model.py

from torch import nn
import torch

input_size = 4
hidden_size = 64
output_size = 5


class SimpleNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x


def predict(model, input):
    model.eval()
    input_tensors = torch.tensor(input, dtype=torch.float32).unsqueeze(0)

    with torch.no_grad():
        output = model(input_tensors)

    probabilities = torch.softmax(output, dim=1)

    predicted_class_index = torch.argmax(probabilities, dim=1).item()
    return predicted_class_index