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import os
import numpy as np
from PIL import Image
import tensorflow as tf
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
import gradio as gr
# Load model from Hugging Face Hub
model = None
HF_MODEL_ID = os.environ.get("HF_MODEL_ID", "Sharris/age_detection_regression")
print(f"Attempting to load model from: {HF_MODEL_ID}")
try:
from huggingface_hub import hf_hub_download
print("Downloading best_model.h5...")
model_path = hf_hub_download(repo_id=HF_MODEL_ID, filename="best_model.h5")
print(f"Model downloaded to: {model_path}")
print("Loading model with TensorFlow...")
model = tf.keras.models.load_model(model_path, compile=False)
print(f"✅ Successfully loaded model from {HF_MODEL_ID}")
except Exception as e:
print(f"❌ Failed to download best_model.h5: {e}")
# Fallback: try to download entire repo and look for model files
try:
print("Trying fallback: downloading entire repository...")
from huggingface_hub import snapshot_download
repo_dir = snapshot_download(repo_id=HF_MODEL_ID)
print(f"Repository downloaded to: {repo_dir}")
# Look for model files in the downloaded repo
possible_files = ["best_model.h5", "final_model.h5", "model.h5"]
for filename in possible_files:
model_file = os.path.join(repo_dir, filename)
if os.path.exists(model_file):
print(f"Found model file: {model_file}")
try:
model = tf.keras.models.load_model(model_file, compile=False)
print(f"✅ Successfully loaded model from {model_file}")
break
except Exception as load_error:
print(f"Failed to load {model_file}: {load_error}")
continue
if model is None:
# List all files in the repo for debugging
import os
print("Files in downloaded repository:")
for root, dirs, files in os.walk(repo_dir):
for file in files:
print(f" {os.path.join(root, file)}")
except Exception as e2:
print(f"❌ Fallback download also failed: {e2}")
if model is None:
raise RuntimeError(
f"❌ Could not load model from {HF_MODEL_ID}. Please ensure the repository contains a valid model file (best_model.h5, final_model.h5, or model.h5)."
)
INPUT_SIZE = (224, 224)
def predict_age(image: Image.Image):
if image.mode != 'RGB':
image = image.convert('RGB')
image = image.resize(INPUT_SIZE)
arr = np.array(image).astype(np.float32)
arr = preprocess_input(arr)
arr = np.expand_dims(arr, 0)
pred = model.predict(arr)[0]
# Ensure scalar
if hasattr(pred, '__len__'):
pred = float(np.asarray(pred).squeeze())
else:
pred = float(pred)
return round(pred, 2), float(pred)
demo = gr.Interface(
fn=predict_age,
inputs=gr.Image(type='pil', label='Face image (crop to face for best results)'),
outputs=[
gr.Number(label='Predicted age (years)'),
gr.Number(label='Raw model output')
],
examples=[],
title='UTKFace Age Estimator',
description='Upload a cropped face image and the model will predict age in years. For Spaces, set the HF_MODEL_ID environment variable to your Hugging Face model repo if you want the app to download a SavedModel from the Hub.'
)
if __name__ == '__main__':
demo.launch(server_name='0.0.0.0', server_port=int(os.environ.get('PORT', 7860)))
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