app.py

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 = [
    ["image.jpg"],
    ["image (1).jpg"],
    ["image (2).jpg"],
    ["image (3).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()