model_hugging_face.py

from transformers import AutoConfig, AutoModel
from transformers import PreTrainedModel, PretrainedConfig
import torch.nn as nn
import torch

class ArchitectureConfig(PretrainedConfig):
    model_type = "architecture"

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.input_size = kwargs.get("input_size", 9)
        self.hidden_size_1 = kwargs.get("hidden_size_1", 9)
        self.hidden_size_2 = kwargs.get("hidden_size_2", 9)
        self.hidden_size_3 = kwargs.get("hidden_size_3", 9)
        self.hidden_size_4 = kwargs.get("hidden_size_4", 9)
        self.hidden_size_5 = kwargs.get("hidden_size_5", 9)
        self.hidden_size_6 = kwargs.get("hidden_size_6", 9)
        self.hidden_size_7 = kwargs.get("hidden_size_7", 9)
        self.output_size = kwargs.get("output_size", 9)

class Architecture(PreTrainedModel):
    config_class = ArchitectureConfig

    def __init__(self, config: ArchitectureConfig):
        super().__init__(config)
        self.input_size = config.input_size
        self.hidden_size_1 = config.hidden_size_1
        self.hidden_size_2 = config.hidden_size_2
        self.hidden_size_3 = config.hidden_size_3
        self.hidden_size_4 = config.hidden_size_4
        self.hidden_size_5 = config.hidden_size_5
        self.hidden_size_6 = config.hidden_size_6
        self.hidden_size_7 = config.hidden_size_7
        self.output_size = config.output_size

        self.fc1 = nn.Linear(self.input_size, self.hidden_size_1)
        self.fc2 = nn.Linear(self.hidden_size_1, self.hidden_size_2)
        self.fc3 = nn.Linear(self.hidden_size_2, self.hidden_size_3)
        self.fc4 = nn.Linear(self.hidden_size_3, self.hidden_size_4)
        self.fc5 = nn.Linear(self.hidden_size_4, self.hidden_size_5)
        self.fc6 = nn.Linear(self.hidden_size_5, self.hidden_size_6)
        self.fc7 = nn.Linear(self.hidden_size_6, self.hidden_size_7)
        self.fc8 = nn.Linear(self.hidden_size_7, self.output_size)

        self.relu = nn.ReLU()

    def forward(self, x):
        x1 = self.relu(self.fc1(x))
        x2 = self.relu(self.fc2(x1))
        x3 = self.relu(self.fc3(x2))
        x4 = self.relu(self.fc4(x3))
        x5 = self.relu(self.fc5(x4))
        x6 = self.relu(self.fc6(x5))
        x7 = self.relu(self.fc7(x6))
        x8 = self.fc8(x7)
        return x8

    def inference(self, x):
        return self.forward(x)

# Loading the model from saved weights
def load_model():
    AutoConfig.register("architecture", ArchitectureConfig)
    AutoModel.register(ArchitectureConfig, Architecture)
    config = ArchitectureConfig()
    model = Architecture(config)
    model.load_state_dict(torch.load('./model_weights.pth'))
    return model

load_model()