app.py

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

# -------- MODEL DEFINITION --------
class ImprovedCNN(nn.Module):
    def __init__(self):
        super(ImprovedCNN, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2),
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(128 * 16 * 16, 512),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(512, 1)
        )

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        return x

# -------- LOAD MODEL --------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
model = ImprovedCNN().to(device)

model_path = "age_prediction_model3.pth"
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
print(f"✅ Model loaded from {model_path}")

# -------- IMAGE PREPROCESSING --------
transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# -------- PREDICTION FUNCTION --------
def predict_age(image: Image.Image) -> float:
    image_tensor = transform(image).unsqueeze(0).to(device)
    with torch.no_grad():
        output = model(image_tensor)
        age = output.item()
    return round(age, 2)

# -------- GRADIO UI --------
demo = gr.Interface(
    fn=predict_age,
    inputs=gr.Image(type="pil", image_mode="RGB", label="Upload Face Image"),
    outputs=gr.Number(label="Predicted Age"),
    title="Face Age Prediction",
    description="Upload a face image to predict age using a CNN model."
)

# -------- LAUNCH --------
if __name__ == "__main__":
    demo.launch()