import torch
import torch.nn as nn

# ================================
# 🧠 MODEL CLASSES
# ================================

class BrainTumorModel(nn.Module):
    def __init__(self):
        super(BrainTumorModel, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(32 * 56 * 56, 128),
            nn.ReLU(),
            nn.Linear(128, 4)  # 4 tumor classes
        )

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

class GliomaStageModel(nn.Module):
    def __init__(self):
        super(GliomaStageModel, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(32 * 56 * 56, 128),
            nn.ReLU(),
            nn.Linear(128, 4)  # 4 glioma stages
        )

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

# ================================
# 💡 PRECAUTIONS
# ================================

def get_precautions_from_gemini(tumor_type):
    precaution_db = {
        "meningioma": "Avoid radiation exposure and get regular check-ups.",
        "pituitary": "Monitor hormonal levels and follow medication strictly.",
        "notumor": "Stay healthy and get annual MRI scans if symptoms appear.",
        "glioma": "Maintain a healthy lifestyle and follow up with neuro-oncologist."
    }
    return precaution_db.get(tumor_type.lower(), "No specific precautions found.")