diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..1880ba8 --- /dev/null +++ b/.env.example @@ -0,0 +1,12 @@ +# Grafana Cloud OTLP credentials +# See https://grafana.com/docs/grafana-cloud/send-data/otlp/send-data-otlp/ +# Note that the metrics, traces and logs endpoints need /v1/metrics, /v1/traces and /v1/logs +# to be appended to the GRAFANA_OTLP_ENDPOINT in order to work. +# You get the following error +# Transient error StatusCode.UNAVAILABLE encountered while exporting metrics to otlp-gateway-prod-us-west-0.grafana.net, retrying in 1s. +# other iwse +GRAFANA_OTLP_USERNAME = ':" | base64 -w0` +GRAFANA_OTLP_API_ENCODED_TOKEN = '' +GRAFANA_OTLP_ENDPOINT = "=1,<2)", "astroid (>=2,<4)"] test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"] +[[package]] +name = "backoff" +version = "2.2.1" +description = "Function decoration for backoff and retry" +optional = false +python-versions = ">=3.7,<4.0" +files = [ + {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"}, + {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"}, +] + [[package]] name = "black" version = "23.12.1" @@ -64,6 +75,17 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"] jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"] uvloop = ["uvloop (>=0.15.2)"] +[[package]] +name = "certifi" +version = "2023.11.17" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, + {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, +] + [[package]] name = "cfgv" version = "3.4.0" @@ -75,6 +97,105 @@ files = [ {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"}, ] +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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"4.5.5.62" face-recognition = "1.3.0" pyusb = "^1.2.1" setuptools = "^68.2.2" -prometheus-client = "^0.19.0" opencv-contrib-python = "4.5.5.62" +python-dotenv = "^1.0.0" +opentelemetry-api = "^1.22.0" +opentelemetry-sdk = "^1.22.0" +opentelemetry-exporter-otlp = "^1.22.0" [tool.poetry.group.dev.dependencies] black = "^23.3.0" diff --git a/src/pygptcourse/camera_manager.py b/src/pygptcourse/camera_manager.py index 0393b8d..56b78af 100644 --- a/src/pygptcourse/camera_manager.py +++ b/src/pygptcourse/camera_manager.py @@ -1,4 +1,4 @@ -import cv2 +import cv2 # type: ignore class CameraManager: diff --git a/src/pygptcourse/credentials.py b/src/pygptcourse/credentials.py new file mode 100644 index 0000000..04ba641 --- /dev/null +++ b/src/pygptcourse/credentials.py @@ -0,0 +1,26 @@ +# credentials.py + +import base64 +import os + +from dotenv import load_dotenv + + +class OpenTelemetryCredentials: + def __init__(self): + load_dotenv() # Load environment variables from .env file + + self.username = os.getenv("GRAFANA_OTLP_USERNAME", "fake_user") + print(f"Grafana OTLP username is: {self.username}") + self.api_token = os.getenv("GRAFANA_OTLP_API_TOKEN", "fake_token") + self.api_encoded_token = base64.b64encode( + f"{self.username}:{self.api_token}".encode("utf-8") + ).decode("utf-8") + self.endpoint = os.getenv("GRAFANA_OTLP_ENDPOINT", "https://fake_endpoint") + self.trace_endpoint = self.endpoint + "/v1/traces" + self.metrics_endpoint = self.endpoint + "/v1/metrics" + self.logs_endpoint = self.endpoint + "/v1/logs" + + def is_configured(self): + # Check if all the necessary variables are present + return all([self.username, self.api_token, self.endpoint]) diff --git a/src/pygptcourse/face_detector.py b/src/pygptcourse/face_detector.py index ef39e32..ca005db 100644 --- a/src/pygptcourse/face_detector.py +++ b/src/pygptcourse/face_detector.py @@ -1,5 +1,7 @@ import face_recognition # type: ignore +from pygptcourse.otel_decorators import otel_handler + class FaceDetector: def __init__(self, face_images, image_loader): @@ -30,5 +32,6 @@ def detect_faces(self, image): name = list(self.face_encodings.keys())[first_match_index] face_names.append(name) + otel_handler.faces_detected_count.add(1, {"name": name}) return face_locations, face_names diff --git a/src/pygptcourse/main.py b/src/pygptcourse/main.py index 64abab0..4c4fa27 100755 --- a/src/pygptcourse/main.py +++ b/src/pygptcourse/main.py @@ -5,22 +5,18 @@ import cv2 # type: ignore # isort: off -from prometheus_client import Summary, start_http_server - from pygptcourse.camera_control import CameraControl from pygptcourse.camera_manager import CameraManager from pygptcourse.face_detector import FaceDetector from pygptcourse.file_system_image_loader import FileSystemImageLoader +from pygptcourse.otel_decorators import otel_handler # isort: on + # the above is required because the local isort adds a new line while default GHA (Github Actions) # adds a new line -# Create a metric to track time spent and requests made. -REQUEST_TIME = Summary("face_detection_seconds", "Time spent detecting faces") - - -@REQUEST_TIME.time() +@otel_handler.trace def detect_faces(face_detector, frame): return face_detector.detect_faces(frame) @@ -207,6 +203,4 @@ def main(): if __name__ == "__main__": - # Start up the server to expose the metrics. - start_http_server(port=18000, addr="0.0.0.0") main() diff --git a/src/pygptcourse/otel_decorators.py b/src/pygptcourse/otel_decorators.py new file mode 100644 index 0000000..82df78c --- /dev/null +++ b/src/pygptcourse/otel_decorators.py @@ -0,0 +1,76 @@ +# ot_decorator.py + +from functools import wraps + +from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter +from opentelemetry.metrics import get_meter_provider, set_meter_provider +from opentelemetry.sdk.metrics import MeterProvider +from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader +from opentelemetry.sdk.resources import SERVICE_NAME, Resource + +from pygptcourse.credentials import OpenTelemetryCredentials + + +class OpenTelemetryHandler: + def __init__(self): + VERSION = "0.1.2" + self.creds = OpenTelemetryCredentials() + self.enabled = self.creds.is_configured() + service_name = "TShirtLauncherControl" + self.resource = Resource.create({SERVICE_NAME: service_name}) + self.otlp_metrics_exporter = OTLPMetricExporter( + endpoint=f"{self.creds.metrics_endpoint}", + headers={ + "authorization": f"Basic {self.creds.api_encoded_token}", + }, + ) + self.metric_reader = PeriodicExportingMetricReader( + exporter=self.otlp_metrics_exporter, + export_interval_millis=10000, + export_timeout_millis=2000, + ) + self.meter_provider = MeterProvider( + resource=self.resource, metric_readers=[self.metric_reader] + ) + set_meter_provider(self.meter_provider) + + self.meter = get_meter_provider().get_meter(service_name, VERSION) + + # Metric definitions + self.usb_failures = self.meter.create_counter( + "usb_connection_failures", + description="Count of USB connection failures", + unit="int", + ) + self.launch_count = self.meter.create_counter( + "launch_count", description="Total number of launches", unit="int" + ) + self.faces_detected_count = self.meter.create_counter( + "faces_detected", + description="Total number of faces detected", + unit="int", + ) + + def trace(self, func): + @wraps(func) + def wrapper(*args, **kwargs): + if self.enabled: + # If OTLP is enabled, do something before the function (e.g., start a span) + # print(f"Starting OpenTelemetry span for {func.__name__}") + + # Execute the function + result = func(*args, **kwargs) + + # Do something after the function (e.g., end the span) + # print(f"Ending OpenTelemetry span for {func.__name__}") + + return result + else: + # If OTLP is not enabled, just execute the function + return func(*args, **kwargs) + + return wrapper + + +# Global instance of the handler +otel_handler = OpenTelemetryHandler() diff --git a/tests/test_unit_otel.py b/tests/test_unit_otel.py new file mode 100644 index 0000000..48cd688 --- /dev/null +++ b/tests/test_unit_otel.py @@ -0,0 +1,94 @@ +import os +import unittest +from unittest.mock import MagicMock, Mock, patch + +from pygptcourse.face_detector import FaceDetector +from pygptcourse.otel_decorators import OpenTelemetryHandler, otel_handler + + +class TestOpenTelemetry(unittest.TestCase): + def setUp(self): + # Mocking environment variables typically found in .env file + self.env_vars = { + "GRAFANA_OTLP_USERNAME": "example_username", + "GRAFANA_OTLP_API_TOKEN": "example_token", + "GRAFANA_OTLP_ENDPOINT": "https://example.com/endpoint", + } + self.mock_exporter = MagicMock() + + def test_otel_configuration(self): + # Mocking the environment variables for the test + with patch.dict(os.environ, self.env_vars): + handler = OpenTelemetryHandler() + self.assertIsNotNone(handler.meter) + # Asserting that the credentials are loaded correctly from the environment + self.assertEqual(handler.creds.username, "example_username") + self.assertEqual(handler.creds.api_token, "example_token") + self.assertEqual(handler.creds.endpoint, "https://example.com/endpoint") + + @patch("opentelemetry.sdk.metrics.export.PeriodicExportingMetricReader") + @patch("opentelemetry.exporter.otlp.proto.http.metric_exporter.OTLPMetricExporter") + def test_otel_export_with_error(self, mock_exporter, mock_reader): + # Configure the mock exporter to raise an exception when exporting + mock_exporter.return_value.export.side_effect = Exception("Export failed") + # Assuming a realistic way to trigger the metric increment + otel_handler.faces_detected_count.add(1, {"name": "Test"}) + try: + mock_reader.return_value.force_flush() + except Exception as e: + self.assertIsInstance(e, Exception) + self.assertEqual(str(e), "Export failed") + + def test_decorator_functionality(self): + expected_result = "expected result" + + @otel_handler.trace + def function_to_test(): + return expected_result + + result = function_to_test() + self.assertEqual(result, expected_result) + + def test_error_handling(self): + with self.assertRaises(Exception): + raise Exception("Simulated realistic failure") + + +class TestFaceDetector(unittest.TestCase): + @patch("face_recognition.compare_faces", return_value=[True, False]) + @patch("face_recognition.face_encodings") + @patch("face_recognition.face_locations") + @patch("face_recognition.load_image_file") + @patch( + "pygptcourse.otel_decorators.otel_handler.faces_detected_count.add" + ) # replace with the actual module name + def test_detect_faces( + self, + mock_otel_handler_add, + mock_load_image_file, + mock_face_locations, + mock_face_encodings, + mock_compare_faces, + ): + # Arrange + mock_image_loader = Mock() + mock_image_loader.get_full_image_path.return_value = "full_image_path" + face_images = {"test": "image_path"} + detector = FaceDetector(face_images, mock_image_loader) + + mock_image = Mock() + mock_load_image_file.return_value = mock_image + mock_face_locations.return_value = ["location"] + mock_face_encodings.return_value = [ + [0.1] * 128 + ] # A list of a single face encoding + + # Act + detector.detect_faces(mock_image) + + # Assert + mock_otel_handler_add.assert_called_once_with(1, {"name": "test"}) + + +if __name__ == "__main__": + unittest.main()