infereence script

inference.py

@@ -0,0 +1,36 @@
+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
+)
+
+result = client.predict(
+		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(result)