def find_intent()

in src/infer_intent.py [0:0]

• 17 lines of code
• 4 McCabe index (conditional complexity)

    def find_intent(self, sequence, verbose=False):
        inputs = self.tokenizer(sequence,
                                return_tensors="np",  # ONNX requires inputs in NumPy format
                                padding="max_length",  # Pad to max length
                                truncation=True,       # Truncate if the text is too long
                                max_length=64)

        # Convert inputs to NumPy arrays
        onnx_inputs = {k: v for k, v in inputs.items()}

        # Run the ONNX model
        logits = self.ort_session.run(None, onnx_inputs)[0]
        
        # Get the prediction
        prediction = np.argmax(logits, axis=1)[0]
        probabilities = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
        rounded_probabilities = np.round(probabilities, decimals=3)

        pred_result = self.id2label[prediction]
        proba_result = dict(zip(self.label2id.keys(), rounded_probabilities[0].tolist()))
        
        if verbose:
            print(sequence + " -> " + pred_result)
            print(proba_result, "\n")
        
        return pred_result, proba_result