app.py

import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image
import gradio as gr

class HybridModel(nn.Module):
    def __init__(self, backbones, num_classes):
        super(HybridModel, self).__init__()
        self.models = nn.ModuleList()
        self.out_features = 0

        for name in backbones:
            if name == "ResNet152":
                model = models.resnet152(weights=None)
                in_features = model.fc.in_features
                model.fc = nn.Identity()
            elif name == "DenseNet201":
                model = models.densenet201(weights=None)
                in_features = model.classifier.in_features
                model.classifier = nn.Identity()
            else:
                raise ValueError(f"Backbone {name} not supported.")

            self.models.append(model)
            self.out_features += in_features

        # ✅ Match checkpoint: 3968 → 1024 → 4
        self.classifier = nn.Sequential(
            nn.Linear(self.out_features, 1024),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(1024, num_classes)
        )

    def forward(self, x):
        features = [m(x) for m in self.models]
        combined = torch.cat(features, dim=1)
        return self.classifier(combined)


# --------- Load Model ----------
MODEL_PATH = "ResNet152_DenseNet201_best.pt"
device = torch.device("cpu")  # force CPU

num_classes = 4
backbones = ["ResNet152", "DenseNet201"]
model = HybridModel(backbones, num_classes)

state_dict = torch.load(MODEL_PATH, map_location=device)
model.load_state_dict(state_dict)
model.to(device)
model.eval()

# --------- Define Preprocessing ----------
transform = transforms.Compose([
    transforms.Resize((224, 224)),  
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], 
                         [0.229, 0.224, 0.225])  
])

# Class labels
class_names = ['No Impairment', 'Very Mild Impairment', 'Moderate Impairment', 'Mild Impairment']

# --------- Prediction Function ----------
def predict(image):
    image = transform(image).unsqueeze(0).to(device)
    with torch.no_grad():
        outputs = model(image)
        _, predicted = torch.max(outputs, 1)
    return class_names[predicted.item()]

# --------- Gradio Interface ----------
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload MRI Scan"),
    outputs=gr.Label(num_top_classes=4, label="Predicted Alzheimer’s Stage"),
    title="Alzheimer’s MRI Classifier",
    description="Upload an MRI brain scan to classify into one of four stages of Alzheimer's disease."
)

if __name__ == "__main__":
    iface.launch()