Upload 3 files

app.py

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+import torch
+import torch.nn as nn
+from torchvision import transforms, models
+from PIL import Image
+import gradio as gr
+
+# === Load trained model ===
+model = models.resnet18()
+in_features = model.fc.in_features
+model.fc = nn.Sequential(
+    nn.Linear(in_features, 256),
+    nn.ReLU(),
+    nn.Dropout(0.4),
+    nn.Linear(256, 2)
+)
+model.load_state_dict(torch.load("tumor_model.pth", map_location=torch.device("cpu")))
+model.eval()
+
+# === Transform (same as validation) ===
+transform = transforms.Compose([
+    transforms.Lambda(lambda x: x.convert('RGB')),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+# === Prediction Function ===
+def predict(image):
+    image = Image.fromarray(image)
+    input_tensor = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        _, pred = torch.max(outputs, 1)
+        prob = torch.softmax(outputs, dim=1)[0][pred.item()].item()
+        label = "Tumor: Yes" if pred.item() == 1 else "Tumor: No"
+        return f"{label} ({prob * 100:.2f}%)"
+
+# === Gradio Interface (No examples) ===
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Brain Scan"),
+    outputs=gr.Label(label="Prediction"),
+    title="🧠 Tumor Detection",
+    description="Upload a brain MRI image to detect if a tumor is present (Yes or No)."
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+torch
+torchvision
+gradio
+Pillow

tumor_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:547764ba3e5b6f4fe1187ec1a90438d2f6605de2b7d1aa9f3e5656f4d72c4dd1
+size 45313338