2023-11-13 07:37:20 +05:30
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def sample_classification(event, context):
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"""Triggered by a change to a Firestore document.
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Args:
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event (dict): Event payload.
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context (google.cloud.functions.Context): Metadata for the event.
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"""
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resource_string = context.resource
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# print out the resource string that triggered the function
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print(f"Function triggered by change to: {resource_string}.")
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# now print out the entire event object
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print(str(event))
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2023-12-08 21:44:55 +05:30
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with open('label_encoder.pkl', 'rb') as label_encoder_file:
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loaded_label_encoder = pickle.load(label_encoder_file)
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with open('gaussian_naive_bayes_model.pkl', 'rb') as model_file:
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loaded_model = pickle.load(model_file)
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input_data = pd.DataFrame({'calculatedRatio': 0.231057205, 'deviceRatio': 0.231057205,'led1Buffer': 23776.33, 'led2Buffer': 26401.67,
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'led1Sample': 16286, 'led2Sample': 6952.67}, index=[0])
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predicted_result = loaded_model.predict(new_data)
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print(predicted_result.item())
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