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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()
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