from pathlib import Path
import sys
import torch
from train import CNN

sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
import gpu_utils


class PredictResult:

    possibilities: torch.Tensor

    def __init__(self, possibilities: torch.Tensor):
        self.possibilities = possibilities

    def chosen_number(self) -> int:
        """获取最终选定的数字"""
        # 依然是找最大的那个index
        _, prediction = self.possibilities.max(1)
        return prediction.item()

    def number_possibilities(self) -> list[float]:
        """获取每个数字出现的概率"""
        return list(self.possibilities[0][i].item() for i in range(10))

class Predictor:
    device: torch.device
    cnn: CNN

    def __init__(self):
        self.device = gpu_utils.get_gpu_device()
        self.cnn = CNN().to(self.device)

        # 加载保存好的模型参数
        file_path = Path(__file__).resolve().parent.parent / 'models' / 'cnn.pth'
        self.cnn.load_state_dict(torch.load(file_path))

    def predict(self, image: list[list[bool]]) -> PredictResult:
        input = torch.Tensor(image).float().to(self.device)
        assert(input.dim() == 2)
        assert(input.size(0) == 28)
        assert(input.size(1) == 28)

        # 为了满足要求，要在第一维度挤出2下
        # 一次是灰度通道，一次是批次。
        # 相当于batch size = 1的计算
        input = input.unsqueeze(0).unsqueeze(0)
       
        # 预测
        with torch.no_grad():
            output = self.cnn(input)
            return PredictResult(output)