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app.py

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+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from PIL import Image
+from torchvision import models
+import gradio as gr
+
+# Define transformations (must be the same as those used during training)
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Load the model architecture and weights
+model = models.resnet50(weights=None)  # Initialize model without pretrained weights
+model.fc = nn.Linear(model.fc.in_features, 4)  # Adjust final layer for 4 classes
+
+# Load the state dictionary with map_location for CPU
+model.load_state_dict(torch.load("alzheimer_model_resnet50.pth", map_location=torch.device('cpu')))
+model.eval()  # Set model to evaluation mode
+
+# Define class labels (must match the dataset used during training)
+class_labels = ["Mild_Demented 0", "Moderate_Demented 1", "Non_Demented 2", "Very_Mild_Demented 3"] # Replace with your class names
+
+# Define the prediction function
+def predict(image):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image.astype('uint8'), 'RGB')
+    else:
+        image = Image.open(image).convert("RGB")
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted = torch.max(outputs.data, 1)
+        label = class_labels[predicted.item()]
+    return label
+
+# Create a Gradio interface with examples
+examples = [
+    ["0.jpg"],
+    ["f1.jpg"],
+    ["image.jpg"]
+]
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload an MRI Image"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Alzheimer MRI Classification",
+    examples=examples
+)
+
+if __name__ == "__main__":
+    iface.launch()