Add custom handler for UFC predictor

handler.py

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+import tensorflow as tf
+import numpy as np
+from typing import Dict, Any
+
+
+class CustomLSTM(tf.keras.layers.LSTM):
+    def __init__(self, *args, **kwargs):
+        kwargs.pop('time_major', None)
+        super().__init__(*args, **kwargs)
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # Load your Keras model
+        tf.keras.utils.get_custom_objects()['LSTM'] = CustomLSTM
+        self.model = tf.keras.models.load_model(f"{path}/tf_model.h5")
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Custom inference handler for UFC predictions.
+
+        Args:
+            data: A dictionary containing the input data. 
+                  Expected keys: "fighter1" and "fighter2".
+
+        Returns:
+            A dictionary with prediction results.
+        """
+        fighter1 = data.get("fighter1")
+        fighter2 = data.get("fighter2")
+
+        # Validate inputs
+        if not fighter1 or not fighter2:
+            return {"error": "Both 'fighter1' and 'fighter2' must be provided."}
+
+        # Prepare input for the model (replace with your logic)
+        input_data = np.random.rand(1, 200, 89)  # Replace with real pre-processing logic
+
+        # Get predictions
+        prediction = self.model.predict(input_data)
+        return {
+            "fighter1_win_probability": float(prediction[0][0]),
+            "fighter2_win_probability": float(1 - prediction[0][0]),
+        }

requirements.txt

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+tensorflow
+numpy