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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.")
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