import os
import warnings

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
warnings.filterwarnings("ignore", message=".*tf.lite.Interpreter is deprecated.*")

import numpy as np
import tensorflow as tf

sample = np.array(
    [[0.15, 0.75, 0.35, 0.45], [0.85, 0.25, 0.65, 0.75]],
    dtype=np.float32,
)

interpreter = tf.lite.Interpreter(model_path="support_score.tflite")
input_details = interpreter.get_input_details()[0]
interpreter.resize_tensor_input(input_details["index"], sample.shape)
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()[0]
output_details = interpreter.get_output_details()[0]

interpreter.set_tensor(input_details["index"], sample)
interpreter.invoke()
predictions = interpreter.get_tensor(output_details["index"])

print(f"input_name={input_details['name']}")
print(f"input_shape={input_details['shape_signature'].tolist()}")
print(f"output_name={output_details['name']}")
print(f"output_shape={output_details['shape_signature'].tolist()}")
print(f"prediction_shape={list(predictions.shape)}")
print(f"prediction_range={predictions.min():.6f}..{predictions.max():.6f}")