model.py

import torch
import torch.nn as nn
from transformers import AutoModel
from config import (
    HIDDEN_SIZE,
    DROPOUT_PROB,
    LAST_NUM_NEURON,
    HF_REPO_NAME,
    WEIGHTS_FILE_NAME,
    PRETRAINED_MODEL,
)
from huggingface_hub import hf_hub_download


class EnergySmellsDetector(nn.Module):
    def __init__(self, model_name):
        super(EnergySmellsDetector, self).__init__()
        self.model = AutoModel.from_pretrained(model_name)
        self.dropout = nn.Dropout(DROPOUT_PROB)
        self.fc = nn.Linear(HIDDEN_SIZE, LAST_NUM_NEURON)

    def forward(self, input_ids, attention_mask):
        outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
        x = self.dropout(outputs.pooler_output)
        logits = self.fc(x)
        return torch.sigmoid(logits).to(float)

    @staticmethod
    def load_model_from_hf():
        model_path = hf_hub_download(repo_id=HF_REPO_NAME, filename=WEIGHTS_FILE_NAME)

        # Load model
        model = EnergySmellsDetector(PRETRAINED_MODEL)
        model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))

        return model