Upload 3 files

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+
+# Load the trained model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Define model
+model = models.resnet50(weights=None)
+in_features = model.fc.in_features
+model.fc = nn.Sequential(
+    nn.Linear(in_features, 512),
+    nn.ReLU(),
+    nn.Dropout(0.4),
+    nn.Linear(512, 47)  # 47 classes
+)
+model.load_state_dict(torch.load("cancer_model.pth", map_location=device))
+model.to(device)
+model.eval()
+
+# Label mapping (update this with actual class names if available)
+class_names = [f"Class {i}" for i in range(47)]
+
+# Transforms (same as validation)
+transform = transforms.Compose([
+    transforms.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+    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(img):
+    img = transform(img).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model(img)
+        probs = torch.softmax(outputs, dim=1)
+        confidences, predicted = torch.max(probs, 1)
+    return {class_names[i]: float(probs[0][i]) for i in range(len(class_names))}
+
+# Gradio UI
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=5),
+    title="Multi-Cancer Classifier",
+    description="Upload a histopathology or cancer-related image. The model will predict its cancer type (47 classes)."
+).launch()

cancer_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df9ce7117323af782c866933372c1f24af027c32e374ee63409c866f4ec9d8a7
+size 98644160

requirements.txt

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+torch>=2.0
+torchvision
+gradio>=4.0
+Pillow