fix: coverage workflow

Signed-off-by: Kushal Batra <[email protected]>

.github/workflows/coverage.yml

@@ -31,7 +31,6 @@ jobs:
       run: |
         poetry env use ${{ matrix.python-version }}
         poetry install --all-extras --with dev
-        poetry run pip install --no-cache -r requirements/requirements-torch.txt
 
     - name: Run Coverage
       run: |

Makefile

@@ -17,7 +17,6 @@ clean:
 
 format: clean
 	poetry run black numaprom/ tests/
-	poetry run black starter.py
 
 lint: format
 	poetry run ruff check --fix .

README.md

@@ -1,55 +1,14 @@
 # numalogic-prometheus
 
 
-Numalogic-prometheus is a AIOps pipeline to do in cluster anomaly detection for any prometheus metrics. It is built on our streaming platform [numaflow](https://numaflow.numaproj.io/quick-start/#installation) using [numalogic](https://github.com/numaproj/numalogic) as the ML library.  
+Numalogic-prometheus is a ML metrics library for Prometheus. It provides a set of metrics to monitor your system in your Kubernetes applications.  
 
-By default, it provides anomaly detection for Argo CD and Argo Rollouts metrics, to identify issues before deploying/rolling out new changes. 
-
-It installs in a few minutes and is easier to onboard and configure any new metrics for anomaly detection. 
-
-## Prerequisites
-- [Numaflow](https://numaflow.numaproj.io/quick-start/#installation)
-- [Prometheus](docs/prometheus.md)
-
-Run the below command to install all the prerequisites.
+## Installation
 ```shell
-kustomize build manifests/prerequisites | kubectl apply -f -
-```
-
-## Quick Start
-
-Run the below command to install `numalogic-prometheus` and its dependencies on you cluster.
-
-```shell
-kustomize build manifests/ | kubectl apply -f - 
-```
-
-
-## Operations
-
-- To view Numaflow UX:
-```
-kubectl port-forward svc/numaflow-server 8443  -n numaflow-system
-```
-
-Here you can see all the pipelines running on the cluster
-
-- To view ML flow server:
-```
-kubectl port-forward svc/mlflow-service 5000 -n numalogic-prometheus
+poetry install
 ```
+It installs in a few minutes and is easier to onboard and configure any new metrics for anomaly detection. 
 
-Here you can see all the model runs, models saved, etc.
 
-- To view Prometheus server:
-```
-kubectl port-forward <prometheus-deployment-xxxxxxxxx-xxxxx> 8490:9090 -n monitoring
-```
 
 
-## Resources
-- [PROMETHEUS](docs/prometheus.md)
-- [METRICS](docs/metrics.md)
-- [MLFLOW_WITH_S3](docs/mlflow-s3.md)
-- [DEVELOPMENT](docs/development/development.md)
-- [CONTRIBUTING](https://github.com/numaproj/numaproj/blob/main/CONTRIBUTING.md)

numaprom/monitoring/metrics.py

@@ -1,5 +1,4 @@
 import logging
-from typing import Optional
 
 from prometheus_client import Counter, Info, Summary, Gauge, Histogram
 
@@ -22,8 +21,8 @@ def __init__(
         self,
         name: str,
         description: str,
-        static_label_pairs: Optional[dict[str, str]],
-        label_pairs: Optional[dict[str, str]],
+        static_label_pairs: dict[str, str] | None,
+        label_pairs: dict[str, str] | None,
     ) -> None:
         self.label_pairs = label_pairs
         self.static_label_pairs = dict(static_label_pairs)  # converting DictConfig to dict type