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BTD_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:269506d46e3854e3561379a7d4eb977194e66430b781b36bc56fab481e224f17
+size 178115794

TumorModel.py

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+import torch.nn as nn
+
+class TumorClassification(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.Conv2d(1, 16, 3, 1, 1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Conv2d(16, 32, 3, 1, 1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Flatten(),
+            nn.Linear(32 * 56 * 56, 128),
+            nn.ReLU(),
+            nn.Linear(128, 4)
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+class GliomaStageModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.Linear(9, 128),
+            nn.ReLU(),
+            nn.Linear(128, 64),
+            nn.ReLU(),
+            nn.Linear(64, 4)
+        )
+
+    def forward(self, x):
+        return self.model(x)

app.py

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+import torch
+import gradio as gr
+from PIL import Image
+from torchvision import transforms
+from TumorModel import TumorClassification, GliomaStageModel
+
+# Load tumor classification model
+tumor_model = TumorClassification()
+tumor_model.load_state_dict(torch.load("BTD_model.pth", map_location=torch.device("cpu")))
+tumor_model.eval()
+
+# Load glioma stage model
+glioma_model = GliomaStageModel()
+glioma_model.load_state_dict(torch.load("glioma_stages.pth", map_location=torch.device("cpu")))
+glioma_model.eval()
+
+# Labels
+tumor_labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
+stage_labels = ['Stage 1', 'Stage 2', 'Stage 3', 'Stage 4']
+
+# Image transform
+transform = transforms.Compose([
+    transforms.Grayscale(),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5], std=[0.5])
+])
+
+# Tumor type prediction
+def predict_tumor(image):
+    image = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        out = tumor_model(image)
+        pred = torch.argmax(out, dim=1).item()
+        return tumor_labels[pred]
+
+# Glioma stage prediction
+def predict_stage(gender, age, idh1, tp53, atrx, pten, egfr, cic, pik3ca):
+    gender_val = 0 if gender == "Male" else 1
+    features = [gender_val, age, idh1, tp53, atrx, pten, egfr, cic, pik3ca]
+    x = torch.tensor(features).float().unsqueeze(0)
+    with torch.no_grad():
+        out = glioma_model(x)
+        pred = torch.argmax(out, dim=1).item()
+        return stage_labels[pred]
+
+# Interface 1: Tumor Detector
+tumor_tab = gr.Interface(
+    fn=predict_tumor,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    title="🧠 Brain Tumor Detection",
+    description="Upload an MRI image to classify tumor type: glioma, meningioma, notumor, or pituitary."
+)
+
+# Interface 2: Glioma Stage Prediction
+stage_tab = gr.Interface(
+    fn=predict_stage,
+    inputs=[
+        gr.Radio(["Male", "Female"], label="Gender"),
+        gr.Slider(0, 100, label="Age"),
+        gr.Slider(0, 1, step=1, label="IDH1 Mutation"),
+        gr.Slider(0, 1, step=1, label="TP53 Mutation"),
+        gr.Slider(0, 1, step=1, label="ATRX Mutation"),
+        gr.Slider(0, 1, step=1, label="PTEN Mutation"),
+        gr.Slider(0, 1, step=1, label="EGFR Mutation"),
+        gr.Slider(0, 1, step=1, label="CIC Mutation"),
+        gr.Slider(0, 1, step=1, label="PIK3CA Mutation")
+    ],
+    outputs=gr.Label(),
+    title="🧬 Glioma Stage Predictor",
+    description="Enter patient’s mutation and demographic data to predict glioma stage."
+)
+
+# Combine in tabs
+demo = gr.TabbedInterface([tumor_tab, stage_tab], tab_names=["Tumor Detector", "Glioma Stage"])
+
+# Run app
+demo.launch()

glioma_stages.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c64f506838505e6cfa74775ceed9260b03a978bf64243d123396393932cb1a2b
+size 33544

requirements.txt

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+torch
+torchvision
+gradio
+pillow