import json
import mlflow
import pandas as pd
from pprint import pprint
from mlflow.tracking import MlflowClient

model_name = "s430705"
model_version = 50

mlflow.set_tracking_uri("http://172.17.0.1:5000")

model = mlflow.keras.pyfunc.load_model(
    model_uri=f"models:/{model_name}/{model_version}",
)

client = MlflowClient()

with open(client.get_model_version(model_name, model_version).source + '/input_example.json', 'r') as input_file:
    data = json.load(input_file)
    example_input = data["inputs"]

input_dictionary = {i: x for i, x in enumerate(example_input)}
input_ex = pd.DataFrame(input_dictionary, index=[0])
print("RESULT: ", model.predict(input_ex))