Merge pull request 'some more fixes' (#49) from fix/frontend/more-pipeline-fixes into main

Reviewed-on: #49

.gitea/workflows/backend_run_lint.yaml

@@ -25,8 +25,6 @@ jobs:
         ls -la
         # only install dev-packages
         pipenv install --categories=dev-packages
-        pipenv run pip freeze        
-
       working-directory: backend
 
     - name: Run linter

.gitea/workflows/backend_run_test.yaml

@@ -25,7 +25,6 @@ jobs:
         ls -la
         # install all packages, including dev-packages
         pipenv install --dev
-        pipenv run pip freeze        
       working-directory: backend
 
     - name: Run Tests

.vscode/launch.json

@@ -9,18 +9,16 @@
             "name": "Backend - debug",
             "type": "debugpy",
             "request": "launch",
-            "module": "uvicorn",
             "env": {
                 "DEBUG": "true"
             },
-            "args": [
-                // "--app-dir",
-                // "src",
-                "src.main:app",
-                "--reload",
-            ],
             "jinja": true,
-            "cwd": "${workspaceFolder}/backend"
+            "cwd": "${workspaceFolder}/backend",
+            "module": "fastapi",
+            "args": [
+                "dev",
+                "src/main.py"
+            ]
         },
         {
             "name": "Backend - tester",

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "cmake.ignoreCMakeListsMissing": true
-}

backend/Dockerfile

@@ -14,5 +14,7 @@ EXPOSE 8000
 ENV NUM_WORKERS=1
 ENV OSM_CACHE_DIR=/cache
 ENV MEMCACHED_HOST_PATH=none
+ENV LOKI_URL=none
 
+# explicitly use a string instead of an argument list to force a shell and variable expansion
 CMD fastapi run src/main.py --port 8000 --workers $NUM_WORKERS

backend/Pipfile

@@ -25,3 +25,4 @@ pymemcache = "*"
 fastapi-cli = "*"
 scikit-learn = "*"
 pyqt6 = "*"
+loki-logger-handler = "*"

backend/Pipfile.lock

@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "bb22b4e28c7aa199c94b688ad93d3ab0ccf1089a172131f4aec03b78e7bd7f1c"
+            "sha256": "6edd6644586e8814a0b4526adb3352dfc17828ca129de7a68c1d5929efe94daa"
         },
         "pipfile-spec": 6,
         "requires": {},
@@ -507,6 +507,15 @@
             "markers": "python_version >= '3.8'",
             "version": "==1.4.7"
         },
+        "loki-logger-handler": {
+            "hashes": [
+                "sha256:aa1a9c933282c134a1e4271aba3cbaa2a3660eab6ea415bad7a072444ab98aa8",
+                "sha256:f6114727a9e5e6f3f2058b9b5324d1cab6d1a04e802079f7b57a8aeb7bd0a112"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '2.7'",
+            "version": "==1.0.2"
+        },
         "lxml": {
             "hashes": [
                 "sha256:01220dca0d066d1349bd6a1726856a78f7929f3878f7e2ee83c296c69495309e",

backend/src/constants.py

@@ -1,6 +1,5 @@
-"""Module allowing to access the parameters of route generation"""
+"""Module setting global parameters for the application such as cache, route generation, etc."""
 
-import logging
 import os
 from pathlib import Path
 
@@ -16,21 +15,6 @@ cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
 OSM_CACHE_DIR = Path(cache_dir_string)
 
 
-# if we are in a debug session, set verbose and rich logging
-if os.getenv('DEBUG', "false") == "true":
-    from rich.logging import RichHandler
-    logging.basicConfig(
-        level=logging.DEBUG,
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-        handlers=[RichHandler()]
-    )
-else:
-    logging.basicConfig(
-        level=logging.INFO,
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    )
-
-
 MEMCACHED_HOST_PATH = os.getenv('MEMCACHED_HOST_PATH', None)
 if MEMCACHED_HOST_PATH == "none":
     MEMCACHED_HOST_PATH = None

backend/src/logging_config.py

@@ -0,0 +1,58 @@
+"""Sets up global logging configuration for the application."""
+
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+
+def configure_logging():
+    """
+    Called at startup of a FastAPI application instance to setup logging. Depending on the environment, it will log to stdout or to Loki.
+    """
+
+    is_debug = os.getenv('DEBUG', "false") == "true"
+    is_kubernetes = os.getenv('KUBERNETES_SERVICE_HOST') is not None
+
+
+    if is_kubernetes:
+        # in that case we want to log to stdout and also to loki
+        from loki_logger_handler.loki_logger_handler import LokiLoggerHandler
+        loki_url = os.getenv('LOKI_URL')
+        loki_url = "http://localhost:3100/loki/api/v1/push"
+        if loki_url is None:
+            raise ValueError("LOKI_URL environment variable is not set")
+        
+        loki_handler = LokiLoggerHandler(
+            url = loki_url,
+            labels = {'app': 'anyway', 'environment': 'staging' if is_debug else 'production'}
+        )
+
+        logger.info(f"Logging to Loki at {loki_url} with {loki_handler.labels} and {is_debug=}")
+        logging_handlers = [loki_handler, logging.StreamHandler()]
+        logging_level = logging.DEBUG if is_debug else logging.INFO
+        # silence the chatty logs loki generates itself
+        logging.getLogger('urllib3.connectionpool').setLevel(logging.WARNING)
+        # no need for time since it's added by loki or can be shown in kube logs
+        logging_format = '%(name)s - %(levelname)s - %(message)s'
+
+    else:
+        # if we are in a debug (local) session, set verbose and rich logging
+        from rich.logging import RichHandler
+        logging_handlers = [RichHandler()]
+        logging_level = logging.DEBUG if is_debug else logging.INFO
+        logging_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+
+
+
+    logging.basicConfig(
+        level = logging_level,
+        format = logging_format,
+        handlers = logging_handlers
+    )
+
+    # also overwrite the uvicorn loggers
+    logging.getLogger('uvicorn').handlers = logging_handlers
+    logging.getLogger('uvicorn.access').handlers = logging_handlers
+    logging.getLogger('uvicorn.error').handlers = logging_handlers
+

backend/src/main.py

@@ -2,7 +2,9 @@
 
 import logging
 from fastapi import FastAPI, HTTPException, Query
+from contextlib import asynccontextmanager
 
+from .logging_config import configure_logging
 from .structs.landmark import Landmark, Toilets
 from .structs.preferences import Preferences
 from .structs.linked_landmarks import LinkedLandmarks
@@ -11,17 +13,28 @@ from .utils.landmarks_manager import LandmarkManager
 from .utils.toilets_manager import ToiletsManager
 from .utils.optimizer import Optimizer
 from .utils.refiner import Refiner
-from .persistence import client as cache_client
-
+from .cache import client as cache_client
 
 logger = logging.getLogger(__name__)
 
-app = FastAPI()
 manager = LandmarkManager()
 optimizer = Optimizer()
 refiner = Refiner(optimizer=optimizer)
 
 
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Function to run at the start of the app"""
+    logger.info("Setting up logging")
+    configure_logging()
+    yield
+    logger.info("Shutting down logging")
+
+
+app = FastAPI(lifespan=lifespan)
+
+
+
 @app.post("/trip/new")
 def new_trip(preferences: Preferences,
              start: tuple[float, float],

backend/src/parameters/landmark_parameters.yaml

@@ -1,11 +1,11 @@
 city_bbox_side: 7500 #m
 radius_close_to: 50
-church_coeff: 0.9
-nature_coeff: 1.25
+church_coeff: 0.65
+nature_coeff: 1.35
 overall_coeff: 10
 tag_exponent: 1.15
 image_bonus: 10
-viewpoint_bonus: 15
+viewpoint_bonus: 5
 wikipedia_bonus: 4
 name_bonus: 3
 N_important: 40

backend/src/structs/landmark.py

@@ -1,7 +1,7 @@
 """Definition of the Landmark class to handle visitable objects across the world."""
 
 from typing import Optional, Literal
-from uuid import uuid4
+from uuid import uuid4, UUID
 from pydantic import BaseModel, Field
 
 
@@ -29,12 +29,12 @@ class Landmark(BaseModel) :
         description (Optional[str]): A text description of the landmark.
         duration (Optional[int]): The estimated time to visit the landmark (in minutes).
         name_en (Optional[str]): The English name of the landmark.
-        uuid (str): A unique identifier for the landmark, generated by default using uuid4.
+        uuid (UUID): A unique identifier for the landmark, generated by default using uuid4.
         must_do (Optional[bool]): Whether the landmark is a "must-do" attraction.
         must_avoid (Optional[bool]): Whether the landmark should be avoided.
         is_secondary (Optional[bool]): Whether the landmark is secondary or less important.
         time_to_reach_next (Optional[int]): Estimated time (in minutes) to reach the next landmark.
-        next_uuid (Optional[str]): UUID of the next landmark in sequence (if applicable).
+        next_uuid (Optional[UUID]): UUID of the next landmark in sequence (if applicable).
     """
 
     # Properties of the landmark
@@ -52,7 +52,7 @@ class Landmark(BaseModel) :
     name_en : Optional[str] = None
 
     # Unique ID of a given landmark
-    uuid: str = Field(default_factory=uuid4)
+    uuid: UUID = Field(default_factory=uuid4)
 
     # Additional properties depending on specific tour
     must_do : Optional[bool] = False
@@ -60,7 +60,7 @@ class Landmark(BaseModel) :
     is_secondary : Optional[bool] = False
 
     time_to_reach_next : Optional[int] = 0
-    next_uuid : Optional[str] = None
+    next_uuid : Optional[UUID] = None
 
     def __str__(self) -> str:
         """
@@ -139,4 +139,4 @@ class Toilets(BaseModel) :
     
     class Config:
         # This allows us to easily convert the model to and from dictionaries
-        orm_mode = True
+        from_attributes = True

backend/src/structs/trip.py

@@ -1,6 +1,6 @@
 """Definition of the Trip class."""
 
-import uuid
+from uuid import uuid4, UUID
 from pydantic import BaseModel, Field
 from pymemcache.client.base import Client
 
@@ -19,9 +19,9 @@ class Trip(BaseModel):
     Methods:
         from_linked_landmarks: create a Trip from LinkedLandmarks object.
     """
-    uuid: str = Field(default_factory=uuid.uuid4)
+    uuid: UUID = Field(default_factory=uuid4)
     total_time: int
-    first_landmark_uuid: str
+    first_landmark_uuid: UUID
 
 
     @classmethod
@@ -31,7 +31,7 @@ class Trip(BaseModel):
         """
         trip = Trip(
             total_time = landmarks.total_time,
-            first_landmark_uuid = str(landmarks[0].uuid)
+            first_landmark_uuid = landmarks[0].uuid
         )
 
         # Store the trip in the cache

backend/src/tests/test_main.py

@@ -53,7 +53,7 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
         client:
         request:
     """
-    duration_minutes = 30
+    duration_minutes = 120
     response = client.post(
         "/trip/new",
         json={
@@ -72,10 +72,16 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     # Add details to report
     log_trip_details(request, landmarks, result['total_time'], duration_minutes)
 
+    for elem in landmarks :
+        print(elem)
+        print(elem.osm_id)
+
     # checks :
     assert response.status_code == 200  # check for successful planning
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
     assert 136200148 in osm_ids         # check for Cathédrale St. Jean in trip
+    # assert response.status_code == 2000  # check for successful planning
+
 
 
 def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
@@ -86,7 +92,7 @@ def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
         client:
         request:
     """
-    duration_minutes = 600
+    duration_minutes = 240
     response = client.post(
         "/trip/new",
         json={
@@ -100,7 +106,6 @@ def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
         )
     result = response.json()
     landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-    # osm_ids = landmarks_to_osmid(landmarks)
 
     # Add details to report
     log_trip_details(request, landmarks, result['total_time'], duration_minutes)

backend/src/tests/test_utils.py

@@ -4,7 +4,7 @@ from fastapi import HTTPException
 from pydantic import ValidationError
 
 from ..structs.landmark import Landmark
-from ..persistence import client as cache_client
+from ..cache import client as cache_client
 
 
 def landmarks_to_osmid(landmarks: list[Landmark]) -> list[int] :

backend/src/utils/cluster_manager.py

@@ -9,12 +9,12 @@ from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
 
 from ..structs.landmark import Landmark
 from ..utils.get_time_separation import get_distance
-from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
+from ..constants import OSM_CACHE_DIR
 
 
-class ShoppingLocation(BaseModel):
+class Cluster(BaseModel):
     """"
-    A classe representing an interesting area for shopping.
+    A class representing an interesting area for shopping or sightseeing.
     
     It can represent either a general area or a specifc route with start and end point.
     The importance represents the number of shops found in this cluster.
@@ -33,7 +33,7 @@ class ShoppingLocation(BaseModel):
     # end: Optional[list] = None
 
 
-class ShoppingManager:
+class ClusterManager:
 
     logger = logging.getLogger(__name__)
 
@@ -42,12 +42,21 @@ class ShoppingManager:
     all_points: list
     cluster_points: list
     cluster_labels: list
-    shopping_locations: list[ShoppingLocation]
+    cluster_type: Literal['sightseeing', 'shopping']
 
-    def __init__(self, bbox: tuple) -> None:
+    def __init__(self, bbox: tuple, cluster_type: Literal['sightseeing', 'shopping']) -> None:
         """
         Upon intialization, generate the point cloud used for cluster detection.
         The points represent bag/clothes shops and general boutiques.
+        If the first step is successful, it applies the DBSCAN clustering algorithm with different
+        parameters depending on the size of the city (number of points). 
+        It filters out noise points and keeps only the largest clusters.
+
+        A successful initialization updates:
+            - `self.cluster_points`: The points belonging to clusters.
+            - `self.cluster_labels`: The labels for the points in clusters.
+        
+        The method also calls `filter_clusters()` to retain only the largest clusters.
 
         Args: 
             bbox: The bounding box coordinates (around:radius, center_lat, center_lon).
@@ -57,13 +66,23 @@ class ShoppingManager:
         self.overpass = Overpass()
         CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
 
+        self.cluster_type = cluster_type
+        if cluster_type == 'shopping' :
+            elem_type = ['node']
+            sel = ['"shop"~"^(bag|boutique|clothes)$"']
+            out = 'skel'
+        else :
+            elem_type = ['way']
+            sel = ['"historic"="building"']
+            out = 'center'
+
         # Initialize the points for cluster detection
         query = overpassQueryBuilder(
             bbox = bbox,
-            elementType = ['node'],
-            selector = ['"shop"~"^(bag|boutique|clothes)$"'],
+            elementType = elem_type,
+            selector = sel,
             includeCenter = True,
-            out = 'skel'
+            out = out
         )
 
         try:
@@ -77,87 +96,50 @@ class ShoppingManager:
         else :
             points = []
             for elem in result.elements() :
-                points.append(tuple((elem.lat(), elem.lon())))
+                coords = tuple((elem.lat(), elem.lon()))
+                if coords[0] is None :
+                    coords = tuple((elem.centerLat(), elem.centerLon()))
+                points.append(coords)
 
             self.all_points = np.array(points)
-            self.valid = True            
+            self.valid = True
+
+            # 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)
+
+            # Separate clustered points and noise points
+            self.cluster_points = self.all_points[labels != -1]
+            self.cluster_labels = labels[labels != -1]
+
+            # filter the clusters to keep only the largest ones
+            self.filter_clusters()        
 
 
-    def generate_shopping_landmarks(self) -> list[Landmark]:
+    def generate_clusters(self) -> list[Landmark]:
         """
-        Generate shopping landmarks based on clustered locations.
-
-        This method first generates clusters of locations and then  extracts shopping-related 
-        locations from these clusters. It transforms each shopping location into a `Landmark` object.
-
-        Returns:
-            list[Landmark]: A list of `Landmark` objects representing shopping locations.
-                            Returns an empty list if no clusters are found.
-        """
-
-        self.generate_clusters()
-
-        if len(set(self.cluster_labels)) == 0 :
-            return []       # Return empty list if no clusters were found
-
-        # Then generate the shopping locations
-        self.generate_shopping_locations()
-
-        # Transform the locations in landmarks and return the list
-        shopping_landmarks = []
-        for location in self.shopping_locations :
-            shopping_landmarks.append(self.create_landmark(location))
-
-        return shopping_landmarks
-
-
-
-    def generate_clusters(self) :
-        """
-        Generate clusters of points using DBSCAN.
-
-        This method applies the DBSCAN clustering algorithm with different
-        parameters depending on the size of the city (number of points). 
-        It filters out noise points and keeps only the largest clusters.
-
-        The method updates:
-            - `self.cluster_points`: The points belonging to clusters.
-            - `self.cluster_labels`: The labels for the points in clusters.
-        
-        The method also calls `filter_clusters()` to retain only the largest clusters.
-        """
-
-        # Apply DBSCAN to find clusters. Choose different settings for different cities.
-        if len(self.all_points) > 200 :
-            dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree')  # for large cities
-        else :
-            dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree')  # for small cities
-
-        labels = dbscan.fit_predict(self.all_points)
-
-        # Separate clustered points and noise points
-        self.cluster_points = self.all_points[labels != -1]
-        self.cluster_labels = labels[labels != -1]
-
-        # filter the clusters to keep only the largest ones
-        self.filter_clusters()
-
-
-    def generate_shopping_locations(self) :
-        """
-        Generate shopping locations based on clustered points.
+        Generate a list of landmarks based on identified clusters.
 
         This method iterates over the different clusters, calculates the centroid 
         (as the mean of the points within each cluster), and assigns an importance 
         based on the size of the cluster.
 
-        The generated shopping locations are stored in `self.shopping_locations` 
-        as a list of `ShoppingLocation` objects, each with:
+        The generated shopping locations are stored in `self.clusters` 
+        as a list of `Cluster` objects, each with:
             - `type`: Set to 'area'.
             - `centroid`: The calculated centroid of the cluster.
             - `importance`: The number of points in the cluster.
         """
 
+        if not self.valid :
+            return []       # Return empty list if no clusters were found
+
         locations = []
 
         # loop through the different clusters
@@ -169,16 +151,25 @@ class ShoppingManager:
             # Calculate the centroid as the mean of the points
             centroid = np.mean(current_cluster, axis=0)
 
-            locations.append(ShoppingLocation(
+            if self.cluster_type == 'shopping' :
+                score = len(current_cluster)*2
+            else :
+                score = len(current_cluster)*8
+            locations.append(Cluster(
                 type='area',
                 centroid=centroid,
-                importance = len(current_cluster)
+                importance = score
             ))
 
-        self.shopping_locations = locations
+        # Transform the locations in landmarks and return the list
+        cluster_landmarks = []
+        for cluster in locations :
+            cluster_landmarks.append(self.create_landmark(cluster))
+
+        return cluster_landmarks
 
 
-    def create_landmark(self, shopping_location: ShoppingLocation) -> Landmark:
+    def create_landmark(self, cluster: Cluster) -> Landmark:
         """
         Create a Landmark object based on the given shopping location.
 
@@ -187,7 +178,7 @@ class ShoppingManager:
         result and creates a landmark with the associated details such as name, type, and OSM ID.
 
         Parameters:
-            shopping_location (ShoppingLocation): A ShoppingLocation object containing 
+            shopping_location (Cluster): A Cluster object containing 
             the centroid and importance of the area.
 
         Returns:
@@ -196,14 +187,21 @@ class ShoppingManager:
         """
 
         # Define the bounding box for a given radius around the coordinates
-        lat, lon = shopping_location.centroid
+        lat, lon = cluster.centroid
         bbox = ("around:1000", str(lat), str(lon))
 
         # Query neighborhoods and shopping malls
-        selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"', '"shop"="mall"']
+        selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"']
+
+        if self.cluster_type == 'shopping' :
+            selectors.append('"shop"="mall"')
+            new_name = 'Shopping Area'
+            t = 40
+        else : 
+            new_name = 'Neighborhood'
+            t = 15
 
         min_dist = float('inf')
-        new_name = 'Shopping Area'
         new_name_en = None
         osm_id = 0
         osm_type = 'node'
@@ -231,7 +229,7 @@ class ShoppingManager:
                     if location[0] is None : 
                         continue
 
-                d = get_distance(shopping_location.centroid, location)
+                d = get_distance(cluster.centroid, location)
                 if  d < min_dist :
                     min_dist = d
                     new_name = elem.tag('name')
@@ -246,13 +244,14 @@ class ShoppingManager:
         
         return Landmark(
             name=new_name,
-            type='shopping',
-            location=shopping_location.centroid,              # TODO: use the fact the we can also recognize streets.
-            attractiveness=shopping_location.importance,
+            type=self.cluster_type,
+            location=cluster.centroid,              # TODO: use the fact the we can also recognize streets.
+            attractiveness=cluster.importance,
             n_tags=0,
             osm_id=osm_id,
             osm_type=osm_type,
-            name_en=new_name_en
+            name_en=new_name_en,
+            duration=t
         )
 
 

backend/src/utils/landmarks_manager.py

@@ -5,7 +5,7 @@ from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
 from ..structs.preferences import Preferences
 from ..structs.landmark import Landmark
 from .take_most_important import take_most_important
-from .cluster_processing import ShoppingManager
+from .cluster_manager import ClusterManager
 
 from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
 
@@ -86,6 +86,11 @@ class LandmarkManager:
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
             all_landmarks.update(current_landmarks)
 
+            # special pipeline for historic neighborhoods
+            neighborhood_manager = ClusterManager(bbox, 'sightseeing')
+            historic_clusters = neighborhood_manager.generate_clusters()
+            all_landmarks.update(historic_clusters)
+
         # list for nature
         if preferences.nature.score != 0:
             score_function = lambda score: score * 10 * self.nature_coeff * preferences.nature.score / 5
@@ -102,11 +107,9 @@ class LandmarkManager:
             all_landmarks.update(current_landmarks)
 
             # special pipeline for shopping malls
-            shopping_manager = ShoppingManager(bbox)
-            if shopping_manager.valid :
-                shopping_clusters = shopping_manager.generate_shopping_landmarks()
-                for landmark in shopping_clusters : landmark.duration = 45
-                all_landmarks.update(shopping_clusters)
+            shopping_manager = ClusterManager(bbox, 'shopping')
+            shopping_clusters = shopping_manager.generate_clusters()
+            all_landmarks.update(shopping_clusters)
             
 
 
@@ -277,6 +280,11 @@ class LandmarkManager:
                         skip = True
                         break
 
+                    if "building:" in tag_key:
+                        # do not count the building description as being particularly useful
+                        n_tags -= 1
+                    
+
                     if "boundary" in tag_key:
                         # skip "areas" like administrative boundaries and stuff
                         skip = True
@@ -327,13 +335,16 @@ class LandmarkManager:
                     continue
 
                 score = score_function(score)
-                if "place_of_worship" in elem.tags().values():
-                    score = score * self.church_coeff
-                    duration = 10
+                if "place_of_worship" in elem.tags().values() :
+                    if "cathedral" not in elem.tags().values() :
+                        score = score * self.church_coeff
+                        duration = 5
+                    else : 
+                        duration = 10
 
-                if 'viewpoint' in elem.tags().values() :
+                elif 'viewpoint' in elem.tags().values() :
                     # viewpoints must count more
-                    score += self.viewpoint_bonus
+                    score = score * self.viewpoint_bonus
                     duration = 10
                 
                 elif "museum" in elem.tags().values() or "aquarium" in elem.tags().values() or "planetarium" in elem.tags().values():

frontend/.github/workflows/build_app_ios.yaml

@@ -8,7 +8,7 @@ jobs:
     runs-on: macos-latest
     env:
       # $BUNDLE_GEMFILE must be set at the job level, so it is set for all steps
-      BUNDLE_GEMFILE: ios/Gemfile
+      BUNDLE_GEMFILE: ${{ github.workspace }}/ios/Gemfile
     steps:
       - uses: actions/checkout@v4