diff --git a/cloud-functions/python/functions/prediction/main.py b/cloud-functions/python/functions/prediction/main.py
new file mode 100644
index 0000000..7b399b2
--- /dev/null
+++ b/cloud-functions/python/functions/prediction/main.py
@@ -0,0 +1,87 @@
+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
+from flask import jsonify
+
+# initialize_app()
+
+@functions_framework.http
+def prediction(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': 'POST',
+            'Access-Control-Allow-Headers': 'Content-Type',
+            # 'Access-Control-Max-Age': '3600'
+        }
+
+        return ('', 204, headers)
+
+    headers = {
+        'Access-Control-Allow-Origin': '*'
+    }
+
+    request_json = request.get_json(silent=True)
+    if request_json and 'calculatedRatio' in request_json and 'deviceRatio' in request_json:
+        calculated_ratio = request_json['calculatedRatio']
+        device_ratio = request_json['deviceRatio']
+        led1_buffer = request_json['led1Buffer']
+        led2_buffer = request_json['led2Buffer']
+        led1_sample = request_json['led1Sample']
+        led2_sample = request_json['led2Sample']
+    else:
+        calculated_ratio = 0.231057205
+        device_ratio = 0.231057205
+        led1_buffer = 23776.33
+        led2_buffer = 26401.67
+        led1_sample = 16286
+        led2_sample = 6952.67
+
+    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': calculated_ratio, 'deviceRatio': device_ratio,'led1Buffer': led1_buffer, 'led2Buffer': led2_buffer, 
+      'led1Sample': led1_sample, 'led2Sample': led2_sample}, 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 (jsonify({"predictedClass": predicted_result.item()}), 200, headers)