fix for cluster manager

.envrc

@@ -1 +0,0 @@
-use nix

.gitea/workflows/backend_build-deploy-prod.yaml

@@ -3,9 +3,6 @@ on:
     tags:
       - v*
 
-permissions:
-  pull-requests: write
-
 name: Build and deploy the backend to production
 
 jobs:
@@ -13,7 +10,15 @@ jobs:
     name: Build and push image
     uses: ./.gitea/workflows/workflow_build-image.yaml
     with:
-      # sets the tag to the git tag that triggered the workflow - the deployment (configured in a separate repository) will use this tag and be deployed to production by argocd
-      tag: ${{ github.ref_name }}
+      tag: stable
     secrets:
       PACKAGE_REGISTRY_ACCESS: ${{ secrets.PACKAGE_REGISTRY_ACCESS }}
+  
+  deploy-prod:
+    name: Deploy to production
+    uses: ./.gitea/workflows/workflow_deploy-container.yaml
+    with:
+      overlay: prod
+    secrets:
+      KUBE_CONFIG: ${{ secrets.KUBE_CONFIG }}
+    needs: build-and-push

.gitea/workflows/backend_build-deploy-stg.yaml

@@ -12,32 +12,15 @@ jobs:
     name: Build and push image
     uses: ./.gitea/workflows/workflow_build-image.yaml
     with:
-      # sets a unique tag for each commit in the PR - this gets deployed to a separate application instance using argocd
-      tag: sha${{ github.sha }}
+      tag: unstable
     secrets:
       PACKAGE_REGISTRY_ACCESS: ${{ secrets.PACKAGE_REGISTRY_ACCESS }}
-
-  notify:
-    runs-on: ubuntu-latest
-    name: Add a comment to the PR to notify about the deployment
-    steps:
-      - name: Download gitea client
-        run: |
-          curl -sSL -o tea https://dl.gitea.com/tea/0.11.0/tea-0.11.0-linux-amd64
-          chmod +x tea
-
-      - name: Login
-        run: |
-          ./tea login add --url git.kluster.moll.re --name bot --token ${{ secrets.GITEA_TOKEN }}
-          ./tea login default
-      - name: Post comment
-        run: |
-          ./tea comment --repo anydev/anyway --login bot ${{ github.event.number }} """
-          The backend has been deployed to staging with url https://pr-${{ github.event.number }}.anyway-stg.anydev.info. Check the deployment status in ArgoCD:
-
-          [![App Status](https://argocd.kluster.moll.re/api/badge?name=anydev-anyway-backend-stg-pr-${{ github.event.number }}&revision=true&showAppName=true)](https://argocd.kluster.moll.re/applications/anydev-anyway-backend-stg-pr-${{ github.event.number }})
-          """
-        env:
-          GITEA_TOKEN: ${{ secrets.GITEA_TOKEN }}
-          GITEA_BASE_URL: ${{ secrets.GITEA_BASE_URL }}
-          GITEA_REPO: ${{ secrets.GITEA_REPO }}
+  
+  deploy-prod:
+    name: Deploy to staging
+    uses: ./.gitea/workflows/workflow_deploy-container.yaml
+    with:
+      overlay: stg
+    secrets:
+      KUBE_CONFIG: ${{ secrets.KUBE_CONFIG }}
+    needs: build-and-push

.gitea/workflows/workflow_deploy-container.yaml

@@ -0,0 +1,35 @@
+on:
+  workflow_call:
+    inputs:
+      overlay:
+        required: true
+        type: string
+    secrets:
+      KUBE_CONFIG:
+        required: true
+
+
+name: Deploy the newly built container
+
+
+jobs:
+  deploy:
+    name: Deploy
+    runs-on: ubuntu-latest
+    steps:
+
+    - uses: https://gitea.com/actions/checkout@v4
+      with:
+        submodules: true
+
+    - name: setup kubectl
+      uses: https://github.com/azure/setup-kubectl@v4
+
+    - name: Set kubeconfig
+      run: |
+        echo "${{ secrets.KUBE_CONFIG }}" > kubeconfig
+
+    - name: Deploy to k8s
+      run: |
+        kubectl apply -k backend/deployment/overlays/${{ inputs.overlay }} --kubeconfig=kubeconfig
+        kubectl -n anyway-backend rollout restart deployment/anyway-backend-${{ inputs.overlay }} --kubeconfig=kubeconfig

.gitignore

@@ -1,2 +1 @@
 cache/
-.direnv/

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "backend/deployment"]
+	path = backend/deployment
+	url = https://git.kluster.moll.re/anydev/anyway-backend-deployment

.vscode/launch.json

@@ -9,7 +9,9 @@
             "name": "Backend - debug",
             "type": "debugpy",
             "request": "launch",
-            "envFile": "${workspaceFolder}/backend/debug.env",
+            "env": {
+                "DEBUG": "true"
+            },
             "jinja": true,
             "cwd": "${workspaceFolder}/backend",
             "module": "fastapi",
@@ -23,7 +25,9 @@
             "type": "debugpy",
             "request": "launch",
             "program": "src/tester.py",
-            "envFile": "${workspaceFolder}/backend/debug.env",
+            "env": {
+                "DEBUG": "true"
+            },
             "cwd": "${workspaceFolder}/backend"
         },
         // frontend - flutter app

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "nixEnvSelector.nixFile": "${workspaceFolder}/default.nix"
-}

backend/.gitignore

@@ -1,6 +1,3 @@
-# all .env files
-*.env
-
 # osm-cache
 cache_XML/
 
@@ -168,4 +165,4 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+#.idea/

backend/src/landmarks/cluster_manager.py

@@ -102,52 +102,57 @@ class ClusterManager:
             selector = sel,
             out = out
         )
-        except Exception as e:
-            self.logger.warning(f"Error fetching clusters: {e}")
-
-        if result is None :
-            self.logger.debug(f"Found no {cluster_type} clusters, overpass query returned no datapoints.")
-            self.valid = False
-
-        else :
-            points = []
-            for elem in result:
-                osm_type = elem.get('type')
-
-                # Get coordinates and append them to the points list
-                _, coords = get_base_info(elem, osm_type)
-                if coords is not None :
-                    points.append(coords)
-
-            if points :
-                self.all_points = np.array(points)
-
-                # Apply DBSCAN to find clusters. Choose different settings for different cities.
-                if self.cluster_type == 'shopping' and len(self.all_points) > 200 :
-                    dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree')  # for large cities
-                elif self.cluster_type == 'sightseeing' :
-                    dbscan = DBSCAN(eps=0.0025, min_samples=15, algorithm='kd_tree')  # for historic neighborhoods
-                else :
-                    dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree')  # for small cities
-
-                labels = dbscan.fit_predict(self.all_points)
-
-                # Check that there are is least 1 cluster
-                if len(set(labels)) > 1 :
-                    self.logger.info(f"Found {len(set(labels))} different {cluster_type} clusters.")
-                    # Separate clustered points and noise points
-                    self.cluster_points = self.all_points[labels != -1]
-                    self.cluster_labels = labels[labels != -1]
-                    self.filter_clusters()      # ValueError here sometimes. I dont know why. # Filter the clusters to keep only the largest ones.
-                    self.valid = True
-
-                else :
-                    self.logger.info(f"Found 0 {cluster_type} clusters.")
-                    self.valid = False
+            if result is None :
+                self.logger.debug(f"Found no {cluster_type} clusters, overpass query returned no datapoints.")
+                self.valid = False
 
             else :
-                self.logger.debug(f"Detected 0 {cluster_type} clusters.")
-                self.valid = False
+                points = []
+                for elem in result:
+                    osm_type = elem.get('type')
+
+                    # Get coordinates and append them to the points list
+                    _, coords = get_base_info(elem, osm_type)
+                    if coords is not None :
+                        points.append(coords)
+
+                if points :
+                    self.all_points = np.array(points)
+
+                    # Apply DBSCAN to find clusters. Choose different settings for different cities.
+                    if self.cluster_type == 'shopping' and len(self.all_points) > 200 :
+                        dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree')  # for large cities
+                    elif self.cluster_type == 'sightseeing' :
+                        dbscan = DBSCAN(eps=0.0025, min_samples=15, algorithm='kd_tree')  # for historic neighborhoods
+                    else :
+                        dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree')  # for small cities
+
+                    labels = dbscan.fit_predict(self.all_points)
+
+                    # Check that there are is least 1 cluster
+                    if len(set(labels)) > 1 :
+                        self.logger.info(f"Found {len(set(labels))} different {cluster_type} clusters.")
+                        # Separate clustered points and noise points
+                        self.cluster_points = self.all_points[labels != -1]
+                        self.cluster_labels = labels[labels != -1]
+                        self.filter_clusters()      # ValueError here sometimes. I dont know why. # Filter the clusters to keep only the largest ones.
+                        self.valid = True
+
+                    else :
+                        self.logger.info(f"Found 0 {cluster_type} clusters.")
+                        self.valid = False
+
+                else :
+                    self.logger.debug(f"Detected 0 {cluster_type} clusters.")
+                    self.valid = False
+
+        except UnboundLocalError as ule: 
+            self.logger.warning(f"Error fetching clusters due to overpass crash: {ule}")
+            self.valid = False
+        except Exception as e:
+            self.logger.warning(f"Error fetching clusters: {e}")
+            raise Exception from e
+
 
 
     def generate_clusters(self) -> list[Landmark]:

default.nix

@@ -1,17 +0,0 @@
-{ pkgs ? import <nixpkgs> { config.android_sdk.accept_license = true; config.allowUnfree = true; } }:
-
-pkgs.mkShell {
-    buildInputs = [
-        pkgs.flutter
-        #pkgs.android-tools  # for adb
-        #pkgs.openjdk        # required for Android builds
-    ];
-
-    # Set up Android SDK paths if needed
-    shellHook = ''
-        export ANDROID_SDK_ROOT=${pkgs.androidsdk}/libexec/android-sdk
-        export PATH=$PATH:${pkgs.androidsdk}/libexec/android-sdk/platform-tools
-        echo "Flutter dev environment ready. 'adb' and 'flutter' are available."
-    '';
-}
-