Upload app.py with huggingface_hub

app.py

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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
+
+# Try to load model from various sources
+model = None
+
+# Try local files first (for development)
+local_model_paths = ["saved_model", "best_model.h5", "final_model.h5"]
+for path in local_model_paths:
+    if os.path.exists(path):
+        try:
+            model = tf.keras.models.load_model(path, compile=False)
+            print(f"Loaded model from local path: {path}")
+            break
+        except Exception as e:
+            print(f"Failed to load local model from {path}: {e}")
+
+# If no local model, try to download from Hugging Face Hub
+if model is None:
+    HF_MODEL_ID = os.environ.get("HF_MODEL_ID", "Sharris/age_detection_regression")
+    try:
+        from huggingface_hub import hf_hub_download
+        # Try to download the .h5 model file
+        model_path = hf_hub_download(repo_id=HF_MODEL_ID, filename="best_model.h5")
+        model = tf.keras.models.load_model(model_path, compile=False)
+        print(f"Loaded model from HF Hub: {HF_MODEL_ID}/best_model.h5")
+    except Exception as e:
+        print(f"Failed to load model from HF Hub ({HF_MODEL_ID}): {e}")
+        # Fallback: try to download entire repo and load from there
+        try:
+            from huggingface_hub import snapshot_download
+            repo_dir = snapshot_download(repo_id=HF_MODEL_ID)
+            model_file = os.path.join(repo_dir, "best_model.h5")
+            if os.path.exists(model_file):
+                model = tf.keras.models.load_model(model_file, compile=False)
+                print(f"Loaded model from downloaded repo: {model_file}")
+        except Exception as e2:
+            print(f"Fallback download also failed: {e2}")
+
+if model is None:
+    raise RuntimeError(
+        "No model found. Ensure 'best_model.h5' exists locally or set HF_MODEL_ID env var to a Hugging Face model repo containing the model."
+    )
+
+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 {
+        "predicted_age": round(pred, 2),
+        "raw_output": 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)))