Upgraded refiner

backend/src/main.py

@@ -1,6 +1,6 @@
 from optimizer import solve_optimization
+from refiner import refine_optimization
 from landmarks_manager import generate_landmarks
-from structs.landmarks import LandmarkTest
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
 from structs.preferences import Preferences, Preference
@@ -12,35 +12,45 @@ app = FastAPI()
 
 # Assuming frontend is calling like this : 
 #"http://127.0.0.1:8000/process?param1={param1}&param2={param2}"
-@app.post("/optimizer_coords/{latitude}/{longitude}/{city_country}")
-def main1(preferences: Preferences = Body(...), latitude: float = None, longitude: float = None, city_country: str = None) -> List[Landmark]:
+@app.post("/optimizer_coords/{start_lat}/{start_lon}/{finish_lat}/{finish_lon}")
+def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(...), finish_lat: float = None, finish_lon: float = None) -> List[Landmark]:
     
     if preferences is None :
         raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
-    elif latitude is None and longitude is None and city_country is None :
-        raise ValueError("Please provide GPS coordinates or a 'city_country' string.")
-    elif latitude is not None and longitude is not None and city_country is not None :
-        raise ValueError("Please provide EITHER GPS coordinates or a 'city_country' string.")
+    if bool(start_lat) ^ bool(start_lon) :
+        raise ValueError("Please provide both latitude and longitude for the starting point")
+    if bool(finish_lat) ^ bool(finish_lon) :
+        raise ValueError("Please provide both latitude and longitude for the finish point")
 
-
-    # From frontend get longitude, latitude and prefence list
-    if city_country is None :
-        coordinates = tuple((latitude, longitude))
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start_lat, start_lon), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    
+    if bool(finish_lat) and bool(finish_lon) :
+        finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(finish_lat, finish_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    else :
+        finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(start_lat, start_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
     
-    [], landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
-
     start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
 
+    # Generate the landmarks from the start location
+    landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
+
+    # insert start and finish to the landmarks list
     landmarks_short.insert(0, start)
     landmarks_short.append(finish)
 
-    max_walking_time = 4 # hours
+    # TODO use these parameters in another way
+    max_walking_time = 4    # hours
+    detour = 30             # minutes
 
-    visiting_list = solve_optimization(landmarks_short, max_walking_time*60, True)
-
-    return visiting_list
+    # First stage optimization
+    base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
+    
+    # Second stage optimization
+    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
+    
+    return refined_tour