Create app.py

app.py

@@ -0,0 +1,68 @@
+# app.py
+import io
+import os
+from typing import List
+import gradio as gr
+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.transforms as T
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class AgeGenderClassifier(nn.Module):
+def __init__(self):
+super(AgeGenderClassifier, self).__init__()
+
+self.intermediate = nn.Sequential(
+nn.Linear(2048, 512),
+nn.ReLU(),
+nn.Dropout(0.4),
+nn.Linear(512, 128),
+nn.ReLU(),
+nn.Dropout(0.4),
+nn.Linear(128, 64),
+nn.ReLU(),
+)
+self.age_classifier = nn.Sequential(
+nn.Linear(64, 1),
+nn.Sigmoid()
+)
+self.gender_classifier = nn.Sequential(
+nn.Linear(64, 1),
+nn.Sigmoid()
+)
+
+
+def forward(self, x):
+x = self.intermediate(x)
+age = self.age_classifier(x)
+gender = self.gender_classifier(x)
+return age, gender
+
+
+
+
+def build_model(weights_path: str):
+"""Rebuild VGG16 backbone + custom avgpool/classifier then load weights."""
+backbone = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1)
+
+for p in backbone.parameters():
+p.requires_grad = False
+
+
+
+for p in backbone.features[24:].parameters():
+p.requires_grad = True
+
+
+backbone.avgpool = nn.Sequential(
+nn.Conv2d(512, 512, kernel_size=3),
+nn.MaxPool2d(2),
+nn.ReLU(),
+nn.Flatten()
+demo.launch()