Added field validation for preferences

- 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.

.gitea/workflows/backend_run_test.yaml

@@ -18,17 +18,17 @@ jobs:
     - name: Install dependencies
       run: |
         apt-get update && apt-get install -y python3 python3-pip
-        pip install pipenv        
+        pip install uv        
 
     - name: Install packages
       run: |
         ls -la
         # install all packages, including dev-packages
-        pipenv install --dev
+        uv sync
       working-directory: backend
 
     - name: Run Tests
-      run: pipenv run pytest src --html=report.html --self-contained-html --log-cli-level=DEBUG
+      run: uv run pytest src --html=report.html --self-contained-html --log-cli-level=DEBUG
       working-directory: backend
 
     - name: Upload HTML report

backend/.gitignore

@@ -12,6 +12,9 @@ __pycache__/
 # C extensions
 *.so
 
+# Pytest reports
+report.html
+
 # Distribution / packaging
 .Python
 build/

backend/.python-version

@@ -0,0 +1 @@
+3.12.9

backend/Dockerfile

@@ -1,11 +1,29 @@
-FROM python:3.11-slim
+FROM python:3.12-slim-bookworm
 
+# The installer requires curl (and certificates) to download the release archive
+RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certificates
+
+# Download the latest installer
+ADD https://astral.sh/uv/install.sh /uv-installer.sh
+
+# Run the installer then remove it
+RUN sh /uv-installer.sh && rm /uv-installer.sh
+
+# Ensure the installed binary is on the `PATH`
+ENV PATH="/root/.local/bin/:$PATH"
+
+# Set the working directory
 WORKDIR /app
-COPY Pipfile Pipfile.lock .
 
-RUN pip install pipenv
+# Copy uv files
-RUN pipenv install --deploy --system
+COPY pyproject.toml pyproject.toml
+COPY uv.lock uv.lock
+COPY .python-version .python-version
 
+# Sync the venv
+RUN uv sync --frozen --no-cache --no-dev
+
+# Copy application files
 COPY src src
 
 EXPOSE 8000
@@ -17,4 +35,4 @@ ENV MEMCACHED_HOST_PATH=none
 ENV LOKI_URL=none
 
 # explicitly use a string instead of an argument list to force a shell and variable expansion
-CMD fastapi run src/main.py --port 8000 --workers $NUM_WORKERS
+CMD uv run fastapi run src/main.py --port 8000 --workers $NUM_WORKERS

backend/Pipfile

@@ -1,27 +0,0 @@
-[[source]]
-url = "https://pypi.org/simple"
-verify_ssl = true
-name = "pypi"
-
-[dev-packages]
-pylint = "*"
-pytest = "*"
-tomli = "*"
-httpx = "*"
-exceptiongroup = "*"
-pytest-html = "*"
-typing-extensions = "*"
-dill = "*"
-
-[packages]
-numpy = "*"
-fastapi = "*"
-pydantic = "*"
-shapely = "*"
-pymemcache = "*"
-fastapi-cli = "*"
-scikit-learn = "*"
-loki-logger-handler = "*"
-pulp = "*"
-scipy = "*"
-requests = "*"

backend/landmarks.json

@@ -1,363 +0,0 @@
-[
-    {
-        "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/main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from backend!")
+
+
+if __name__ == "__main__":
+    main()

backend/pyproject.toml

@@ -0,0 +1,55 @@
+[project]
+name = "backend"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "annotated-types==0.7.0 ; python_full_version >= '3.8'",
+    "anyio==4.8.0 ; python_full_version >= '3.9'",
+    "certifi==2024.12.14 ; python_full_version >= '3.6'",
+    "charset-normalizer==3.4.1 ; python_full_version >= '3.7'",
+    "click==8.1.8 ; python_full_version >= '3.7'",
+    "fastapi==0.115.7 ; python_full_version >= '3.8'",
+    "fastapi-cli==0.0.7 ; python_full_version >= '3.8'",
+    "h11==0.14.0 ; python_full_version >= '3.7'",
+    "httptools==0.6.4",
+    "idna==3.10 ; python_full_version >= '3.6'",
+    "joblib==1.4.2 ; python_full_version >= '3.8'",
+    "loki-logger-handler==1.1.0 ; python_full_version >= '2.7'",
+    "markdown-it-py==3.0.0 ; python_full_version >= '3.8'",
+    "mdurl==0.1.2 ; python_full_version >= '3.7'",
+    "numpy==2.2.2 ; python_full_version >= '3.10'",
+    "pulp==2.9.0 ; python_full_version >= '3.7'",
+    "pydantic==2.10.6 ; python_full_version >= '3.8'",
+    "pydantic-core==2.27.2 ; python_full_version >= '3.8'",
+    "pygments==2.19.1 ; python_full_version >= '3.8'",
+    "pymemcache==4.0.0 ; python_full_version >= '3.7'",
+    "python-dotenv==1.0.1",
+    "pyyaml==6.0.2",
+    "requests==2.32.3 ; python_full_version >= '3.8'",
+    "rich==13.9.4 ; python_full_version >= '3.8'",
+    "rich-toolkit==0.13.2 ; python_full_version >= '3.8'",
+    "scikit-learn==1.6.1 ; python_full_version >= '3.9'",
+    "scipy==1.15.1 ; python_full_version >= '3.10'",
+    "shapely==2.0.6 ; python_full_version >= '3.7'",
+    "shellingham==1.5.4 ; python_full_version >= '3.7'",
+    "sniffio==1.3.1 ; python_full_version >= '3.7'",
+    "starlette==0.45.3 ; python_full_version >= '3.9'",
+    "threadpoolctl==3.5.0 ; python_full_version >= '3.8'",
+    "typer==0.15.1 ; python_full_version >= '3.7'",
+    "typing-extensions==4.12.2 ; python_full_version >= '3.8'",
+    "urllib3==2.3.0 ; python_full_version >= '3.9'",
+    "uvicorn[standard]==0.34.0 ; python_full_version >= '3.9'",
+    "uvloop==0.21.0",
+    "watchfiles==1.0.4",
+    "websockets==14.2",
+]
+
+[dependency-groups]
+dev = [
+    "httpx>=0.28.1",
+    "ipykernel>=6.30.0",
+    "pytest>=8.4.1",
+    "pytest-html>=4.1.1",
+]

backend/src/landmarks/cluster_manager.py

@@ -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
 
 

backend/src/landmarks/landmarks_manager.py

@@ -4,7 +4,6 @@ import yaml
 
 from ..structs.preferences import Preferences
 from ..structs.landmark import Landmark
-from ..utils.take_most_important import take_most_important
 from .cluster_manager import ClusterManager
 from ..overpass.overpass import Overpass, get_base_info
 from ..utils.bbox import create_bbox
@@ -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:
+        Parameters :
-        center_coordinates (tuple[float, float]): The latitude and longitude of the center location around which to search.
+            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.
+            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')
+                neighborhood_manager = ClusterManager(bbox, 'sightseeing')
-            historic_clusters = neighborhood_manager.generate_clusters()
+                historic_clusters = neighborhood_manager.generate_clusters()
-            all_landmarks.update(historic_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
+            if allow_clusters :
-            shopping_manager = ClusterManager(bbox, 'shopping')
+                # special pipeline for shopping malls
-            shopping_clusters = shopping_manager.generate_clusters()
+                shopping_manager = ClusterManager(bbox, 'shopping')
-            all_landmarks.update(shopping_clusters)
+                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) :
         """
@@ -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,
@@ -244,180 +264,36 @@ class LandmarkManager:
                                 osm_id=id,
                                 osm_type=osm_type,
                                 attractiveness=0,
-                                n_tags=len(tags))
+                                n_tags=n_tags)
 
-            # Browse through tags to add information to landmark.
+            # Extract useful information for score calculation later down the road.
-            for key, value in tags.items():
+            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')
 
-                # Skip this landmark if not suitable.
+            # Check for place of worship
-                if key == 'building:part' and value == 'yes' :
+            if tags.get('place_of_worship') is not None :
-                    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' :
                     landmark.is_place_of_worship = True
-                if key == 'wikipedia' :
+                    landmark.name_en = tags.get('place_of_worship')
-                    landmark.wiki_url = value
-                if key == 'name:en' :
-                    landmark.name_en = value
-                if 'building:' in key or 'pay' in key :
-                    landmark.n_tags -= 1
 
+            # 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
 
-                # Set the duration.
+            # Compute the score and add landmark to the list.
-                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
-
-            landmark.description, landmark.keywords = self.description_and_keywords(tags)
             self.set_landmark_score(landmark, landmarktype, preference_level)
             landmarks.append(landmark)
 
-            continue
-
-
         return landmarks
 
 
-    def description_and_keywords(self, 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 = self.get_place_type(tags)
-        date = self.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(self, data):
-        """
-        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:
-            data (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 = data.get('amenity', None)
-        building = data.get('building', None)
-        historic = data.get('historic', None)
-        leisure = data.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(self, data):
-        """
-        Extracts the most relevant date from the available tags, prioritizing 'construction_date',
-        'start_date', 'year_of_construction', and 'opening_date' in that order.
-
-        Params:
-            data (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 = data.get('construction_date', None)
-        opening_date = data.get('opening_date', None)
-        start_date = data.get('start_date', None)
-        year_of_construction = data.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
-
-
 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

@@ -1,19 +1,18 @@
 """Main app for backend api"""
 import logging
-import time
 from contextlib import asynccontextmanager
-from fastapi import FastAPI, HTTPException, BackgroundTasks
+from fastapi import FastAPI, HTTPException
 
 from .logging_config import configure_logging
 from .structs.landmark import Landmark
-from .structs.preferences import Preferences
 from .structs.linked_landmarks import LinkedLandmarks
 from .structs.trip import Trip
 from .landmarks.landmarks_manager import LandmarkManager
-from .toilets.toilet_routes import router as toilets_router
+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
 
 
@@ -37,115 +36,22 @@ app = FastAPI(lifespan=lifespan)
 
 
 
+# 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)
+
+
+# Optimizes the trip given preferences. Second step in the main trip generation pipeline
+# Call with "/optimize/trip"
+app.include_router(optimization_router)
+
+
+# Fetches toilets near given coordinates.
+# Call with "/get/toilets" for fetching toilets around coordinates
 app.include_router(toilets_router)
 
 
-@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.")
-
-    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)
-
-    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.")
-
-    # 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:
-        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
-    # 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 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'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
 @app.get("/trip/{trip_uuid}")
@@ -224,3 +130,4 @@ def update_trip_time(trip_uuid: str, removed_landmark_uuid: str) -> Trip:
     trip = Trip.from_linked_landmarks(linked_tour, cache_client)
 
     return trip
+

backend/src/optimization/optimization_router.py

@@ -0,0 +1,141 @@
+"""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/refiner.py

@@ -6,7 +6,6 @@ from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
 
 from ..structs.landmark import Landmark
 from ..utils.get_time_distance import get_time
-from ..utils.take_most_important import take_most_important
 from .optimizer import Optimizer
 from ..constants import OPTIMIZER_PARAMETERS_PATH
 
@@ -238,7 +237,7 @@ class Refiner :
             if self.is_in_area(area, landmark.location) and landmark.name not in visited_names:
                 second_order_landmarks.append(landmark)
 
-        return take_most_important(second_order_landmarks, int(self.max_landmarks_refiner*0.75))
+        return sorted(second_order_landmarks, key=lambda x: x.attractiveness, reverse=True)[:int(self.max_landmarks_refiner*0.75)]
 
 
     # Try fix the shortest path using shapely

backend/src/overpass/overpass.py

@@ -402,6 +402,8 @@ def fill_cache():
     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_'):

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: 80
+max_iter: 80
+N_important: 60

backend/src/structs/landmark.py

@@ -49,8 +49,8 @@ class Landmark(BaseModel) :
     image_url : Optional[str] = None
     website_url : Optional[str] = None
     wiki_url : Optional[str] = None
-    keywords: Optional[dict] = {}
+    # keywords: Optional[dict] = {}
-    description : Optional[str] = None
+    # description : Optional[str] = None
     duration : Optional[int] = 5
     name_en : Optional[str] = None
 

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) :
@@ -32,3 +39,16 @@ class Preferences(BaseModel) :
 
     max_time_minute: Optional[int] = 3*60
     detour_tolerance_minute: Optional[int] = 0
+
+
+    def model_post_init(self, __context):
+        """
+        Method to validate proper initialization of individual Preferences.
+        Raises ValueError if the Preference type does not match with the field name.
+        """
+        if self.sightseeing.type != 'sightseeing':
+            raise ValueError(f'The sightseeing preference cannot be {self.sightseeing.type}.')
+        if self.nature.type != 'nature':
+            raise ValueError(f'The nature preference cannot be {self.nature.type}.')
+        if self.shopping.type != 'shopping':
+            raise ValueError(f'The shopping preference cannot be {self.shopping.type}.')

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",
+        "/get/landmarks",
-        json={
+        json ={
             "preferences": preferences,
             "start": start
         }

backend/src/tests/test_main.py

@@ -1,343 +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)
-
-
-    # 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

@@ -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
@@ -30,7 +30,7 @@ def test_invalid_input(client, location, radius, status_code):    # pylint: disa
         request:
     """
     response = client.post(
-        "/toilets/new",
+        "/get/toilets",
         params={
             "location": location,
             "radius": radius
@@ -58,7 +58,7 @@ def test_no_toilets(client, location, status_code):    # pylint: disable=redefin
         request:
     """
     response = client.post(
-        "/toilets/new",
+        "/get/toilets",
         params={
             "location": location
         }
@@ -87,7 +87,7 @@ def test_toilets(client, location, status_code):    # pylint: disable=redefined-
         request:
     """
     response = client.post(
-        "/toilets/new",
+        "/get/toilets",
         params={
             "location": location,
             "radius" : 600

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

@@ -1,9 +1,12 @@
 """Helper methods for testing."""
+import time
 import logging
+from functools import wraps
 from fastapi import HTTPException
 
-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] :
@@ -91,3 +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

@@ -70,6 +70,8 @@ class ToiletsManager:
 
         toilets_list = self.to_toilets(result)
 
+        self.logger.debug(f'Found {len(toilets_list)} toilets around {self.location}')
+
         return toilets_list
 
 

backend/src/toilets/toilets_router.py

@@ -1,16 +1,20 @@
-"""Defines the endpoint for fetching toilet locations."""
+"""API entry point for fetching toilet locations."""
+
 from fastapi import HTTPException, APIRouter, Query
 
-from ..structs.toilets import Toilets
 from .toilets_manager import ToiletsManager
+from ..structs.toilets import Toilets
 
 
-# Define the API router
+# Initialize the API router
 router = APIRouter()
 
 
-@router.post("/toilets/new")
+@router.post("/get/toilets")
-def get_toilets(location: tuple[float, float] = Query(...), radius: int = 500) -> list[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.
 
@@ -34,5 +38,6 @@ def get_toilets(location: tuple[float, float] = Query(...), radius: int = 500) -
         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/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]