backend/src/main.py

@@ -63,7 +63,7 @@ def new_trip(preferences: Preferences, start: tuple[float, float], end: tuple[fl
     refined_tour = refiner.refine_optimization(landmarks, base_tour, preferences.max_time_minute, preferences.detour_tolerance_minute)
 
     linked_tour = LinkedLandmarks(refined_tour)
-    # upon creation of the trip, persistence of both the trip and its landmarks is ensured
+    # upon creation of the trip, persistence of both the trip and its landmarks is ensured. Ca
     trip = Trip.from_linked_landmarks(linked_tour, cache_client)
     return trip
 

backend/src/parameters/landmark_parameters.yaml

@@ -1,6 +1,6 @@
 city_bbox_side: 7500 #m
 radius_close_to: 50
-church_coeff: 0.5
+church_coeff: 0.75
 nature_coeff: 1.25
 overall_coeff: 10
 tag_exponent: 1.15

backend/src/structs/landmark.py

@@ -14,22 +14,26 @@ class Landmark(BaseModel) :
     osm_id : int
     attractiveness : int
     n_tags : int
-    image_url : Optional[str] = None
+    image_url : Optional[str] = None                            # TODO future
     website_url : Optional[str] = None
+    wikipedia_url : Optional[str] = None
     description : Optional[str] = None                          # TODO future
-    duration : Optional[int] = 0
+    duration : Optional[int] = 0                                # TODO future
     name_en : Optional[str] = None
 
     # Unique ID of a given landmark
-    uuid: str = Field(default_factory=uuid4)
+    uuid: str = Field(default_factory=uuid4)                    # TODO implement this ASAP
     
     # Additional properties depending on specific tour
     must_do : Optional[bool] = False
     must_avoid : Optional[bool] = False
     is_secondary : Optional[bool] = False                       # TODO future   
     
-    time_to_reach_next : Optional[int] = 0
-    next_uuid : Optional[str] = None
+    time_to_reach_next : Optional[int] = 0                      # TODO fix this in existing code
+    next_uuid : Optional[str] = None                            # TODO implement this ASAP
+
+    def __hash__(self) -> int:
+        return self.uuid.int
     
     def __str__(self) -> str:
         time_to_next_str = f", time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
@@ -38,15 +42,3 @@ class Landmark(BaseModel) :
         if self.type in ["start", "finish", "nature", "shopping"] : type_str += '\t '
         return f'Landmark{type_str}: [{self.name} @{self.location}, score={self.attractiveness}{time_to_next_str}{is_secondary_str}]'
     
-    def distance(self, value: 'Landmark') -> float:
-        return (self.location[0] - value.location[0])**2 + (self.location[1] - value.location[1])**2
-
-    def __hash__(self) -> int:
-        return hash(self.name)
-
-    def __eq__(self, value: 'Landmark') -> bool:
-        # eq and hash must be consistent
-        # in particular, if two objects are equal, their hash must be equal
-        # uuid and osm_id are just shortcuts to avoid comparing all the properties
-        # if they are equal, we know that the name is also equal and in turn the hash is equal
-        return self.uuid == value.uuid or self.osm_id == value.osm_id or (self.name == value.name and self.distance(value) < 0.001)

backend/src/utils/get_time_separation.py

@@ -16,6 +16,8 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     Args:
         p1 (Tuple[float, float]): Coordinates of the starting location.
         p2 (Tuple[float, float]): Coordinates of the destination.
+        detour (float): Detour factor affecting the distance.
+        speed (float): Walking speed in kilometers per hour.
 
     Returns:
         int: Time to travel from p1 to p2 in minutes.

backend/src/utils/landmarks_manager.py

@@ -1,11 +1,15 @@
-import math
+import math as m
 import yaml
 import logging
 
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
 from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
+from pywikibot import ItemPage, Site
+from pywikibot import config
+config.put_throttle = 0
+config.maxlag = 0
 
-from structs.preferences import Preferences
+from structs.preferences import Preferences, Preference
 from structs.landmark import Landmark
 from .take_most_important import take_most_important
 import constants
@@ -73,34 +77,79 @@ class LandmarkManager:
         max_walk_dist = (preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor
         reachable_bbox_side = min(max_walk_dist, self.max_bbox_side)
 
-        # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
-        all_landmarks = set()
-
+        L = []
         bbox = self.create_bbox(center_coordinates, reachable_bbox_side)
         # list for sightseeing
         if preferences.sightseeing.score != 0:
-            score_function = lambda score: score * 10 * preferences.sightseeing.score / 5
-            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
-            all_landmarks.update(current_landmarks)
+            score_function = lambda score: int(score*10*preferences.sightseeing.score/5)   # self.count_elements_close_to(loc) +
+            L1 = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
+            L += L1
 
         # list for nature
         if preferences.nature.score != 0:
-            score_function = lambda score: score * 10 * self.nature_coeff * preferences.nature.score / 5
-            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
-            all_landmarks.update(current_landmarks)
+            score_function = lambda score: int(score*10*self.nature_coeff*preferences.nature.score/5)   # self.count_elements_close_to(loc) +
+            L2 = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
+            L += L2
 
         # list for shopping
         if preferences.shopping.score != 0:
-            score_function = lambda score: score * 10 * preferences.shopping.score / 5
-            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
-            all_landmarks.update(current_landmarks)
+            score_function = lambda score: int(score*10*preferences.shopping.score/5)   # self.count_elements_close_to(loc) +
+            L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
+            L += L3
 
 
-        landmarks_constrained = take_most_important(all_landmarks, self.N_important)
-        self.logger.info(f'Generated {len(all_landmarks)} landmarks around {center_coordinates}, and constrained to {len(landmarks_constrained)} most important ones.')
+        L = self.remove_duplicates(L)
+        # self.correct_score(L, preferences)
 
-        return all_landmarks, landmarks_constrained
+        L_constrained = take_most_important(L, self.N_important)
+        self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
 
+        return L, L_constrained
+
+
+    def remove_duplicates(self, landmarks: list[Landmark]) -> list[Landmark]:
+        """
+        Removes duplicate landmarks based on their names from the given list. Only retains the landmark with highest score
+
+        Parameters:
+        landmarks (list[Landmark]): A list of Landmark objects.
+
+        Returns:
+        list[Landmark]: A list of unique Landmark objects based on their names.
+        """
+
+        L_clean = []
+        names = []
+
+        for landmark in landmarks:
+            if landmark.name in names: 
+                continue  
+            else:
+                names.append(landmark.name)
+                L_clean.append(landmark)
+        
+        return L_clean
+        
+
+    def correct_score(self, landmarks: list[Landmark], preferences: Preferences) -> None:
+        """
+        Adjust the attractiveness score of each landmark in the list based on user preferences.
+
+        This method updates the attractiveness of each landmark by scaling it according to the user's preference score.
+        The score adjustment is computed using a simple linear transformation based on the preference score.
+
+        Args:
+            landmarks (list[Landmark]): A list of landmarks whose scores need to be corrected.
+            preferences (Preferences): The user's preference settings that influence the attractiveness score adjustment.
+        """
+
+        score_dict = {
+            preferences.sightseeing.type: preferences.sightseeing.score,
+            preferences.nature.type: preferences.nature.score,
+            preferences.shopping.type: preferences.shopping.score
+        }
+        for landmark in landmarks:
+            landmark.attractiveness = int(landmark.attractiveness * score_dict[landmark.type] / 5)        
 
 
     def count_elements_close_to(self, coordinates: tuple[float, float]) -> int:
@@ -123,7 +172,7 @@ class LandmarkManager:
 
         radius = self.radius_close_to
 
-        alpha = (180 * radius) / (6371000 * math.pi)
+        alpha = (180*radius) / (6371000*m.pi)
         bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
 
         # Build the query to find elements within the radius
@@ -167,7 +216,7 @@ class LandmarkManager:
 
         # Convert distance to degrees
         lat_diff = half_side_length_km / 111  # 1 degree latitude is approximately 111 km
-        lon_diff = half_side_length_km / (111 * math.cos(math.radians(lat)))  # Adjust for longitude based on latitude
+        lon_diff = half_side_length_km / (111 * m.cos(m.radians(lat)))  # Adjust for longitude based on latitude
 
         # Calculate bbox
         min_lat = lat - lat_diff
@@ -216,18 +265,22 @@ class LandmarkManager:
                 result = self.overpass.query(query)
             except Exception as e:
                 self.logger.error(f"Error fetching landmarks: {e}")
-                continue
+                return
             
             for elem in result.elements():
 
-                name = elem.tag('name')
-                location = (elem.centerLat(), elem.centerLon())
+                name = elem.tag('name')                             # Add name
+                location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
 
                 # TODO: exclude these from the get go
                 # skip if unprecise location
                 if name is None or location[0] is None:
                     continue
 
+                # skip if unused
+                # if 'disused:leisure' in elem.tags().keys():
+                #     continue
+                
                 # skip if part of another building
                 if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
                     continue
@@ -238,6 +291,7 @@ class LandmarkManager:
                 n_tags = len(elem.tags().keys())    # Add number of tags
                 score = n_tags**self.tag_exponent   # Add score
                 website_url = None
+                wikpedia_url = None
                 image_url = None
                 name_en = None
 
@@ -245,17 +299,22 @@ class LandmarkManager:
                 skip = False
                 for tag in elem.tags().keys():
                     if "pay" in tag:
-                        # payment options are a good sign
-                        score += self.pay_bonus
+                        score += self.pay_bonus             # discard payment options for tags
 
                     if "disused" in tag:
-                        # skip disused amenities
-                        skip = True
+                        skip = True             # skip disused amenities
                         break
 
                     if "wiki" in tag:
-                        # wikipedia entries count more
-                        score += self.wikipedia_bonus
+                        score += self.wikipedia_bonus             # wikipedia entries count more
+                        
+                    # if tag == "wikidata":
+                    #     Q = elem.tag('wikidata')
+                    #     site = Site("wikidata", "wikidata")
+                    #     item = ItemPage(site, Q)
+                    #     item.get()
+                    #     n_languages = len(item.labels)
+                    #     n_tags += n_languages/10
 
                     if "viewpoint" in tag:
                         score += self.viewpoint_bonus
@@ -277,42 +336,46 @@ class LandmarkManager:
                             skip = True
                             break
                     
-                    if tag in ['website', 'contact:website']:
+                    # Get additional information
+                    # if tag == 'wikipedia' :
+                    #     wikpedia_url = elem.tag('wikipedia')
+                    if tag in ['website', 'contact:website'] :
                         website_url = elem.tag(tag)
-                    if tag == 'image':
+                    if tag == 'image' :
                         image_url = elem.tag('image')
-                    if tag =='name:en':
+                    if tag =='name:en' :
                         name_en = elem.tag('name:en')
 
                 if skip:
                     continue
 
                 score = score_function(score)
-                if "place_of_worship" in elem.tags().values():
-                    score = score * self.church_coeff
+                if "place_of_worship" in elem.tags().values() :
+                    score = int(score*self.church_coeff)
                     duration = 15
                 
-                elif "museum" in elem.tags().values():
-                    score = score * self.church_coeff
+                elif "museum" in elem.tags().values() :
+                    score = int(score*self.church_coeff)
                     duration = 60
                 
-                else:
+                else : 
                     duration = 5
 
-                # finally create our own landmark object
+                # Generate the landmark and append it to the list
                 landmark = Landmark(
-                    name = name,
-                    type = elem_type,
-                    location = location,
-                    osm_type = osm_type,
-                    osm_id = osm_id,
-                    attractiveness = int(score),
-                    must_do = False,
-                    n_tags = int(n_tags),
-                    duration = int(duration),
-                    name_en = name_en,
-                    image_url = image_url,
-                    website_url = website_url
+                    name=name,
+                    type=elem_type,
+                    location=location,
+                    osm_type=osm_type,
+                    osm_id=osm_id,
+                    attractiveness=score,
+                    must_do=False,
+                    n_tags=int(n_tags),
+                    duration = duration, 
+                    name_en=name_en,
+                    image_url=image_url,
+                    # wikipedia_url=wikpedia_url,
+                    website_url=website_url
                 )
                 return_list.append(landmark)
         

backend/src/utils/take_most_important.py

@@ -1,16 +1,38 @@
 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
-    Parameters:
-    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
-    """
+def take_most_important(landmarks: list[Landmark], N_important) -> list[Landmark] :
+    L = len(landmarks)
+    L_copy = []
+    L_clean = []
+    scores = [0]*len(landmarks)
+    names = []
+    name_id = {}
 
-    # Sort landmarks by attractiveness (descending)
-    sorted_landmarks = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
+    for i, elem in enumerate(landmarks) :
+        if elem.name not in names :
+            names.append(elem.name)
+            name_id[elem.name] = [i]
+            L_copy.append(elem)
+        else :
+            name_id[elem.name] += [i]
+            scores = []
+            for j in name_id[elem.name] :
+                scores.append(L[j].attractiveness)
+            best_id = max(range(len(scores)), key=scores.__getitem__)
+            t = name_id[elem.name][best_id]
+            if t == i :
+                for old in L_copy :
+                    if old.name == elem.name :
+                        old.attractiveness = L[t].attractiveness
     
-    return sorted_landmarks[:n_important]
+    scores = [0]*len(L_copy)
+    for i, elem in enumerate(L_copy) :
+        scores[i] = elem.attractiveness
+
+    res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-L):]
+
+    for i, elem in enumerate(L_copy) :
+        if i in res :
+            L_clean.append(elem)
+
+    return L_clean

frontend/.github/workflows/build_app_android.yaml

@@ -44,7 +44,7 @@ jobs:
           echo "${{ secrets.ANDROID_SECRET_PROPERTIES }}" > secrets.properties
           echo "${{ secrets.ANDROID_GOOGLE_PLAY_JSON }}" > google-key.json
           # decode the base64 encoded google key
-          echo "${{ secrets.ANDROID_KEYSTORE_BASE64 }}" | base64 -d > release.keystore
+          echo "${{ secrets.ANDROID_KEYSTORE_BASE64 }}" | base64 -d - > release.keystore
         working-directory: android
 
       - name: Install fastlane

frontend/lib/constants.dart

@@ -3,7 +3,6 @@ import 'package:flutter/material.dart';
 const String APP_NAME = 'AnyWay';
 
 String API_URL_BASE = 'https://anyway.anydev.info';
-String API_URL_DEBUG = 'https://anyway.anydev.info';
 String PRIVACY_URL = 'https://anydev.info/privacy';
 
 const String MAP_ID = '41c21ac9b81dbfd8';

frontend/lib/modules/new_trip_button.dart

@@ -34,7 +34,7 @@ class _NewTripButtonState extends State<NewTripButton> {
         }
         return FloatingActionButton.extended(
           onPressed: onPressed,
-          icon: const Icon(Icons.directions),
+          icon: const Icon(Icons.add),
           label: AutoSizeText('Start planning!'),
         ); 
       }

frontend/lib/pages/new_trip_preferences.dart

@@ -63,7 +63,7 @@ class _NewTripPreferencesPageState extends State<NewTripPreferencesPage> {
       margin: const EdgeInsets.only(left: 10, right: 10, top: 10, bottom: 0),
       shadowColor: Colors.grey,
       child: ListTile(
-        leading: preferences.maxTime.icon,
+        leading: Icon(Icons.timer),
         title: Text(preferences.maxTime.description),
         subtitle: CupertinoTimerPicker(
           mode: CupertinoTimerPickerMode.hm,

frontend/lib/pages/settings.dart

@@ -61,7 +61,9 @@ class _SettingsPageState extends State<SettingsPage> {
                     return AlertDialog(
                       title: Text('Debug mode - use a custom API endpoint'),
                       content: TextField(
-                        controller: TextEditingController(text: API_URL_DEBUG),
+                        decoration: InputDecoration(
+                          hintText: 'https://anyway-stg.anydev.info'
+                        ),
                         onChanged: (value) {
                           setState(() {
                             API_URL_BASE = value;