language/models.py

import torch
import torch.nn as nn


# ===============================
# Soil Model (matches training)
# ===============================

class SoilModel(nn.Module):
    def __init__(self, input_size=8, num_classes=10):
        super().__init__()

        self.num_classes = num_classes

        self.net = nn.Sequential(
            nn.Linear(input_size, 64),
            nn.ReLU(),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, num_classes)
        )

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


# ===============================
# Vision Model (matches training CNN)
# ===============================

class VisionModel(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()

        self.num_classes = num_classes

        self.conv = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(32, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(64, 128, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )

        self.fc = nn.Sequential(
            nn.Linear(128 * 16 * 16, 256),
            nn.ReLU(),
            nn.Linear(256, num_classes)
        )

    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        return self.fc(x)