Create app.py

app.py

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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()