cfc/cfc-langgraph/app/agents/multimodal_agent.py

from typing import Optional
import logging

from src.graphs.tongue import get_tongue_graph

logger = logging.getLogger(__name__)


class TongueDiagnosisAgent:
    """舌诊分析 Agent — 调用 LangGraph 舌诊 graph（glm-5 视觉模型）"""

    async def diagnose(
        self,
        image_url: str,
        user_id: int,
        additional_context: Optional[dict] = None,
        prompt_template: Optional[str] = None,
    ) -> dict:
        return await self._via_llm(image_url, prompt_template=prompt_template)

    async def _via_llm(self, image_url: str, prompt_template: Optional[str] = None) -> dict:
        try:
            graph = get_tongue_graph()
            result = graph.invoke({
                "request": {"image_url": image_url, "prompt_template": prompt_template or ""},
                "image_base64": None,
                "raw_response": "",
                "overall_assessment": "",
                "indicators": [],
                "error": None,
            })
            if result.get("error"):
                logger.warning("舌诊 graph 失败: %s", result["error"])
                return self._mock_result()
            return {
                "overall_assessment": result["overall_assessment"],
                "indicators": result["indicators"],
            }
        except Exception as e:
            logger.warning("舌诊 graph 执行异常: %s", e)
            return self._mock_result()

    def _mock_result(self) -> dict:
        return {
            "overall_assessment": "舌象基本正常, 舌质淡红, 苔薄白, 提示脾胃功能尚可。",
            "indicators": [
                {"code": "tongue_color", "value": "淡红"},
                {"code": "coating_color", "value": "薄白"},
                {"code": "coating_texture", "value": "润"},
                {"code": "fissure", "value": "无"},
                {"code": "teeth_mark", "value": "轻"},
                {"code": "sublingual_vein", "value": "正常"},
                {"code": "constitution", "value": "平和质"},
            ],
        }