讓證據分類不再綁 Google Drive，分完直接掛進任務

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flowchart LR
  subgraph FE[前端]
    U1[上傳到批次頁]
    U2[審閱頁（沿用）]
    U3[歸檔對話框]
  end
  subgraph PKG[套件 jedi-evidence-classification]
    R[route：批次／分類／歸檔]
    S[app service：批次狀態機＋分類編排]
    P1[port IEvidenceStorage]
    P2[port IControlCatalog]
    P3[port IClassificationProfile]
    P4[port ITaskEvidenceSink]
  end
  subgraph HOST[主專案]
    A1[adapter → IUploadFileProvider]
    A2[adapter → build_classifier_catalog_by_ssp_id]
    A3[adapter → classification_profiles 表]
    A4[adapter → job_evidences ＋ get_jobs_by_round_id]
    DB[(PostgreSQL：evidence_batches／batch_files／runs／profiles／job_evidences)]
  end
  subgraph STOR[儲存後端（擇一）]
    L[local]
    M[minio／seaweedfs]
    RA[remote agent]
  end
  C[容器 cmmc-classifier：只吃 job 目錄裡的檔＋prompt 三段，寫結果 JSON]
  U1 --> R
  U2 --> R
  U3 --> R
  R --> S
  S --> P1
  S --> P2
  S --> P3
  S --> P4
  P1 -.-> A1
  P2 -.-> A2
  P3 -.-> A3
  P4 -.-> A4
  A1 --> L
  A1 --> M
  A1 --> RA
  A2 --> DB
  A3 --> DB
  A4 --> DB
  S -->|拉檔到 job 目錄後起容器| C
  C -->|結果 JSON| S

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sequenceDiagram
    participant U as 使用者（專案經理）
    participant FE as 前端
    participant BE as 主專案 BE（含套件）
    participant ST as 儲存後端
    participant C as 分類容器
    participant DB as PostgreSQL

    U->>FE: 選輪次，開新批次，拖入 N 份檔
    FE->>BE: POST /evidence-batches（round_uid）→ batch_uid
    loop 每一份檔
      FE->>BE: POST /evidence-batches/{uid}/files（multipart）
      BE->>ST: IUploadFileProvider.save_file()（帶 tenant 脈絡）
      BE->>DB: upload_files（tenant_id／owner）＋ evidence_batch_files
    end
    U->>FE: 按「開始分類」
    FE->>BE: POST /evidence-batches/{uid}/classify（model／threshold 可選）
    BE->>DB: 批次 status → classifying（之後加檔一律 409）
    BE->>DB: 解析 profile（專案→SSP→catalog→framework_version→通用）＋ 動態目標集
    BE->>ST: 逐檔 get_file() 拉到 job 工作目錄（D2 甲案）
    BE->>C: docker run（掛 job 目錄；環境變數只有 AI 金鑰）
    C->>C: 讀 prompt 三段＋目標集，逐檔呼叫 AI
    C-->>BE: 結果 JSON（每檔 → [part_id, confidence]）
    BE->>DB: evidence_classification_runs（掛 batch）＋ status → review
    U->>FE: 進審閱頁：加減 AO、標不適用、刪檔、儲存
    FE->>BE: PUT /classification-runs/{id}/state
    U->>FE: 按「歸檔進任務」
    FE->>BE: POST /evidence-batches/{uid}/archive
    BE->>DB: get_jobs_by_round_id(round_id) 建 part_id → job 對照
    BE->>DB: 每檔每 AO：job_evidences（source=AI_CLASSIFIED，冪等）；沒任務的標 no_task
    BE->>DB: 批次 status → archived
    BE-->>FE: 結果摘要：已掛 X 檔／Y 筆證據，Z 檔無對應任務
    FE->>U: 問「批次剩餘 Z 檔要清掉嗎？」
    U->>FE: 選清掉
    FE->>BE: POST /evidence-batches/{uid}/purge
    BE->>ST: delete_file()（依 D7 實刪或軟刪）

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stateDiagram-v2
    [*] --> uploading : 建批次
    uploading --> uploading : 加檔／刪檔
    uploading --> ready : 至少一檔且使用者按「完成上傳」（或按分類時自動）
    ready --> uploading : 使用者再加檔（分類尚未開始）
    ready --> classifying : 按「開始分類」（封口，加檔 409）
    classifying --> review : 容器回結果
    classifying --> failed : 容器失敗／逾時
    failed --> classifying : 重跑（同批次，不必重傳）
    review --> review : 審閱調整、儲存
    review --> classifying : 重新分類（換 model／threshold）
    review --> archived : 歸檔進任務
    archived --> archived : 清掉剩餘檔（purge，只動未掛任務的檔）
    archived --> [*]