| 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() | |