import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download

class SimpleCNN(nn.Module):
    def __init__(self, model_type='c', num_classes=4):
        super(SimpleCNN, self).__init__()
        self.num_classes = num_classes
        if model_type == 'f':
            self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
            self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
            self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
            self.fc1 = nn.Linear(64 * 28 * 28, 256)
            self.dropout = nn.Dropout(0.5)
        elif model_type == 'c':
            self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
            self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
            self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
            self.fc1 = nn.Linear(128 * 28 * 28, 512)
            self.dropout = nn.Dropout(0.5)
        elif model_type == 'q':
            self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
            self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
            self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
            self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
            self.fc1 = nn.Linear(512 * 14 * 14, 1024)
            self.dropout = nn.Dropout(0.3)
        self.fc2 = nn.Linear(self.fc1.out_features, num_classes)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = self.pool(self.relu(self.conv3(x)))
        if hasattr(self, 'conv4'):
            x = self.pool(self.relu(self.conv4(x)))
        x = x.view(x.size(0), -1)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x


def load_model(version='c', device='cpu'):
    model_type = version.lower()
    filename = f"Vbai-TS 1.0{model_type}.pt"

    weights_path = hf_hub_download(
        repo_id="Neurazum/Vbai-TS-1.0",
        filename=filename,
        repo_type="model"
    )

    model = SimpleCNN(model_type=model_type, num_classes=6).to(device)
    state_dict = torch.load(weights_path, map_location=device)
    model.load_state_dict(state_dict, strict=False)
    model.eval()

    return model