app.py

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

# ------------------- Model Definition -------------------
class SimpleCNN(nn.Module):
    def __init__(self, num_classes=1):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(128 * 28 * 28, 512)
        self.fc2 = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = x.view(-1, 128 * 28 * 28)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# ------------------- Load Model -------------------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SimpleCNN()
model.load_state_dict(torch.load("age_prediction_model1.pth", map_location=device))
model.to(device)
model.eval()

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

# ------------------- Prediction Function -------------------
def predict(image):
    image = transform(image).unsqueeze(0).to(device)
    with torch.no_grad():
        output = model(image).squeeze().item()
    return f"Predicted Age: {round(output, 2)} years"

# ------------------- Gradio Interface -------------------
iface = gr.Interface(fn=predict, 
                     inputs=gr.Image(type="pil"), 
                     outputs="text",
                     title="Face Age Prediction",
                     description="Upload a face image and get a predicted age")

iface.launch()