Deploy to Hugging Face Spaces: Fix model loading and add documentation

- Fix model_utils.py: Use self.model_path instead of model_path parameter
- Fix AnomalyDetector: Return all required fields (threshold, top_drivers)
- Update test_api.py: Add support for Hugging Face Spaces URL
- Add comprehensive documentation (TESTING.md, SPACE_INFO.md)
- Update README.md with Hugging Face Spaces frontmatter

model_utils.py

@@ -266,8 +266,42 @@ class AnomalyDetector:
         
         if os.path.exists(self.model_path):
             from tensorflow import keras
-            self.model = keras.models.load_model(self.model_path)
-            print(f"Loaded autoencoder from {self.model_path}")
+            import warnings
+            warnings.filterwarnings('ignore', category=UserWarning)
+            
+            try:
+                # Try loading with compile=False first (for inference only)
+                self.model = keras.models.load_model(self.model_path, compile=False)
+                print(f"Loaded autoencoder from {self.model_path}")
+            except Exception as e:
+                # If that fails, try with safe_mode=False (for Keras 3.x compatibility)
+                try:
+                    # Check if safe_mode parameter exists (Keras 3.x)
+                    import inspect
+                    load_model_sig = inspect.signature(keras.models.load_model)
+                    if 'safe_mode' in load_model_sig.parameters:
+                        self.model = keras.models.load_model(
+                            self.model_path, 
+                            compile=False,
+                            safe_mode=False
+                        )
+                        print(f"Loaded autoencoder from {self.model_path} (with safe_mode=False)")
+                    else:
+                        raise e
+                except Exception as e2:
+                    # Last resort: try using tf.keras instead of keras
+                    try:
+                        import tensorflow as tf
+                        self.model = tf.keras.models.load_model(
+                            self.model_path,
+                            compile=False
+                        )
+                        print(f"Loaded autoencoder from {self.model_path} (using tf.keras)")
+                    except Exception as e3:
+                        print(f"Error loading model. This might be a version compatibility issue.")
+                        print(f"Error details: {str(e3)[:200]}")
+                        print(f"Please ensure TensorFlow version matches the training environment.")
+                        self.model = None
         else:
             self.model = None
             print(f"Warning: Model not found at {self.model_path}")

requirements.txt

@@ -4,7 +4,7 @@ pydantic
 numpy
 scikit-learn
 xgboost
-tensorflow
+tensorflow>=2.13.0,<3.0.0
 shap
 joblib
 python-multipart