persistence for recurring api calls

backend/Dockerfile

@@ -13,5 +13,6 @@ EXPOSE 8000
 # Set environment variables used by the deployment. These can be overridden by the user using this image.
 ENV NUM_WORKERS=1
 ENV OSM_CACHE_DIR=/cache
+ENV MEMCACHED_HOST=none
 
 CMD fastapi run src/main.py --port 8000 --workers $NUM_WORKERS

backend/Pipfile

@@ -14,3 +14,4 @@ shapely = "*"
 scipy = "*"
 osmpythontools = "*"
 pywikibot = "*"
+pymemcache = "*"

backend/Pipfile.lock

@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "f0de801038593d42d8b780d14c2c72bb4f5f5e66df02f72244917ede5d5ebce6"
+            "sha256": "4f8b3f0395b4e5352330616870da13acf41e16d1b69ba31b15fd688e90b8b628"
         },
         "pipfile-spec": 6,
         "requires": {},
@@ -1102,6 +1102,15 @@
             "markers": "python_version >= '3.8'",
             "version": "==2.18.0"
         },
+        "pymemcache": {
+            "hashes": [
+                "sha256:27bf9bd1bbc1e20f83633208620d56de50f14185055e49504f4f5e94e94aff94",
+                "sha256:f507bc20e0dc8d562f8df9d872107a278df049fa496805c1431b926f3ddd0eab"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.7'",
+            "version": "==4.0.0"
+        },
         "pyparsing": {
             "hashes": [
                 "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad",
@@ -1142,12 +1151,12 @@
         },
         "pywikibot": {
             "hashes": [
-                "sha256:3f4fbc57f1765aa0fa1ccf84125bcfa475cae95b9cc0291867b751f3d4ac8fa2",
-                "sha256:a26d918cf88ef56fdb1421b65b09def200cc28031cdc922d72a4198fbfddd225"
+                "sha256:0dd8291f1a26abb9fce2c2108a90dc338274988e60d21723aec1d3b0de321b5e",
+                "sha256:7953fc4a6c498057e6eb7d9b762bbccb61348af0a599b89d7e246d5175b20a9b"
             ],
             "index": "pypi",
             "markers": "python_full_version >= '3.7.0'",
-            "version": "==9.2.1"
+            "version": "==9.3.0"
         },
         "pyyaml": {
             "hashes": [
@@ -1349,7 +1358,7 @@
                 "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d",
                 "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"
             ],
-            "markers": "python_version >= '3.8'",
+            "markers": "python_version < '3.13'",
             "version": "==4.12.2"
         },
         "tzdata": {
@@ -1658,11 +1667,11 @@
         },
         "xarray": {
             "hashes": [
-                "sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7",
-                "sha256:721a7394e8ec3d592b2d8ebe21eed074ac077dc1bb1bd777ce00e41700b4866c"
+                "sha256:1b0fd51ec408474aa1f4a355d75c00cc1c02bd425d97b2c2e551fd21810e7f64",
+                "sha256:4cae512d121a8522d41e66d942fb06c526bc1fd32c2c181d5fe62fe65b671638"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==2024.6.0"
+            "version": "==2024.7.0"
         }
     },
     "develop": {}

backend/src/constants.py

@@ -25,3 +25,7 @@ logging.config.dictConfig(config)
 # if we are in a debug session, set the log level to debug
 if os.getenv('DEBUG', False):
     logging.getLogger().setLevel(logging.DEBUG)
+
+MEMCACHE_HOST = os.getenv('MEMCACHE_HOST', None)
+if MEMCACHE_HOST == "none":
+    MEMCACHE_HOST = None

backend/src/main.py

@@ -1,12 +1,14 @@
 import logging
-from fastapi import FastAPI, Query, Body
+from fastapi import FastAPI, Query, Body, HTTPException
 
 from structs.landmark import Landmark
 from structs.preferences import Preferences
 from structs.linked_landmarks import LinkedLandmarks
+from structs.trip import Trip
 from utils.landmarks_manager import LandmarkManager
 from utils.optimizer import Optimizer
 from utils.refiner import Refiner
+from persistence import client as cache_client
 
 
 logger = logging.getLogger(__name__)
@@ -17,8 +19,8 @@ optimizer = Optimizer()
 refiner = Refiner(optimizer=optimizer)
 
 
-@app.post("/route/new")
-def get_route(preferences: Preferences, start: tuple[float, float], end: tuple[float, float] | None = None) -> str:
+@app.post("/trip/new")
+def new_trip(preferences: Preferences, start: tuple[float, float], end: tuple[float, float] | None = None) -> Trip:
     '''
     Main function to call the optimizer.
     :param preferences: the preferences specified by the user as the post body
@@ -47,22 +49,32 @@ def get_route(preferences: Preferences, start: tuple[float, float], end: tuple[f
     landmarks_short.insert(0, start_landmark)
     landmarks_short.append(end_landmark)
     
-    # TODO infer these parameters from the preferences
-    max_walking_time = 4    # hours
-    detour = 30             # minutes
-
     # First stage optimization
-    base_tour = optimizer.solve_optimization(max_walking_time*60, landmarks_short)
+    base_tour = optimizer.solve_optimization(preferences.max_time_minute, landmarks_short)
     
     # Second stage optimization
-    refined_tour = refiner.refine_optimization(landmarks, base_tour, max_walking_time*60, detour)
+    refined_tour = refiner.refine_optimization(landmarks, base_tour, preferences.max_time_minute, preferences.detour_tolerance_minute)
 
     linked_tour = LinkedLandmarks(refined_tour)
-    return linked_tour[0].uuid
+    # 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
 
 
+#### For already existing trips/landmarks
+@app.get("/trip/{trip_uuid}")
+def get_trip(trip_uuid: str) -> Trip:
+    try:
+        trip = cache_client.get(f"trip_{trip_uuid}")
+        return trip
+    except KeyError:
+        raise HTTPException(status_code=404, detail="Trip not found")
+
 
 @app.get("/landmark/{landmark_uuid}")
 def get_landmark(landmark_uuid: str) -> Landmark:
-    #cherche dans linked_tour et retourne le landmark correspondant
-    pass
+    try:
+        landmark = cache_client.get(f"landmark_{landmark_uuid}")
+        return landmark
+    except KeyError:
+        raise HTTPException(status_code=404, detail="Landmark not found")

backend/src/persistence.py

@@ -0,0 +1,18 @@
+from pymemcache.client.base import Client
+
+import constants
+
+
+class DummyClient:
+    _data = {}
+    def set(self, key, value, **kwargs):
+        self._data[key] = value
+
+    def get(self, key, **kwargs):
+        return self._data[key]
+
+
+if constants.MEMCACHE_HOST is None:
+    client = DummyClient()
+else:
+    client = Client(constants.MEMCACHE_HOST, timeout=1)

backend/src/structs/linked_landmarks.py

@@ -1,4 +1,3 @@
-import uuid
 from .landmark import Landmark
 from utils.get_time_separation import get_time
 
@@ -9,8 +8,7 @@ class LinkedLandmarks:
     """
     
     _landmarks = list[Landmark]
-    total_time = int
-    uuid = str
+    total_time: int = 0
 
     def __init__(self, data: list[Landmark] = None) -> None:
         """
@@ -19,7 +17,6 @@ class LinkedLandmarks:
         Args:
             data (list[Landmark], optional): The list of landmarks that are linked together. Defaults to None.
         """
-        self.uuid = uuid.uuid4()
         self._landmarks = data if data else []
         self._link_landmarks()
 
@@ -28,7 +25,6 @@ class LinkedLandmarks:
         """
         Create the links between the landmarks in the list by setting their .next_uuid and the .time_to_next attributes.
         """
-        self.total_time = 0
         for i, landmark in enumerate(self._landmarks[:-1]):
             landmark.next_uuid = self._landmarks[i + 1].uuid
             time_to_next = get_time(landmark.location, self._landmarks[i + 1].location)
@@ -44,18 +40,4 @@ class LinkedLandmarks:
     
 
     def __str__(self) -> str:
-        return f"LinkedLandmarks, total time: {self.total_time} minutes, {len(self._landmarks)} stops: [{','.join([str(landmark) for landmark in self._landmarks])}]"
-    
-
-    def asdict(self) -> dict:
-        """
-        Convert the linked landmarks to a json serializable dictionary.
-        
-        Returns:
-            dict: A dictionary representation of the linked landmarks.
-        """
-        return {
-            'uuid': self.uuid,
-            'total_time': self.total_time,
-            'landmarks': [landmark.dict() for landmark in self._landmarks]
-        }
+        return f"LinkedLandmarks [{' ->'.join([str(landmark) for landmark in self._landmarks])}]"

backend/src/structs/preferences.py

@@ -2,7 +2,6 @@ from pydantic import BaseModel
 from typing import Optional, Literal
 
 class Preference(BaseModel) :
-    name: str
     type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
     score: int          # score could be from 1 to 5
 

backend/src/structs/trip.py

@@ -0,0 +1,28 @@
+from pydantic import BaseModel, Field
+
+from .landmark import Landmark
+from .linked_landmarks import LinkedLandmarks
+import uuid
+
+class Trip(BaseModel):
+    uuid: str = Field(default_factory=uuid.uuid4)
+    total_time: int
+    first_landmark_uuid: str
+
+
+    @classmethod
+    def from_linked_landmarks(self, landmarks: LinkedLandmarks, cache_client) -> "Trip":
+        """
+        Initialize a new Trip object and ensure it is stored in the cache.
+        """
+        trip = Trip(
+            total_time = landmarks.total_time,
+            first_landmark_uuid = str(landmarks[0].uuid)
+        )
+
+        # Store the trip in the cache
+        cache_client.set(f"trip_{trip.uuid}", trip)
+        for landmark in landmarks:
+            cache_client.set(f"landmark_{landmark.uuid}", landmark)
+
+        return trip

backend/src/tester.py

@@ -20,18 +20,9 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
 
     
     preferences = Preferences(
-                    sightseeing=Preference(
-                                  name='sightseeing', 
-                                  type='sightseeing',
-                                  score = 5),
-                    nature=Preference(
-                                  name='nature', 
-                                  type='nature',
-                                  score = 5),
-                    shopping=Preference(
-                                  name='shopping', 
-                                  type='shopping',
-                                  score = 5),
+        sightseeing=Preference(type='sightseeing', score = 5),
+        nature=Preference(type='nature', score = 5),
+        shopping=Preference(type='shopping', score = 5),
 
         max_time_minute=180,
         detour_tolerance_minute=30

backend/src/utils/landmarks_manager.py

@@ -15,10 +15,6 @@ from .take_most_important import take_most_important
 import constants
 
 
-SIGHTSEEING = 'sightseeing'
-NATURE = 'nature'
-SHOPPING = 'shopping'
-
 
 
 class LandmarkManager:
@@ -74,25 +70,25 @@ class LandmarkManager:
         # list for sightseeing
         if preferences.sightseeing.score != 0:
             score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.church_coeff)  
-            L1 = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], SIGHTSEEING, score_function)
-            self.correct_score(L1, preferences.sightseeing)
+            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 loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.park_coeff)  
-            L2 = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], NATURE, score_function)
-            self.correct_score(L2, preferences.nature)
+            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 loc, n_tags: int(self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff))
-            L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], SHOPPING, score_function)
-            self.correct_score(L3, preferences.shopping)
+            L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
             L += L3
 
+
         L = self.remove_duplicates(L)
+        self.correct_score(L, preferences)
+
         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.')
 
@@ -123,7 +119,7 @@ class LandmarkManager:
         return L_clean
         
 
-    def correct_score(self, landmarks: list[Landmark], preference: Preference):
+    def correct_score(self, landmarks: list[Landmark], preferences: Preferences) -> None:
         """
         Adjust the attractiveness score of each landmark in the list based on user preferences.
 
@@ -132,20 +128,16 @@ class LandmarkManager:
 
         Args:
             landmarks (list[Landmark]): A list of landmarks whose scores need to be corrected.
-            preference (Preference): The user's preference settings that influence the attractiveness score adjustment.
-
-        Raises:
-            TypeError: If the type of any landmark in the list does not match the expected type in the preference.
+            preferences (Preferences): The user's preference settings that influence the attractiveness score adjustment.
         """
 
-        if len(landmarks) == 0:
-            return
-        
-        if landmarks[0].type != preference.type:
-            raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {landmarks[0].name}")
-
-        for elem in landmarks:
-            elem.attractiveness = int(elem.attractiveness*preference.score/5)     # arbitrary computation
+        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:
@@ -310,7 +302,7 @@ class LandmarkManager:
                         if "leisure" in tag and elem.tag('leisure') == "park":
                             elem_type = "nature"
 
-                    if landmarktype != SHOPPING:
+                    if landmarktype != "shopping":
                         if "shop" in tag:
                             skip = True
                             break