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""" |
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Obtain Predictions for Machine Failure Predictor Model using Gradio Client |
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====================================================================== |
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This script connects to a deployed machine failure predictor model using Gradio Client, |
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fetches the dataset, preprocesses the data, and generates predictions for a |
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sample of test data using the deployed model. The resulting predictions are |
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stored in a list. A time delay of one second is added after each prediction |
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submission to avoid overloading the model server. |
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""" |
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import time |
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from gradio_client import Client |
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from sklearn.datasets import fetch_openml |
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from sklearn.model_selection import train_test_split |
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client = Client("akdiwahar/testModel") |
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dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto") |
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data_df = dataset.data |
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target = 'Machine failure' |
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numeric_features = [ |
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'Air temperature [K]', |
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'Process temperature [K]', |
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'Rotational speed [rpm]', |
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'Torque [Nm]', |
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'Tool wear [min]' |
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] |
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categorical_features = ['Type'] |
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X = data_df[numeric_features + categorical_features] |
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y = data_df[target] |
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Xtrain, Xtest, ytrain, ytest = train_test_split( |
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X, y, |
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test_size=0.2, |
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random_state=42 |
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) |
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Xtest_sample = Xtest.sample(100) |
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Xtest_sample_rows = list(Xtest_sample.itertuples(index=False, name=None)) |
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batch_predictions = [] |
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for row in Xtest_sample_rows: |
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try: |
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job = client.submit( |
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air_temperature=row[0], |
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process_temperature=row[1], |
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rotational_speed=row[2], |
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torque=row[3], |
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tool_wear=row[4], |
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type=row[5], |
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api_name="/predict" |
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) |
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batch_predictions.append(job.result()) |
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time.sleep(1) |
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except Exception as e: |
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print(e) |