inference-schema

from inference_schema.schema_decorators import input_schema, output_schema from inference_schema.parameter_types import PandasParameterType import pandas as pd import json # Define sample input and output data structures # These samples are used to infer the schema for validation and serialization sample_input_df = pd.DataFrame({'feature1': [10.0, 20.0], 'feature2': [30.0, 40.0]}) sample_output_dict = {'prediction': [40.0, 60.0]} @input_schema(PandasParameterType(sample_input_df)) @output_schema(sample_output_dict) def predict(input_data: pd.DataFrame) -> dict: """ A dummy prediction function that takes a DataFrame and returns a dictionary. The decorators handle validation of `input_data` and serialization of the return value. """ # Example prediction logic: sum of features predictions = (input_data['feature1'] + input_data['feature2']).tolist() return {'prediction': predictions} # --- Example Usage --- # This is how you'd typically call it, with input that matches the schema input_for_prediction = pd.DataFrame({'feature1': [5.0, 15.0], 'feature2': [25.0, 35.0]}) result = predict(input_for_prediction) print(f"Predicted result: {result}") # If used in a web service, the input might come as JSON and be deserialized # and validated into a DataFrame before reaching `predict` function. # Example: raw_json_input = '{"feature1": [5.0, 15.0], "feature2": [25.0, 35.0]}' # (framework would parse, inference-schema would validate/convert)