from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
import torch, torch.nn as nn, torch.optim as optim

DATA_DIR = "data_soil_visibility"
MODEL_PATH = "soil_visibility_classifier.pt"

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

dataset = datasets.ImageFolder(DATA_DIR, transform=transform)
dataloader = DataLoader(dataset, batch_size=16, shuffle=True)

model = models.resnet18(pretrained=True)
model.fc = nn.Linear(model.fc.in_features, 1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    model.train()
    for inputs, labels in dataloader:
        inputs, labels = inputs.to(device), labels.float().unsqueeze(1).to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

torch.save(model, MODEL_PATH)
print(f"✅ Saved to {MODEL_PATH}")