: User = Depends(get_current_user),
+):
+    criteria_list = (
+        [c.strip() for c in criteria.split(",") if c.strip()] if criteria else None
+    )
+    return get_bot_comments(
+        db,
+        dataset_id=dataset_id,
+        video_id=video_id,
+        author=author,
+        search=search,
+        criteria_filter=criteria_list,
+        page=page,
+        page_size=page_size,
+    )

backend/schemas/dashboard.py

@@ -0,0 +1,126 @@
+"""Schemas Pydantic da US-06 — Dashboard de Análise."""
+
+import uuid
+
+from pydantic import BaseModel
+
+# ── Visão Geral (Global) ───────────────────────────────────────────
+
+
+class GlobalSummary(BaseModel):
+    total_datasets: int
+    total_comments_annotated: int
+    total_comments_in_datasets: int
+    annotation_progress: float
+    total_bots: int
+    total_humans: int
+    total_conflicts: int
+    pending_conflicts: int
+    agreement_rate: float
+
+
+class GlobalDashboardResponse(BaseModel):
+    summary: GlobalSummary
+    active_criteria_filter: list[str]
+    label_distribution_chart: str
+    comparativo_por_dataset_chart: str
+    annotations_over_time_chart: str
+    bot_rate_by_dataset_chart: str
+    agreement_by_dataset_chart: str
+    criteria_effectiveness_chart: str
+
+
+# ── Eficácia por Critério ──────────────────────────────────────────
+
+
+class CriteriaEffectivenessItem(BaseModel):
+    criteria: str
+    group: str
+    total_datasets: int
+    total_comments_selected: int
+    total_bots: int
+    bot_rate: float
+
+
+# ── Por Vídeo ──────────────────────────────────────────────────────
+
+
+class VideoSummary(BaseModel):
+    total_comments_collected: int
+    total_comments_in_datasets: int
+    total_annotated: int
+    total_bots: int
+    total_humans: int
+    total_conflicts: int
+    pending_conflicts: int
+    agreement_rate: float
+
+
+class VideoHighlight(BaseModel):
+    label: str
+    value: str
+    detail: str | None = None
+
+
+class VideoDashboardResponse(BaseModel):
+    video_id: str
+    summary: VideoSummary
+    highlights: list[VideoHighlight]
+    active_criteria_filter: list[str]
+    label_distribution_chart: str
+    comparativo_por_dataset_chart: str
+    bot_rate_by_criteria_chart: str
+    comment_timeline_chart: str
+
+
+# ── Meu Progresso ──────────────────────────────────────────────────
+
+
+class UserSummary(BaseModel):
+    total_datasets_assigned: int
+    datasets_completed: int
+    datasets_pending: int
+    total_annotated: int
+    total_pending: int
+    bots: int
+    humans: int
+    conflicts_generated: int
+
+
+class UserDatasetProgress(BaseModel):
+    dataset_id: uuid.UUID
+    dataset_name: str
+    video_id: str
+    total_comments: int
+    annotated_by_me: int
+    pending: int
+    percent_complete: float
+    my_bots: int
+    my_conflicts: int
+    status: str
+
+
+class UserDashboardResponse(BaseModel):
+    summary: UserSummary
+    datasets: list[UserDatasetProgress]
+    my_label_distribution_chart: str
+    my_progress_by_dataset_chart: str
+    my_annotations_over_time_chart: str
+
+
+# ── Tabela de Bots ─────────────────────────────────────────────────
+
+
+class BotCommentItem(BaseModel):
+    dataset_name: str
+    author_display_name: str
+    text_original: str
+    concordance_pct: int
+    conflict_status: str | None = None
+    annotators_count: int = 0
+    criteria: list[str] = []
+
+
+class BotCommentsResponse(BaseModel):
+    total: int
+    items: list[BotCommentItem]