added more structure

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

@@ -55,7 +55,12 @@ 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
+            ) -> tuple[list[Landmark], list[Landmark]]:
         """
         Generate and prioritize a list of landmarks based on user preferences.
 
@@ -63,16 +68,17 @@ 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 +95,11 @@ 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:
@@ -113,14 +120,18 @@ 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
 
@@ -236,188 +247,63 @@ 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
+
+            # Maybe a bit too inefficient
+            # for key in tags.keys():
+            #     if 'disused:' in key or 'boundary:' in key :
+            #         break
+            #     if 'building:' in key or 'pay' in key :
+            #         n_tags -= 1
 
             # Convert this to Landmark object
+            # TODO: convert to proto landmark and store rest to memcache
             landmark = Landmark(name=name,
                                 type=landmarktype,
                                 location=coords,
                                 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/main.py

@@ -1,12 +1,13 @@
 """Main app for backend api"""
 import logging
 import time
+import random
 from contextlib import asynccontextmanager
 from fastapi import FastAPI, HTTPException, BackgroundTasks
 
 from .logging_config import configure_logging
 from .structs.landmark import Landmark
-from .structs.preferences import Preferences
+from .structs.preferences import Preferences, Preference
 from .structs.linked_landmarks import LinkedLandmarks
 from .structs.trip import Trip
 from .landmarks.landmarks_manager import LandmarkManager
@@ -140,7 +141,7 @@ def new_trip(preferences: Preferences,
     # 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))
+    logger.info('Detailed trip :\n\t' + '\n\t'.join(f'{landmark}' for landmark in refined_tour))
 
     background_tasks.add_task(fill_cache)
 
@@ -224,3 +225,45 @@ def update_trip_time(trip_uuid: str, removed_landmark_uuid: str) -> Trip:
     trip = Trip.from_linked_landmarks(linked_tour, cache_client)
 
     return trip
+
+
+# TODO: get stuff to do nearby. The idea is to have maybe thhe 3 best things to do within 500m and 2 hidden gems. 
+@app.post("/landmarks/get-nearby/{lat}/{lon}")
+def get_landmarks_nearby(lat: float, lon: float) -> list[Landmark] :
+
+    # preferences = {"sightseeing": {"type": "sightseeing", "score": 0},
+    #                         "nature": {"type": "nature", "score": 0},
+    #                         "shopping": {"type": "shopping", "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 = 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),
+        allow_clusters=False
+    )
+
+    if len(landmarks_around) == 0 :
+        raise HTTPException(status_code=500, detail="No landmarks were found.")
+
+    # select 5 landmarks from there
+    if len(landmarks_around) > 6 :
+        landmarks_around = landmarks_around[:2] + random.sample(landmarks_around[3:], 2)
+    
+    logger.info('Suggested landmarks :\n\t' + '\n\t'.join(f'{landmark}' for landmark in landmarks_around))
+    return landmarks_around

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/proto_landmark.py

@@ -0,0 +1,16 @@
+from typing import Optional
+from uuid import uuid4, UUID
+from pydantic import BaseModel, Field
+
+# Output to frontend
+class ProtoLandmark(BaseModel) :
+    """fef"""
+
+    uuid: UUID = Field(default_factory=uuid4)
+
+    location : tuple
+    attractiveness : int
+    duration : Optional[int] = 5
+
+    must_do : Optional[bool] = False
+    must_avoid : Optional[bool] = False

backend/src/tests/test_nearby.py

@@ -0,0 +1,38 @@
+"""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], 2000),     # Lyon, Bellecour.
+        ([41.4020572, 2.1818985], 2000),       # Barcelona
+    ]
+)
+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"/landmarks/get-nearby/{location[0]}/{location[1]}")
+    print(response)
+    print(response.json())
+    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/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