hpos-data/cloud-functions/python/functions/main.py

import functions_framework
# from google.cloud.firestore_v1.base_query import FieldFilter
# from firebase_admin import initialize_app, credentials, firestore
import os
from datetime import datetime
import pickle
# import pandas as pd

@functions_framework.http
def my_function(request):
    """HTTP Cloud Function.
    Args:
        request (flask.Request): The request object.
        <https://flask.palletsprojects.com/en/1.1.x/api/#incoming-request-data>
    Returns:
        The response text, or any set of values that can be turned into a
        Response object using `make_response`
        <https://flask.palletsprojects.com/en/1.1.x/api/#flask.make_response>.
    """
    if request.method == 'OPTIONS':
        headers = {
            'Access-Control-Allow-Origin': '*',
            'Access-Control-Allow-Methods': 'GET',
            'Access-Control-Allow-Headers': 'Content-Type',
            # 'Access-Control-Max-Age': '3600'
        }

        return ('', 204, headers)

    headers = {
        'Access-Control-Allow-Origin': '*'
    }

    with open('label_encoder.pkl', 'rb') as label_encoder_file:
      loaded_label_encoder = pickle.load(label_encoder_file)

    with open('gaussian_naive_bayes_model.pkl', 'rb') as model_file:
      loaded_model = pickle.load(model_file)

    input_data = pd.DataFrame({'calculatedRatio': 0.231057205, 'deviceRatio': 0.231057205,'led1Buffer': 23776.33, 'led2Buffer': 26401.67, 
      'led1Sample': 16286, 'led2Sample': 6952.67}, index=[0])

    predicted_result = loaded_model.predict(input_data)
    print(predicted_result.item())
    
#     db = firestore.client()
#     source_collection = "testData"

    # # Get all documents from the source collection
    # source_docs = db.collection(source_collection).where(filter=FieldFilter("createdAt", ">=", datetime.today().strftime("%Y-%m-%d"))).stream()
    # count = 0
    # for doc in source_docs:
    #     # Extract the document ID
    #     doc_id = doc.id
    #     print(doc_id)

    #     # Get the document data
    #     doc_data = doc.to_dict()

    #     try:
    #         # Delete the document from the source collection
    #         db.collection(source_collection).document(doc_id).delete()
    #         print(f"Document with ID '{doc_id}' updated")
    #         count = count + 1
    #     except Exception as e:
    #         print(f"Error deleting document with ID '{doc_id}' from the source collection: {e}")
    
    return ('prediction applied: {}!'.format(0), 200, headers)