import json
import mlflow as mlf
import pandas as pd

model_name = "s430705"
model_version = 8
mlf.set_tracking_uri("http://172.17.0.1:5000")
mlf.keras.log_model("runs:/dd2657243e2c4176a2ec2e3340c3ff13/run-relative/path/to/model", registered_model_name="s430705")
model = mlf.keras.load_model(
    model_uri=f"models:/{model_name}/{model_version}"
)
with open('movies_imdb2/input_example.json', 'r') as datafile:
    data = json.load(datafile)['inputs']

input_dictionary = {idx:x for idx, x in enumerate(data)}
input_ex = pd.DataFrame([input_dictionary])
print(model.predict(input_ex))