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94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
import sys
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import pandas as pd
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import requests
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from ludwig.datasets import titanic
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# Ludwig model server default values
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LUDWIG_HOST = "0.0.0.0"
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LUDWIG_PORT = "8000"
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#
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# retrieve data to make predictions
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#
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test_df = titanic.load()
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print(f"retrieved {test_df.shape[0]:d} records for predictions")
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#
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# execute REST API /predict for a single record
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#
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# get a single record from dataframe and convert to list of dictionaries
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prediction_request_dict_list = test_df.head(1).to_dict(orient="records")
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# extract dictionary for the single record only
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prediction_request_dict = prediction_request_dict_list[0]
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print("single record for prediction:\n", prediction_request_dict)
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# construct URL
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predict_url = "".join(["http://", LUDWIG_HOST, ":", LUDWIG_PORT, "/predict"])
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print("\ninvoking REST API /predict for single record...")
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# connect using the default host address and port number
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try:
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response = requests.post(predict_url, data=prediction_request_dict)
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except requests.exceptions.ConnectionError as e:
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print(e)
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print("REST API /predict failed")
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sys.exit(1)
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# check if REST API worked
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if response.status_code == 200:
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# REST API successful
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# convert JSON response to panda dataframe
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pred_df = pd.read_json("[" + response.text + "]", orient="records")
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print(f"\nReceived {pred_df.shape[0]:d} predictions")
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print("Sample predictions:")
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print(pred_df.head())
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else:
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# Error encountered during REST API processing
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print("\nError during predictions, error code: ", response.status_code, "reason code: ", response.text)
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#
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# execute REST API /batch_predict on a pandas dataframe
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#
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# create json representation of dataset for REST API
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prediction_request_json = test_df.to_json(orient="split")
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print("\ninvoking REST API /batch_predict for entire dataframe...")
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# construct URL
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batch_predict_url = "".join(["http://", LUDWIG_HOST, ":", LUDWIG_PORT, "/batch_predict"])
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# connect using the default host address and port number
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response = requests.post(batch_predict_url, data={"dataset": prediction_request_json})
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try:
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response = requests.post(batch_predict_url, data={"dataset": prediction_request_json})
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except requests.exceptions.ConnectionError as e:
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print(e)
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print("REST API /batch_predict failed")
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sys.exit(1)
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# check if REST API worked
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if response.status_code == 200:
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# REST API successful
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# convert JSON response to panda dataframe
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pred_df = pd.read_json(response.text, orient="split")
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print(f"\nReceived {pred_df.shape[0]:d} predictions")
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print("Sample predictions:")
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print(pred_df.head())
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else:
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# Error encountered during REST API processing
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print("\nError during predictions, error code: ", response.status_code, "reason code: ", response.text)
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