import gradio as gr
from surrogate import CrabNetSurrogateModel

model = CrabNetSurrogateModel()

example_parameterization = parameterization = {
    "N": 3,
    "alpha": 0.5,
    "d_model": 512,
    "dim_feedforward": 2048,
    "dropout": 0.1,
    "emb_scaler": 1.0,
    "epochs_step": 10,
    "eps": 0.000001,
    "fudge": 0.02,
    "heads": 4,
    "k": 6,
    "lr": 0.001,
    "pe_resolution": 5000,
    "ple_resolution": 5000,
    "pos_scaler": 1.0,
    "weight_decay": 0,
    "batch_size": 32,
    "out_hidden4": 128,
    "betas2": 0.9,
    "betas1": 0.999,
    "losscurve": False,
    "learningcurve": False,
    "bias": False,
    "criterion": "RobustL1",
    "elem_prop": "mat2vec",
    "train_frac": 0.5,
}

model.surrogate_evaluate(example_parameterization)


def greet(name):
    return "Hello " + name + "!!"


iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()