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.resnet50 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-resnet50-model")
+
+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 = (256, 256)
+
+
+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)))