#!/usr/bin/env python3

import numpy as np
from PyRuntime import OMExecutionSession

# Load the model mnist.so compiled with onnx-mlir.
model = './mnist.so'
session = OMExecutionSession(model)
# Print the models input/output signature, for display.
# Signature functions for info only, commented out if they cause problems.
print("input signature in json", session.input_signature())
print("output signature in json",session.output_signature())
# Create an input arbitrarily filled of 1.0 values.
input = np.array([-0.4242129623889923, -0.4242129623889923,
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    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
    -0.4242129623889923, -0.4242129623889923], np.dtype(np.float32)).reshape(1,1,28,28)
# Run the model. It is best to always use the [] around the inputs as the inputs
# are an vector of numpy arrays.
outputs = session.run([input])
# Analyze the output (first array in the list, of signature 1x10xf32).
prediction = outputs[0]
digit = -1
prob = 0.0
for i in range(0, 10):
    print("prediction ", i, "=", prediction[0, i])
    if prediction[0, i] > prob:
        digit = i
        prob = prediction[0, i]
# Print the value with the highest prediction (8 here).
print("The digit is", digit)
