from typing import List

import torch
from pydantic import BaseModel

# 设备配置
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"

# 默认检测参数
DEFAULT_CONF = 0.25
DEFAULT_IOU = 0.5
DEFAULT_MIN_SIZE = 8
DEFAULT_POS_THRESH = 5

MODEL_CONFIGS = {
    "安全施工模型": {
        "model_path": "models/ppe_state_model/best.pt",
        "types": ["novest", "nohelmet"],
        "type_to_id": {"novest": 0, "nohelmet": 2},
        "params": {
            "enable_primary": True,
            "primary_conf": 0.55,
            "secondary_conf": 0.6,
            "final_conf": 0.65,
            "enable_multi_scale": True,
            "multi_scales": [0.75, 1.0, 1.25],
            "enable_secondary": True,
            "slice_size": 512,
            "overlap_ratio": 0.3,
            "weight_primary": 0.4,
            "weight_secondary": 0.6
        }
    },
    "烟雾火灾模型": {
        "model_path": "models/fire_smoke_model/best.pt",
        "types": ["fire", "smoke"],
        "type_to_id": {"fire": 0, "smoke": 1},
        "params": {
            "enable_primary": True,
            "primary_conf": 0.99,
            "secondary_conf": 0.99,
            "final_conf": 0.99,
            "enable_multi_scale": True,
            "multi_scales": [0.75, 1.0, 1.25],
            "enable_secondary": True,
            "slice_size": 512,
            "overlap_ratio": 0.3,
            "weight_primary": 0.4,
            "weight_secondary": 0.6
        }
    }
}

# SAHI自适应切片配置
SLICE_RULES = [
    (12_000_000, (384, 0.35)),
    (3_000_000, (512, 0.3)),
    (0, (640, 0.25))
]


class DetectionResponse(BaseModel):
    hasTarget: int
    originalImgSize: List[int]
    targets: List[dict]
    processing_errors: List[str] = []

# 智能体相关
OLLAMA_MODEL = "alibayram/Qwen3-30B-A3B-Instruct-2507:latest"  # 当前最强本地模型
OLLAMA_BASE_URL = "http://192.168.110.5:11434"

VLLM_BASE_URL = "http://192.168.110.5:8000/v1"
VLLM_MODEL_NAME = "/home/zrway/vllm_models/LLM/new"