diff --git a/alembic/versions/0004_park_screen_nullable_tenant.py b/alembic/versions/0004_park_screen_nullable_tenant.py new file mode 100644 index 0000000..51e1e88 --- /dev/null +++ b/alembic/versions/0004_park_screen_nullable_tenant.py @@ -0,0 +1,35 @@ +"""park_screens.tenant_id 改为可空（纠正早期 create_all 遗留的 NOT NULL） + +Revision ID: 0004_park_screen_nullable_tenant +Revises: 0003_training_tables +Create Date: 2026-08-24 +""" +from __future__ import annotations + +import sqlalchemy as sa +from alembic import op + +revision = "0004_park_screen_nullable_tenant" +down_revision = "0003_training_tables" +branch_labels = None +depends_on = None + + +def upgrade() -> None: + # SQLite 不能直接 ALTER 去除 NOT NULL → 用 batch（内部 rename+copy 重建表，保留既有行） + with op.batch_alter_table("park_screens") as batch_op: + batch_op.alter_column( + "tenant_id", + existing_type=sa.String(), + nullable=True, + ) + + +def downgrade() -> None: + # 回退：恢复 NOT NULL（如无数据则安全；有数据且为 NULL 时需先处理） + with op.batch_alter_table("park_screens") as batch_op: + batch_op.alter_column( + "tenant_id", + existing_type=sa.String(), + nullable=False, + ) diff --git a/alembic/versions/0005_park_settings.py b/alembic/versions/0005_park_settings.py new file mode 100644 index 0000000..74deab7 --- /dev/null +++ b/alembic/versions/0005_park_settings.py @@ -0,0 +1,30 @@ +"""park_settings 全局配置 kv 表（视频识别开关/地址等，admin 可写） + +Revision ID: 0005_park_settings +Revises: 0004_park_screen_nullable_tenant +Create Date: 2026-08-24 +""" +from __future__ import annotations + +import sqlalchemy as sa +from alembic import op + +revision = "0005_park_settings" +down_revision = "0004_park_screen_nullable_tenant" +branch_labels = None +depends_on = None + + +def upgrade() -> None: + conn = op.get_bind() + if not conn.dialect.has_table(conn, "park_settings"): + op.create_table( + "park_settings", + sa.Column("key", sa.String(), primary_key=True), + sa.Column("value", sa.Text(), nullable=False, server_default=""), + sa.Column("updated_at", sa.String(), nullable=False, server_default=""), + ) + + +def downgrade() -> None: + op.drop_table("park_settings") diff --git a/app/infrastructure/models.py b/app/infrastructure/models.py index 4dc4038..9a85fc4 100644 --- a/app/infrastructure/models.py +++ b/app/infrastructure/models.py @@ -755,6 +755,14 @@ class ParkKbDoc(Base): created_at: Mapped[str] = mapped_column(String, default="") +class ParkSetting(Base): + """园区全局配置 kv（如视频识别开关/地址），admin 经 /park/admin/* 读写，运行时不建表/不灌种子。""" + __tablename__ = "park_settings" + key: Mapped[str] = mapped_column(String, primary_key=True) + value: Mapped[str] = mapped_column(Text, default="") + updated_at: Mapped[str] = mapped_column(String, default="") + + class ParkScreen(Base): __tablename__ = "park_screens" id: Mapped[str] = mapped_column(String, primary_key=True) diff --git a/app/park/config.py b/app/park/config.py index 7ecb39c..3a89bd4 100644 --- a/app/park/config.py +++ b/app/park/config.py @@ -43,10 +43,6 @@ class Settings: DATA_FILE = Path(_env("DPM_DATA_FILE", str(PARK_DIR / "data.json"))) DIST_DIR = Path(_env("DPM_DIST_DIR", str(PARK_DIR / "dist"))) # 大屏前端产物（可选托管） - # ---- 视频帧保存（摄像头识别帧）---- - SAVE_VISION_FRAMES = _env("DPM_SAVE_VISION_FRAMES", "1") == "1" - VISION_FRAME_SAVE_DIR = Path(_env("DPM_VISION_SAVE_DIR", str(MEDIA_DIR / "video"))) - # ---- MQTT（EMQX broker；凭据走 server-core/.env，勿入库）---- MQTT_ENABLED = _env("DPM_MQTT_ENABLED", "1") == "1" MQTT_HOST = _env("DPM_MQTT_HOST", _env("MQTT_BROKER_HOST", "192.168.1.9")) @@ -82,7 +78,6 @@ class Settings: S2S_HOST = _env("DPM_S2S_HOST", "0.0.0.0") S2S_PORT = int(_env("DPM_S2S_PORT", "8765")) S2S_NUM_PIPELINES = int(_env("DPM_S2S_NUM_PIPELINES", "1")) - VISION_LLM_MODEL = _env("DPM_VISION_LLM_MODEL", "qwen3-vl-flash") S2S_STT_MODEL = _env("DPM_S2S_STT_MODEL", "qwen3-asr-flash-realtime") S2S_LLM_MODEL = _env("DPM_S2S_LLM_MODEL", "qwen-plus") S2S_TTS_MODEL = _env("DPM_S2S_TTS_MODEL", "qwen3-tts-flash-realtime") diff --git a/app/park/routers.py b/app/park/routers.py index 948f7a5..e54cc78 100644 --- a/app/park/routers.py +++ b/app/park/routers.py @@ -412,6 +412,27 @@ async def runtime_config(request: Request): } +class VisionConfigBody(BaseModel): + api: str = "" + + +@router.get("/api/vision/config") +async def vision_config_get(authorization: str | None = Header(None), tenant_id: str | None = None): + """园区端读：本园区视频识别服务地址（空=禁用）。识别能力在独立 park-vision。""" + tid = await _resolve_tenant(authorization, tenant_id) + api = await tenants.get_tenant_vision(tid) + return {"ok": True, "api": api} + + +@router.put("/api/vision/config") +async def vision_config_set(body: VisionConfigBody, authorization: str | None = Header(None), tenant_id: str | None = None): + """园区端写：配置本园区视频识别服务地址（可填局域网地址；空=禁用）。""" + tid = await _resolve_tenant(authorization, tenant_id) + api = (body.api or "").strip().rstrip("/") + await tenants.set_tenant_vision(tid, api) + return {"ok": True, "api": api} + + # ==================== 大屏数据（sim_engine） ==================== @router.get("/api/dashboard/snapshot") @@ -513,9 +534,11 @@ async def bind_screen_by_code(tid: str, body: BindByCodeBody): screen = await tenants.bind_device(tid, dev_id, body.name or "", body.role or "main", body.location or "") token = create_device_token(tid, dev_id) tenant = await tenants.get_tenant(tid) + vision_api = await tenants.get_tenant_vision(tid) # 本园区视频识别地址(可局域网)，随绑定下发大屏 _publish_bind(dev_id, {"event": "bound", "token": token, "tenant_id": tid, - "name": (tenant or {}).get("name", ""), "intro": (tenant or {}).get("intro", [])}) - return {"ok": True, "screen": screen, "token": token} + "name": (tenant or {}).get("name", ""), "intro": (tenant or {}).get("intro", []), + "vision_api": vision_api}) + return {"ok": True, "screen": screen, "token": token, "vision_api": vision_api} @router.post("/api/devices/{device_id}/unbind") @@ -636,16 +659,6 @@ class VisionEventBody(BaseModel): detail: str = "" -class VisionFrameBody(BaseModel): - image: str = "" - conf: float = 0.0 - - -class VisionLlmBody(BaseModel): - image: str = "" - prompt: str = "" - - AI_GROUPED_QUESTIONS = [ {"title": "入驻与流程", "questions": ["如何申请入驻园区？", "园区入驻的条件有哪些？", "入驻需要准备哪些材料？", "入驻流程是怎样的？", "入驻评审如何打分？", "入驻需要多长时间？"]}, {"title": "政策与扶持", "questions": ["园区有哪些创业政策扶持？", "如何申请创业补贴？", "如何申请创业担保贷款？", "对高校毕业生有什么优惠？", "科技成果转化有哪些支持？"]}, @@ -726,27 +739,6 @@ async def vision_event(body: VisionEventBody): return {"ok": True} -@router.post("/api/vision/frame") -def vision_frame(body: VisionFrameBody): - img_b64 = (body.image or "").strip() - if not img_b64: - return {"ok": False, "error": "missing image"} - from .vision_yolo import predict_base64 - return predict_base64(img_b64) - - -@router.post("/api/vision/llm") -def vision_llm(body: VisionLlmBody): - img = (body.image or "").strip() - if not img: - return {"ok": False, "error": "missing image"} - try: - from .vision_llm import analyze_scene - return analyze_scene(img, body.prompt) - except Exception as e: # noqa: BLE001 - return {"ok": False, "error": str(e)} - - # ==================== 展示控制（管理端 → MQTT） ==================== # 全部 MQTT 前端控制命令白名单（与 park-desktop docs/mqtt-commands.md 一致） _VALID_DISPLAY_ACTIONS = { diff --git a/app/park/tenants.py b/app/park/tenants.py index 2627a70..6087e7b 100644 --- a/app/park/tenants.py +++ b/app/park/tenants.py @@ -7,6 +7,8 @@ sim_engine 需同步 tick，故另提供同步只读访问器 get_tenant_data_sy """ from __future__ import annotations +from datetime import datetime + import hashlib import json import secrets @@ -18,7 +20,7 @@ from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker from ..config import DATABASE_URL from ..infrastructure.db import make_async_engine -from ..infrastructure.models import ParkCompany, ParkKbDoc, ParkScreen, ParkTenant +from ..infrastructure.models import ParkCompany, ParkKbDoc, ParkScreen, ParkSetting, ParkTenant from .sim_engine import DEFAULT_DATA, refresh_engine _DEFAULT_TENANT_ID = "T001" @@ -445,7 +447,16 @@ async def set_device_code(device_id: str, code: str, expires: str) -> dict: async def find_device_by_code(code: str) -> dict | None: async with _get_session() as s: row = (await s.execute(select(ParkScreen).where(ParkScreen.code == code, ParkScreen.status == "unbound").limit(1))).scalars().first() - return _sc(row) if row else None + if row is None: + return None + # 连接码轮换/过期校验：过期码不可再绑定（保证当前展示的最新码才有效） + if row.code_expires: + try: + if datetime.fromisoformat(row.code_expires) < datetime.now(): + return None + except ValueError: + return None + return _sc(row) async def bind_device(tenant_id: str, device_id: str, name: str = "", role: str = "main", location: str = "") -> dict | None: @@ -522,3 +533,33 @@ async def update_screen(tenant_id: str, payload: dict) -> dict: await s.refresh(row) refresh_engine(tenant_id, d) return await screen_view(tenant_id) + + +# ---------------- 全局配置（park_settings kv） ---------------- + +async def get_setting(key: str, default: str = "") -> str: + async with _get_session() as s: + row = await s.get(ParkSetting, key) + return row.value if row else default + + +async def set_setting(key: str, value: str) -> None: + async with _get_session() as s: + row = await s.get(ParkSetting, key) + if row is None: + s.add(ParkSetting(key=key, value=value, updated_at=_now())) + else: + row.value = value + row.updated_at = _now() + await s.commit() + + +# ---------------- 视频识别（per-tenant，园区端控制，存 park_settings 命名键） ---------------- + +async def get_tenant_vision(tenant_id: str) -> str: + """本园区的视频识别服务地址（空=禁用）。""" + return await get_setting(f"vision_api:{tenant_id}", "") + + +async def set_tenant_vision(tenant_id: str, api: str) -> None: + await set_setting(f"vision_api:{tenant_id}", (api or "").strip().rstrip("/")) diff --git a/app/park/vision_llm.py b/app/park/vision_llm.py deleted file mode 100644 index 6d653f1..0000000 --- a/app/park/vision_llm.py +++ /dev/null @@ -1,83 +0,0 @@ -# -*- coding: utf-8 -*- -"""对话开场画面识别 —— 用多模态 LLM（qwen3-vl-flash）识别实时画面中的人数、性别等， -并把结果作为上下文注入对话，让 AI 了解当前在场观众。 - -与 vision_yolo（YOLO 数人脸/姿态）互补：这里用 LLM 做语义级理解（人数、性别构成、场景描述）。 -""" -import json -import logging -import ssl -import urllib.request - -import certifi - -from .config import settings - -log = logging.getLogger("dpm.vision") - -_SSL_CTX = ssl.create_default_context(cafile=certifi.where()) - -_DEFAULT_PROMPT = ( - "请识别这张实时画面，只输出一个 JSON 对象（不要输出任何其他文字）：" - '{"people": 画面中人数(int), "males": 其中男性人数(int), "females": 其中女性人数(int), ' - '"desc": 一句话中文描述画面（含大致人数、性别构成、人物大致状态，如年龄/坐站/是否看屏幕）。' - "若画面无人或不确定，则 people=0、males=0、females=0，desc='画面中暂时没有人'。" -) - - -def analyze_scene(jpeg_b64: str, prompt: str = "") -> dict: - """调用多模态 LLM 识别画面中人数/性别，返回 {ok, people, males, females, desc}。""" - payload = { - "model": settings.VISION_LLM_MODEL, - "messages": [{ - "role": "user", - "content": [ - {"type": "text", "text": prompt or _DEFAULT_PROMPT}, - {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{jpeg_b64}"}}, - ], - }], - } - req = urllib.request.Request( - f"{settings.LLM_BASE_URL}/chat/completions", - data=json.dumps(payload, ensure_ascii=False).encode("utf-8"), - headers={ - "Content-Type": "application/json", - "Authorization": f"Bearer {settings.DASHSCOPE_API_KEY}", - }, - ) - with urllib.request.urlopen(req, timeout=30, context=_SSL_CTX) as resp: - data = json.loads(resp.read().decode("utf-8")) - content = data["choices"][0]["message"].get("content") or "" - return _parse(content) - - -def _parse(text: str) -> dict: - """从模型输出中抽取 JSON 对象并规范化为结果 dict。""" - text = (text or "").strip() - if text.startswith("```"): - lines = [l for l in text.splitlines() if not l.strip().startswith("```")] - text = "\n".join(lines).strip() - obj: dict = {} - i, j = text.find("{"), text.rfind("}") - if i != -1 and j > i: - try: - obj = json.loads(text[i:j + 1]) - except Exception: # noqa: BLE001 - log.warning("vision llm: 返回 JSON 解析失败: %s", text[:120]) - people = _as_int(obj.get("people")) - males = _as_int(obj.get("males")) - females = _as_int(obj.get("females")) - return { - "ok": True, - "people": people, - "males": males, - "females": females, - "desc": str(obj.get("desc") or "").strip(), - } - - -def _as_int(v): - try: - return int(float(v)) - except (TypeError, ValueError): - return 0 diff --git a/app/park/vision_yolo.py b/app/park/vision_yolo.py deleted file mode 100644 index a33d0b0..0000000 --- a/app/park/vision_yolo.py +++ /dev/null @@ -1,243 +0,0 @@ -# -*- coding: utf-8 -*- -"""YOLO 人脸检测 + 姿态估计（后端推理） - -链路：前端摄像头抽帧 → JPEG base64 → POST /api/vision/frame → 本模块推理 → 返回人脸/姿态/举手特征。 -模型： - - backend/models/yolov8n-face.pt （lindevs/yolov8-face，WIDERFACE 训练，torch 6.3MB） - - backend/models/yolov8n-pose.pt （ultralytics 官方，COCO 17 关键点，torch 6.8MB） -推理：ultralytics 加载（torch 原生，自动 letterbox + NMS），CPU 设备。 - -手势判定分工： - - 后端只做"单帧几何特征"（举手 = 手腕高于肩），无状态 - - 前端做时序判定（举手持续 1.5s → 开始对话；举手后手腕摆动 → 挥手结束对话） -""" -import base64 -import io -import logging -import time -from pathlib import Path - -from PIL import Image - -from .config import settings - -log = logging.getLogger("dpm.vision") - -_frame_seq = 0 - -MODEL_DIR = Path(__file__).resolve().parent.parent / "models" -FACE_MODEL_PATH = MODEL_DIR / "yolov8n-face.pt" -POSE_MODEL_PATH = MODEL_DIR / "yolov8n-pose.pt" -CONF_THRESHOLD = 0.25 # 远距离小脸可调低（如 0.15） -IMGSZ = 640 - -# COCO 17 关键点索引 -KP_LEFT_SHOULDER = 5 -KP_RIGHT_SHOULDER = 6 -KP_LEFT_ELBOW = 7 -KP_RIGHT_ELBOW = 8 -KP_LEFT_WRIST = 9 -KP_RIGHT_WRIST = 10 -RAISE_LIFT = 0.06 # 举手：手腕高于对应肩 6% 画面高（降低阈值，更易稳定激活） -FIST_ELBOW_LIFT = 0.05 # 举拳：手腕高于肘 5% 画面高（前臂上举） -FIST_SHOULDER_GAP = 0.15 # 举拳：拳不高于肩 15% 画面高（收在胸前） -FIST_CHEST_DIST = 0.25 # 举拳：拳与肩水平距离 < 25% 画面宽（贴近躯干） -KP_CONF_MIN = 0.3 # 关键点置信度过滤 - -# ── 扩展手势几何阈值（基于 COCO 17 关键点） ── -POINT_REACH_X = 0.30 # 指向：手腕水平伸出距肩 ≥30% 画面宽（手臂向前/侧伸） -POINT_Y_RANGE = 0.28 # 指向：手腕与肩同高 ±28% 画面高（排除高举/下垂） -HANDS_CLOSE_DIST = 0.18 # 双手合十/靠近：双腕欧氏距离 <18% 画面宽 - -_face_model = None -_pose_model = None - - -def get_face_model(): - global _face_model - if _face_model is None: - from ultralytics import YOLO - log.info("vision: 加载 YOLO 人脸模型 %s（首次约 2-5s）", FACE_MODEL_PATH) - _face_model = YOLO(str(FACE_MODEL_PATH), task="detect") - log.info("vision: YOLO 人脸模型就绪") - return _face_model - - -def get_pose_model(): - global _pose_model - if _pose_model is None: - from ultralytics import YOLO - log.info("vision: 加载 YOLO 姿态模型 %s（首次约 2-5s）", POSE_MODEL_PATH) - _pose_model = YOLO(str(POSE_MODEL_PATH), task="pose") - log.info("vision: YOLO 姿态模型就绪") - return _pose_model - - -def _parse_pose(results, W, H): - """解析姿态结果 → persons + raised(举手) + fists(举拳) - + both_up(双臂举起) + pointing(指向) + hands_close(双手合十) - + hands(每手几何摘要，供前端挥手/手势跟随时序判定) - 注意：ultralytics keypoints.data 为【原图像素坐标】， - 此处统一归一化为 [0,1]（x/W, y/H）后再判定阈值与输出 - """ - persons = [] - raised = [] - fists = [] - pointing = [] - hands = [] - for r in results: - if r.keypoints is None: - continue - kps = r.keypoints.data # [N,17,3] 像素坐标 - for i in range(kps.shape[0]): - kp = kps[i] - person = [ - [round(float(kp[j][0]), 3), round(float(kp[j][1]), 3), round(float(kp[j][2]), 3)] - for j in range(17) - ] - persons.append(person) - # 每只手臂：举手（腕明显高于肩）或 举拳（前臂上举、拳收胸前），两者互斥 - arm = {} - for side, wrist_i, elbow_i, shoulder_i in ( - ("left", KP_LEFT_WRIST, KP_LEFT_ELBOW, KP_LEFT_SHOULDER), - ("right", KP_RIGHT_WRIST, KP_RIGHT_ELBOW, KP_RIGHT_SHOULDER), - ): - w = kp[wrist_i] - e = kp[elbow_i] - s = kp[shoulder_i] - if float(w[2]) < KP_CONF_MIN or float(s[2]) < KP_CONF_MIN or float(e[2]) < KP_CONF_MIN: - continue - # 像素 → 归一化 [0,1] - wx, wy = float(w[0]) / W, float(w[1]) / H - sx, sy = float(s[0]) / W, float(s[1]) / H - ey = float(e[1]) / H - is_raised = wy < sy - RAISE_LIFT - is_fist = ( - wy < ey - FIST_ELBOW_LIFT - and wy >= sy - FIST_SHOULDER_GAP - and abs(wx - sx) < FIST_CHEST_DIST - ) - if is_raised: - raised.append({"side": side, "x": round(wx, 4), "y": round(wy, 4)}) - elif is_fist: - fists.append({"side": side, "x": round(wx, 4), "y": round(wy, 4)}) - # 指向：手腕水平伸出距肩较远、且与肩同高区间（前伸/侧伸，排除高举与下垂） - if ( - abs(wx - sx) > POINT_REACH_X - and abs(wy - sy) < POINT_Y_RANGE - ): - pointing.append({"side": side, "x": round(wx, 4), "y": round(wy, 4)}) - arm[side] = { - "side": side, - "wx": round(wx, 4), "wy": round(wy, 4), - "sx": round(sx, 4), "sy": round(sy, 4), - "raised": is_raised, "fist": is_fist, - } - if arm: - hands.append(arm) - # 双臂举起：左、右腕都高于各自肩 - both_up = False - if len(hands) >= 1: - h0 = hands[0] - if "left" in h0 and "right" in h0: - both_up = h0["left"]["raised"] and h0["right"]["raised"] - # 双手合十/靠近：同一人的双腕欧氏距离 < 阈值 - hands_close = False - if len(hands) >= 1: - h0 = hands[0] - if "left" in h0 and "right" in h0: - dx = h0["left"]["wx"] - h0["right"]["wx"] - dy = h0["left"]["wy"] - h0["right"]["wy"] - hands_close = (dx * dx + dy * dy) ** 0.5 < HANDS_CLOSE_DIST - return persons, raised, fists, both_up, pointing, hands_close, hands - - -def predict_jpeg(jpeg_bytes: bytes): - """人脸检测 + 姿态估计 → {faces, boxes, pose, raised, fists, both_up, pointing, hands_close, hands, latency_ms}""" - t0 = time.time() - img = Image.open(io.BytesIO(jpeg_bytes)).convert("RGB") - - # 人脸 - face_res = get_face_model().predict(img, conf=CONF_THRESHOLD, imgsz=IMGSZ, verbose=False, device="cpu") - boxes = [] - for r in face_res: - if r.boxes is None: - continue - for b in r.boxes: - xyxy = [round(float(v), 1) for v in b.xyxy[0].tolist()] - conf = round(float(b.conf[0]), 3) - boxes.append({"box": xyxy, "conf": conf}) - - # 姿态 - W, H = img.size # 原图尺寸（关键点像素坐标 → 归一化基准） - pose_res = get_pose_model().predict(img, conf=CONF_THRESHOLD, imgsz=IMGSZ, verbose=False, device="cpu") - persons, raised, fists, both_up, pointing, hands_close, hands = _parse_pose(pose_res, W, H) - - latency_ms = round((time.time() - t0) * 1000, 1) - log.info( - "vision: 人脸 %d 姿态 %d 举手 %d 举拳 %d 指向 %d 合十 %s · %.0fms", - len(boxes), len(persons), len(raised), len(fists), len(pointing), hands_close, latency_ms, - ) - return { - "faces": len(boxes), - "boxes": boxes, - "pose": persons, - "raised": raised, - "fists": fists, - "both_up": both_up, - "pointing": pointing, - "hands_close": hands_close, - "hands": hands, - "latency_ms": latency_ms, - } - - -def _should_save(data: dict) -> bool: - """仅当画面检测到人脸/手势时才值得落盘保存。""" - return ( - int(data.get("faces") or 0) > 0 - or bool(data.get("raised")) - or bool(data.get("fists")) - or bool(data.get("both_up")) - or bool(data.get("pointing")) - or bool(data.get("hands_close")) - ) - - -def maybe_save_frame(jpeg: bytes, data: dict): - """仅将命中（有人/有动作）的 JPEG 帧落盘到 MEDIA_DIR/video/<小时目录>/，便于排查识别链路。 - 目录按小时分割（YYYYMMDD_HH），不自动清理。 - 可通过 DPM_SAVE_VISION_FRAMES=0 关闭，或 DPM_VISION_SAVE_DIR 改目录。""" - global _frame_seq - if not settings.SAVE_VISION_FRAMES: - return - if not _should_save(data): - return - _frame_seq += 1 - ts = time.strftime("%Y%m%d_%H%M%S") - hour = time.strftime("%Y%m%d_%H") - try: - save_dir = settings.VISION_FRAME_SAVE_DIR / hour - save_dir.mkdir(parents=True, exist_ok=True) - path = save_dir / f"{ts}_{_frame_seq:05d}.jpg" - with open(path, "wb") as f: - f.write(jpeg) - except Exception as e: # noqa: BLE001 - log.warning("vision: 保存帧失败 %s", e) - - -def predict_base64(b64: str): - """入口：base64 JPEG → 检测结果 dict（含 ok 标记，失败时带 error）""" - try: - jpeg = base64.b64decode(b64) - 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