Delete app.py

app.py

@@ -1,57 +0,0 @@
-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.inputs.Image(type="numpy", label="Upload an MRI Image"),
-    outputs="text",
-    title="Alzheimer MRI Classification",
-    examples=examples
-)
-
-if __name__ == "__main__":
-    iface.launch()