removed unused landmark attributes

- Added landmarks_router.py to handle landmark retrieval based on user preferences and location.
- Implemented optimization_router.py for trip optimization, including handling preferences and landmarks.
- Created toilets_router.py to fetch toilet locations within a specified radius from a given location.
- Enhanced error handling and logging across all new endpoints.
- Generated a comprehensive report.html for test results and environment details.

backend/.gitignore

@@ -1,6 +1,9 @@
 # osm-cache
 cache_XML/
 
+# secrets
+*secrets.yaml
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -9,6 +12,9 @@ __pycache__/
 # C extensions
 *.so
 
+# Pytest reports
+report.html
+
 # Distribution / packaging
 .Python
 build/

backend/landmarks.json

@@ -0,0 +1,363 @@
+[
+    {
+        "name": "Chinatown",
+        "type": "shopping",
+        "location": [
+            45.7554934,
+            4.8444852
+        ],
+        "osm_type": "way",
+        "osm_id": 996515596,
+        "attractiveness": 129,
+        "n_tags": 0,
+        "image_url": null,
+        "website_url": null,
+        "wiki_url": null,
+        "keywords": {},
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "285d159c-68ee-4b37-8d71-f27ee3d38b02",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Galeries Lafayette",
+        "type": "shopping",
+        "location": [
+            45.7627107,
+            4.8556833
+        ],
+        "osm_type": "way",
+        "osm_id": 1069872743,
+        "attractiveness": 197,
+        "n_tags": 11,
+        "image_url": null,
+        "website_url": "http://www.galerieslafayette.com/",
+        "wiki_url": null,
+        "keywords": null,
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "28f1bc30-10d3-4944-8861-0ed9abca012d",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Muji",
+        "type": "shopping",
+        "location": [
+            45.7615971,
+            4.8543781
+        ],
+        "osm_type": "way",
+        "osm_id": 1044165817,
+        "attractiveness": 259,
+        "n_tags": 14,
+        "image_url": null,
+        "website_url": "https://www.muji.com/fr/",
+        "wiki_url": null,
+        "keywords": null,
+        "description": null,
+        "duration": 30,
+        "name_en": "Muji",
+        "uuid": "957f86a5-6c00-41a2-815d-d6f739052be4",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "HEMA",
+        "type": "shopping",
+        "location": [
+            45.7619133,
+            4.8565239
+        ],
+        "osm_type": "way",
+        "osm_id": 1069872750,
+        "attractiveness": 156,
+        "n_tags": 9,
+        "image_url": null,
+        "website_url": "https://fr.westfield.com/lapartdieu/store/HEMA/www.hema.fr",
+        "wiki_url": null,
+        "keywords": null,
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "8dae9d3e-e4c4-4e80-941d-0b106e22c85b",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Cordeliers",
+        "type": "shopping",
+        "location": [
+            45.7622752,
+            4.8337998
+        ],
+        "osm_type": "node",
+        "osm_id": 5545183519,
+        "attractiveness": 813,
+        "n_tags": 0,
+        "image_url": null,
+        "website_url": null,
+        "wiki_url": null,
+        "keywords": {},
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "ba02adb5-e28f-4645-8c2d-25ead6232379",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Halles de Lyon Paul Bocuse",
+        "type": "shopping",
+        "location": [
+            45.7628282,
+            4.8505601
+        ],
+        "osm_type": "relation",
+        "osm_id": 971529,
+        "attractiveness": 272,
+        "n_tags": 12,
+        "image_url": null,
+        "website_url": "https://www.halles-de-lyon-paulbocuse.com/",
+        "wiki_url": "fr:Halles de Lyon-Paul Bocuse",
+        "keywords": {
+            "importance": "national",
+            "height": null,
+            "place_type": "marketplace",
+            "date": null
+        },
+        "description": "Halles de Lyon Paul Bocuse is a marketplace of national importance.",
+        "duration": 30,
+        "name_en": null,
+        "uuid": "bbd50de3-aa91-425d-90c2-d4abfd1b4abe",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Grand Bazar",
+        "type": "shopping",
+        "location": [
+            45.7632141,
+            4.8361975
+        ],
+        "osm_type": "way",
+        "osm_id": 82399951,
+        "attractiveness": 93,
+        "n_tags": 7,
+        "image_url": null,
+        "website_url": null,
+        "wiki_url": null,
+        "keywords": null,
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "3de9131c-87c5-4efb-9fa8-064896fb8b29",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Shopping Area",
+        "type": "shopping",
+        "location": [
+            45.7673452,
+            4.8438683
+        ],
+        "osm_type": "node",
+        "osm_id": 0,
+        "attractiveness": 156,
+        "n_tags": 0,
+        "image_url": null,
+        "website_url": null,
+        "wiki_url": null,
+        "keywords": {},
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "df2482a8-7e2e-4536-aad3-564899b2fa65",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Cour Oxyg\u00e8ne",
+        "type": "shopping",
+        "location": [
+            45.7620905,
+            4.8568873
+        ],
+        "osm_type": "way",
+        "osm_id": 132673030,
+        "attractiveness": 63,
+        "n_tags": 5,
+        "image_url": null,
+        "website_url": null,
+        "wiki_url": null,
+        "keywords": null,
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "ed134f76-9a02-4bee-9c10-78454f7bc4ce",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "P\u00f4le de Commerces et de Loisirs Confluence",
+        "type": "shopping",
+        "location": [
+            45.7410414,
+            4.8171031
+        ],
+        "osm_type": "way",
+        "osm_id": 440270633,
+        "attractiveness": 259,
+        "n_tags": 14,
+        "image_url": null,
+        "website_url": "https://www.confluence.fr/",
+        "wiki_url": null,
+        "keywords": null,
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "dd7e2f5f-0e60-4560-b903-e5ded4b6e36a",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Grand H\u00f4tel-Dieu",
+        "type": "shopping",
+        "location": [
+            45.7586955,
+            4.8364597
+        ],
+        "osm_type": "relation",
+        "osm_id": 300128,
+        "attractiveness": 546,
+        "n_tags": 22,
+        "image_url": null,
+        "website_url": "https://grand-hotel-dieu.com",
+        "wiki_url": "fr:H\u00f4tel-Dieu de Lyon",
+        "keywords": {
+            "importance": "international",
+            "height": null,
+            "place_type": "building",
+            "date": "C17"
+        },
+        "description": "Grand H\u00f4tel-Dieu is an internationally famous building. It was constructed in C17.",
+        "duration": 30,
+        "name_en": null,
+        "uuid": "a91265a8-ffbd-44f7-a7ab-3ff75f08fbab",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Westfield La Part-Dieu",
+        "type": "shopping",
+        "location": [
+            45.761331,
+            4.855676
+        ],
+        "osm_type": "way",
+        "osm_id": 62338376,
+        "attractiveness": 546,
+        "n_tags": 22,
+        "image_url": null,
+        "website_url": "https://fr.westfield.com/lapartdieu",
+        "wiki_url": "fr:La Part-Dieu (centre commercial)",
+        "keywords": null,
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "7d60316f-d689-4fcf-be68-ffc09353b826",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    },
+    {
+        "name": "Ainay",
+        "type": "shopping",
+        "location": [
+            45.7553105,
+            4.8312084
+        ],
+        "osm_type": "node",
+        "osm_id": 5545126047,
+        "attractiveness": 132,
+        "n_tags": 0,
+        "image_url": null,
+        "website_url": null,
+        "wiki_url": null,
+        "keywords": {},
+        "description": null,
+        "duration": 30,
+        "name_en": null,
+        "uuid": "ad214f3d-a4b9-4078-876a-446caa7ab01c",
+        "must_do": false,
+        "must_avoid": false,
+        "is_secondary": false,
+        "time_to_reach_next": 0,
+        "next_uuid": null,
+        "is_viewpoint": false,
+        "is_place_of_worship": false
+    }
+]

backend/src/landmarks/cluster_manager.py

@@ -8,8 +8,8 @@ from pydantic import BaseModel
 
 from ..overpass.overpass import Overpass, get_base_info
 from ..structs.landmark import Landmark
-from .get_time_distance import get_distance
-from .utils import create_bbox
+from ..utils.get_time_distance import get_distance
+from ..utils.bbox import create_bbox
 
 
 
@@ -103,7 +103,7 @@ class ClusterManager:
             out = out
         )
         except Exception as e:
-            self.logger.error(f"Error fetching clusters: {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.")
@@ -113,7 +113,7 @@ class ClusterManager:
             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 :
@@ -146,7 +146,7 @@ class ClusterManager:
                     self.valid = False
 
             else :
-                self.logger.debug(f"Detected 0 {cluster_type} clusters.")
+                self.logger.debug(f"Found 0 {cluster_type} clusters.")
                 self.valid = False
 
 
@@ -217,7 +217,7 @@ class ClusterManager:
 
         # Define the bounding box for a given radius around the coordinates
         bbox = create_bbox(cluster.centroid, 300)
-        
+
         # Query neighborhoods and shopping malls
         selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"']
 
@@ -242,27 +242,25 @@ class ClusterManager:
                                                   out = 'ids center tags'
                                                   )
             except Exception as e:
-                self.logger.error(f"Error fetching clusters: {e}")
+                self.logger.warning(f"Error fetching clusters: {e}")
                 continue
 
             if result is None :
-                self.logger.error(f"Error fetching clusters: {e}")
+                self.logger.warning(f"Error fetching clusters: query result is None")
                 continue
 
             for elem in result:
-                osm_type = elem.get('type')
-
-                id, coords, name = get_base_info(elem, osm_type, with_name=True)
-
+                # Get basic info
+                id, coords, name = get_base_info(elem, elem.get('type'), with_name=True)
                 if name is None or coords is None :
                     continue
 
                 d = get_distance(cluster.centroid, coords)
                 if  d < min_dist :
                     min_dist = d
-                    new_name = name         # add name
-                    osm_type = osm_type     # add type: 'way' or 'relation'
-                    osm_id = id             # add OSM id
+                    new_name = name             # add name
+                    osm_type = elem.get('type') # add type: 'way' or 'relation'
+                    osm_id = id                 # add OSM id
 
         return Landmark(
             name=new_name,

backend/src/landmarks/landmarks_manager.py

@@ -4,10 +4,9 @@ import yaml
 
 from ..structs.preferences import Preferences
 from ..structs.landmark import Landmark
-from .take_most_important import take_most_important
 from .cluster_manager import ClusterManager
 from ..overpass.overpass import Overpass, get_base_info
-from .utils import create_bbox
+from ..utils.bbox import create_bbox
 
 from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH
 
@@ -23,7 +22,7 @@ class LandmarkManager:
     church_coeff: float     # coeff to adjsut score of churches
     nature_coeff: float       # coeff to adjust score of parks
     overall_coeff: float        # coeff to adjust weight of tags
-    n_important: int        # number of important landmarks to consider
+    # n_important: int        # number of important landmarks to consider
 
 
     def __init__(self) -> None:
@@ -42,7 +41,7 @@ class LandmarkManager:
             self.wikipedia_bonus = parameters['wikipedia_bonus']
             self.viewpoint_bonus = parameters['viewpoint_bonus']
             self.pay_bonus = parameters['pay_bonus']
-            self.n_important = parameters['N_important']
+            # self.n_important = parameters['N_important']
 
         with OPTIMIZER_PARAMETERS_PATH.open('r') as f:
             parameters = yaml.safe_load(f)
@@ -55,7 +54,12 @@ class LandmarkManager:
         self.logger.info('LandmakManager successfully initialized.')
 
 
-    def generate_landmarks_list(self, center_coordinates: tuple[float, float], preferences: Preferences) -> tuple[list[Landmark], list[Landmark]]:
+    def generate_landmarks_list(
+            self,
+            center_coordinates: tuple[float, float],
+            preferences: Preferences,
+            allow_clusters: bool = True
+            ) -> list[Landmark] :
         """
         Generate and prioritize a list of landmarks based on user preferences.
 
@@ -63,16 +67,17 @@ class LandmarkManager:
         and current location. It scores and corrects these landmarks, removes duplicates, and then selects the most important
         landmarks based on a predefined criterion.
 
-        Args:
-        center_coordinates (tuple[float, float]): The latitude and longitude of the center location around which to search.
-        preferences (Preferences): The user's preference settings that influence the landmark selection.
+        Parameters :
+            center_coordinates (tuple[float, float]): The latitude and longitude of the center location around which to search.
+            preferences (Preferences): The user's preference settings that influence the landmark selection.
+            allow_clusters (bool, optional) : If set to False, no clusters will be fetched. Mainly used for the option to fetch landmarks nearby.
 
         Returns:
         tuple[list[Landmark], list[Landmark]]:
         - A list of all existing landmarks.
         - A list of the most important landmarks based on the user's preferences.
         """
-        self.logger.debug('Starting to fetch landmarks...')
+        self.logger.info(f'Starting to fetch landmarks around {center_coordinates}...')
         max_walk_dist = int((preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor)
         radius = min(max_walk_dist, int(self.max_bbox_side/2))
 
@@ -89,10 +94,11 @@ class LandmarkManager:
             all_landmarks.update(current_landmarks)
             self.logger.info(f'Found {len(current_landmarks)} sightseeing landmarks')
 
+            if allow_clusters :
             # special pipeline for historic neighborhoods
-            neighborhood_manager = ClusterManager(bbox, 'sightseeing')
-            historic_clusters = neighborhood_manager.generate_clusters()
-            all_landmarks.update(historic_clusters)
+                neighborhood_manager = ClusterManager(bbox, 'sightseeing')
+                historic_clusters = neighborhood_manager.generate_clusters()
+                all_landmarks.update(historic_clusters)
 
         # list for nature
         if preferences.nature.score != 0:
@@ -113,16 +119,19 @@ class LandmarkManager:
                 landmark.duration = 30
             all_landmarks.update(current_landmarks)
 
-            # special pipeline for shopping malls
-            shopping_manager = ClusterManager(bbox, 'shopping')
-            shopping_clusters = shopping_manager.generate_clusters()
-            all_landmarks.update(shopping_clusters)
+            if allow_clusters :
+                # special pipeline for shopping malls
+                shopping_manager = ClusterManager(bbox, 'shopping')
+                shopping_clusters = shopping_manager.generate_clusters()
+                all_landmarks.update(shopping_clusters)
 
 
-        landmarks_constrained = take_most_important(all_landmarks, self.n_important)
+        # DETAILS HERE
         # self.logger.info(f'All landmarks generated : {len(all_landmarks)} landmarks around {center_coordinates}, and constrained to {len(landmarks_constrained)} most important ones.')
+        self.logger.info(f'Found {len(all_landmarks)} landmarks in total.')
 
-        return all_landmarks, landmarks_constrained
+        return sorted(all_landmarks, key=lambda x: x.attractiveness, reverse=True)
+    
 
     def set_landmark_score(self, landmark: Landmark, landmarktype: str, preference_level: int) :
         """
@@ -197,7 +206,7 @@ class LandmarkManager:
                     out = 'ids center tags'
                     )
             except Exception as e:
-                self.logger.error(f"Error fetching landmarks: {e}")
+                self.logger.debug(f"Failed to fetch landmarks, proceeding without: {str(e)}")
                 continue
 
             return_list += self._to_landmarks(result, landmarktype, preference_level)
@@ -236,6 +245,17 @@ class LandmarkManager:
                 continue
 
             tags = elem.get('tags')
+            n_tags=len(tags)
+
+            # Skip this landmark if not suitable
+            if tags.get('building:part') is not None :
+                continue
+            if tags.get('disused') is not None :
+                continue
+            if tags.get('boundary') is not None :
+                continue
+            if tags.get('shop') is not None and landmarktype != 'shopping' :
+                continue
 
             # Convert this to Landmark object
             landmark = Landmark(name=name,
@@ -244,57 +264,36 @@ class LandmarkManager:
                                 osm_id=id,
                                 osm_type=osm_type,
                                 attractiveness=0,
-                                n_tags=len(tags))
+                                n_tags=n_tags)
 
-            # self.logger.debug('added landmark.')
+            # Extract useful information for score calculation later down the road.
+            landmark.image_url = tags.get('image')
+            landmark.website_url = tags.get('website')
+            landmark.wiki_url = tags.get('wikipedia')
+            landmark.name_en = tags.get('name:en')
 
-            # Browse through tags to add information to landmark.
-            for key, value in tags.items():
-
-                # Skip this landmark if not suitable.
-                if key == 'building:part' and value == 'yes' :
-                    break
-                if 'disused:' in key :
-                    break
-                if 'boundary:' in key :
-                    break
-                if 'shop' in key and landmarktype != 'shopping' :
-                    break
-                # if value == 'apartments' :
-                #     break
-
-                # Fill in the other attributes.
-                if key == 'image' :
-                    landmark.image_url = value
-                if key == 'website' :
-                    landmark.website_url = value
-                if value == 'place_of_worship' :
+            # Check for place of worship
+            if tags.get('place_of_worship') is not None :
                     landmark.is_place_of_worship = True
-                if key == 'wikipedia' :
-                    landmark.wiki_url = value
-                if key == 'name:en' :
-                    landmark.name_en = value
-                if 'building:' in key or 'pay' in key :
-                    landmark.n_tags -= 1
+                    landmark.name_en = tags.get('place_of_worship')
 
-                # Set the duration.
-                if value in ['museum', 'aquarium', 'planetarium'] :
-                    landmark.duration = 60
-                elif value == 'viewpoint' :
-                    landmark.is_viewpoint = True
-                    landmark.duration = 10
-                elif value == 'cathedral' :
-                    landmark.is_place_of_worship = False
-                    landmark.duration = 10
+            # Set the duration. Needed for the optimization.
+            if tags.get('amenity') in ['aquarium', 'planetarium'] or tags.get('tourism') in ['aquarium', 'museum', 'zoo']:
+                landmark.duration = 60
+            elif tags.get('tourism') == 'viewpoint' :
+                landmark.is_viewpoint = True
+                landmark.duration = 10
+            elif tags.get('building') == 'cathedral' :
+                landmark.is_place_of_worship = False
+                landmark.duration = 10
 
-            else:
-                self.set_landmark_score(landmark, landmarktype, preference_level)
-                landmarks.append(landmark)
-
-            continue
+            # Compute the score and add landmark to the list.
+            self.set_landmark_score(landmark, landmarktype, preference_level)
+            landmarks.append(landmark)
 
         return landmarks
 
+
 def dict_to_selector_list(d: dict) -> list:
     """
     Convert a dictionary of key-value pairs to a list of Overpass query strings.

backend/src/landmarks/landmarks_router.py

@@ -0,0 +1,123 @@
+"""Main app for backend api"""
+import logging
+import time
+import random
+from fastapi import HTTPException, APIRouter
+
+from ..structs.landmark import Landmark
+from ..structs.preferences import Preferences, Preference
+from .landmarks_manager import LandmarkManager
+
+
+# Setup the logger and the Landmarks Manager
+logger = logging.getLogger(__name__)
+manager = LandmarkManager()
+
+
+# Initialize the API router
+router = APIRouter()
+
+
+@router.post("/get/landmarks")
+def get_landmarks(
+    preferences: Preferences,
+    start: tuple[float, float],
+) -> list[Landmark]:
+    """
+    Function that returns all available landmarks given some preferences and a start position.
+
+    Args:
+        preferences : the preferences specified by the user as the post body
+        start       : the coordinates of the starting point
+    Returns: 
+        list[Landmark] : The full list of fetched landmarks
+    """
+    if preferences is None:
+        raise HTTPException(status_code=406, detail="Preferences not provided or incomplete.")
+    if (preferences.shopping.score == 0 and
+        preferences.sightseeing.score == 0 and
+        preferences.nature.score == 0) :
+        raise HTTPException(status_code=406, detail="All preferences are 0.")
+    if start is None:
+        raise HTTPException(status_code=406, detail="Start coordinates not provided")
+    if not (-90 <= start[0] <= 90 or -180 <= start[1] <= 180):
+        raise HTTPException(status_code=422, detail="Start coordinates not in range")
+
+    logger.info(f"Requested new trip generation. Details:\n\tCoordinates: {start}\n\tTime: {preferences.max_time_minute}\n\tSightseeing: {preferences.sightseeing.score}\n\tNature: {preferences.nature.score}\n\tShopping: {preferences.shopping.score}")
+
+    start_time = time.time()
+
+    # Generate the landmarks from the start location
+    landmarks = manager.generate_landmarks_list(
+        center_coordinates = start,
+        preferences = preferences
+    )
+
+    if len(landmarks) == 0 :
+        raise HTTPException(status_code=500, detail="No landmarks were found.")
+
+    t_generate_landmarks = time.time() - start_time
+    logger.info(f'Fetched {len(landmarks)} landmarks in  \t: {round(t_generate_landmarks,3)} seconds')
+    
+    return landmarks
+
+
+@router.post("/get-nearby/landmarks/{lat}/{lon}")
+def get_landmarks_nearby(
+    lat: float,
+    lon: float
+) -> list[Landmark] :
+    """
+    Suggests nearby landmarks based on a given latitude and longitude.
+
+    This endpoint returns a curated list of up to 5 landmarks around the given geographical coordinates. It uses fixed preferences for
+    sightseeing, shopping, and nature, with a maximum time constraint of 30 minutes to limit the number of landmarks returned.
+
+    Args:
+        lat (float): Latitude of the user's current location.
+        lon (float): Longitude of the user's current location.
+
+    Returns:
+        list[Landmark]: A list of selected nearby landmarks.
+    """
+    logger.info(f'Fetching landmarks nearby ({lat}, {lon}).')
+
+    # Define fixed preferences:
+    prefs = Preferences(
+        sightseeing = Preference(
+            type='sightseeing',
+            score=5
+        ),
+        shopping = Preference(
+            type='shopping',
+            score=2
+        ),
+        nature = Preference(
+            type='nature',
+            score=5
+        ),
+        max_time_minute=30,
+        detour_tolerance_minute=0,
+    )
+
+    # Find the landmarks around the location
+    landmarks_around = manager.generate_landmarks_list(
+        center_coordinates = (lat, lon),
+        preferences = prefs,
+        allow_clusters=False,
+    )
+
+    if len(landmarks_around) == 0 :
+        raise HTTPException(status_code=500, detail="No landmarks were found.")
+
+    # select 8 - 12 landmarks from there
+    if len(landmarks_around) > 8 :
+        n_imp = random.randint(2,5)
+        rest = random.randint(8 - n_imp, min(12, len(landmarks_around))-n_imp)
+
+        print(f'len = {len(landmarks_around)}\nn_imp = {n_imp}\nrest = {rest}')
+        landmarks_around = landmarks_around[:n_imp] + random.sample(landmarks_around[n_imp:], rest)
+    
+    logger.info(f'Found {len(landmarks_around)} landmarks to suggest nearby ({lat}, {lon}).')
+    # logger.debug('Suggested landmarks :\n\t' + '\n\t'.join(f'{landmark}' for landmark in landmarks_around))
+    return landmarks_around

backend/src/logging_config.py

@@ -33,14 +33,14 @@ def configure_logging():
         # 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'
+        logging_format = '%(name)-55s - %(levelname)-7s - %(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_format = '%(asctime)s - %(name)-55s - %(levelname)-7s - %(message)s'
 
 
 

backend/src/main.py

@@ -1,20 +1,18 @@
 """Main app for backend api"""
-
 import logging
-import time
 from contextlib import asynccontextmanager
-from fastapi import FastAPI, HTTPException, BackgroundTasks, Query
+from fastapi import FastAPI, HTTPException
 
 from .logging_config import configure_logging
-from .structs.landmark import Landmark, Toilets
-from .structs.preferences import Preferences
+from .structs.landmark import Landmark
 from .structs.linked_landmarks import LinkedLandmarks
 from .structs.trip import Trip
-from .utils.landmarks_manager import LandmarkManager
-from .utils.toilets_manager import ToiletsManager
+from .landmarks.landmarks_manager import LandmarkManager
+from .toilets.toilets_router import router as toilets_router
+from .optimization.optimization_router import router as optimization_router
+from .landmarks.landmarks_router import router as landmarks_router, get_landmarks_nearby
 from .optimization.optimizer import Optimizer
 from .optimization.refiner import Refiner
-from .overpass.overpass import fill_cache
 from .cache import client as cache_client
 
 
@@ -37,109 +35,22 @@ async def lifespan(app: FastAPI):
 app = FastAPI(lifespan=lifespan)
 
 
-@app.post("/trip/new")
-def new_trip(preferences: Preferences,
-             start: tuple[float, float],
-             end: tuple[float, float] | None = None, 
-             background_tasks: BackgroundTasks = None) -> Trip:
-    """
-    Main function to call the optimizer.
 
-    Args:
-        preferences : the preferences specified by the user as the post body
-        start       : the coordinates of the starting point
-        end         : the coordinates of the finishing point
-    Returns: 
-        (uuid) : The uuid of the first landmark in the optimized route
-    """
-    if preferences is None:
-        raise HTTPException(status_code=406, detail="Preferences not provided or incomplete.")
-    if (preferences.shopping.score == 0 and
-        preferences.sightseeing.score == 0 and
-        preferences.nature.score == 0) :
-        raise HTTPException(status_code=406, detail="All preferences are 0.")
-    if start is None:
-        raise HTTPException(status_code=406, detail="Start coordinates not provided")
-    if not (-90 <= start[0] <= 90 or -180 <= start[1] <= 180):
-        raise HTTPException(status_code=422, detail="Start coordinates not in range")
-    if end is None:
-        end = start
-        logger.info("No end coordinates provided. Using start=end.")
+# Fetches the global list of landmarks given preferences and start/end coordinates. Two routes
+# Call with "/get/landmarks/" for main entry point of the trip generation pipeline.
+# Call with "/get-nearby/landmarks/" for the NEARBY feature.
+app.include_router(landmarks_router)
 
-    start_landmark = Landmark(name='start',
-                              type='start',
-                              location=(start[0], start[1]),
-                              osm_type='start',
-                              osm_id=0,
-                              attractiveness=0,
-                              duration=0,
-                              must_do=True,
-                              n_tags = 0)
 
-    end_landmark = Landmark(name='finish',
-                            type='finish',
-                            location=(end[0], end[1]),
-                            osm_type='end',
-                            osm_id=0,
-                            attractiveness=0,
-                            duration=0,
-                            must_do=True,
-                            n_tags=0)
+# Optimizes the trip given preferences. Second step in the main trip generation pipeline
+# Call with "/optimize/trip"
+app.include_router(optimization_router)
 
-    start_time = time.time()
-    # Generate the landmarks from the start location
-    landmarks, landmarks_short = manager.generate_landmarks_list(
-        center_coordinates = start,
-        preferences = preferences
-    )
 
-    if len(landmarks) == 0 :
-        raise HTTPException(status_code=500, detail="No landmarks were found.")
+# Fetches toilets near given coordinates.
+# Call with "/get/toilets" for fetching toilets around coordinates
+app.include_router(toilets_router)
 
-    # insert start and finish to the landmarks list
-    landmarks_short.insert(0, start_landmark)
-    landmarks_short.append(end_landmark)
-
-    t_generate_landmarks = time.time() - start_time
-    logger.info(f'Fetched {len(landmarks)} landmarks in  \t: {round(t_generate_landmarks,3)} seconds')
-    start_time = time.time()
-
-    # First stage optimization
-    try:
-        base_tour = optimizer.solve_optimization(preferences.max_time_minute, landmarks_short)
-    except Exception as exc:
-        raise HTTPException(status_code=500, detail=f"Optimization failed: {str(exc)}") from exc
-
-    t_first_stage = time.time() - start_time
-    start_time = time.time()
-
-    # Second stage optimization
-    # TODO : only if necessary (not enough landmarks for ex.)
-    try :
-        refined_tour = refiner.refine_optimization(landmarks, base_tour,
-                                               preferences.max_time_minute,
-                                               preferences.detour_tolerance_minute)
-    except TimeoutError as te :
-        logger.error(f'Refiner failed : {str(te)} Using base tour.')
-        refined_tour = base_tour
-    except Exception as exc :
-        raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {str(exc)}") from exc
-
-    t_second_stage = time.time() - start_time
-
-    logger.debug(f'First stage optimization\t: {round(t_first_stage,3)} seconds')
-    logger.debug(f'Second stage optimization\t: {round(t_second_stage,3)} seconds')
-    logger.info(f'Total computation time\t: {round(t_first_stage + t_second_stage,3)} seconds')
-    linked_tour = LinkedLandmarks(refined_tour)
-
-    # upon creation of the trip, persistence of both the trip and its landmarks is ensured.
-    trip = Trip.from_linked_landmarks(linked_tour, cache_client)
-    logger.info(f'Generated a trip of {trip.total_time} minutes with {len(refined_tour)} landmarks in {round(t_generate_landmarks + t_first_stage + t_second_stage,3)} seconds.')
-    logger.debug('Detailed trip :\n\t' + '\n\t'.join(f'{landmark}' for landmark in refined_tour))
-
-    background_tasks.add_task(fill_cache)
-
-    return trip
 
 
 #### For already existing trips/landmarks
@@ -158,6 +69,7 @@ def get_trip(trip_uuid: str) -> Trip:
         trip = cache_client.get(f"trip_{trip_uuid}")
         return trip
     except KeyError as exc:
+        logger.error(f"Failed to fetch trip with UUID {trip_uuid}: {str(exc)}")
         raise HTTPException(status_code=404, detail="Trip not found") from exc
 
 
@@ -176,6 +88,7 @@ def get_landmark(landmark_uuid: str) -> Landmark:
         landmark = cache_client.get(f"landmark_{landmark_uuid}")
         return landmark
     except KeyError as exc:
+        logger.error(f"Failed to fetch landmark with UUID {landmark_uuid}: {str(exc)}")
         raise HTTPException(status_code=404, detail="Landmark not found") from exc
 
 
@@ -194,6 +107,7 @@ def update_trip_time(trip_uuid: str, removed_landmark_uuid: str) -> Trip:
     try:
         trip = cache_client.get(f'trip_{trip_uuid}')
     except KeyError as exc:
+        logger.error(f"Failed to update trip with UUID {trip_uuid} (trip not found): {str(exc)}")
         raise HTTPException(status_code=404, detail='Trip not found') from exc
 
     landmarks = []
@@ -208,6 +122,7 @@ def update_trip_time(trip_uuid: str, removed_landmark_uuid: str) -> Trip:
                 landmarks.append(landmark)
             next_uuid = landmark.next_uuid  # Prepare for the next iteration
     except KeyError as exc:
+        logger.error(f"Failed to update trip with UUID {trip_uuid} : {str(exc)}")
         raise HTTPException(status_code=404, detail=f'landmark {next_uuid} not found') from exc
 
     # Re-link every thing and compute times again
@@ -216,31 +131,3 @@ def update_trip_time(trip_uuid: str, removed_landmark_uuid: str) -> Trip:
 
     return trip
 
-
-
-@app.post("/toilets/new")
-def get_toilets(location: tuple[float, float] = Query(...), radius: int = 500) -> list[Toilets] :
-    """
-    Endpoint to find toilets within a specified radius from a given location.
-
-    This endpoint expects the `location` and `radius` as **query parameters**, not in the request body.
-
-    Args:
-        location (tuple[float, float]): The latitude and longitude of the location to search from.
-        radius (int, optional): The radius (in meters) within which to search for toilets. Defaults to 500 meters.
-
-    Returns:
-        list[Toilets]: A list of Toilets objects that meet the criteria.
-    """
-    if location is None:
-        raise HTTPException(status_code=406, detail="Coordinates not provided or invalid")
-    if not (-90 <= location[0] <= 90 or -180 <= location[1] <= 180):
-        raise HTTPException(status_code=422, detail="Start coordinates not in range")
-
-    toilets_manager = ToiletsManager(location, radius)
-
-    try :
-        toilets_list = toilets_manager.generate_toilet_list()
-        return toilets_list
-    except KeyError as exc:
-        raise HTTPException(status_code=404, detail="No toilets found") from exc

backend/src/optimization/optimization_router.py

@@ -0,0 +1,139 @@
+"""API entry point for the trip optimization."""
+
+import logging
+import time
+import yaml
+from fastapi import HTTPException, APIRouter, BackgroundTasks
+
+from .optimizer import Optimizer
+from .refiner import Refiner
+from ..structs.landmark import Landmark
+from ..structs.preferences import Preferences
+from ..structs.linked_landmarks import LinkedLandmarks
+from ..structs.trip import Trip
+from ..overpass.overpass import fill_cache
+from ..cache import client as cache_client
+from ..constants import OPTIMIZER_PARAMETERS_PATH
+
+
+# Setup the Logger, Optimizer and Refiner
+logger = logging.getLogger(__name__)
+optimizer = Optimizer()
+refiner = Refiner(optimizer=optimizer)
+
+
+# Initialize the API router
+router = APIRouter()
+
+
+@router.post("/optimize/trip")
+def optimize_trip(
+        preferences: Preferences,
+        landmarks: list[Landmark],
+        start: tuple[float, float],
+        end: tuple[float, float] | None = None,
+        background_tasks: BackgroundTasks = None
+    ) -> Trip:
+    """
+    Main function to call the optimizer.
+
+    Args:
+        preferences (Preferences)   : the preferences specified by the user as the post body.
+        start (tuple[float, float]) : the coordinates of the starting point.
+        end tuple[float, float]     : the coordinates of the finishing point.
+        backgroud_tasks (BackgroundTasks) : necessary to fill the cache after the trip has been returned.
+    Returns: 
+        (uuid) : The uuid of the first landmark in the optimized route
+    """
+    if preferences is None:
+        raise HTTPException(status_code=406, detail="Preferences not provided or incomplete.")
+    if len(landmarks) == 0 :
+        raise HTTPException(status_code=406, detail="No landmarks provided for computing the trip.")
+    if (preferences.shopping.score == 0 and
+        preferences.sightseeing.score == 0 and
+        preferences.nature.score == 0) :
+        raise HTTPException(status_code=406, detail="All preferences are 0.")
+    if start is None:
+        raise HTTPException(status_code=406, detail="Start coordinates not provided")
+    if not (-90 <= start[0] <= 90 or -180 <= start[1] <= 180):
+        raise HTTPException(status_code=422, detail="Start coordinates not in range")
+    if end is None:
+        end = start
+        logger.info("No end coordinates provided. Using start=end.")
+
+    # Start the timer
+    start_time = time.time()
+
+    logger.info(f"Requested new trip generation. Details:\n\tCoordinates: {start}\n\tTime: {preferences.max_time_minute}\n\tSightseeing: {preferences.sightseeing.score}\n\tNature: {preferences.nature.score}\n\tShopping: {preferences.shopping.score}")
+
+    start_landmark = Landmark(
+        name='start',
+        type='start',
+        location=(start[0], start[1]),
+        osm_type='start',
+        osm_id=0,
+        attractiveness=0,
+        duration=0,
+        must_do=True,
+        n_tags = 0
+    )
+
+    end_landmark = Landmark(
+        name='finish',
+        type='finish',
+        location=(end[0], end[1]),
+        osm_type='end',
+        osm_id=0,
+        attractiveness=0,
+        duration=0,
+        must_do=True,
+        n_tags=0
+    )
+    
+    # From the parameters load the length at which to truncate the landmarks list.
+    with OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+            parameters = yaml.safe_load(f)
+            n_important = parameters['N_important']
+
+    # Truncate to the  most important landmarks for a shorter list
+    landmarks_short = landmarks[:n_important]
+
+    # insert start and finish to the shorter landmarks list
+    landmarks_short.insert(0, start_landmark)
+    landmarks_short.append(end_landmark)
+
+    # First stage optimization
+    try:
+        base_tour = optimizer.solve_optimization(preferences.max_time_minute, landmarks_short)
+    except Exception as exc:
+        logger.error(f"Trip generation failed: {str(exc)}")
+        raise HTTPException(status_code=500, detail=f"Optimization failed: {str(exc)}") from exc
+
+    t_first_stage = time.time() - start_time
+    start_time = time.time()
+
+    # Second stage optimization
+    try :
+        refined_tour = refiner.refine_optimization(landmarks, base_tour,
+                                               preferences.max_time_minute,
+                                               preferences.detour_tolerance_minute)
+    except Exception as exc :
+        logger.warning(f"Refiner failed. Proceeding with base trip {str(exc)}")
+        refined_tour = base_tour
+
+    t_second_stage = time.time() - start_time
+
+    logger.debug(f'First stage optimization\t: {round(t_first_stage,3)} seconds')
+    logger.debug(f'Second stage optimization\t: {round(t_second_stage,3)} seconds')
+    logger.info(f'Total computation time\t: {round(t_first_stage + t_second_stage,3)} seconds')
+    linked_tour = LinkedLandmarks(refined_tour)
+
+    # upon creation of the trip, persistence of both the trip and its landmarks is ensured.
+    trip = Trip.from_linked_landmarks(linked_tour, cache_client)
+    logger.info(f'Optimized a trip of {trip.total_time} minutes with {len(refined_tour)} landmarks in {round(t_first_stage + t_second_stage,3)} seconds.')
+    logger.info('Detailed trip :\n\t' + '\n\t'.join(f'{landmark}' for landmark in refined_tour))
+
+    background_tasks.add_task(fill_cache)
+
+    return trip
+

backend/src/optimization/optimizer.py

@@ -257,7 +257,6 @@ class Optimizer:
         Returns:
             None: This function modifies the `prob` object by adding L-2 equality constraints in-place.
         """
-        # FIXME: weird 0 artifact in the coefficients popping up
         # Loop through rows 1 to L-2 to prevent stacked ones
         for i in range(1, L-1):
             # Add the constraint that sums across each "row" or "block" in the decision variables
@@ -590,7 +589,7 @@ class Optimizer:
         try :
             prob.solve(pl.PULP_CBC_CMD(msg=False, timeLimit=self.time_limit+1, gapRel=self.gap_rel))
         except Exception as exc :
-            raise Exception(f"No solution found: {exc}") from exc
+            raise Exception(f"No solution found: {str(exc)}") from exc
         status = pl.LpStatus[prob.status]
         solution = [pl.value(var) for var in x]  # The values of the decision variables (will be 0 or 1)
 
@@ -598,7 +597,7 @@ class Optimizer:
 
         # Raise error if no solution is found. FIXME: for now this throws the internal server error
         if status != 'Optimal' :
-            self.logger.error("The problem is overconstrained, no solution on first try.")
+            self.logger.warning("The problem is overconstrained, no solution on first try.")
             raise ArithmeticError("No solution could be found. Please try again with more time or different preferences.")
 
         # If there is a solution, we're good to go, just check for connectiveness
@@ -608,7 +607,7 @@ class Optimizer:
         while circles is not None :
             i += 1
             if i == self.max_iter :
-                self.logger.error(f'Timeout: No solution found after {self.max_iter} iterations.')
+                self.logger.warning(f'Timeout: No solution found after {self.max_iter} iterations.')
                 raise TimeoutError(f"Optimization took too long. No solution found after {self.max_iter} iterations.")
 
             for circle in circles :
@@ -618,12 +617,13 @@ class Optimizer:
             try :
                 prob.solve(pl.PULP_CBC_CMD(msg=False, timeLimit=self.time_limit, gapRel=self.gap_rel))
             except Exception as exc :
-                raise Exception(f"No solution found: {exc}") from exc
+                self.logger.warning("No solution found: {str(exc)")
+                raise Exception(f"No solution found: {str(exc)}") from exc
 
             solution = [pl.value(var) for var in x]
 
             if pl.LpStatus[prob.status] != 'Optimal' :
-                self.logger.error("The problem is overconstrained, no solution after {i} cycles.")
+                self.logger.warning("The problem is overconstrained, no solution after {i} cycles.")
                 raise ArithmeticError("No solution could be found. Please try again with more time or different preferences.")
 
             circles = self.is_connected(solution)

backend/src/optimization/refiner.py

@@ -278,7 +278,7 @@ class Refiner :
             better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
             xs, ys = better_tour_poly.exterior.xy
             """ 
-            ERROR HERE : 
+            FIXED : ERROR HERE : 
                 Exception has occurred: AttributeError
                 'LineString' object has no attribute 'exterior'
             """
@@ -356,7 +356,7 @@ class Refiner :
 
         # If unsuccessful optimization, use the base_tour.
         if new_tour is None:
-            self.logger.warning("No solution found for the refined tour. Returning the initial tour.")
+            self.logger.warning("Refiner failed: No solution found during second stage optimization.")
             new_tour = base_tour
 
         # If only one landmark, return it.
@@ -369,6 +369,7 @@ class Refiner :
         # Fix the tour using Polygons if the path looks weird.
         # Conditions : circular trip and invalid polygon.
         if base_tour[0].location == base_tour[-1].location and not better_poly.is_valid :
+            self.logger.debug("Tours might be funky, attempting to correct with polygons")
             better_tour = self.fix_using_polygon(better_tour)
 
         return better_tour

backend/src/overpass/caching_strategy.py

@@ -1,3 +1,4 @@
+"""Module defining the handling of cache data from Overpass requests."""
 import os
 import json
 import hashlib
@@ -61,7 +62,7 @@ class JSONCache(CachingStrategyBase):
         return None
 
     def set(self, key, value):
-        """Save the JSON data as an ElementTree to the cache."""
+        """Save the JSON data in the cache."""
         filename = self._filename(key)
         try:
             # Write the JSON data to the cache file
@@ -94,7 +95,7 @@ class JSONCache(CachingStrategyBase):
 
     def close(self):
         """Cleanup method, if needed."""
-        pass
+
 
 class CachingStrategy:
     """
@@ -107,6 +108,7 @@ class CachingStrategy:
 
     @classmethod
     def use(cls, strategy_name='JSON', **kwargs):
+        """Define the caching strategy to use."""
         if cls.__strategy:
             cls.__strategy.close()
 
@@ -119,10 +121,12 @@ class CachingStrategy:
 
     @classmethod
     def get(cls, key):
+        """Get the data from the cache."""
         return cls.__strategy.get(key)
 
     @classmethod
     def set(cls, key, value):
+        """Save the data in the cache."""
         cls.__strategy.set(key, value)
 
     @classmethod

backend/src/overpass/overpass.py

@@ -1,5 +1,6 @@
 """Module allowing connexion to overpass api and fectch data from OSM."""
 import os
+import time
 import urllib
 import math
 import logging
@@ -59,19 +60,17 @@ class Overpass :
             return Overpass._filter_landmarks(cached_responses, bbox)
 
         # If there is no cached data, fetch all from Overpass.
-        elif not cached_responses :
+        if not cached_responses :
             query_str = Overpass.build_query(bbox, osm_types, selector, conditions, out)
             self.logger.debug(f'Query string: {query_str}')
             return self.fetch_data_from_api(query_str)
 
-        # Hybrid cache: some data from Overpass, some data from cache.
-        else :
-            # Resize the bbox for smaller search area and build new query string.
-            non_cached_bbox = Overpass._get_non_cached_bbox(non_cached_cells, bbox)
-            query_str = Overpass.build_query(non_cached_bbox, osm_types, selector, conditions, out)
-            self.logger.debug(f'Query string: {query_str}')
-            non_cached_responses = self.fetch_data_from_api(query_str)
-            return Overpass._filter_landmarks(cached_responses, bbox) + non_cached_responses
+        # Resize the bbox for smaller search area and build new query string.
+        non_cached_bbox = Overpass._get_non_cached_bbox(non_cached_cells, bbox)
+        query_str = Overpass.build_query(non_cached_bbox, osm_types, selector, conditions, out)
+        self.logger.debug(f'Query string: {query_str}')
+        non_cached_responses = self.fetch_data_from_api(query_str)
+        return Overpass._filter_landmarks(cached_responses, bbox) + non_cached_responses
 
 
     def fetch_data_from_api(self, query_str: str) -> List[dict]:
@@ -96,9 +95,10 @@ class Overpass :
                 return elements
 
         except urllib.error.URLError as e:
-            self.logger.error(f"Error connecting to Overpass API: {e}")
-            raise ConnectionError(f"Error connecting to Overpass API: {e}") from e
+            self.logger.error(f"Error connecting to Overpass API: {str(e)}")
+            raise ConnectionError(f"Error connecting to Overpass API: {str(e)}") from e
         except Exception as exc :
+            self.logger.error(f"unexpected error while fetching data from Overpass: {str(exc)}")
             raise Exception(f'An unexpected error occured: {str(exc)}') from exc
 
 
@@ -114,7 +114,7 @@ class Overpass :
             with urllib.request.urlopen(request) as response:
 
                 # Convert the HTTPResponse to a string and load data
-                response_data = response.read().decode('utf-8')  
+                response_data = response.read().decode('utf-8')
                 data = json.loads(response_data)
 
                 # Get elements and set cache
@@ -122,7 +122,7 @@ class Overpass :
                 self.caching_strategy.set(cache_key, elements)
                 self.logger.debug(f'Cache set for {cache_key}')
         except urllib.error.URLError as e:
-            raise ConnectionError(f"Error connecting to Overpass API: {e}") from e
+            raise ConnectionError(f"Error connecting to Overpass API: {str(e)}") from e
         except Exception as exc :
             raise Exception(f'An unexpected error occured: {str(exc)}') from exc
 
@@ -153,7 +153,7 @@ class Overpass :
             - If no conditions are provided, the query will just use the `selector` to filter the OSM 
             elements without additional constraints.
         """
-        query = '[out:json];('
+        query = '[out:json][timeout:20];('
 
         # convert the bbox to string.
         bbox_str = f"({','.join(map(str, bbox))})"
@@ -309,9 +309,9 @@ class Overpass :
         if min_lat == float('inf') or min_lon == float('inf'):
             return None
 
-        return (max(min_lat, original_bbox[0]), 
-                max(min_lon, original_bbox[1]), 
-                min(max_lat, original_bbox[2]), 
+        return (max(min_lat, original_bbox[0]),
+                max(min_lon, original_bbox[1]),
+                min(max_lat, original_bbox[2]),
                 min(max_lon, original_bbox[3]))
 
 
@@ -388,8 +388,8 @@ def get_base_info(elem: dict, osm_type: OSM_TYPES, with_name=False) :
     if with_name :
         name = elem.get('tags', {}).get('name')
         return osm_id, coords, name
-    else :
-        return osm_id, coords
+
+    return osm_id, coords
 
 
 def fill_cache():
@@ -399,18 +399,27 @@ def fill_cache():
     """
     overpass = Overpass()
 
+    n_files = 0
+    total = 0
+
+    overpass.logger.info('Trip successfully returned, starting to fill cache.')
+
     with os.scandir(OSM_CACHE_DIR) as it:
         for entry in it:
             if entry.is_file() and entry.name.startswith('hollow_'):
-
+                total += 1
                 try :
                     # Read the whole file content as a string
-                    with open(entry.path, 'r') as f:
+                    with open(entry.path, 'r', encoding='utf-8') as f:
                         # load data and fill the cache with the query and key
                         json_data = json.load(f)
                         overpass.fill_cache(json_data)
+                        n_files += 1
+                        time.sleep(1)
                     # Now delete the file as the cache is filled
                     os.remove(entry.path)
 
                 except Exception as exc :
-                    overpass.logger.error(f'An error occured while parsing file {entry.path} as .json file')
+                    overpass.logger.error(f'An error occured while parsing file {entry.path} as .json file: {str(exc)}')
+
+    overpass.logger.info(f"Successfully filled {n_files}/{total} cache files.")

backend/src/parameters/amenity_selectors.yaml

@@ -72,6 +72,7 @@ sightseeing:
     # - castle
     # - museum
 
+
 museums:
   tourism:
     - museum

backend/src/parameters/landmark_parameters.yaml

@@ -7,5 +7,4 @@ tag_exponent: 1.15
 image_bonus: 1.1
 viewpoint_bonus: 10
 wikipedia_bonus: 1.25
-N_important: 60
 pay_bonus: -1

backend/src/parameters/optimizer_parameters.yaml

@@ -6,4 +6,5 @@ max_landmarks_refiner: 20
 overshoot: 0.0016
 time_limit: 1
 gap_rel: 0.025
-max_iter: 40
+max_iter: 80
+N_important: 60

backend/src/structs/landmark.py

@@ -1,8 +1,7 @@
 """Definition of the Landmark class to handle visitable objects across the world."""
-
 from typing import Optional, Literal
 from uuid import uuid4, UUID
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field
 
 
 # Output to frontend
@@ -50,7 +49,8 @@ class Landmark(BaseModel) :
     image_url : Optional[str] = None
     website_url : Optional[str] = None
     wiki_url : Optional[str] = None
-    description : Optional[str] = None                          # TODO future
+    # keywords: Optional[dict] = {}
+    # description : Optional[str] = None
     duration : Optional[int] = 5
     name_en : Optional[str] = None
 
@@ -69,6 +69,7 @@ class Landmark(BaseModel) :
     is_viewpoint : Optional[bool] = False
     is_place_of_worship : Optional[bool] = False
 
+
     def __str__(self) -> str:
         """
         String representation of the Landmark object.
@@ -122,26 +123,3 @@ class Landmark(BaseModel) :
         return (self.uuid == value.uuid or
                 self.osm_id == value.osm_id or
                 (self.name == value.name and self.distance(value) < 0.001))
-
-
-class Toilets(BaseModel) :
-    """
-    Model for toilets. When false/empty the information is either false either not known.
-    """
-    location : tuple
-    wheelchair : Optional[bool] = False
-    changing_table : Optional[bool] = False
-    fee : Optional[bool] = False
-    opening_hours : Optional[str] = ""
-
-
-    def __str__(self) -> str:
-        """
-        String representation of the Toilets object.
-
-        Returns:
-            str: A formatted string with the toilets location.
-        """
-        return f'Toilets @{self.location}'
-
-    model_config = ConfigDict(from_attributes=True)

backend/src/structs/linked_landmarks.py

@@ -2,6 +2,7 @@
 
 from .landmark import Landmark
 from ..utils.get_time_distance import get_time
+from ..utils.description import description_and_keywords
 
 class LinkedLandmarks:
     """
@@ -35,18 +36,23 @@ class LinkedLandmarks:
         Create the links between the landmarks in the list by setting their 
         .next_uuid and the .time_to_next attributes.
         """
-
         # Mark secondary landmarks as such
         self.update_secondary_landmarks()
 
-
         for i, landmark in enumerate(self._landmarks[:-1]):
+            # Set uuid of the next landmark
             landmark.next_uuid = self._landmarks[i + 1].uuid
+
+            # Adjust time to reach and total time
             time_to_next = get_time(landmark.location, self._landmarks[i + 1].location)
             landmark.time_to_reach_next = time_to_next
             self.total_time += time_to_next
             self.total_time += landmark.duration
 
+            # Fill in the keywords and description. GOOD IDEA, BAD EXECUTION, tags aren't available anymore at this stage
+            # landmark.description, landmark.keywords = description_and_keywords(tags)
+
+
         self._landmarks[-1].next_uuid = None
         self._landmarks[-1].time_to_reach_next = 0
 

backend/src/structs/preferences.py

@@ -1,7 +1,7 @@
 """Defines the Preferences used as input for trip generation."""
 
 from typing import Optional, Literal
-from pydantic import BaseModel
+from pydantic import BaseModel, field_validator
 
 
 class Preference(BaseModel) :
@@ -15,6 +15,13 @@ class Preference(BaseModel) :
     type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
     score: int          # score could be from 1 to 5
 
+    @field_validator("type")
+    @classmethod
+    def validate_type(cls, v):
+        if v not in {'sightseeing', 'nature', 'shopping', 'start', 'finish'}:
+            raise ValueError(f"Invalid type: {v}")
+        return v
+
 
 # Input for optimization
 class Preferences(BaseModel) :

backend/src/structs/toilets.py

@@ -0,0 +1,26 @@
+"""Definition of the Toilets class."""
+from typing import Optional
+from pydantic import BaseModel, ConfigDict
+
+
+class Toilets(BaseModel) :
+    """
+    Model for toilets. When false/empty the information is either false either not known.
+    """
+    location : tuple
+    wheelchair : Optional[bool] = False
+    changing_table : Optional[bool] = False
+    fee : Optional[bool] = False
+    opening_hours : Optional[str] = ""
+
+
+    def __str__(self) -> str:
+        """
+        String representation of the Toilets object.
+
+        Returns:
+            str: A formatted string with the toilets location.
+        """
+        return f'Toilets @{self.location}'
+
+    model_config = ConfigDict(from_attributes=True)

backend/src/tests/test_invalid_input.py

@@ -19,30 +19,50 @@ def invalid_client():
         ([48.8566, 2.3522], {}, 422),
 
         # Invalid cases: incomplete preferences.
-        ([48.084588, 7.280405], {"sightseeing": {"type": "nature", "score": 5}, # no shopping
+        ([48.084588, 7.280405], {"sightseeing": {"type": "sightseeing", "score": 5},    # no shopping pref
                                  "nature": {"type": "nature", "score": 5},
                                  }, 422),
-        ([48.084588, 7.280405], {"sightseeing": {"type": "nature", "score": 5}, # no nature
+        ([48.084588, 7.280405], {"sightseeing": {"type": "sightseeing", "score": 5},    # no nature pref
                                  "shopping": {"type": "shopping", "score": 5},
                                  }, 422),
-        ([48.084588, 7.280405], {"nature": {"type": "nature", "score": 5},      # no sightseeing
+        ([48.084588, 7.280405], {"nature": {"type": "nature", "score": 5},              # no sightseeing pref
                                  "shopping": {"type": "shopping", "score": 5},
                                  }, 422),
+        ([48.084588, 7.280405], {"sightseeing": {"type": "nature", "score": 1},         # mixed up preferences types. TODO: i suggest reducing the complexity by remove the Preference object.
+                                 "nature": {"type": "shopping", "score": 1},
+                                 "shopping": {"type": "shopping", "score": 1},
+                                 }, 422),
+        ([48.084588, 7.280405], {"doesnotexist": {"type": "sightseeing", "score": 2},   # non-existing preferences types
+                                 "nature": {"type": "nature", "score": 2},
+                                 "shopping": {"type": "shopping", "score": 2},
+                                 }, 422),
+        ([48.084588, 7.280405], {"sightseeing": {"type": "sightseeing", "score": 3},    # non-existing preferences types
+                                 "nature": {"type": "doesntexisteither", "score": 3},
+                                 "shopping": {"type": "shopping", "score": 3},
+                                 }, 422),
+        ([48.084588, 7.280405], {"sightseeing": {"type": "sightseeing", "score": -1},   # negative preference value
+                                 "nature": {"type": "doesntexisteither", "score": 4},
+                                 "shopping": {"type": "shopping", "score": 4},
+                                 }, 422),
+        ([48.084588, 7.280405], {"sightseeing": {"type": "sightseeing", "score": 10},   # too high preference value
+                                 "nature": {"type": "doesntexisteither", "score": 4},
+                                 "shopping": {"type": "shopping", "score": 4},
+                                 }, 422),
 
         # Invalid cases: unexisting coords
-        ([91, 181], {"sightseeing": {"type": "nature", "score": 5},
+        ([91, 181], {"sightseeing": {"type": "sightseeing", "score": 5},
                      "nature": {"type": "nature", "score": 5},
                      "shopping": {"type": "shopping", "score": 5},
                     }, 422),
-        ([-91, 181], {"sightseeing": {"type": "nature", "score": 5},
+        ([-91, 181], {"sightseeing": {"type": "sightseeing", "score": 5},
                      "nature": {"type": "nature", "score": 5},
                      "shopping": {"type": "shopping", "score": 5},
                     }, 422),
-        ([91, -181], {"sightseeing": {"type": "nature", "score": 5},
+        ([91, -181], {"sightseeing": {"type": "sightseeing", "score": 5},
                      "nature": {"type": "nature", "score": 5},
                      "shopping": {"type": "shopping", "score": 5},
                     }, 422),
-        ([-91, -181], {"sightseeing": {"type": "nature", "score": 5},
+        ([-91, -181], {"sightseeing": {"type": "sightseeing", "score": 5},
                      "nature": {"type": "nature", "score": 5},
                      "shopping": {"type": "shopping", "score": 5},
                     }, 422),
@@ -53,8 +73,8 @@ def test_input(invalid_client, start, preferences, status_code):   # pylint: dis
     Test new trip creation with different sets of preferences and locations.
     """
     response = invalid_client.post(
-        "/trip/new",
-        json={
+        "/get/landmarks",
+        json ={
             "preferences": preferences,
             "start": start
         }

backend/src/tests/test_main.py

@@ -1,345 +0,0 @@
-"""Collection of tests to ensure correct implementation and track progress. """
-import time
-from fastapi.testclient import TestClient
-import pytest
-
-from .test_utils import load_trip_landmarks, log_trip_details
-from ..main import app
-
-@pytest.fixture(scope="module")
-def client():
-    """Client used to call the app."""
-    return TestClient(app)
-
-
-def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
-    """
-    Test n°1 : Custom test in Turckheim to ensure small villages are also supported.
-
-    Args:
-        client:
-        request:
-    """
-    start_time = time.time()  # Start timer
-    duration_minutes = 20
-
-    response = client.post(
-        "/trip/new",
-        json={
-            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-            "nature": {"type": "nature", "score": 0},
-            "shopping": {"type": "shopping", "score": 0},
-            "max_time_minute": duration_minutes,
-            "detour_tolerance_minute": 0},
-            "start": [48.084588, 7.280405]
-            # "start": [45.74445023349939, 4.8222687890538865]
-            # "start": [45.75156398104873, 4.827154464827647]
-            }
-        )
-    result = response.json()
-    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-
-
-    # Get computation time
-    comp_time = time.time() - start_time
-
-    # Add details to report
-    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
-
-    # for elem in landmarks :
-    #     print(elem)
-
-    # checks :
-    assert response.status_code == 200  # check for successful planning
-    assert isinstance(landmarks, list)  # check that the return type is a list
-    assert len(landmarks) > 2           # check that there is something to visit
-    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-    assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
-    assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"
-    # assert 2!= 3
-
-def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
-    """
-    Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
-    
-    Args:
-        client:
-        request:
-    """
-    start_time = time.time()  # Start timer
-    duration_minutes = 120
-
-
-    response = client.post(
-        "/trip/new",
-        json={
-            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-                            "nature": {"type": "nature", "score": 5},
-                            "shopping": {"type": "shopping", "score": 5},
-                            "max_time_minute": duration_minutes,
-                            "detour_tolerance_minute": 0},
-            "start": [45.7576485, 4.8330241]
-            }
-        )
-    result = response.json()
-    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-
-    # Get computation time
-    comp_time = time.time() - start_time
-
-    # Add details to report
-    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
-
-    # for elem in landmarks :
-    #     print(elem)
-
-    # checks :
-    assert response.status_code == 200  # check for successful planning
-    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-    assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
-    assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"
-
-def test_cologne(client, request) :   # pylint: disable=redefined-outer-name
-    """
-    Test n°3 : Custom test in Cologne to ensure proper decision making in crowded area.
-    
-    Args:
-        client:
-        request:
-    """
-    start_time = time.time()  # Start timer
-    duration_minutes = 240
-
-    response = client.post(
-        "/trip/new",
-        json={
-            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-                            "nature": {"type": "nature", "score": 5},
-                            "shopping": {"type": "shopping", "score": 5},
-                            "max_time_minute": duration_minutes,
-                            "detour_tolerance_minute": 0},
-            "start": [50.942352665, 6.957777972392]
-            }
-        )
-    result = response.json()
-    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-
-    # Get computation time
-    comp_time = time.time() - start_time
-
-    # Add details to report
-    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
-
-    # for elem in landmarks :
-    #     print(elem)
-
-    # checks :
-    assert response.status_code == 200  # check for successful planning
-    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-    assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
-    assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"
-
-
-def test_strasbourg(client, request) :   # pylint: disable=redefined-outer-name
-    """
-    Test n°4 : Custom test in Strasbourg to ensure proper decision making in crowded area.
-    
-    Args:
-        client:
-        request:
-    """
-    start_time = time.time()  # Start timer
-    duration_minutes = 180
-
-    response = client.post(
-        "/trip/new",
-        json={
-            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-                            "nature": {"type": "nature", "score": 5},
-                            "shopping": {"type": "shopping", "score": 5},
-                            "max_time_minute": duration_minutes,
-                            "detour_tolerance_minute": 0},
-            "start": [48.5846589226, 7.74078715721]
-            }
-        )
-    result = response.json()
-    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-
-    # Get computation time
-    comp_time = time.time() - start_time
-
-    # Add details to report
-    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
-
-    # for elem in landmarks :
-    #     print(elem)
-
-    # checks :
-    assert response.status_code == 200  # check for successful planning
-    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-    assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
-    assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"
-
-
-def test_zurich(client, request) :   # pylint: disable=redefined-outer-name
-    """
-    Test n°5 : Custom test in Zurich to ensure proper decision making in crowded area.
-    
-    Args:
-        client:
-        request:
-    """
-    start_time = time.time()  # Start timer
-    duration_minutes = 180
-
-    response = client.post(
-        "/trip/new",
-        json={
-            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-                            "nature": {"type": "nature", "score": 5},
-                            "shopping": {"type": "shopping", "score": 5},
-                            "max_time_minute": duration_minutes,
-                            "detour_tolerance_minute": 0},
-            "start": [47.377884227, 8.5395114066]
-            }
-        )
-    result = response.json()
-    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-
-    # Get computation time
-    comp_time = time.time() - start_time
-
-    # Add details to report
-    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
-
-    # for elem in landmarks :
-    #     print(elem)
-
-    # checks :
-    assert response.status_code == 200  # check for successful planning
-    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-    assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
-    assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"
-
-
-def test_paris(client, request) :   # pylint: disable=redefined-outer-name
-    """
-    Test n°6 : Custom test in Paris (les Halles) centre to ensure proper decision making in crowded area.
-    
-    Args:
-        client:
-        request:
-    """
-    start_time = time.time()  # Start timer
-    duration_minutes = 200
-
-    response = client.post(
-        "/trip/new",
-        json={
-            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-                            "nature": {"type": "nature", "score": 0},
-                            "shopping": {"type": "shopping", "score": 5},
-                            "max_time_minute": duration_minutes,
-                            "detour_tolerance_minute": 0},
-            "start": [48.85468881798671, 2.3423925755998374]
-            }
-        )
-    result = response.json()
-    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-
-    # Get computation time
-    comp_time = time.time() - start_time
-
-    # Add details to report
-    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
-
-    # for elem in landmarks :
-    #     print(elem)
-
-    # checks :
-    assert response.status_code == 200  # check for successful planning
-    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-    assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
-    assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"
-
-
-def test_new_york(client, request) :   # pylint: disable=redefined-outer-name
-    """
-    Test n°7 : Custom test in New York to ensure proper decision making in crowded area.
-    
-    Args:
-        client:
-        request:
-    """
-    start_time = time.time()  # Start timer
-    duration_minutes = 600
-
-    response = client.post(
-        "/trip/new",
-        json={
-            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-                            "nature": {"type": "nature", "score": 5},
-                            "shopping": {"type": "shopping", "score": 5},
-                            "max_time_minute": duration_minutes,
-                            "detour_tolerance_minute": 0},
-            "start": [40.72592726802, -73.9920434795]
-            }
-        )
-    result = response.json()
-    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-
-    # Get computation time
-    comp_time = time.time() - start_time
-
-    # Add details to report
-    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
-
-    # for elem in landmarks :
-    #     print(elem)
-
-    # checks :
-    assert response.status_code == 200  # check for successful planning
-    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-    assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
-    assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"
-
-
-def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
-    """
-    Test n°8 : Custom test in Lyon centre to ensure shopping clusters are found.
-    
-    Args:
-        client:
-        request:
-    """
-    start_time = time.time()  # Start timer
-    duration_minutes = 240
-
-    response = client.post(
-        "/trip/new",
-        json={
-            "preferences": {"sightseeing": {"type": "sightseeing", "score": 0},
-                            "nature": {"type": "nature", "score": 0},
-                            "shopping": {"type": "shopping", "score": 5},
-                            "max_time_minute": duration_minutes,
-                            "detour_tolerance_minute": 0},
-            "start": [45.7576485, 4.8330241]
-            }
-        )
-    result = response.json()
-    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
-
-    # Get computation time
-    comp_time = time.time() - start_time
-
-    # Add details to report
-    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
-
-    # for elem in landmarks :
-    #     print(elem)
-
-    # checks :
-    assert response.status_code == 200  # check for successful planning
-    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-    assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
-    assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"

backend/src/tests/test_nearby.py

@@ -0,0 +1,46 @@
+"""Collection of tests to ensure correct implementation and track progress of the get_landmarks_nearby feature. """
+
+from fastapi.testclient import TestClient
+import pytest
+
+from ..main import app
+
+
+@pytest.fixture(scope="module")
+def client():
+    """Client used to call the app."""
+    return TestClient(app)
+
+
+@pytest.mark.parametrize(
+    "location,status_code",
+    [
+        ([45.7576485, 4.8330241], 200),    # Lyon, France
+        ([41.4020572, 2.1818985], 200),    # Barcelona, Spain
+        ([59.3293, 18.0686], 200),         # Stockholm, Sweden
+        ([43.6532, -79.3832], 200),        # Toronto, Canada
+        ([38.7223, -9.1393], 200),         # Lisbon, Portugal
+        ([6.5244, 3.3792], 200),           # Lagos, Nigeria
+        ([17.3850, 78.4867], 200),         # Hyderabad, India
+        ([30.0444, 31.2357], 200),         # Cairo, Egypt
+        ([50.8503, 4.3517], 200),          # Brussels, Belgium
+        ([35.2271, -80.8431], 200),        # Charlotte, USA
+        ([10.4806, -66.9036], 200),        # Caracas, Venezuela
+        ([9.51074, -13.71118], 200),       # Conakry, Guinea
+    ]
+)
+def test_nearby(client, location, status_code):    # pylint: disable=redefined-outer-name
+    """
+    Test n°1 : Verify handling of invalid input.
+
+    Args:
+        client:
+        request:
+    """
+    response = client.post(f"/get-nearby/landmarks/{location[0]}/{location[1]}")
+    suggestions = response.json()
+
+    # checks :
+    assert response.status_code == status_code  # check for successful planning
+    assert isinstance(suggestions, list)  # check that the return type is a list
+    assert len(suggestions) > 0

backend/src/tests/test_toilets.py

@@ -3,7 +3,7 @@
 from fastapi.testclient import TestClient
 import pytest
 
-from ..structs.landmark import Toilets
+from ..structs.toilets import Toilets
 from ..main import app
 
 
@@ -18,7 +18,7 @@ def client():
     [
         ({}, None, 422),                # Invalid case: no location at all.
         ([443], None, 422),             # Invalid cases: invalid location.
-        ([443, 433], None, 422),             # Invalid cases: invalid location.
+        ([443, 433], None, 422),        # Invalid cases: invalid location.
     ]
 )
 def test_invalid_input(client, location, radius, status_code):    # pylint: disable=redefined-outer-name
@@ -30,12 +30,13 @@ def test_invalid_input(client, location, radius, status_code):    # pylint: disa
         request:
     """
     response = client.post(
-        "/toilets/new",
+        "/get/toilets",
         params={
             "location": location,
             "radius": radius
         }
     )
+    print(response.json())
 
     # checks :
     assert response.status_code == status_code
@@ -58,11 +59,12 @@ def test_no_toilets(client, location, status_code):    # pylint: disable=redefin
         request:
     """
     response = client.post(
-        "/toilets/new",
+        "/get/toilets",
         params={
             "location": location
         }
     )
+    print(response.json())
     toilets_list = [Toilets.model_validate(toilet) for toilet in response.json()]
 
     # checks :
@@ -87,12 +89,14 @@ def test_toilets(client, location, status_code):    # pylint: disable=redefined-
         request:
     """
     response = client.post(
-        "/toilets/new",
+        "/get/toilets",
         params={
             "location": location,
             "radius" : 600
         }
     )
+
+    print(response.json())
     toilets_list = [Toilets.model_validate(toilet) for toilet in response.json()]
 
     # checks :

backend/src/tests/test_trip_generation.py

@@ -0,0 +1,81 @@
+"""Collection of tests to ensure correct implementation and track progress."""
+import time
+from fastapi.testclient import TestClient
+import pytest
+
+from .test_utils import load_trip_landmarks, log_trip_details
+from ..structs.preferences import Preferences, Preference
+from ..main import app
+
+
+@pytest.fixture(scope="module")
+def client():
+    """Client used to call the app."""
+    return TestClient(app)
+
+
+@pytest.mark.parametrize(
+    "sightseeing, shopping, nature, max_time_minute, start_coords, end_coords",
+    [
+        # Edge cases
+        (0, 0, 5, 240, [45.7576485, 4.8330241], None),                  # Lyon, Bellecour - test shopping only
+        
+        # Realistic
+        (5, 0, 0, 20, [48.0845881, 7.2804050], None),                   # Turckheim
+        (5, 5, 5, 120, [45.7576485, 4.8330241], None),                  # Lyon, Bellecour
+        (5, 2, 5, 240, [50.9423526, 6.9577780], None),                  # Cologne, centre
+        (3, 5, 0, 180, [48.5846589226, 7.74078715721], None),           # Strasbourg, centre
+        (2, 4, 5, 180, [47.377884227, 8.5395114066], None),             # Zurich, centre
+        (5, 0, 5, 200, [48.85468881798671, 2.3423925755998374], None),  # Paris, centre
+        (5, 5, 5, 600, [40.72592726802, -73.9920434795], None),         # New York, Lower Manhattan
+    ]
+)
+def test_trip(client, request, sightseeing, shopping, nature, max_time_minute, start_coords, end_coords):
+
+    start_time = time.time()  # Start timer
+
+    prefs = Preferences(
+        sightseeing=Preference(type='sightseeing', score=sightseeing),
+        shopping=Preference(type='shopping', score=shopping),
+        nature=Preference(type='nature', score=nature),
+        max_time_minute=max_time_minute,
+        detour_tolerance_minute=0,
+    )
+    start = start_coords
+    end = end_coords
+
+    # Step 1: request the list of landmarks in the vicinty of the starting point
+    response = client.post(
+        "/get/landmarks",
+        json={
+            "preferences": prefs.model_dump(),
+            "start": start_coords,
+            "end": end_coords,
+            }
+        )
+    landmarks = response.json()
+
+    # Step 2: Feed the landmarks to the optimizer to compute the trip
+    response = client.post(
+        "/optimize/trip",
+        json={
+            "preferences": prefs.model_dump(),
+            "landmarks": landmarks,
+            "start": start,
+            "end": end,
+            }
+        )
+    result = response.json()
+    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
+
+    # Get computation time
+    comp_time = time.time() - start_time
+
+    # Add details to report
+    log_trip_details(request, landmarks, result['total_time'], prefs.max_time_minute)
+
+    # checks :
+    assert response.status_code == 200  # check for successful planning
+    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
+    assert prefs.max_time_minute*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {prefs.max_time_minute}"
+    assert prefs.max_time_minute*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {prefs.max_time_minute}"

backend/src/tests/test_utils.py

@@ -1,10 +1,12 @@
 """Helper methods for testing."""
+import time
 import logging
+from functools import wraps
 from fastapi import HTTPException
-from pydantic import ValidationError
 
-from ..structs.landmark import Landmark
 from ..cache import client as cache_client
+from ..structs.landmark import Landmark
+from ..structs.preferences import Preferences, Preference
 
 
 def landmarks_to_osmid(landmarks: list[Landmark]) -> list[int] :
@@ -39,7 +41,7 @@ def fetch_landmark(landmark_uuid: str):
     try:
         landmark = cache_client.get(f'landmark_{landmark_uuid}')
         if not landmark :
-            logger.warning(f'Cache miss for landmark UUID: {landmark_uuid}')
+            logger.error(f'Cache miss for landmark UUID: {landmark_uuid}')
             raise HTTPException(status_code=404, detail=f'Landmark with UUID {landmark_uuid} not found in cache.')
 
         # Validate that the fetched data is a dictionary
@@ -92,3 +94,34 @@ def log_trip_details(request, landmarks: list[Landmark], duration: int, target_d
     request.node.trip_details = trip_string
     request.node.trip_duration = str(duration)              # result['total_time']
     request.node.target_duration = str(target_duration)
+
+
+
+
+def trip_params(
+    sightseeing: int,
+    shopping: int,
+    nature: int,
+    max_time_minute: int,
+    start_coords: tuple[float, float] = None,
+    end_coords: tuple[float, float] = None,
+):
+    def decorator(test_func):
+        @wraps(test_func)
+        def wrapper(client, request):
+            prefs = Preferences(
+                sightseeing=Preference(type='sightseeing', score=sightseeing),
+                shopping=Preference(type='shopping', score=shopping),
+                nature=Preference(type='nature', score=nature),
+                max_time_minute=max_time_minute,
+                detour_tolerance_minute=0,
+            )
+
+            start = start_coords
+            end = end_coords
+
+            # Inject into test function
+            return test_func(client, request, prefs, start, end)
+
+        return wrapper
+    return decorator

backend/src/toilets/toilets_manager.py

@@ -2,8 +2,8 @@
 import logging
 
 from ..overpass.overpass import Overpass, get_base_info
-from ..structs.landmark import Toilets
-from .utils import create_bbox
+from ..structs.toilets import Toilets
+from ..utils.bbox import create_bbox
 
 
 # silence the overpass logger
@@ -65,7 +65,7 @@ class ToiletsManager:
         try:
             result = self.overpass.fetch_data_from_api(query_str=query)
         except Exception as e:
-            self.logger.error(f"Error fetching landmarks: {e}")
+            self.logger.error(f"Error fetching toilets: {e}")
             return None
 
         toilets_list = self.to_toilets(result)

backend/src/toilets/toilets_router.py

@@ -0,0 +1,42 @@
+"""API entry point for fetching toilet locations."""
+
+from fastapi import HTTPException, APIRouter, Query
+
+from .toilets_manager import ToiletsManager
+from ..structs.toilets import Toilets
+
+
+# Initialize the API router
+router = APIRouter()
+
+
+@router.post("/get/toilets")
+def get_toilets(
+        location: tuple[float, float] = Query(...),
+        radius: int = 500
+    ) -> list[Toilets] :
+    """
+    Endpoint to find toilets within a specified radius from a given location.
+
+    This endpoint expects the `location` and `radius` as **query parameters**, not in the request body.
+
+    Args:
+        location (tuple[float, float]): The latitude and longitude of the location to search from.
+        radius (int, optional): The radius (in meters) within which to search for toilets. Defaults to 500 meters.
+
+    Returns:
+        list[Toilets]: A list of Toilets objects that meet the criteria.
+    """
+    if location is None:
+        raise HTTPException(status_code=406, detail="Coordinates not provided or invalid")
+    if not (-90 <= location[0] <= 90 or -180 <= location[1] <= 180):
+        raise HTTPException(status_code=422, detail="Start coordinates not in range")
+
+    toilets_manager = ToiletsManager(location, radius)
+
+    try :
+        toilets_list = toilets_manager.generate_toilet_list()
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail="No toilets found") from exc
+
+    return toilets_list

backend/src/utils/bbox.py

@@ -24,4 +24,4 @@ def create_bbox(coords: tuple[float, float], radius: int):
     lon_min = lon - d_lon * 180 / m.pi
     lon_max = lon + d_lon * 180 / m.pi
 
-    return (lat_min, lon_min, lat_max, lon_max)
+    return (lat_min, lon_min, lat_max, lon_max)

backend/src/utils/description.py

@@ -0,0 +1,123 @@
+"""Add more information about the landmarks by writing a short description and keywords. """
+
+
+def description_and_keywords(tags: dict):
+    """
+    Generates a description and a set of keywords for a given landmark based on its tags.
+
+    Params:
+        tags (dict): A dictionary containing metadata about the landmark, including its name,
+                importance, height, date of construction, and visitor information.
+
+    Returns:
+        description (str): A string description of the landmark.
+        keywords (dict): A dictionary of keywords with fields such as 'importance', 'height',
+                    'place_type', and 'date'.
+    """
+    # Extract relevant fields
+    name = tags.get('name')
+    importance = tags.get('importance', None)
+    n_visitors = tags.get('tourism:visitors', None)
+    height = tags.get('height')
+    place_type = get_place_type(tags)
+    date = get_date(tags)
+
+    if place_type is None :
+        return None, None
+
+    # Start the description.
+    if importance is None :
+        if len(tags.keys()) < 5 :
+            return None, None
+        if len(tags.keys()) < 10 :
+            description = f"{name} is a well known {place_type}."
+        elif len(tags.keys()) < 17 :
+            importance = 'national'
+            description = f"{name} is a {place_type} of national importance."
+        else :
+            importance = 'international'
+            description = f"{name} is an internationally famous {place_type}."
+    else :
+        description = f"{name} is a {place_type} of {importance} importance."
+
+    if height is not None and date is not None :
+        description += f" This {place_type} was constructed in {date} and is ca. {height} meters high."
+    elif height is not None :
+        description += f" This {place_type} stands ca. {height} meters tall."
+    elif date is not None:
+        description += f" It was constructed in {date}."
+
+    # Format the visitor number
+    if n_visitors is not None :
+        n_visitors = int(n_visitors)
+        if n_visitors < 1000000 :
+            description += f" It welcomes {int(n_visitors/1000)} thousand visitors every year."
+        else :
+            description += f" It welcomes {round(n_visitors/1000000, 1)} million visitors every year."
+
+    # Set the keywords.
+    keywords = {"importance": importance,
+                "height": height,
+                "place_type": place_type,
+                "date": date}
+
+    return description, keywords
+
+
+def get_place_type(tags):
+    """
+    Determines the type of the place based on available tags such as 'amenity', 'building',
+    'historic', and 'leisure'. The priority order is: 'historic' > 'building' (if not generic) > 
+    'amenity' > 'leisure'.
+
+    Params:
+        tags (dict): A dictionary containing metadata about the place.
+
+    Returns:
+        place_type (str): The determined type of the place, or None if no relevant type is found.
+    """
+    amenity = tags.get('amenity', None)
+    building = tags.get('building', None)
+    historic = tags.get('historic', None)
+    leisure = tags.get('leisure')
+
+    if historic and historic != "yes":
+        return historic
+    if building and building not in ["yes", "civic", "government", "apartments", "residential", "commericial", "industrial", "retail", "religious", "public", "service"]:
+        return building
+    if amenity:
+        return amenity
+    if leisure:
+        return leisure
+
+
+    return None
+
+
+def get_date(tags):
+    """
+    Extracts the most relevant date from the available tags, prioritizing 'construction_date',
+    'start_date', 'year_of_construction', and 'opening_date' in that order.
+
+    Params:
+        tags (dict): A dictionary containing metadata about the place.
+
+    Returns:
+        date (str): The most relevant date found, or None if no date is available.
+    """
+    construction_date = tags.get('construction_date', None)
+    opening_date = tags.get('opening_date', None)
+    start_date = tags.get('start_date', None)
+    year_of_construction = tags.get('year_of_construction', None)
+
+    # Prioritize based on availability
+    if construction_date:
+        return construction_date
+    if start_date:
+        return start_date
+    if year_of_construction:
+        return year_of_construction
+    if opening_date:
+        return opening_date
+
+    return None

backend/src/utils/take_most_important.py

@@ -1,17 +0,0 @@
-"""Helper function to return only the major landmarks from a large list."""
-from ..structs.landmark import Landmark
-
-def take_most_important(landmarks: list[Landmark], n_important) -> list[Landmark]:
-    """
-    Given a list of landmarks, return the n_important most important landmarks
-    Args:
-    landmarks: list[Landmark] - list of landmarks
-    n_important: int - number of most important landmarks to return
-    Returns:
-    list[Landmark] - list of the n_important most important landmarks
-    """
-
-    # Sort landmarks by attractiveness (descending)
-    sorted_landmarks = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
-
-    return sorted_landmarks[:n_important]

status

@@ -0,0 +1,48 @@
+error: wrong number of arguments, should be from 1 to 2
+usage: git config [<options>]
+
+Config file location
+    --[no-]global         use global config file
+    --[no-]system         use system config file
+    --[no-]local          use repository config file
+    --[no-]worktree       use per-worktree config file
+    -f, --[no-]file <file>
+                          use given config file
+    --[no-]blob <blob-id> read config from given blob object
+
+Action
+    --[no-]get            get value: name [value-pattern]
+    --[no-]get-all        get all values: key [value-pattern]
+    --[no-]get-regexp     get values for regexp: name-regex [value-pattern]
+    --[no-]get-urlmatch   get value specific for the URL: section[.var] URL
+    --[no-]replace-all    replace all matching variables: name value [value-pattern]
+    --[no-]add            add a new variable: name value
+    --[no-]unset          remove a variable: name [value-pattern]
+    --[no-]unset-all      remove all matches: name [value-pattern]
+    --[no-]rename-section rename section: old-name new-name
+    --[no-]remove-section remove a section: name
+    -l, --[no-]list       list all
+    --[no-]fixed-value    use string equality when comparing values to 'value-pattern'
+    -e, --[no-]edit       open an editor
+    --[no-]get-color      find the color configured: slot [default]
+    --[no-]get-colorbool  find the color setting: slot [stdout-is-tty]
+
+Type
+    -t, --[no-]type <type>
+                          value is given this type
+    --bool                value is "true" or "false"
+    --int                 value is decimal number
+    --bool-or-int         value is --bool or --int
+    --bool-or-str         value is --bool or string
+    --path                value is a path (file or directory name)
+    --expiry-date         value is an expiry date
+
+Other
+    -z, --[no-]null       terminate values with NUL byte
+    --[no-]name-only      show variable names only
+    --[no-]includes       respect include directives on lookup
+    --[no-]show-origin    show origin of config (file, standard input, blob, command line)
+    --[no-]show-scope     show scope of config (worktree, local, global, system, command)
+    --[no-]default <value>
+                          with --get, use default value when missing entry
+