69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
import functions_framework
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# from google.cloud.firestore_v1.base_query import FieldFilter
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# from firebase_admin import initialize_app, credentials, firestore
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import os
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from datetime import datetime
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import pickle
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# import pandas as pd
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@functions_framework.http
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def my_function(request):
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"""HTTP Cloud Function.
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Args:
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request (flask.Request): The request object.
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<https://flask.palletsprojects.com/en/1.1.x/api/#incoming-request-data>
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Returns:
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The response text, or any set of values that can be turned into a
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Response object using `make_response`
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<https://flask.palletsprojects.com/en/1.1.x/api/#flask.make_response>.
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"""
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if request.method == 'OPTIONS':
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headers = {
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'Access-Control-Allow-Origin': '*',
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'Access-Control-Allow-Methods': 'GET',
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'Access-Control-Allow-Headers': 'Content-Type',
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# 'Access-Control-Max-Age': '3600'
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}
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return ('', 204, headers)
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headers = {
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'Access-Control-Allow-Origin': '*'
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}
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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(input_data)
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print(predicted_result.item())
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# db = firestore.client()
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# source_collection = "testData"
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# # Get all documents from the source collection
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# source_docs = db.collection(source_collection).where(filter=FieldFilter("createdAt", ">=", datetime.today().strftime("%Y-%m-%d"))).stream()
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# count = 0
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# for doc in source_docs:
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# # Extract the document ID
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# doc_id = doc.id
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# print(doc_id)
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# # Get the document data
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# doc_data = doc.to_dict()
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# try:
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# # Delete the document from the source collection
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# db.collection(source_collection).document(doc_id).delete()
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# print(f"Document with ID '{doc_id}' updated")
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# count = count + 1
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# except Exception as e:
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# print(f"Error deleting document with ID '{doc_id}' from the source collection: {e}")
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return ('prediction applied: {}!'.format(0), 200, headers)
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