CRITICAL FIX: Correct preprocessing normalization mismatch - Replace ResNet50 preprocess_input with simple /255.0 normalization - Fixes negative age predictions

app.py

@@ -2,7 +2,7 @@ import os
 import numpy as np
 from PIL import Image
 import tensorflow as tf
-from tensorflow.keras.applications.resnet50 import preprocess_input
+# Remove ResNet50 preprocessing - using simple normalization instead
 import gradio as gr
 
 # Load model from Hugging Face Hub
@@ -69,7 +69,8 @@ def predict_age(image: Image.Image):
         image = image.convert('RGB')
     image = image.resize(INPUT_SIZE)
     arr = np.array(image).astype(np.float32)
-    arr = preprocess_input(arr)
+    # Use same normalization as training: [0,1] range instead of ResNet50 preprocessing
+    arr = arr / 255.0
     arr = np.expand_dims(arr, 0)
 
     pred = model.predict(arr)[0]