| from sklearn.datasets import fetch_openml | |
| from sklearn.model_selection import train_test_split | |
| from gradio_client import Client | |
| client = Client("pgurazada1/diamond-price-predictor") | |
| dataset = fetch_openml(data_id=43355, as_frame=True, parser='auto') | |
| diamond_prices = dataset.data | |
| target = ['price'] | |
| numeric_features = ['carat'] | |
| categorical_features = ['shape', 'cut', 'color', 'clarity', 'report', 'type'] | |
| X = diamond_prices.drop(columns=target) | |
| y = diamond_prices[target] | |
| Xtrain, Xtest, ytrain, ytest = train_test_split( | |
| X, y, | |
| test_size=0.2, | |
| random_state=42 | |
| ) | |
| job = client.submit( | |
| 3, # float in 'Carat' Number component | |
| "Round", # Literal['Round', 'Princess', 'Emerald', 'Asscher', 'Cushion', 'Radiant', 'Oval', 'Pear', 'Marquise'] in 'Shape' Dropdown component | |
| "Ideal", # Literal['Ideal', 'Premium', 'Very Good', 'Good', 'Fair'] in 'Cut' Dropdown component | |
| "D", # Literal['D', 'E', 'F', 'G', 'H', 'I', 'J'] in 'Color' Dropdown component | |
| "IF", # Literal['IF', 'VVS1', 'VVS2', 'VS1', 'VS2', 'SI1', 'SI2', 'I1'] in 'Clarity' Dropdown component | |
| "GIA", # Literal['GIA', 'IGI', 'HRD', 'AGS'] in 'Report' Dropdown component | |
| "Natural", # Literal['Natural', 'Lab Grown'] in 'Type' Dropdown component | |
| api_name="/predict" | |
| ) | |
| print(job.result()) |