cfc/cfc-langgraph/app/api/tongue.py

from fastapi import APIRouter, UploadFile, File, Form
from typing import Optional
from app.agents.multimodal_agent import TongueDiagnosisAgent
import logging

logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/v1", tags=["tongue"])

_agent: Optional[TongueDiagnosisAgent] = None


def get_agent() -> TongueDiagnosisAgent:
    global _agent
    if _agent is None:
        _agent = TongueDiagnosisAgent()
    return _agent


@router.post("/tongue/diagnose")
async def tongue_diagnose(
    file: UploadFile = File(...),
    user_id: int = Form(...),
    prompt_template: Optional[str] = Form(None),
):
    """舌诊分析: 上传舌苔图片, 返回分析结果"""
    agent = get_agent()

    import tempfile, os
    ext = os.path.splitext(file.filename or "tongue.jpg")[1] or ".jpg"
    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=ext)
    content = await file.read()
    tmp.write(content)
    tmp.close()

    try:
        import base64
        b64 = base64.b64encode(content).decode()
        data_url = f"data:image/{ext[1:]};base64,{b64}"

        result = await agent.diagnose(image_url=data_url, user_id=user_id, prompt_template=prompt_template)
        return {"code": 200, "data": result}
    except Exception as e:
        logger.error("舌诊分析失败: %s", e, exc_info=True)
        return {"code": 500, "message": "舌诊分析失败"}
    finally:
        os.unlink(tmp.name)


# ── 舌象快速分类（纯颜色启发式，<50ms）────────────────────────────
def _quick_tongue_detect(content: bytes) -> bool:
    """基于颜色分布的轻量级舌象分类，避免调用 LLM。

    判据：
    1. 全图中偏红/暖色像素占比 >= 30%（舌体是红/粉色）
    2. 白色/近白像素占比 < 65%（排除大面积白纸报告）
    3. 纯黑文字像素占比 < 25%（排除文档/报告截图）
    """
    from PIL import Image
    import io

    try:
        img = Image.open(io.BytesIO(content)).convert("RGB")
        small = img.resize((200, 200), Image.LANCZOS)
        pixels = list(small.getdata())
        n = len(pixels)

        reddish = warm = white_px = black_px = 0
        for r, g, b in pixels:
            if r > g * 1.15 and r > b * 1.15:
                reddish += 1
            if r > g and r > b and (r - b) > 15:
                warm += 1
            if r > 200 and g > 200 and b > 200:
                white_px += 1
            if r < 80 and g < 80 and b < 80:
                black_px += 1

        red_ratio = reddish / n
        warm_ratio = warm / n
        white_ratio = white_px / n
        text_ratio = black_px / n

        # 舌象：≥30% 偏红 + 白色占比不高 + 文字占比不高
        is_tongue = (red_ratio >= 0.30) and (white_ratio < 0.65) and (text_ratio < 0.25)
        logger.debug(
            "舌象分类 [red=%.3f warm=%.3f white=%.3f text=%.3f] → %s",
            red_ratio, warm_ratio, white_ratio, text_ratio, is_tongue,
        )
        return is_tongue
    except Exception as e:
        logger.warning("舌象颜色分析失败，降级为 false: %s", e)
        return False


@router.post("/tongue/detect")
async def tongue_detect(file: UploadFile = File(...)):
    """舌象照片分类: 判断上传图片是否为舌头（舌象）照片，用于图片上传分流。

    返回 {"is_tongue": bool}。非舌象（报告截图/文档/其他部位）返回 false。
    分类失败时返回 false（安全降级：走通用报告流程，不误伤正常报告）。
    """
    content = await file.read()
    try:
        is_tongue = _quick_tongue_detect(content)
        logger.info("舌象分类结果 is_tongue=%s (filename=%s)", is_tongue, file.filename)
        return {"code": 200, "data": {"is_tongue": is_tongue}}
    except Exception as e:
        logger.error("舌象分类失败: %s", e, exc_info=True)
        return {"code": 200, "data": {"is_tongue": False}}