import torch
import torchvision
from torchvision.models import EfficientNet_B2_Weights
from torch import nn

def create_model(num_classes=7):
    weights = EfficientNet_B2_Weights.DEFAULT
    model = torchvision.models.efficientnet_b2(weights=weights)

    for param in model.parameters():
        param.requires_grad = False  # Freeze for inference

    model.classifier = nn.Sequential(
        nn.Dropout(p=0.3),
        nn.Linear(model.classifier[1].in_features, num_classes)
    )

    return model

def load_model(weights_path="model/effnetb2_dermamnist.pth"):
    model = create_model(num_classes=7)
    model.load_state_dict(torch.load(weights_path, map_location=torch.device("cpu")))
    model.eval()
    return model