.gitea/workflows/backend_run_test.yaml

@@ -28,7 +28,7 @@ jobs:
       working-directory: backend
 
     - name: Run Tests
-      run: pipenv run pytest src --html=report.html --self-contained-html --log-cli-level=INFO
+      run: pipenv run pytest src --html=report.html --self-contained-html --log-cli-level=DEBUG
       working-directory: backend
 
     - name: Upload HTML report

backend/.pylintrc

@@ -445,7 +445,9 @@ disable=raw-checker-failed,
         logging-fstring-interpolation,
         duplicate-code,
         relative-beyond-top-level, 
-        invalid-name
+        invalid-name,
+        too-many-arguments,
+        too-many-positional-arguments
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

backend/src/constants.py

@@ -2,6 +2,7 @@
 
 import os
 from pathlib import Path
+from typing import List, Literal, Tuple
 
 
 LOCATION_PREFIX = Path('src')
@@ -14,6 +15,8 @@ OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
 cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
 OSM_CACHE_DIR = Path(cache_dir_string)
 
+OSM_TYPES = List[Literal['way', 'node', 'relation']]
+BBOX = Tuple[float, float, float, float]
 
 MEMCACHED_HOST_PATH = os.getenv('MEMCACHED_HOST_PATH', None)
 if MEMCACHED_HOST_PATH == "none":

backend/src/main.py

@@ -3,7 +3,7 @@
 import logging
 import time
 from contextlib import asynccontextmanager
-from fastapi import FastAPI, HTTPException, Query
+from fastapi import FastAPI, HTTPException, BackgroundTasks, Query
 
 from .logging_config import configure_logging
 from .structs.landmark import Landmark, Toilets
@@ -14,8 +14,10 @@ from .utils.landmarks_manager import LandmarkManager
 from .utils.toilets_manager import ToiletsManager
 from .optimization.optimizer import Optimizer
 from .optimization.refiner import Refiner
+from .overpass.overpass import fill_cache
 from .cache import client as cache_client
 
+
 logger = logging.getLogger(__name__)
 
 manager = LandmarkManager()
@@ -35,11 +37,11 @@ async def lifespan(app: FastAPI):
 app = FastAPI(lifespan=lifespan)
 
 
-
 @app.post("/trip/new")
 def new_trip(preferences: Preferences,
              start: tuple[float, float],
-             end: tuple[float, float] | None = None) -> Trip:
+             end: tuple[float, float] | None = None, 
+             background_tasks: BackgroundTasks = None) -> Trip:
     """
     Main function to call the optimizer.
 
@@ -91,6 +93,9 @@ def new_trip(preferences: Preferences,
         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)
@@ -114,6 +119,9 @@ def new_trip(preferences: Preferences,
         refined_tour = refiner.refine_optimization(landmarks, base_tour,
                                                preferences.max_time_minute,
                                                preferences.detour_tolerance_minute)
+    except TimeoutError as te :
+        logger.error(f'Refiner failed : {str(te)} Using base tour.')
+        refined_tour = base_tour
     except Exception as exc :
         raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {str(exc)}") from exc
 
@@ -127,6 +135,9 @@ 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.')
+
+    background_tasks.add_task(fill_cache)
+
     return trip
 
 

backend/src/optimization/optimizer.py

@@ -55,6 +55,9 @@ class Optimizer:
             self.average_walking_speed = parameters['average_walking_speed']
             self.max_landmarks = parameters['max_landmarks']
             self.overshoot = parameters['overshoot']
+            self.time_limit = parameters['time_limit']
+            self.gap_rel = parameters['gap_rel']
+            self.max_iter = parameters['max_iter']
 
 
     def init_ub_time(self, prob: pl.LpProblem, x: pl.LpVariable, L: int, landmarks: list[Landmark], max_time: int):
@@ -490,7 +493,18 @@ class Optimizer:
 
 
     def warm_start(self, x: list[pl.LpVariable], L: int) :
+        """
+        This function sets the initial values of the decision variables to a feasible solution.
+        This can help the solver start with a feasible or heuristic solution,
+        potentially speeding up convergence.
 
+        Args:
+        x (list[pl.LpVariable]): A list of PuLP decision variables (binary variables).
+        L (int): The size parameter, representing a dimension (likely related to a grid or matrix).
+
+        Returns:
+        list[pl.LpVariable]: The modified list of PuLP decision variables with initial values set.
+        """
         for i in range(L*L) :
             x[i].setInitialValue(0)
 
@@ -573,7 +587,10 @@ class Optimizer:
         prob, x = self.pre_processing(L, landmarks, max_time, max_landmarks)
 
         # Solve the problem and extract results.
-        prob.solve(pl.PULP_CBC_CMD(msg=False, gapRel=0.1, timeLimit=10, warmStart=False))
+        try :
+            prob.solve(pl.PULP_CBC_CMD(msg=False, timeLimit=self.time_limit+1, gapRel=self.gap_rel))
+        except Exception as exc :
+            raise Exception(f"No solution found: {exc}") from exc
         status = pl.LpStatus[prob.status]
         solution = [pl.value(var) for var in x]  # The values of the decision variables (will be 0 or 1)
 
@@ -588,18 +605,21 @@ class Optimizer:
         circles = self.is_connected(solution)
 
         i = 0
-        timeout = 40
         while circles is not None :
             i += 1
-            if i == timeout :
+            if i == self.max_iter :
-                self.logger.error(f'Timeout: No solution found after {timeout} iterations.')
+                self.logger.error(f'Timeout: No solution found after {self.max_iter} iterations.')
-                raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
+                raise TimeoutError(f"Optimization took too long. No solution found after {self.max_iter} iterations.")
 
             for circle in circles :
                 self.prevent_circle(prob, x, circle, L)
 
             # Solve the problem again
-            prob.solve(pl.PULP_CBC_CMD(msg=False))
+            try :
+                prob.solve(pl.PULP_CBC_CMD(msg=False, timeLimit=self.time_limit, gapRel=self.gap_rel))
+            except Exception as exc :
+                raise Exception(f"No solution found: {exc}") from exc
+
             solution = [pl.value(var) for var in x]
 
             if pl.LpStatus[prob.status] != 'Optimal' :
@@ -614,5 +634,5 @@ class Optimizer:
         order = self.get_order(solution)
         tour =  [landmarks[i] for i in order]
 
-        self.logger.debug(f"Re-optimized {i} times, objective value : {int(pl.value(prob.objective))}")
+        self.logger.info(f"Re-optimized {i} times, objective value : {int(pl.value(prob.objective))}")
         return tour

backend/src/overpass/caching_strategy.py

@@ -1,9 +1,8 @@
-"""Module defining the caching strategy for overpass requests."""
 import os
-import xml.etree.ElementTree as ET
+import json
 import hashlib
 
-from ..constants import OSM_CACHE_DIR
+from ..constants import OSM_CACHE_DIR, OSM_TYPES
 
 
 def get_cache_key(query: str) -> str:
@@ -17,10 +16,6 @@ def get_cache_key(query: str) -> str:
 class CachingStrategyBase:
     """
     Base class for implementing caching strategies.
-
-    This class defines the structure for a caching strategy with basic methods
-    that must be implemented by subclasses. Subclasses should define how to
-    retrieve, store, and close the cache.
     """
     def get(self, key):
         """Retrieve the cached data associated with the provided key."""
@@ -30,111 +25,108 @@ class CachingStrategyBase:
         """Store data in the cache with the specified key."""
         raise NotImplementedError('Subclass should implement set')
 
+    def set_hollow(self, key, **kwargs):
+        """Create a hollow (empty) cache entry with a specific key."""
+        raise NotImplementedError('Subclass should implement set_hollow')
+
     def close(self):
         """Clean up or close any resources used by the caching strategy."""
 
 
-class XMLCache(CachingStrategyBase):
+class JSONCache(CachingStrategyBase):
     """
-    A caching strategy that stores and retrieves data in XML format.
+    A caching strategy that stores and retrieves data in JSON format.
-
-    This class provides methods to cache data as XML files in a specified directory.
-    The directory is automatically suffixed with '_XML' to distinguish it from other
-    caching strategies. The data is stored and retrieved using XML serialization.
-
-    Args:
-        cache_dir (str): The base directory where XML cache files will be stored.
-                         Defaults to 'OSM_CACHE_DIR' with a '_XML' suffix.
-
-    Methods:
-        get(key): Retrieve cached data from a XML file associated with the given key.
-        set(key, value): Store data in a XML file with the specified key.
     """
     def __init__(self, cache_dir=OSM_CACHE_DIR):
         # Add the class name as a suffix to the directory
-        self._cache_dir = f'{cache_dir}_XML'
+        self._cache_dir = f'{cache_dir}'
         if not os.path.exists(self._cache_dir):
             os.makedirs(self._cache_dir)
 
     def _filename(self, key):
-        return os.path.join(self._cache_dir, f'{key}.xml')
+        return os.path.join(self._cache_dir, f'{key}.json')
 
     def get(self, key):
-        """Retrieve XML data from the cache and parse it as an ElementTree."""
+        """Retrieve JSON data from the cache and parse it as an ElementTree."""
         filename = self._filename(key)
         if os.path.exists(filename):
             try:
-                # Parse and return the cached XML data
+                # Open and parse the cached JSON data
-                tree = ET.parse(filename)
+                with open(filename, 'r', encoding='utf-8') as file:
-                return tree.getroot()  # Return the root element of the parsed XML
+                    data = json.load(file)
-            except ET.ParseError:
+                # Return the data as a list of dicts.
-                # print(f"Error parsing cached XML file: {filename}")
+                return data
-                return None
+            except json.JSONDecodeError:
+                return None  # Return None if parsing fails
         return None
 
     def set(self, key, value):
-        """Save the XML data as an ElementTree to the cache."""
+        """Save the JSON data as an ElementTree to the cache."""
         filename = self._filename(key)
-        tree = ET.ElementTree(value)  # value is expected to be an ElementTree root element
         try:
-            # Write the XML data to a file
+            # Write the JSON data to the cache file
-            with open(filename, 'wb') as file:
+            with open(filename, 'w', encoding='utf-8') as file:
-                tree.write(file, encoding='utf-8', xml_declaration=True)
+                json.dump(value, file, ensure_ascii=False, indent=4)
         except IOError as e:
             raise IOError(f"Error writing to cache file: {filename} - {e}") from e
 
+    def set_hollow(self, key, cell: tuple, osm_types: list,
+                    selector: str, conditions: list=None, out='center'):
+        """Create an empty placeholder cache entry for a future fill."""
+        hollow_key = f'hollow_{key}'
+        filename = self._filename(hollow_key)
+
+        # Create the hollow JSON structure
+        hollow_data = {
+            "key": key,
+            "cell": list(cell),
+            "osm_types": list(osm_types),
+            "selector": selector,
+            "conditions": conditions,
+            "out": out
+        }
+        # Write the hollow data to the cache file
+        try:
+            with open(filename, 'w', encoding='utf-8') as file:
+                json.dump(hollow_data, file, ensure_ascii=False, indent=4)
+        except IOError as e:
+            raise IOError(f"Error writing hollow cache to file: {filename} - {e}") from e
+
+    def close(self):
+        """Cleanup method, if needed."""
+        pass
 
 class CachingStrategy:
     """
     A class to manage different caching strategies.
-
-    This class provides an interface to switch between different caching strategies 
-    (e.g., XMLCache, JSONCache) dynamically. It allows caching data in different formats, 
-    depending on the strategy being used. By default, it uses the XMLCache strategy.
-
-    Attributes:
-    __strategy (CachingStrategyBase): The currently active caching strategy.
-    __strategies (dict): A mapping between strategy names (as strings) and their corresponding
-                         classes, allowing dynamic selection of caching strategies.
     """
-    __strategy = XMLCache()  # Default caching strategy
+    __strategy = JSONCache()  # Default caching strategy
     __strategies = {
-        'XML': XMLCache,
+        'JSON': JSONCache,
     }
 
     @classmethod
-    def use(cls, strategy_name='XML', **kwargs):
+    def use(cls, strategy_name='JSON', **kwargs):
-        """
-        Set the caching strategy based on the strategy_name provided.
-
-        Args:
-            strategy_name (str): The name of the caching strategy (e.g., 'XML').
-            **kwargs: Additional keyword arguments to pass when initializing the strategy.
-        """
-        # If a previous strategy exists, close it
         if cls.__strategy:
             cls.__strategy.close()
 
-        # Retrieve the strategy class based on the strategy name
         strategy_class = cls.__strategies.get(strategy_name)
-
         if not strategy_class:
             raise ValueError(f"Unknown caching strategy: {strategy_name}")
 
-        # Instantiate the new strategy with the provided arguments
         cls.__strategy = strategy_class(**kwargs)
         return cls.__strategy
 
     @classmethod
     def get(cls, key):
-        """Get data from the current strategy's cache."""
-        if not cls.__strategy:
-            raise RuntimeError("Caching strategy has not been set.")
         return cls.__strategy.get(key)
 
     @classmethod
     def set(cls, key, value):
-        """Set data in the current strategy's cache."""
-        if not cls.__strategy:
-            raise RuntimeError("Caching strategy has not been set.")
         cls.__strategy.set(key, value)
+
+    @classmethod
+    def set_hollow(cls, key, cell: tuple, osm_types: OSM_TYPES,
+                    selector: str, conditions: list=None, out='center'):
+        """Create a hollow cache entry."""
+        cls.__strategy.set_hollow(key, cell, osm_types, selector, conditions, out)

backend/src/overpass/overpass.py

@@ -1,14 +1,17 @@
 """Module allowing connexion to overpass api and fectch data from OSM."""
-from typing import Literal, List
+import os
 import urllib
+import math
 import logging
-import xml.etree.ElementTree as ET
+import json
+from typing import List, Tuple
 
 from .caching_strategy import get_cache_key, CachingStrategy
-from ..constants import OSM_CACHE_DIR
+from ..constants import OSM_CACHE_DIR, OSM_TYPES, BBOX
 
-logger = logging.getLogger('Overpass')
+
-osm_types = List[Literal['way', 'node', 'relation']]
+RESOLUTION = 0.05
+CELL = Tuple[int, int]
 
 
 class Overpass :
@@ -16,7 +19,10 @@ class Overpass :
     Overpass class to manage the query building and sending to overpass api.
     The caching strategy is a part of this class and initialized upon creation of the Overpass object.
     """
-    def __init__(self, caching_strategy: str = 'XML', cache_dir: str = OSM_CACHE_DIR) :
+    logger = logging.getLogger(__name__)
+
+
+    def __init__(self, caching_strategy: str = 'JSON', cache_dir: str = OSM_CACHE_DIR) :
         """
         Initialize the Overpass instance with the url, headers and caching strategy.
         """
@@ -25,17 +31,109 @@ class Overpass :
         self.caching_strategy = CachingStrategy.use(caching_strategy, cache_dir=cache_dir)
 
 
-    @classmethod
+    def send_query(self, bbox: BBOX, osm_types: OSM_TYPES,
-    def build_query(self, area: tuple, osm_types: osm_types,
+                    selector: str, conditions: list=None, out='center') -> List[dict]:
-                    selector: str, conditions=[], out='center') -> str:
+        """
+        Sends the Overpass QL query to the Overpass API and returns the parsed json response.
+
+        Args:
+            bbox (tuple): Bounding box for the query.
+            osm_types (list[str]): List of OSM element types (e.g., 'node', 'way').
+            selector (str): Key or tag to filter OSM elements (e.g., 'highway').
+            conditions (list): Optional list of additional filter conditions in Overpass QL format.
+            out (str): Output format ('center', 'body', etc.). Defaults to 'center'.
+
+        Returns:
+            list:   Parsed json response from the Overpass API, or cached data if available.
+        """
+        # Determine which grid cells overlap with this bounding box.
+        overlapping_cells = Overpass._get_overlapping_cells(bbox)
+
+        # Retrieve cached data and identify missing cache entries
+        cached_responses, non_cached_cells = self._retrieve_cached_data(overlapping_cells, osm_types, selector, conditions, out)
+
+        self.logger.info(f'Cache hit for {len(overlapping_cells)-len(non_cached_cells)}/{len(overlapping_cells)} quadrants.')
+
+        # If there is no missing data, return the cached responses after filtering.
+        if not non_cached_cells :
+            return Overpass._filter_landmarks(cached_responses, bbox)
+
+        # If there is no cached data, fetch all from Overpass.
+        elif not cached_responses :
+            query_str = Overpass.build_query(bbox, osm_types, selector, conditions, out)
+            return self.fetch_data_from_api(query_str)
+
+        # Hybrid cache: some data from Overpass, some data from cache.
+        else :
+            # Resize the bbox for smaller search area and build new query string.
+            non_cached_bbox = Overpass._get_non_cached_bbox(non_cached_cells, bbox)
+            query_str = Overpass.build_query(non_cached_bbox, osm_types, selector, conditions, out)
+            non_cached_responses = self.fetch_data_from_api(query_str)
+            return Overpass._filter_landmarks(cached_responses, bbox) + non_cached_responses
+
+
+    def fetch_data_from_api(self, query_str: str) -> List[dict]:
+        """
+        Fetch data from the Overpass API and return the json data.
+
+        Args:
+            query_str (str): The Overpass query string.
+
+        Returns:
+            dict: Combined cached and fetched data.
+        """
+        try:
+            data = urllib.parse.urlencode({'data': query_str}).encode('utf-8')
+            request = urllib.request.Request(self.overpass_url, data=data, headers=self.headers)
+
+            with urllib.request.urlopen(request) as response:
+                response_data = response.read().decode('utf-8')  # Convert the HTTPResponse to a string
+                data = json.loads(response_data)  # Load the JSON from the string
+                elements = data.get('elements', [])
+                # self.logger.debug(f'Query = {query_str}')
+                return elements
+
+        except urllib.error.URLError as e:
+            self.logger.error(f"Error connecting to Overpass API: {e}")
+            raise ConnectionError(f"Error connecting to Overpass API: {e}") from e
+        except Exception as exc :
+            raise Exception(f'An unexpected error occured: {str(exc)}') from exc
+
+
+    def fill_cache(self, json_data: dict) :
+        """
+        Fill cache with data by using a hollow cache entry's information.
+        """
+        query_str, cache_key = Overpass._build_query_from_hollow(json_data)
+        try:
+            data = urllib.parse.urlencode({'data': query_str}).encode('utf-8')
+            request = urllib.request.Request(self.overpass_url, data=data, headers=self.headers)
+
+            with urllib.request.urlopen(request) as response:
+
+                # Convert the HTTPResponse to a string and load data
+                response_data = response.read().decode('utf-8')  
+                data = json.loads(response_data)
+
+                # Get elements and set cache
+                elements = data.get('elements', [])
+                self.caching_strategy.set(cache_key, elements)
+                self.logger.debug(f'Cache set for {cache_key}')
+        except urllib.error.URLError as e:
+            raise ConnectionError(f"Error connecting to Overpass API: {e}") from e
+        except Exception as exc :
+            raise Exception(f'An unexpected error occured: {str(exc)}') from exc
+
+
+    @staticmethod
+    def build_query(bbox: BBOX, osm_types: OSM_TYPES,
+                    selector: str, conditions: list=None, out='center') -> str:
         """
         Constructs a query string for the Overpass API to retrieve OpenStreetMap (OSM) data.
 
         Args:
-            area (tuple): A tuple representing the geographical search area, typically in the format 
+            bbox (tuple): A tuple representing the geographical search area, typically in the format 
-                        (radius, latitude, longitude). The first element is a string like "around:2000" 
+                        (lat_min, lon_min, lat_max, lon_max).
-                        specifying the search radius, and the second and third elements represent 
-                        the latitude and longitude as floats or strings.
             osm_types (list[str]): A list of OSM element types to search for. Must be one or more of 
                                     'Way', 'Node', or 'Relation'.
             selector (str): The key or tag to filter the OSM elements (e.g., 'amenity', 'highway', etc.).
@@ -52,82 +150,203 @@ class Overpass :
         Notes:
             - If no conditions are provided, the query will just use the `selector` to filter the OSM 
             elements without additional constraints.
-            - The search area must always formatted as "(radius, lat, lon)".
         """
-        if not isinstance(conditions, list) :
+        query = '[out:json];('
-            conditions = [conditions]
-        if not isinstance(osm_types, list) :
-            osm_types = [osm_types]
 
-        query = '('
+        # convert the bbox to string.
+        bbox_str = f"({','.join(map(str, bbox))})"
 
-        # Round the radius to nearest 50 and coordinates to generate less queries
+        if conditions is not None and len(conditions) > 0:
-        if area[0] > 500 :
-            search_radius = round(area[0] / 50) * 50
-            loc = tuple((round(area[1], 2), round(area[2], 2)))
-        else :
-            search_radius = round(area[0] / 25) * 25
-            loc = tuple((round(area[1], 3), round(area[2], 3)))
-
-        search_area = f"(around:{search_radius}, {str(loc[0])}, {str(loc[1])})"
-
-        if conditions :
             conditions = '(if: ' + ' && '.join(conditions) + ')'
         else :
             conditions = ''
 
         for elem in osm_types :
-            query += elem + '[' + selector + ']' + conditions + search_area + ';'
+            query += elem + '[' + selector + ']' + conditions + bbox_str + ';'
 
         query += ');' + f'out {out};'
 
         return query
 
 
-    def send_query(self, query: str) -> ET:
+    def _retrieve_cached_data(self, overlapping_cells: CELL, osm_types: OSM_TYPES,
+                              selector: str, conditions: list, out: str) -> Tuple[List[dict], list[CELL]]:
         """
-        Sends the Overpass QL query to the Overpass API and returns the parsed JSON response.
+        Retrieve cached data and identify missing cache quadrants.
 
         Args:
-            query (str): The Overpass QL query to be sent to the Overpass API.
+            overlapping_cells (list): Cells to check for cached data.
+            osm_types (list): OSM types (e.g., 'node', 'way').
+            selector (str): Key or tag to filter OSM elements.
+            conditions (list): Additional conditions to apply.
+            out (str): Output format.
 
         Returns:
-            dict: The parsed JSON response from the Overpass API, or None if the request fails.
+            tuple: A tuple containing:
+                - cached_responses (list): List of cached data found.
+                - non_cached_cells (list(tuple)): List of cells with missing data.
+        """
+        cell_key_dict = {}
+        for cell in overlapping_cells :
+            for elem in osm_types :
+                key_str = f"{elem}[{selector}]{conditions}({','.join(map(str, cell))})"
+
+            cell_key_dict[cell] = get_cache_key(key_str)
+
+        cached_responses = []
+        non_cached_cells = []
+
+        # Retrieve the cached data and mark the missing entries as hollow
+        for cell, key in cell_key_dict.items():
+            cached_data = self.caching_strategy.get(key)
+            if cached_data is not None :
+                cached_responses += cached_data
+            else:
+                self.caching_strategy.set_hollow(key, cell, osm_types, selector, conditions, out)
+                non_cached_cells.append(cell)
+
+        return cached_responses, non_cached_cells
+
+
+    @staticmethod
+    def _build_query_from_hollow(json_data: dict) -> Tuple[str, str]:
+        """
+        Build query string using information from a hollow cache entry.
+        """
+        # Extract values from the JSON object
+        key = json_data.get('key')
+        cell = tuple(json_data.get('cell'))
+        bbox = Overpass._get_bbox_from_grid_cell(cell)
+        osm_types = json_data.get('osm_types')
+        selector = json_data.get('selector')
+        conditions = json_data.get('conditions')
+        out = json_data.get('out')
+
+
+        query_str = Overpass.build_query(bbox, osm_types, selector, conditions, out)
+        return query_str, key
+
+
+    @staticmethod
+    def _get_overlapping_cells(query_bbox: tuple) -> List[CELL]:
+        """
+        Returns a set of all grid cells that overlap with the given bounding box.
+        """
+        # Extract location from the query bbox
+        lat_min, lon_min, lat_max, lon_max = query_bbox
+
+        min_lat_cell, min_lon_cell = Overpass._get_grid_cell(lat_min, lon_min)
+        max_lat_cell, max_lon_cell = Overpass._get_grid_cell(lat_max, lon_max)
+
+        overlapping_cells = set()
+        for lat_idx in range(min_lat_cell, max_lat_cell + 1):
+            for lon_idx in range(min_lon_cell, max_lon_cell + 1):
+                overlapping_cells.add((lat_idx, lon_idx))
+
+        return overlapping_cells
+
+
+    @staticmethod
+    def _get_grid_cell(lat: float, lon: float) -> CELL:
+        """
+        Returns the grid cell coordinates for a given latitude and longitude.
+        Each grid cell is 0.05°lat x 0.05°lon resolution in size.
+        """
+        lat_index = math.floor(lat / RESOLUTION)
+        lon_index = math.floor(lon / RESOLUTION)
+        return (lat_index, lon_index)
+
+
+    @staticmethod
+    def _get_bbox_from_grid_cell(cell: CELL) -> BBOX:
+        """
+        Returns the bounding box for a given grid cell index.
+        Each grid cell is resolution x resolution in size.
+
+        The bounding box is returned as (min_lat, min_lon, max_lat, max_lon).
+        """
+        # Calculate the southwest (min_lat, min_lon) corner of the bounding box
+        min_lat = round(cell[0] * RESOLUTION, 2)
+        min_lon = round(cell[1] * RESOLUTION, 2)
+
+        # Calculate the northeast (max_lat, max_lon) corner of the bounding box
+        max_lat = round((cell[0] + 1) * RESOLUTION, 2)
+        max_lon = round((cell[1] + 1) * RESOLUTION, 2)
+
+        return (min_lat, min_lon, max_lat, max_lon)
+
+
+    @staticmethod
+    def _get_non_cached_bbox(non_cached_cells: List[CELL], original_bbox: BBOX):
+        """
+        Calculate the non-cached bounding box by excluding cached cells.
+
+        Args:
+            non_cached_cells (list): The list of cells that were not found in the cache.
+            original_bbox (tuple): The original bounding box (min_lat, min_lon, max_lat, max_lon).
+
+        Returns:
+            tuple: The new bounding box that excludes cached cells, or None if all cells are cached.
+        """
+        if not non_cached_cells:
+            return None  # All cells were cached
+
+        # Initialize the non-cached bounding box with extreme values
+        min_lat, min_lon, max_lat, max_lon = float('inf'), float('inf'), float('-inf'), float('-inf')
+
+        # Iterate over non-cached cells to find the new bounding box
+        for cell in non_cached_cells:
+            cell_min_lat, cell_min_lon, cell_max_lat, cell_max_lon = Overpass._get_bbox_from_grid_cell(cell)
+
+            min_lat = min(min_lat, cell_min_lat)
+            min_lon = min(min_lon, cell_min_lon)
+            max_lat = max(max_lat, cell_max_lat)
+            max_lon = max(max_lon, cell_max_lon)
+
+        # If no update to bounding box, return the original
+        if min_lat == float('inf') or min_lon == float('inf'):
+            return None
+
+        return (max(min_lat, original_bbox[0]), 
+                max(min_lon, original_bbox[1]), 
+                min(max_lat, original_bbox[2]), 
+                min(max_lon, original_bbox[3]))
+
+
+    @staticmethod
+    def _filter_landmarks(elements: List[dict], bbox: BBOX) -> List[dict]:
+        """
+        Filters elements based on whether their coordinates are inside the given bbox.
+
+        Args:
+        - elements (list of dict): List of elements containing coordinates.
+        - bbox (tuple): A bounding box defined as (min_lat, min_lon, max_lat, max_lon).
+
+        Returns:
+        - list: A list of elements whose coordinates are inside the bounding box.
         """
 
-        # Generate a cache key for the current query
+        filtered_elements = []
-        cache_key = get_cache_key(query)
+        min_lat, min_lon, max_lat, max_lon = bbox
 
-        # Try to fetch the result from the cache
+        for elem in elements:
-        cached_response = self.caching_strategy.get(cache_key)
+            # Extract coordinates based on the 'type' of element
-        if cached_response is not None :
+            if elem.get('type') != 'node':
-            logger.debug("Cache hit.")
+                center = elem.get('center', {})
-            return cached_response
+                lat = float(center.get('lat', 0))
+                lon = float(center.get('lon', 0))
+            else:
+                lat = float(elem.get('lat', 0))
+                lon = float(elem.get('lon', 0))
 
-        # Prepare the data to be sent as POST request, encoded as bytes
+            # Check if the coordinates fall within the given bounding box
-        data = urllib.parse.urlencode({'data': query}).encode('utf-8')
+            if min_lat <= lat <= max_lat and min_lon <= lon <= max_lon:
+                filtered_elements.append(elem)
 
-        try:
+        return filtered_elements
-            # Create a Request object with the specified URL, data, and headers
-            request = urllib.request.Request(self.overpass_url, data=data, headers=self.headers)
-
-            # Send the request and read the response
-            with urllib.request.urlopen(request) as response:
-                # Read and decode the response
-                response_data = response.read().decode('utf-8')
-                root = ET.fromstring(response_data)
-
-                # Cache the response data as an ElementTree root
-                self.caching_strategy.set(cache_key, root)
-                logger.debug("Response data added to cache.")
-
-                return root
-
-        except urllib.error.URLError as e:
-            raise ConnectionError(f"Error connecting to Overpass API: {e}") from e
 
 
-def get_base_info(elem: ET.Element, osm_type: osm_types, with_name=False) :
+def get_base_info(elem: dict, osm_type: OSM_TYPES, with_name=False) :
     """
     Extracts base information (coordinates, OSM ID, and optionally a name) from an OSM element.
 
@@ -136,7 +355,7 @@ def get_base_info(elem: ET.Element, osm_type: osm_types, with_name=False) :
     extracting coordinates either directly or from a center tag, depending on the element type.
 
     Args:
-        elem (ET.Element): The XML element representing the OSM entity.
+        elem (dict): The JSON element representing the OSM entity.
         osm_type (str): The type of the OSM entity (e.g., 'node', 'way'). If 'node', the coordinates
                         are extracted directly from the element; otherwise, from the 'center' tag.
         with_name (bool): Whether to extract and return the name of the element. If True, it attempts
@@ -150,7 +369,7 @@ def get_base_info(elem: ET.Element, osm_type: osm_types, with_name=False) :
     """
     # 1. extract coordinates
     if osm_type != 'node' :
-        center = elem.find('center')
+        center = elem.get('center')
         lat = float(center.get('lat'))
         lon = float(center.get('lon'))
 
@@ -165,7 +384,31 @@ def get_base_info(elem: ET.Element, osm_type: osm_types, with_name=False) :
 
     # 3. Extract name if specified and return
     if with_name :
-        name = elem.find("tag[@k='name']").get('v') if elem.find("tag[@k='name']") is not None else None
+        name = elem.get('tags', {}).get('name')
         return osm_id, coords, name
     else :
         return osm_id, coords
+
+
+def fill_cache():
+    """
+    Scans the specified cache directory for files starting with 'hollow_' and attempts to load
+    their contents as JSON to fill the cache of the Overpass system.
+    """
+    overpass = Overpass()
+
+    with os.scandir(OSM_CACHE_DIR) as it:
+        for entry in it:
+            if entry.is_file() and entry.name.startswith('hollow_'):
+
+                try :
+                    # Read the whole file content as a string
+                    with open(entry.path, 'r') as f:
+                        # load data and fill the cache with the query and key
+                        json_data = json.load(f)
+                        overpass.fill_cache(json_data)
+                    # Now delete the file as the cache is filled
+                    os.remove(entry.path)
+
+                except Exception as exc :
+                    overpass.logger.error(f'An error occured while parsing file {entry.path} as .json file')

backend/src/parameters/landmark_parameters.yaml

@@ -1,4 +1,4 @@
-city_bbox_side: 7500 #m
+max_bbox_side: 4000   #m
 radius_close_to: 50
 church_coeff: 0.55
 nature_coeff: 1.4
@@ -8,5 +8,5 @@ image_bonus: 1.1
 viewpoint_bonus: 5
 wikipedia_bonus: 1.25
 name_bonus: 3
-N_important: 40
+N_important: 60
 pay_bonus: -1

backend/src/parameters/optimizer_parameters.yaml

@@ -4,3 +4,6 @@ average_walking_speed: 4.8
 max_landmarks: 10
 max_landmarks_refiner: 20
 overshoot: 0.0016
+time_limit: 1
+gap_rel: 0.05
+max_iter: 40

backend/src/structs/landmark.py

@@ -2,7 +2,7 @@
 
 from typing import Optional, Literal
 from uuid import uuid4, UUID
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 # Output to frontend
@@ -144,8 +144,4 @@ class Toilets(BaseModel) :
         """
         return f'Toilets @{self.location}'
 
-    class Config:
+    model_config = ConfigDict(from_attributes=True)
-        """
-        This allows us to easily convert the model to and from dictionaries
-        """
-        from_attributes = True

backend/src/tests/test_main.py

@@ -27,11 +27,13 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
         "/trip/new",
         json={
             "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-            "nature": {"type": "nature", "score": 5},
+            "nature": {"type": "nature", "score": 0},
-            "shopping": {"type": "shopping", "score": 5},
+            "shopping": {"type": "shopping", "score": 0},
             "max_time_minute": duration_minutes,
             "detour_tolerance_minute": 0},
-            "start": [48.084588, 7.280405]
+            # "start": [48.084588, 7.280405]
+            # "start": [45.74445023349939, 4.8222687890538865]
+            "start": [45.75156398104873, 4.827154464827647]
             }
         )
     result = response.json()
@@ -51,11 +53,11 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
     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.
@@ -97,10 +99,9 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     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°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
+    Test n°3 : Custom test in Cologne to ensure proper decision making in crowded area.
     
     Args:
         client:
@@ -141,7 +142,7 @@ def test_cologne(client, request) :   # pylint: disable=redefined-outer-name
 
 def test_strasbourg(client, request) :   # pylint: disable=redefined-outer-name
     """
-    Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
+    Test n°4 : Custom test in Strasbourg to ensure proper decision making in crowded area.
     
     Args:
         client:
@@ -182,7 +183,7 @@ def test_strasbourg(client, request) :   # pylint: disable=redefined-outer-name
 
 def test_zurich(client, request) :   # pylint: disable=redefined-outer-name
     """
-    Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
+    Test n°5 : Custom test in Zurich to ensure proper decision making in crowded area.
     
     Args:
         client:
@@ -223,24 +224,24 @@ def test_zurich(client, request) :   # pylint: disable=redefined-outer-name
 
 def test_paris(client, request) :   # pylint: disable=redefined-outer-name
     """
-    Test n°2 : Custom test in Paris (les Halles) centre to ensure proper decision making in crowded area.
+    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 = 300
+    duration_minutes = 200
 
     response = client.post(
         "/trip/new",
         json={
             "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-                            "nature": {"type": "nature", "score": 5},
+                            "nature": {"type": "nature", "score": 0},
                             "shopping": {"type": "shopping", "score": 5},
                             "max_time_minute": duration_minutes,
                             "detour_tolerance_minute": 0},
-            "start": [48.86248803298562, 2.346451131285925]
+            "start": [48.85468881798671, 2.3423925755998374]
             }
         )
     result = response.json()
@@ -264,7 +265,7 @@ def test_paris(client, request) :   # pylint: disable=redefined-outer-name
 
 def test_new_york(client, request) :   # pylint: disable=redefined-outer-name
     """
-    Test n°2 : Custom test in New York (les Halles) centre to ensure proper decision making in crowded area.
+    Test n°7 : Custom test in New York to ensure proper decision making in crowded area.
     
     Args:
         client:
@@ -305,7 +306,7 @@ def test_new_york(client, request) :   # pylint: disable=redefined-outer-name
 
 def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
     """
-    Test n°3 : Custom test in Lyon centre to ensure shopping clusters are found.
+    Test n°8 : Custom test in Lyon centre to ensure shopping clusters are found.
     
     Args:
         client:
@@ -334,8 +335,8 @@ def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
     # Add details to report
     log_trip_details(request, landmarks, result['total_time'], duration_minutes)
 
-    for elem in landmarks :
+    # for elem in landmarks :
-        print(elem)
+    #     print(elem)
 
     # checks :
     assert response.status_code == 200  # check for successful planning

backend/src/utils/cluster_manager.py

@@ -9,7 +9,8 @@ from pydantic import BaseModel
 from ..overpass.overpass import Overpass, get_base_info
 from ..structs.landmark import Landmark
 from .get_time_distance import get_distance
-from ..constants import OSM_CACHE_DIR
+from .utils import create_bbox
+
 
 
 # silence the overpass logger
@@ -79,8 +80,7 @@ class ClusterManager:
             bbox: The bounding box coordinates (around:radius, center_lat, center_lon).
         """
         # Setup the caching in the Overpass class.
-        self.overpass = Overpass(caching_strategy='XML', cache_dir=OSM_CACHE_DIR)
+        self.overpass = Overpass()
-
 
         self.cluster_type = cluster_type
         if cluster_type == 'shopping' :
@@ -95,27 +95,24 @@ class ClusterManager:
             raise NotImplementedError("Please choose only an available option for cluster detection")
 
         # Initialize the points for cluster detection
-        query = self.overpass.build_query(
+        try:
-            area = bbox,
+            result = self.overpass.send_query(
+            bbox = bbox,
             osm_types = osm_types,
             selector = sel,
             out = out
         )
-        self.logger.debug(f"Cluster query: {query}")
-
-        try:
-            result = self.overpass.send_query(query)
         except Exception as e:
-            self.logger.error(f"Error fetching landmarks: {e}")
+            self.logger.error(f"Error fetching clusters: {e}")
 
         if result is None :
-            self.logger.error(f"Error fetching {cluster_type} clusters, overpass query returned None.")
+            self.logger.debug(f"Found no {cluster_type} clusters, overpass query returned no datapoints.")
             self.valid = False
 
         else :
             points = []
-            for osm_type in osm_types :
+            for elem in result:
-                for elem in result.findall(osm_type):
+                osm_type = elem.get('type')
                 
                 # Get coordinates and append them to the points list
                 _, coords = get_base_info(elem, osm_type)
@@ -137,7 +134,7 @@ class ClusterManager:
 
                 # Check that there are is least 1 cluster
                 if len(set(labels)) > 1 :
-                    self.logger.debug(f"Found {len(set(labels))} different clusters.")
+                    self.logger.info(f"Found {len(set(labels))} different {cluster_type} clusters.")
                     # Separate clustered points and noise points
                     self.cluster_points = self.all_points[labels != -1]
                     self.cluster_labels = labels[labels != -1]
@@ -145,7 +142,7 @@ class ClusterManager:
                     self.valid = True
 
                 else :
-                    self.logger.debug(f"Detected 0 {cluster_type} clusters.")
+                    self.logger.info(f"Found 0 {cluster_type} clusters.")
                     self.valid = False
 
             else :
@@ -218,8 +215,7 @@ class ClusterManager:
         """
 
         # Define the bounding box for a given radius around the coordinates
-        lat, lon = cluster.centroid
+        bbox = create_bbox(cluster.centroid, 1000)
-        bbox = (1000, lat, lon)
         
         # Query neighborhoods and shopping malls
         selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"']
@@ -238,25 +234,22 @@ class ClusterManager:
         osm_types = ['node', 'way', 'relation']
 
         for sel in selectors :
-            query = self.overpass.build_query(
+            try:
-                area = bbox,
+                result = self.overpass.send_query(bbox = bbox,
                                                   osm_types = osm_types,
                                                   selector = sel,
                                                   out = 'ids center'
                                                   )
-
-            try:
-                result = self.overpass.send_query(query)
             except Exception as e:
-                self.logger.error(f"Error fetching landmarks: {e}")
+                self.logger.error(f"Error fetching clusters: {e}")
                 continue
 
             if result is None :
-                self.logger.error(f"Error fetching landmarks: {e}")
+                self.logger.error(f"Error fetching clusters: {e}")
                 continue
 
-            for osm_type in osm_types :
+            for elem in result:
-                for elem in result.findall(osm_type):
+                osm_type = elem.get('type')
 
                 id, coords, name = get_base_info(elem, osm_type, with_name=True)
 

backend/src/utils/landmarks_manager.py

@@ -1,19 +1,15 @@
 """Module used to import data from OSM and arrange them in categories."""
 import logging
-import xml.etree.ElementTree as ET
 import yaml
 
-
 from ..structs.preferences import Preferences
 from ..structs.landmark import Landmark
 from .take_most_important import take_most_important
 from .cluster_manager import ClusterManager
 from ..overpass.overpass import Overpass, get_base_info
+from .utils import create_bbox
 
-from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
+from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH
-
-# silence the overpass logger
-logging.getLogger('Overpass').setLevel(level=logging.CRITICAL)
 
 
 class LandmarkManager:
@@ -37,8 +33,7 @@ class LandmarkManager:
 
         with LANDMARK_PARAMETERS_PATH.open('r') as f:
             parameters = yaml.safe_load(f)
-            self.max_bbox_side = parameters['city_bbox_side']
+            self.max_bbox_side = parameters['max_bbox_side']
-            self.radius_close_to = parameters['radius_close_to']
             self.church_coeff = parameters['church_coeff']
             self.nature_coeff = parameters['nature_coeff']
             self.overall_coeff = parameters['overall_coeff']
@@ -56,7 +51,7 @@ class LandmarkManager:
             self.detour_factor = parameters['detour_factor']
 
         # Setup the caching in the Overpass class.
-        self.overpass = Overpass(caching_strategy='XML', cache_dir=OSM_CACHE_DIR)
+        self.overpass = Overpass()
 
         self.logger.info('LandmakManager successfully initialized.')
 
@@ -80,39 +75,39 @@ class LandmarkManager:
         """
         self.logger.debug('Starting to fetch landmarks...')
         max_walk_dist = int((preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor)
-        reachable_bbox_side = min(max_walk_dist, self.max_bbox_side)
+        radius = min(max_walk_dist, int(self.max_bbox_side/2))
 
         # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
         all_landmarks = set()
 
         # Create a bbox using the around technique, tuple of strings
-        bbox = tuple((min(2000, reachable_bbox_side/2), center_coordinates[0], center_coordinates[1]))
+        bbox = create_bbox(center_coordinates, radius)
 
         # list for sightseeing
         if preferences.sightseeing.score != 0:
             self.logger.debug('Fetching sightseeing landmarks...')
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, preferences.sightseeing.score)
             all_landmarks.update(current_landmarks)
-            self.logger.debug('Fetching sightseeing clusters...')
+            self.logger.info(f'Found {len(current_landmarks)} sightseeing landmarks')
 
             # special pipeline for historic neighborhoods
             neighborhood_manager = ClusterManager(bbox, 'sightseeing')
             historic_clusters = neighborhood_manager.generate_clusters()
             all_landmarks.update(historic_clusters)
-            self.logger.debug('Sightseeing clusters done')
 
         # list for nature
         if preferences.nature.score != 0:
             self.logger.debug('Fetching nature landmarks...')
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, preferences.nature.score)
             all_landmarks.update(current_landmarks)
+            self.logger.info(f'Found {len(current_landmarks)} nature landmarks')
 
 
         # list for shopping
         if preferences.shopping.score != 0:
             self.logger.debug('Fetching shopping landmarks...')
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, preferences.shopping.score)
-            self.logger.debug('Fetching shopping clusters...')
+            self.logger.info(f'Found {len(current_landmarks)} shopping landmarks')
 
             # set time for all shopping activites :
             for landmark in current_landmarks :
@@ -123,8 +118,6 @@ class LandmarkManager:
             shopping_manager = ClusterManager(bbox, 'shopping')
             shopping_clusters = shopping_manager.generate_clusters()
             all_landmarks.update(shopping_clusters)
-            self.logger.debug('Shopping clusters done')
-
 
 
         landmarks_constrained = take_most_important(all_landmarks, self.n_important)
@@ -179,7 +172,7 @@ class LandmarkManager:
         """
         return_list = []
 
-        if landmarktype == 'nature' : query_conditions = []
+        if landmarktype == 'nature' : query_conditions = None
         else : query_conditions = ['count_tags()>5']
 
         # caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
@@ -190,60 +183,58 @@ class LandmarkManager:
             osm_types = ['way', 'relation']
 
             if 'viewpoint' in sel :
-                query_conditions = []
+                query_conditions = None
                 osm_types.append('node')
 
-            query = self.overpass.build_query(
+            # Send the overpass query
-                area = bbox,
+            try:
+                result = self.overpass.send_query(
+                    bbox = bbox,
                     osm_types = osm_types,
                     selector = sel,
                     conditions = query_conditions,        # except for nature....
-                out = 'center'
+                    out = 'ids center tags'
                     )
-            self.logger.debug(f"Query: {query}")
-
-            try:
-                result = self.overpass.send_query(query)
             except Exception as e:
                 self.logger.error(f"Error fetching landmarks: {e}")
                 continue
 
-            return_list += self.xml_to_landmarks(result, landmarktype, preference_level)
+            return_list += self._to_landmarks(result, landmarktype, preference_level)
 
         self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
 
         return return_list
 
 
-    def xml_to_landmarks(self, root: ET.Element, landmarktype, preference_level) -> list[Landmark]:
+    def _to_landmarks(self, elements: list, landmarktype, preference_level) -> list[Landmark]:
         """
         Parse the Overpass API result and extract landmarks.
 
-        This method processes the XML root element returned by the Overpass API and 
+        This method processes the JSON elements returned by the Overpass API and 
         extracts landmarks of types 'node', 'way', and 'relation'. It retrieves 
         relevant information such as name, coordinates, and tags, and converts them 
         into Landmark objects.
 
         Args:
-        root (ET.Element): The root element of the XML response from Overpass API.
+        elements (list): The elements of json response from Overpass API.
         elem_type (str): The type of landmark (e.g., node, way, relation).
 
         Returns:
-        list[Landmark]: A list of Landmark objects extracted from the XML data.
+        list[Landmark]: A list of Landmark objects extracted from the JSON data.
         """
-        if root is None :
+        if elements is None :
             return []
 
         landmarks = []
-        for osm_type in ['node', 'way', 'relation'] :
+        for elem in elements:
-            for elem in root.findall(osm_type):
+            osm_type = elem.get('type')
 
             id, coords, name = get_base_info(elem, osm_type, with_name=True)
 
             if name is None or coords is None :
                 continue
 
-                tags = elem.findall('tag')
+            tags = elem.get('tags')
 
             # Convert this to Landmark object
             landmark = Landmark(name=name,
@@ -254,10 +245,10 @@ class LandmarkManager:
                                 attractiveness=0,
                                 n_tags=len(tags))
 
+            # self.logger.debug('added landmark.')
+
             # Browse through tags to add information to landmark.
-                for tag in tags:
+            for key, value in tags.items():
-                    key = tag.get('k')
-                    value = tag.get('v')
 
                 # Skip this landmark if not suitable.
                 if key == 'building:part' and value == 'yes' :

backend/src/utils/toilets_manager.py

@@ -1,10 +1,9 @@
 """Module for finding public toilets around given coordinates."""
 import logging
-import xml.etree.ElementTree as ET
 
 from ..overpass.overpass import Overpass, get_base_info
 from ..structs.landmark import Toilets
-from ..constants import OSM_CACHE_DIR
+from .utils import create_bbox
 
 
 # silence the overpass logger
@@ -41,7 +40,7 @@ class ToiletsManager:
         self.location = location
 
         # Setup the caching in the Overpass class.
-        self.overpass = Overpass(caching_strategy='XML', cache_dir=OSM_CACHE_DIR)
+        self.overpass = Overpass()
 
 
     def generate_toilet_list(self) -> list[Toilets] :
@@ -53,51 +52,49 @@ class ToiletsManager:
         list[Toilets]: A list of `Toilets` objects containing detailed information 
                        about the toilets found around the given coordinates.
         """
-        bbox = tuple((self.radius, self.location[0], self.location[1]))
+        bbox = create_bbox(self.location, self.radius)
         osm_types = ['node', 'way', 'relation']
         toilets_list = []
 
-        query = self.overpass.build_query(
+        query = Overpass.build_query(
-                area = bbox,
+            bbox = bbox,
             osm_types = osm_types,
             selector = '"amenity"="toilets"',
             out = 'ids center tags'
             )
-        self.logger.debug(f"Query: {query}")
-
         try:
-            result = self.overpass.send_query(query)
+            result = self.overpass.fetch_data_from_api(query_str=query)
         except Exception as e:
             self.logger.error(f"Error fetching landmarks: {e}")
             return None
 
-        toilets_list = self.xml_to_toilets(result)
+        toilets_list = self.to_toilets(result)
 
         return toilets_list
 
 
-    def xml_to_toilets(self, root: ET.Element) -> list[Toilets]:
+    def to_toilets(self, elements: list) -> list[Toilets]:
         """
         Parse the Overpass API result and extract landmarks.
 
-        This method processes the XML root element returned by the Overpass API and 
+        This method processes the JSON elements returned by the Overpass API and 
         extracts landmarks of types 'node', 'way', and 'relation'. It retrieves 
         relevant information such as name, coordinates, and tags, and converts them 
         into Landmark objects.
 
         Args:
-        root (ET.Element): The root element of the XML response from Overpass API.
+        list (osm elements): The root element of the JSON response from Overpass API.
         elem_type (str): The type of landmark (e.g., node, way, relation).
 
         Returns:
-        list[Landmark]: A list of Landmark objects extracted from the XML data.
+        list[Landmark]: A list of Landmark objects extracted from the JSON data.
         """
-        if root is None :
+        if elements is None :
             return []
 
         toilets_list = []
-        for osm_type in ['node', 'way', 'relation'] :
+        for elem in elements:
-            for elem in root.findall(osm_type):
+            osm_type = elem.get('type')
             # Get coordinates and append them to the points list
             _, coords = get_base_info(elem, osm_type)
             if coords is None :
@@ -106,7 +103,7 @@ class ToiletsManager:
             toilets = Toilets(location=coords)
 
             # Extract tags as a dictionary
-                tags = {tag.get('k'): tag.get('v') for tag in elem.findall('tag')}
+            tags = elem.get('tags')
 
             if 'wheelchair' in tags.keys() and tags['wheelchair'] == 'yes':
                 toilets.wheelchair = True

backend/src/utils/utils.py

@@ -0,0 +1,27 @@
+"""Various helper functions"""
+import math as m
+
+def create_bbox(coords: tuple[float, float], radius: int):
+    """
+    Create a bounding box around the given coordinates.
+
+    Args:
+        coords (tuple[float, float]): The latitude and longitude of the center of the bounding box.
+        radius (int): The half-side length of the bounding box in meters.
+
+    Returns:
+        tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
+                                            defining the bounding box.
+    """
+    # Earth's radius in meters
+    R = 6378137
+    lat, lon = coords
+    d_lat = radius / R
+    d_lon = radius / (R * m.cos(m.pi * lat / 180))
+
+    lat_min = lat - d_lat * 180 / m.pi
+    lat_max = lat + d_lat * 180 / m.pi
+    lon_min = lon - d_lon * 180 / m.pi
+    lon_max = lon + d_lon * 180 / m.pi
+
+    return (lat_min, lon_min, lat_max, lon_max)