.gitea/workflows/backed_build-image.yaml

@@ -0,0 +1,31 @@
+on:
+  pull_request:
+    branches:
+      - main
+
+name: Build and push docker image
+
+jobs:
+  build:
+    name: Build
+    runs-on: ubuntu-latest
+    steps:
+
+    - uses: https://gitea.com/actions/checkout@v4
+    
+    - name: Login to Docker Registry
+      uses: docker/login-action@v3
+      with:
+        registry: git.kluster.moll.re
+        username: ${{ gitea.repository_owner }}
+        password: ${{ secrets.DOCKER_PUSH_TOKEN }}
+
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v3
+
+    - name: Build and push
+      uses: docker/build-push-action@v5
+      with:
+        context: backend
+        tags: git.kluster.moll.re/remoll/fast_network_navigation/backend:latest
+        push: true

.gitea/workflows/frontend_build-android.yaml

@@ -2,44 +2,53 @@ on:
   pull_request:
     branches:
       - main
+    paths:
+      - frontend/**
+
 
 name: Build and release APK
 
 jobs:
   build:
     name: Build APK
-    runs-on: k8s
+    runs-on: ubuntu-latest
     steps:
 
     - name: Install prerequisites
       run: |
-        sudo apt-get update
-        sudo apt-get install -y xz-utils unzip
+        apt-get update
+        apt-get install -y jq
 
     - uses: https://gitea.com/actions/checkout@v4
 
+
     - uses: https://github.com/actions/setup-java@v4
       with:
         java-version: '17'
         distribution: 'zulu'
 
+    - name: Fix flutter SDK folder permission
+      run: git config --global --add safe.directory "*"
+
     - uses: https://github.com/subosito/flutter-action@v2
       with:
         channel: stable
-        flutter-version: 3.19.6
+        flutter-version: 3.22.0
         cache: true
 
     - name: Setup Android SDK
       uses: https://github.com/android-actions/setup-android@v3
 
     - run: flutter pub get
+      working-directory: ./frontend
 
-    - run: flutter build apk --debug --split-per-abi
+    - run: flutter build apk --release --split-per-abi
+      working-directory: ./frontend
 
     - name: Release APK
       uses: https://gitea.com/akkuman/gitea-release-action@v1
       with:
-        files: build/app/outputs/flutter-apk/*.apk
+        files: ./frontend/build/app/outputs/flutter-apk/*.apk
         name: Testing release
         release_name: testing
         tag: testing
@@ -49,3 +58,4 @@ jobs:
         token: ${{ secrets.GITEA_TOKEN }}
       env:
         NODE_OPTIONS: '--experimental-fetch'
+      

.gitea/workflows/frontend_build-web.yaml

@@ -2,13 +2,16 @@ on:
   pull_request:
     branches:
       - main
+    paths:
+      - frontend/**
+
 
 name: Build web
 
 jobs:
   build:
     name: Build Web
-    runs-on: k8s
+    runs-on: ubuntu-latest
     steps:
 
     - name: Install prerequisites
@@ -25,6 +28,7 @@ jobs:
         cache: true
 
     - run: flutter pub get
+      working-directory: ./frontend
 
     - run: flutter build web
-
+      working-directory: ./frontend

.gitea/workflows/test.yaml

@@ -1,31 +0,0 @@
-on:
-  push:
-  pull_request:
-    branches:
-      - main
-
-
-name: Test code
-
-jobs:
-  test:
-    name: Test code
-    runs-on: k8s
-    steps:
-
-    - name: Install prerequisites
-      run: |
-        sudo apt-get update
-        sudo apt-get install -y xz-utils
-
-    - uses: actions/checkout@v4
-
-    - uses: https://github.com/subosito/flutter-action@v2
-      with:
-        channel: stable
-        flutter-version: 3.19.6
-        cache: true
-
-    - run: flutter pub get
-
-    - run: flutter test

.gitignore

@@ -1,43 +1 @@
-# Miscellaneous
-*.class
-*.log
-*.pyc
-*.swp
-.DS_Store
-.atom/
-.buildlog/
-.history
-.svn/
-migrate_working_dir/
-
-# IntelliJ related
-*.iml
-*.ipr
-*.iws
-.idea/
-
-# The .vscode folder contains launch configuration and tasks you configure in
-# VS Code which you may wish to be included in version control, so this line
-# is commented out by default.
-#.vscode/
-
-# Flutter/Dart/Pub related
-**/doc/api/
-**/ios/Flutter/.last_build_id
-.dart_tool/
-.flutter-plugins
-.flutter-plugins-dependencies
-.pub-cache/
-.pub/
-/build/
-
-# Symbolication related
-app.*.symbols
-
-# Obfuscation related
-app.*.map.json
-
-# Android Studio will place build artifacts here
-/android/app/debug
-/android/app/profile
-/android/app/release
+cache/

backend/.gitignore

@@ -0,0 +1,164 @@
+# osm-cache
+cache/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

backend/Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3
+
+WORKDIR /app
+COPY Pipfile Pipfile.lock .
+
+RUN pip install pipenv
+RUN pipenv install --deploy --system
+
+COPY . /src
+
+CMD ["pipenv", "run", "python", "/app/src/main.py"]

backend/Pipfile

@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+numpy = "*"
+scipy = "*"
+fastapi = "*"
+osmpythontools = "*"
+pydantic = "*"
+shapely = "*"
+
+[dev-packages]

backend/src/amenities/nature.am

@@ -0,0 +1,11 @@
+'leisure'='park'
+geological
+'natural'='geyser'
+'natural'='hot_spring'
+'natural'='arch'
+'natural'='volcano'
+'natural'='stone'
+'tourism'='alpine_hut'
+'tourism'='viewpoint'
+'tourism'='zoo'
+'waterway'='waterfall'

backend/src/amenities/shopping.am

@@ -0,0 +1,2 @@
+'shop'='department_store'
+'shop'='mall'

backend/src/amenities/sightseeing.am

@@ -0,0 +1,8 @@
+'tourism'='museum'
+'tourism'='attraction'
+'tourism'='gallery'
+historic
+'amenity'='planetarium'
+'amenity'='place_of_worship'
+'amenity'='fountain'
+'water'='reflecting_pool'

backend/src/landmarks_manager.py

@@ -0,0 +1,365 @@
+import math as m
+import json, os
+
+from typing import List, Tuple, Optional
+from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+
+from structs.landmarks import Landmark, LandmarkType
+from structs.preferences import Preferences, Preference
+
+
+SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
+NATURE = LandmarkType(landmark_type='nature')
+SHOPPING = LandmarkType(landmark_type='shopping')
+
+
+# Include the json here
+# Create a list of all things to visit given some preferences and a city. Ready for the optimizer
+def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float]) :
+
+    l_sights, l_nature, l_shop = get_amenities()
+    L = []
+
+    # List for sightseeing
+    if preferences.sightseeing.score != 0 :
+        L1 = get_landmarks(l_sights, SIGHTSEEING, coordinates=coordinates)
+        correct_score(L1, preferences.sightseeing)
+        L += L1
+    
+    # List for nature
+    if preferences.nature.score != 0 :
+        L2 = get_landmarks(l_nature, NATURE, coordinates=coordinates)
+        correct_score(L2, preferences.nature)
+        L += L2
+    
+    # List for shopping
+    if preferences.shopping.score != 0 :
+        L3 = get_landmarks(l_shop, SHOPPING, coordinates=coordinates)
+        correct_score(L3, preferences.shopping)
+        L += L3
+
+    L = remove_duplicates(L)
+
+    return L, take_most_important(L)
+
+
+"""def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
+
+    l_sights, l_nature, l_shop = get_amenities()
+    L = []
+
+    # List for sightseeing
+    if preferences.sightseeing.score != 0 :
+        L1 = get_landmarks(l_sights, SIGHTSEEING, city_country=city_country, coordinates=coordinates)
+        correct_score(L1, preferences.sightseeing)
+        L += L1
+    
+    # List for nature
+    if preferences.nature.score != 0 :
+        L2 = get_landmarks(l_nature, NATURE, city_country=city_country, coordinates=coordinates)
+        correct_score(L2, preferences.nature)
+        L += L2
+    
+    # List for shopping
+    if preferences.shopping.score != 0 :
+        L3 = get_landmarks(l_shop, SHOPPING, city_country=city_country, coordinates=coordinates)
+        correct_score(L3, preferences.shopping)
+        L += L3
+
+    return remove_duplicates(L), take_most_important(L)
+"""
+# Helper function to gather the amenities list
+def get_amenities() -> List[List[str]] :
+    
+    # Get the list of amenities from the files
+    sightseeing = get_list('/amenities/sightseeing.am')
+    nature = get_list('/amenities/nature.am')
+    shopping = get_list('/amenities/shopping.am')
+
+    return sightseeing, nature, shopping
+
+
+# Helper function to read a .am file and generate the corresponding list
+def get_list(path: str) -> List[str] :
+
+    with open(os.path.dirname(os.path.abspath(__file__)) + path) as f :
+        content = f.readlines()
+
+        amenities = []
+        for line in content :
+            amenities.append(line.strip('\n'))
+
+    return amenities
+
+
+# Take the most important landmarks from the list
+def take_most_important(L: List[Landmark], N = 0) -> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+        parameters = json.loads(f.read())
+        N_important = parameters['N important']
+    
+    L_copy = []
+    L_clean = []
+    scores = [0]*len(L)
+    names = []
+    name_id = {}
+
+    for i, elem in enumerate(L) :
+        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
+    
+    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-N):]
+
+    for i, elem in enumerate(L_copy) :
+        if i in res :
+            L_clean.append(elem)
+
+    return L_clean
+
+
+# Remove duplicate elements and elements with low score
+def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
+    """
+    Removes duplicate landmarks based on their names from the given list.
+
+    Parameters:
+    L (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 L :
+        if landmark.name in names: 
+            continue  
+
+
+        else :
+            names.append(landmark.name)
+            L_clean.append(landmark)
+    
+    return L_clean
+    
+
+# Correct the score of a list of landmarks by taking into account preference settings
+def correct_score(L: List[Landmark], preference: Preference) :
+
+    if len(L) == 0 :
+        return
+    
+    if L[0].type != preference.type :
+        raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
+
+    for elem in L :
+        elem.attractiveness = int(elem.attractiveness*preference.score/500)     # arbitrary computation
+
+
+# Function to count elements within a certain radius of a location
+def count_elements_within_radius(coordinates: Tuple[float, float], radius: int) -> int:
+    
+    lat = coordinates[0]
+    lon = coordinates[1]
+
+    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
+    radius_query = overpassQueryBuilder(bbox=[bbox['latLower'],bbox['lonLower'],bbox['latHigher'],bbox['lonHigher']],
+                             elementType=['node', 'way', 'relation'])
+
+    try : 
+        overpass = Overpass()
+        radius_result = overpass.query(radius_query)
+        return radius_result.countElements()
+    
+    except :
+        return None
+
+
+# Creates a bounding box around given coordinates
+def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[float, float, float, float]:
+
+    lat = coordinates[0]
+    lon = coordinates[1]
+
+    # Half the side length in km (since it's a square bbox)
+    half_side_length_km = side_length / 2.0
+
+    # 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 * m.cos(m.radians(lat)))  # Adjust for longitude based on latitude
+
+    # Calculate bbox
+    min_lat = lat - lat_diff
+    max_lat = lat + lat_diff
+    min_lon = lon - lon_diff
+    max_lon = lon + lon_diff
+
+    return min_lat, min_lon, max_lat, max_lon
+
+
+def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: Tuple[float, float]) -> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+        parameters = json.loads(f.read())
+        tag_coeff = parameters['tag coeff']
+        park_coeff = parameters['park coeff']
+        church_coeff = parameters['church coeff']
+        radius = parameters['radius close to']
+        bbox_side = parameters['city bbox side']
+    
+    # Create bbox around start location
+    bbox = create_bbox(coordinates, bbox_side)
+
+    # Initialize some variables
+    N = 0
+    L = []
+    overpass = Overpass()
+
+    for amenity in list_amenity :
+        query = overpassQueryBuilder(bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
+        result = overpass.query(query)
+        N += result.countElements()
+
+        for elem in result.elements():
+
+            name = elem.tag('name')                             # Add name
+            location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
+
+            # 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
+            
+            else :
+                osm_type = elem.type()              # Add type : 'way' or 'relation'
+                osm_id = elem.id()                  # Add OSM id 
+                elem_type = landmarktype            # Add the landmark type as 'sightseeing
+                n_tags = len(elem.tags().keys())    # Add number of tags
+                
+                # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
+                if amenity == "'amenity'='place_of_worship'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
+                elif amenity == "'leisure'='park'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
+                else :
+                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+
+                if score is not None :
+                    # 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=score, must_do=False, n_tags=n_tags)
+                    L.append(landmark)
+
+    return L
+
+
+
+"""def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country: str = None, coordinates: Tuple[float, float] = None) -> List[Landmark] :
+    
+    if city_country is None and coordinates is None :
+        raise ValueError("Either one of 'city_country' and 'coordinates' arguments must be specified")
+
+    if city_country is not None and coordinates is not None :
+        raise ValueError("Cannot specify both 'city_country' and 'coordinates' at the same time, please choose either one")
+
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+        parameters = json.loads(f.read())
+        tag_coeff = parameters['tag coeff']
+        park_coeff = parameters['park coeff']
+        church_coeff = parameters['church coeff']
+        radius = parameters['radius close to']
+        bbox_side = parameters['city bbox side']
+    
+    # If city_country is specified :
+    if city_country is not None :
+        nominatim = Nominatim()
+        areaId = nominatim.query(city_country).areaId()
+        bbox = None
+
+    # If coordinates are specified :
+    elif coordinates is not None :
+        bbox = create_bbox(coordinates, bbox_side)
+        areaId = None
+
+    else :
+        raise ValueError("Argument number is not corresponding.")
+    
+    # Initialize some variables
+    N = 0
+    L = []
+    overpass = Overpass()
+
+    for amenity in list_amenity :
+        query = overpassQueryBuilder(area=areaId, bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
+        result = overpass.query(query)
+        N += result.countElements()
+
+        for elem in result.elements():
+
+            name = elem.tag('name')                             # Add name
+            location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
+
+            # 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
+            
+            else :
+                osm_type = elem.type()              # Add type : 'way' or 'relation'
+                osm_id = elem.id()                  # Add OSM id 
+                elem_type = landmarktype            # Add the landmark type as 'sightseeing
+                n_tags = len(elem.tags().keys())    # Add number of tags
+                
+                # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
+                if amenity == "'amenity'='place_of_worship'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
+                elif amenity == "'leisure'='park'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
+                else :
+                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+
+                if score is not None :
+                    # 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=score, must_do=False, n_tags=n_tags)
+                    L.append(landmark)
+
+    return L
+"""

backend/src/main.py

@@ -0,0 +1,70 @@
+from optimizer import solve_optimization
+from refiner import refine_optimization
+from landmarks_manager import generate_landmarks
+from structs.landmarks import Landmark
+from structs.landmarktype import LandmarkType
+from structs.preferences import Preferences, Preference
+from fastapi import FastAPI, Query, Body
+from typing import List
+
+app = FastAPI()
+
+
+# Assuming frontend is calling like this : 
+#"http://127.0.0.1:8000/process?param1={param1}&param2={param2}"
+@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.")
+    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")
+
+    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)
+    
+    
+    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)
+    
+
+    # TODO use these parameters in another way
+    max_walking_time = 4    # hours
+    detour = 30             # minutes
+
+    # 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
+
+
+
+# input city, country in the form of 'Paris, France'
+@app.post("/test2/{city_country}")
+def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landmark]:
+
+    landmarks = generate_landmarks(city_country, preferences)
+
+    max_steps = 9000000
+
+    visiting_order = solve_optimization(landmarks, max_steps, True)
+
+
+
+

backend/src/main_example.py

@@ -0,0 +1,23 @@
+import fastapi
+from dataclasses import dataclass
+
+
+@dataclass
+class Destination:
+    name: str
+    location: tuple
+    attractiveness: int
+
+
+
+d = Destination()
+
+
+
+def get_route() -> list[Destination]:
+    return {"route": "Hello World"}
+
+endpoint = ("/get_route", get_route)
+end
+if __name__ == "__main__":
+    fastapi.run()

backend/src/optimizer.py

@@ -0,0 +1,411 @@
+import numpy as np
+import json, os
+
+from typing import List, Tuple
+from scipy.optimize import linprog
+from math import radians, sin, cos, acos
+from shapely import Polygon
+
+from structs.landmarks import Landmark
+
+    
+# Function to print the result
+def print_res(L: List[Landmark], L_tot):
+
+    if len(L) == L_tot: 
+        print('\nAll landmarks can be visited within max_steps, the following order is suggested : ')
+    else :
+        print('Could not visit all the landmarks, the following order is suggested : ')
+
+    dist = 0
+    for elem in L : 
+        if elem.name != 'start' :
+            print('- ' + elem.name + ', time to reach = ' + str(elem.time_to_reach))
+            dist += elem.time_to_reach
+        else : 
+            print('- ' + elem.name)
+
+    print("\nMinutes walked : " + str(dist))
+    print(f"Visited {len(L)-2} out of {L_tot-2} landmarks")
+
+
+# Prevent the use of a particular solution
+def prevent_config(resx, A_ub, b_ub) -> bool:
+    
+    for i, elem in enumerate(resx):
+        resx[i] = round(elem)
+    
+    N = len(resx)               # Number of edges
+    L = int(np.sqrt(N))         # Number of landmarks
+
+    nonzeroind = np.nonzero(resx)[0]                    # the return is a little funky so I use the [0]
+    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
+
+    ind_a = nonzero_tup[0].tolist()
+    vertices_visited = ind_a
+    vertices_visited.remove(0)
+
+    ones = [1]*L
+    h = [0]*N
+    for i in range(L) :
+        if i in vertices_visited :
+            h[i*L:i*L+L] = ones
+
+    A_ub = np.vstack((A_ub, h))
+    b_ub.append(len(vertices_visited)-1)
+
+    return A_ub, b_ub
+
+
+# Prevent the possibility of a given solution bit
+def break_cricle(circle_vertices: list, L: int, A_ub: list, b_ub: list) -> bool:
+
+    if L-1 in circle_vertices :
+        circle_vertices.remove(L-1)
+
+    h = [0]*L*L
+    for i in range(L) :
+        if i in circle_vertices :
+            h[i*L:i*L+L] = [1]*L
+
+    A_ub = np.vstack((A_ub, h))
+    b_ub.append(len(circle_vertices)-1)
+
+    return A_ub, b_ub
+
+
+# Checks if the path is connected, returns a circle if it finds one and the RESULT
+def is_connected(resx) -> bool:
+    
+    # first round the results to have only 0-1 values
+    for i, elem in enumerate(resx):
+        resx[i] = round(elem)
+    
+    N = len(resx)               # length of res
+    L = int(np.sqrt(N))         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
+    n_edges = resx.sum()        # number of edges
+
+    nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
+
+    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
+
+    ind_a = nonzero_tup[0].tolist()
+    ind_b = nonzero_tup[1].tolist()
+
+    edges = []
+    edges_visited = []
+    vertices_visited = []
+
+    edge1 = (ind_a[0], ind_b[0])
+    edges_visited.append(edge1)
+    vertices_visited.append(edge1[0])
+
+    for i, a in enumerate(ind_a) :
+        edges.append((a, ind_b[i]))      # Create the list of edges
+
+    remaining = edges
+    remaining.remove(edge1)
+
+    break_flag = False
+    while len(remaining) > 0 and not break_flag:
+        for edge2 in remaining :
+            if edge2[0] == edge1[1] :
+                if edge1[1] in vertices_visited :
+                    edges_visited.append(edge2)
+                    break_flag = True
+                    break
+                else :    
+                    vertices_visited.append(edge1[1])
+                    edges_visited.append(edge2)
+                    remaining.remove(edge2)
+                    edge1 = edge2
+
+            elif edge1[1] == L-1 or edge1[1] in vertices_visited:
+                        break_flag = True
+                        break
+
+    vertices_visited.append(edge1[1])
+
+
+    if len(vertices_visited) == n_edges +1 :
+        return vertices_visited, []
+    else: 
+        return vertices_visited, edges_visited
+
+
+# Function that returns the distance in meters from one location to another
+def get_distance(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
+    
+    # Compute the straight-line distance in km
+    if p1 == p2 :
+        return 0, 0
+    else: 
+        dist = 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
+
+    # Consider the detour factor for average cityto deterline walking distance (in km)
+    walk_dist = dist*detour
+
+    # Time to walk this distance (in minutes)
+    walk_time = walk_dist/speed*60
+
+    if walk_time > 15 :
+        walk_time = 5*round(walk_time/5)
+    else :
+        walk_time = round(walk_time)
+  
+
+    return round(walk_dist, 1), walk_time
+
+
+# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
+# We want to maximize the sightseeing :  max(c) st. A*x < b   and   A_eq*x = b_eq
+def init_ub_dist(landmarks: List[Landmark], max_steps: int):
+    
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
+    c = []
+    # Coefficients of inequality constraints (left-hand side)
+    A_ub = []
+
+    for spot1 in landmarks :
+        dist_table = [0]*len(landmarks)
+        c.append(-spot1.attractiveness)
+        for j, spot2 in enumerate(landmarks) :
+            t = get_distance(spot1.location, spot2.location, detour, speed)[1]
+            dist_table[j] = t
+        A_ub += dist_table
+    c = c*len(landmarks)
+
+    return c, A_ub, [max_steps]
+
+
+# Constraint to respect only one travel per landmark. Also caps the total number of visited landmarks
+def respect_number(L:int, A_ub, b_ub):
+
+    ones = [1]*L
+    zeros = [0]*L
+    for i in range(L) :
+        h = zeros*i + ones + zeros*(L-1-i)
+        A_ub = np.vstack((A_ub, h))
+        b_ub.append(1)
+
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        max_landmarks = parameters['max landmarks']
+
+    A_ub = np.vstack((A_ub, ones*L))
+    b_ub.append(max_landmarks+1)
+
+    return A_ub, b_ub
+
+
+# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
+def break_sym(L, A_ub, b_ub):
+    upper_ind = np.triu_indices(L,0,L)
+
+    up_ind_x = upper_ind[0]
+    up_ind_y = upper_ind[1]
+
+    for i, _ in enumerate(up_ind_x) :
+        l = [0]*L*L
+        if up_ind_x[i] != up_ind_y[i] :
+            l[up_ind_x[i]*L + up_ind_y[i]] = 1
+            l[up_ind_y[i]*L + up_ind_x[i]] = 1
+
+            A_ub = np.vstack((A_ub,l))
+            b_ub.append(1)
+
+    return A_ub, b_ub
+
+
+# Constraint to not stay in position. Removes d11, d22, d33, etc.
+def init_eq_not_stay(L: int): 
+    l = [0]*L*L
+
+    for i in range(L) :
+        for j in range(L) :
+            if j == i :
+                l[j + i*L] = 1
+    
+    l = np.array(np.array(l))
+
+    return [l], [0]
+
+
+# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
+def respect_user_mustsee(landmarks: List[Landmark], A_eq: list, b_eq: list) :
+    L = len(landmarks)
+
+    for i, elem in enumerate(landmarks) :
+        if elem.must_do is True and elem.name not in ['finish', 'start']:
+            l = [0]*L*L
+            for j in range(L) :     # sets the horizontal ones (go from)
+                l[j +i*L] = 1       # sets the vertical ones (go to)        double check if good
+                
+            for k in range(L-1) :
+                l[k*L+L-1] = 1  
+
+            A_eq = np.vstack((A_eq,l))
+            b_eq.append(2)
+
+    return A_eq, b_eq
+
+
+# Constraint to ensure start at start and finish at goal
+def respect_start_finish(L: int, A_eq: list, b_eq: list):
+    ls = [1]*L + [0]*L*(L-1)    # sets only horizontal ones for start (go from)
+    ljump = [0]*L*L
+    ljump[L-1] = 1              # Prevent start finish jump
+    lg = [0]*L*L
+    ll = [0]*L*(L-1) + [1]*L
+    for k in range(L-1) :       # sets only vertical ones for goal (go to)
+        ll[k*L] = 1
+        if k != 0 :             # Prevent the shortcut start -> finish
+            lg[k*L+L-1] = 1 
+            
+
+    A_eq = np.vstack((A_eq,ls))
+    A_eq = np.vstack((A_eq,ljump))
+    A_eq = np.vstack((A_eq,lg))
+    A_eq = np.vstack((A_eq,ll))
+    b_eq.append(1)
+    b_eq.append(0)
+    b_eq.append(1)
+    b_eq.append(0)
+
+    return A_eq, b_eq
+
+
+# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
+def respect_order(N: int, A_eq, b_eq): 
+    for i in range(N-1) :           # Prevent stacked ones
+        if i == 0 or i == N-1:      # Don't touch start or finish
+            continue
+        else : 
+            l = [0]*N
+            l[i] = -1
+            l = l*N
+            for j in range(N) :
+                l[i*N + j] = 1
+
+            A_eq = np.vstack((A_eq,l))
+            b_eq.append(0)
+
+    return A_eq, b_eq
+
+
+# Computes the time to reach from each landmark to the next
+def add_time_to_reach(order: List[int], landmarks: List[Landmark])->List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour_factor = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    j = 0
+    L =  []
+    prev = landmarks[0]
+
+    while(len(L) != len(order)) :
+        
+        elem = landmarks[order[j]]
+        if elem != prev :
+            elem.time_to_reach = get_distance(elem.location, prev.location, detour_factor, speed)[1]
+            elem.must_do = True
+        L.append(elem)
+        prev = elem   
+        j += 1
+    
+    return L
+
+
+def add_time_to_reach_simple(ordered_visit: List[Landmark])-> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour_factor = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    L =  []
+    prev = ordered_visit[0]
+    L.append(prev)
+    total_dist = 0
+
+    for elem in ordered_visit[1:] :
+        elem.time_to_reach = get_distance(elem.location, prev.location, detour_factor, speed)[1]
+        elem.must_do = True
+        L.append(elem)
+        prev = elem
+        total_dist += get_distance(elem.location, prev.location, detour_factor, speed)[1]
+        
+
+    return L, total_dist
+
+
+# Main optimization pipeline
+def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool) :
+
+    L = len(landmarks)
+
+    # SET CONSTRAINTS FOR INEQUALITY
+    c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)              # Add the distances from each landmark to the other
+    A_ub, b_ub = respect_number(L, A_ub, b_ub)                      # Respect max number of visits (no more possible stops than landmarks). 
+    A_ub, b_ub = break_sym(L, A_ub, b_ub)                           # break the 'zig-zag' symmetry
+
+    # SET CONSTRAINTS FOR EQUALITY
+    A_eq, b_eq = init_eq_not_stay(L)                                # Force solution not to stay in same place
+    A_eq, b_eq = respect_user_mustsee(landmarks, A_eq, b_eq)     # Check if there are user_defined must_see. Also takes care of start/goal
+    A_eq, b_eq = respect_start_finish(L, A_eq, b_eq)                # Force start and finish positions
+    A_eq, b_eq = respect_order(L, A_eq, b_eq)                       # Respect order of visit (only works when max_steps is limiting factor)
+
+    # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
+    x_bounds = [(0, 1)]*L*L
+
+    # Solve linear programming problem
+    res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
+
+    # Raise error if no solution is found
+    if not res.success :
+            raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
+
+    # If there is a solution, we're good to go, just check for connectiveness
+    else :
+        order, circle = is_connected(res.x)
+        i = 0
+        timeout = 80
+        while len(circle) != 0 and i < timeout:
+            A_ub, b_ub = prevent_config(res.x, A_ub, b_ub)
+            A_ub, b_ub = break_cricle(order, len(landmarks), A_ub, b_ub)
+            res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
+            order, circle = is_connected(res.x)
+            if len(circle) == 0 :
+                break
+            print(i)
+            i += 1
+        
+        if i == timeout :
+            raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
+
+        # Add the times to reach and stop optimizing
+        L = add_time_to_reach(order, landmarks)
+            
+        if printing_details is True :
+            if i != 0 :
+                print(f"Neded to recompute paths {i} times because of unconnected loops...")
+            print_res(L, len(landmarks))
+            print("\nTotal score : " + str(int(-res.fun)))
+
+        return L
+
+
+
+
+
+

backend/src/parameters/landmarks_manager.params

@@ -0,0 +1,8 @@
+{
+  "city bbox side" : 10,
+  "radius close to" : 27.5,
+  "church coeff" : 0.6,
+  "park coeff" : 1.5,
+  "tag coeff" : 100,
+  "N important" : 40
+}

backend/src/parameters/optimizer.params

@@ -0,0 +1,5 @@
+{
+  "detour factor" : 1.4,
+  "average walking speed" : 4.8,
+  "max landmarks" : 10
+}

backend/src/refiner.py

@@ -0,0 +1,293 @@
+from collections import defaultdict
+from heapq import heappop, heappush
+from itertools import permutations
+import os, json
+
+from shapely import buffer, LineString, Point, Polygon, MultiPoint, convex_hull, concave_hull, LinearRing
+from typing import List, Tuple
+from math import pi
+
+from structs.landmarks import Landmark
+from landmarks_manager import take_most_important
+from optimizer import solve_optimization, add_time_to_reach_simple, print_res, get_distance
+
+
+def create_corridor(landmarks: List[Landmark], width: float) :
+
+  corrected_width = (180*width)/(6371000*pi)
+  
+  path = create_linestring(landmarks)
+  obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2)
+
+  return obj
+
+
+def create_linestring(landmarks: List[Landmark])->List[Point] :
+
+  points = []
+
+  for landmark in landmarks :
+    points.append(Point(landmark.location))
+
+  return LineString(points)
+
+
+def is_in_area(area: Polygon, coordinates) -> bool :
+  point = Point(coordinates)
+  return point.within(area)
+
+
+def is_close_to(location1: Tuple[float], location2: Tuple[float]):
+    """Determine if two locations are close by rounding their coordinates to 3 decimals."""
+    absx = abs(location1[0] - location2[0])
+    absy = abs(location1[1] - location2[1])
+
+    return absx < 0.001 and absy < 0.001
+    #return (round(location1[0], 3), round(location1[1], 3)) == (round(location2[0], 3), round(location2[1], 3))
+
+
+def rearrange(landmarks: List[Landmark]) -> List[Landmark]:
+  
+  i = 1
+  while i < len(landmarks):
+    j = i+1
+    while j < len(landmarks):
+      if is_close_to(landmarks[i].location, landmarks[j].location) and landmarks[i].name not in ['start', 'finish'] and landmarks[j].name not in ['start', 'finish']:
+        # If they are not adjacent, move the j-th element to be adjacent to the i-th element
+        if j != i + 1:
+          landmarks.insert(i + 1, landmarks.pop(j))
+        break  # Move to the next i-th element after rearrangement
+      j += 1
+    i += 1
+  
+  return landmarks
+
+"""
+def find_shortest_path(landmarks: List[Landmark]) -> List[Landmark]:
+    
+  # Read from data
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    detour = parameters['detour factor']
+    speed = parameters['average walking speed']
+
+  # Step 1: Build the graph
+  graph = defaultdict(list)
+  for i in range(len(landmarks)):
+    for j in range(len(landmarks)):
+      if i != j:
+        distance = get_distance(landmarks[i].location, landmarks[j].location, detour, speed)[1]
+        graph[i].append((distance, j))
+
+  # Step 2: Dijkstra's algorithm to find the shortest path from start to finish
+  start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
+  finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
+
+  distances = {i: float('inf') for i in range(len(landmarks))}
+  previous_nodes = {i: None for i in range(len(landmarks))}
+  distances[start_idx] = 0
+  priority_queue = [(0, start_idx)]
+
+  while priority_queue:
+    current_distance, current_index = heappop(priority_queue)
+
+    if current_distance > distances[current_index]:
+      continue
+
+    for neighbor_distance, neighbor_index in graph[current_index]:
+      distance = current_distance + neighbor_distance
+
+      if distance < distances[neighbor_index]:
+        distances[neighbor_index] = distance
+        previous_nodes[neighbor_index] = current_index
+        heappush(priority_queue, (distance, neighbor_index))
+
+  # Step 3: Backtrack from finish to start to find the path
+  path = []
+  current_index = finish_idx
+  while current_index is not None:
+    path.append(landmarks[current_index])
+    current_index = previous_nodes[current_index]
+  path.reverse()
+
+  return path
+"""
+"""
+def total_path_distance(path: List[Landmark], detour, speed) -> float:
+  total_distance = 0
+  for i in range(len(path) - 1):
+    total_distance += get_distance(path[i].location, path[i + 1].location, detour, speed)[1]
+  return total_distance
+"""
+
+
+def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[Landmark]:
+    
+  # Read from data
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    detour = parameters['detour factor']
+    speed = parameters['average walking speed']
+    
+  # Step 1: Find 'start' and 'finish' landmarks
+  start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
+  finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
+  
+  start_landmark = landmarks[start_idx]
+  finish_landmark = landmarks[finish_idx]
+  
+
+  # Step 2: Create a list of unvisited landmarks excluding 'start' and 'finish'
+  unvisited_landmarks = [lm for i, lm in enumerate(landmarks) if i not in [start_idx, finish_idx]]
+  
+  # Step 3: Initialize the path with the 'start' landmark
+  path = [start_landmark]
+  coordinates = [landmarks[start_idx].location]
+
+  current_landmark = start_landmark
+  
+  # Step 4: Use nearest neighbor heuristic to visit all landmarks
+  while unvisited_landmarks:
+    nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_distance(current_landmark.location, lm.location, detour, speed)[1])
+    path.append(nearest_landmark)
+    coordinates.append(nearest_landmark.location)
+    current_landmark = nearest_landmark
+    unvisited_landmarks.remove(nearest_landmark)
+
+  # Step 5: Finally add the 'finish' landmark to the path
+  path.append(finish_landmark)
+  coordinates.append(landmarks[finish_idx].location)
+
+  path_poly = Polygon(coordinates)
+
+  return path, path_poly
+
+def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] :
+
+  second_order_landmarks = []
+  visited_names = []
+  area = create_corridor(visited_landmarks, width)
+
+  for visited in visited_landmarks :
+    visited_names.append(visited.name)
+  
+  for landmark in all_landmarks :
+    if is_in_area(area, landmark.location) and landmark.name not in visited_names:
+      second_order_landmarks.append(landmark)
+
+  return take_most_important(second_order_landmarks, len(visited_landmarks))
+
+
+
+"""def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
+
+  minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
+
+  if print_infos : print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
+
+  full_set = base_tour[:-1] + minor_landmarks   # create full set of possible landmarks (without finish)
+  full_set.append(base_tour[-1])                # add finish back
+
+  new_tour = solve_optimization(full_set, max_time, print_infos)
+
+  return new_tour"""
+
+
+def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
+
+  # Read from the file
+  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+    parameters = json.loads(f.read())
+    max_landmarks = parameters['max landmarks']
+  
+  if len(base_tour)-2 >= max_landmarks :
+    return base_tour
+  
+
+  minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
+
+  if print_infos : print("Using " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
+
+  # full set of visitable landmarks
+  full_set = base_tour[:-1] + minor_landmarks   # create full set of possible landmarks (without finish)
+  full_set.append(base_tour[-1])                # add finish back
+
+  # get a new tour
+  new_tour = solve_optimization(full_set, max_time, False)
+  new_tour, new_dist = add_time_to_reach_simple(new_tour)
+  
+  """#if base_tour[0].location == base_tour[-1].location :
+  if False :
+    coords = []         # Coordinates of the new tour
+    coords_dict = {}    # maps the location of an element to the element itself. Used to access the elements back once we get the geometry
+
+    # Iterate through the new tour without finish
+    for elem in new_tour[:-1] :
+      coords.append(Point(elem.location))
+      coords_dict[elem.location] = elem         # if start = goal, only finish remains
+
+    # Create a concave polygon using the coordinates
+    better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
+    xs, ys = better_tour_poly.exterior.xy
+
+    # reverse the xs and ys
+    xs.reverse()
+    ys.reverse()
+
+    better_tour = []   # List of ordered visit
+    name_index = {}     # Maps the name of a landmark to its index in the concave polygon
+
+    # Loop through the polygon and generate the better (ordered) tour
+    for i,x in enumerate(xs[:-1]) :
+      better_tour.append(coords_dict[tuple((x,ys[i]))])
+      name_index[coords_dict[tuple((x,ys[i]))].name] = i
+
+
+    # Scroll the list to have start in front again
+    start_index = name_index['start']
+    better_tour = better_tour[start_index:] + better_tour[:start_index]
+
+    # Append the finish back and correct the time to reach
+    better_tour.append(new_tour[-1])
+
+    # Rearrange only if polygon
+    better_tour = rearrange(better_tour)
+
+    # Add the time to reach
+    better_tour = add_time_to_reach_simple(better_tour)
+  """
+  
+
+  """
+  if not better_poly.is_simple :
+    
+    coords_dict = {}
+    better_tour2 = []
+    for elem in better_tour :
+      coords_dict[elem.location] = elem
+
+    better_poly2 = better_poly.buffer(0)
+    new_coords = better_poly2.exterior.coords[:]
+    start_coords = base_tour[0].location
+    start_index = new_coords.
+
+    #for point in new_coords :
+  """
+
+
+  better_tour, better_poly = find_shortest_path_through_all_landmarks(new_tour)
+  better_tour, better_dist = add_time_to_reach_simple(better_tour)
+
+  if new_dist < better_dist :
+    final_tour = new_tour
+  else :
+    final_tour = better_tour
+
+  if print_infos :
+    print("\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n")
+    print("\nRefined tour (result of second stage optimization): ")
+    print_res(final_tour, len(full_set))
+
+
+  
+  return final_tour

backend/src/structs/landmarks.py

@@ -0,0 +1,20 @@
+from typing import Optional
+from pydantic import BaseModel
+
+from .landmarktype import LandmarkType
+
+
+
+# Output to frontend
+class Landmark(BaseModel) :
+    name : str
+    type: LandmarkType      # De facto mapping depending on how the query was executed with overpass. Should still EXACTLY correspond to the preferences
+    location : tuple
+    osm_type : str
+    osm_id : int
+    attractiveness : int
+    must_do : bool
+    n_tags : int
+    time_to_reach : Optional[int] = 0
+
+

backend/src/structs/landmarktype.py

@@ -0,0 +1,4 @@
+from pydantic import BaseModel
+
+class LandmarkType(BaseModel):
+    landmark_type: str

backend/src/structs/preferences.py

@@ -0,0 +1,28 @@
+from pydantic import BaseModel
+from .landmarktype import LandmarkType
+
+class Preference(BaseModel) :
+    name: str
+    type: LandmarkType  # should match the attributes of the Preferences class
+    score: int          # score could be from 1 to 5
+
+# Input for optimization
+class Preferences(BaseModel) :
+    # Sightseeing / History & Culture (Musées, bâtiments historiques, opéras, églises)
+    sightseeing : Preference
+
+    # Nature (parcs, jardins, rivières, plages)
+    nature: Preference
+
+    # Shopping (diriger plutôt vers des zones / rues commerçantes)
+    shopping : Preference
+
+"""    # Food (price low or high. Combien on veut dépenser pour manger à midi/soir)
+    food_budget : Preference
+    
+    # Tolérance au détour (ce qui détermine (+ ou -) le chemin emprunté)
+    detour_tol : Preference"""
+
+
+
+

backend/src/tester.py

@@ -0,0 +1,116 @@
+import pandas as pd
+
+from typing import List
+from landmarks_manager import generate_landmarks
+from fastapi.encoders import jsonable_encoder
+
+from optimizer import solve_optimization
+from refiner import refine_optimization
+from structs.landmarks import Landmark
+from structs.landmarktype import LandmarkType
+from structs.preferences import Preferences, Preference
+
+
+# Helper function to create a .txt file with results
+def write_data(L: List[Landmark], file_name: str): 
+
+    data = pd.DataFrame()
+    i = 0
+
+    for landmark in L :
+        data[i] = jsonable_encoder(landmark)
+        i += 1
+
+    data.to_json(file_name, indent = 2, force_ascii=False)
+
+def test3(city_country: str) -> List[Landmark]:
+
+
+    preferences = Preferences(
+                    sightseeing=Preference(
+                                  name='sightseeing', 
+                                  type=LandmarkType(landmark_type='sightseeing'),
+                                  score = 5),
+                    nature=Preference(
+                                  name='nature', 
+                                  type=LandmarkType(landmark_type='nature'),
+                                  score = 0),
+                    shopping=Preference(
+                                  name='shopping', 
+                                  type=LandmarkType(landmark_type='shopping'),
+                                  score = 5))
+
+    coordinates = None
+
+    landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
+
+    #write_data(landmarks)
+
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.2044576, 16.3870242), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.2044576, 16.3870242), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    
+
+    test = landmarks_short
+    
+    test.insert(0, start)
+    test.append(finish)
+
+    max_walking_time = 2 # hours
+
+    visiting_list = solve_optimization(test, max_walking_time*60, True)
+
+
+    
+
+
+def test4(coordinates: tuple[float, float]) -> List[Landmark]:
+
+
+    preferences = Preferences(
+                    sightseeing=Preference(
+                                  name='sightseeing', 
+                                  type=LandmarkType(landmark_type='sightseeing'),
+                                  score = 5),
+                    nature=Preference(
+                                  name='nature', 
+                                  type=LandmarkType(landmark_type='nature'),
+                                  score = 5),
+                    shopping=Preference(
+                                  name='shopping', 
+                                  type=LandmarkType(landmark_type='shopping'),
+                                  score = 5))
+
+
+    # Create start and finish 
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    #start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), 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)
+    #write_data(landmarks, "landmarks.txt")
+
+    # Insert start and finish to the landmarks list
+    landmarks_short.insert(0, start)
+    landmarks_short.append(finish)
+
+    # TODO use these parameters in another way
+    max_walking_time = 2    # hours
+    detour = 30             # minutes
+
+    # 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
+
+
+#test4(tuple((48.8344400, 2.3220540)))       # Café Chez César 
+#test4(tuple((48.8375946, 2.2949904)))       # Point random
+test4(tuple((47.377859, 8.540585)))         # Zurich HB
+#test3('Vienna, Austria')

frontend/.gitignore

@@ -0,0 +1,43 @@
+# Miscellaneous
+*.class
+*.log
+*.pyc
+*.swp
+.DS_Store
+.atom/
+.buildlog/
+.history
+.svn/
+migrate_working_dir/
+
+# IntelliJ related
+*.iml
+*.ipr
+*.iws
+.idea/
+
+# The .vscode folder contains launch configuration and tasks you configure in
+# VS Code which you may wish to be included in version control, so this line
+# is commented out by default.
+#.vscode/
+
+# Flutter/Dart/Pub related
+**/doc/api/
+**/ios/Flutter/.last_build_id
+.dart_tool/
+.flutter-plugins
+.flutter-plugins-dependencies
+.pub-cache/
+.pub/
+/build/
+
+# Symbolication related
+app.*.symbols
+
+# Obfuscation related
+app.*.map.json
+
+# Android Studio will place build artifacts here
+/android/app/debug
+/android/app/profile
+/android/app/release

frontend/android/app/build.gradle

@@ -45,6 +45,9 @@ android {
         applicationId "com.example.fast_network_navigation"
         // You can update the following values to match your application needs.
         // For more information, see: https://docs.flutter.dev/deployment/android#reviewing-the-gradle-build-configuration.
+        // Minimum Android version for Google Maps SDK
+        // https://developers.google.com/maps/flutter-package/config#android
+        minSdk = 21
         minSdkVersion flutter.minSdkVersion
         targetSdkVersion flutter.targetSdkVersion
         versionCode flutterVersionCode.toInteger()

frontend/android/app/src/main/AndroidManifest.xml

@@ -28,7 +28,14 @@
              This is used by the Flutter tool to generate GeneratedPluginRegistrant.java -->
         <meta-data
             android:name="flutterEmbedding"
-            android:value="2" />
+            android:value="2"
+        />
+        <meta-data
+            android:name="com.google.android.geo.API_KEY"
+            android:value="AIzaSyCeWk_D2xvfOHLidvV56EZeQCUybypEntw"
+        />
+
+
     </application>
     <!-- Required to query activities that can process text, see:
          https://developer.android.com/training/package-visibility?hl=en and
@@ -41,4 +48,7 @@
             <data android:mimeType="text/plain"/>
         </intent>
     </queries>
+
+    <!-- Required to fetch data from the internet. -->
+    <uses-permission android:name="android.permission.INTERNET" />
 </manifest>

frontend/lib/layout.dart

@@ -4,9 +4,10 @@ import 'package:fast_network_navigation/modules/overview.dart';
 import 'package:fast_network_navigation/modules/profile.dart';
 
 
+// BasePage is the scaffold that holds all other pages
+// A side drawer is used to switch between pages
 class BasePage extends StatefulWidget {
   const BasePage({super.key, required this.title});
-
   final String title;
 
   @override
@@ -22,7 +23,7 @@ class _BasePageState extends State<BasePage> {
     });
   }
 
-  Widget currentView = MapPage();
+  Widget currentView = NavigationOverview();
   @override
   Widget build(BuildContext context) {
     final ThemeData theme = Theme.of(context);
@@ -51,7 +52,7 @@ class _BasePageState extends State<BasePage> {
                 // Update the state of the app
                 _onItemTapped(0);
                 // Then close the drawer
-                currentView = MapPage();
+                currentView = NavigationOverview();
                 Navigator.pop(context);
               },
             ),
@@ -87,3 +88,4 @@ class _BasePageState extends State<BasePage> {
     );
   }
 }
+

frontend/lib/main.dart

@@ -1,5 +1,5 @@
 import 'package:flutter/material.dart';
-import 'package:fast_network_navigation/modules/scaffold.dart';
+import 'package:fast_network_navigation/layout.dart';
 
 void main() => runApp(const App());
 

frontend/lib/modules/map.dart

@@ -0,0 +1,81 @@
+import 'package:flutter/material.dart';
+
+import 'package:google_maps_flutter/google_maps_flutter.dart';
+
+class MapWidget extends StatefulWidget {
+  @override
+  State<MapWidget> createState() => _MapWidgetState();
+}
+
+class _MapWidgetState extends State<MapWidget> {
+  late GoogleMapController mapController;
+
+  final LatLng _center = const LatLng(45.521563, -122.677433);
+
+  void _onMapCreated(GoogleMapController controller) {
+    mapController = controller;
+  }
+  void _onCameraIdle() {
+    // print(mapController.getLatLng());
+  }
+
+  @override
+  Widget build(BuildContext context) {
+    return GoogleMap(
+      onMapCreated: _onMapCreated,
+      initialCameraPosition: CameraPosition(
+        target: _center,
+        zoom: 11.0,
+      ),
+      onCameraIdle: _onCameraIdle,
+    );
+  }
+}
+
+
+
+  // GeoCode geoCode = GeoCode();
+  // String _currentCityName = "...";
+  // final Debounce _debounce = Debounce(Duration(seconds: 3));
+
+  // final LatLng _center = const LatLng(45.521563, -122.677433);
+  // late GoogleMapController mapController;
+
+  // void _onMapCreated(GoogleMapController controller) {
+  //   mapController = controller;
+  // }
+
+  // // void _setCurrentCityName() async {
+  //   if (mapController.camera.zoom < 9) {
+  //     return; // Don't bother if the view is too wide
+  //   }
+  //   var currentCoordinates = mapController.camera.center;
+  //   String? city;
+    
+  //   try{
+  //     List<Placemark> placemarks = await placemarkFromCoordinates(currentCoordinates.latitude, currentCoordinates.longitude);
+  //     city = placemarks[0].locality.toString();
+  //   } catch (e) {
+  //     debugPrint("Error: $e");
+  //     try {
+  //       Address address = await geoCode.reverseGeocoding(latitude: currentCoordinates.latitude, longitude: currentCoordinates.longitude);
+        
+  //       if (address.city == null || address.city.toString().contains("Throttled!")){
+  //         throw Exception("Probably rate limited");
+  //       }
+  //       city = address.city.toString();
+  //     } catch (e) {
+  //       debugPrint("Error: $e");
+
+  //     }
+  //   }
+  //   if (city != null) {
+  //     setState(() {
+  //       _currentCityName = city!;
+  //     });
+  //   } else {
+  //     _debounce(() async {_setCurrentCityName();});
+  //   }
+  // }
+
+  

frontend/lib/modules/overview.dart

@@ -0,0 +1,99 @@
+import 'package:flutter/material.dart';
+import 'package:sliding_up_panel/sliding_up_panel.dart';
+import 'package:geocode/geocode.dart';
+import 'dart:async';
+import 'package:google_maps_flutter/google_maps_flutter.dart';
+
+import 'package:fast_network_navigation/modules/navigation.dart';
+import 'package:fast_network_navigation/modules/map.dart';
+
+
+
+class NavigationOverview extends StatefulWidget {
+  @override
+  State<NavigationOverview> createState() => _NavigationOverviewState();
+}
+
+
+
+class Debounce {
+  Duration delay;
+  Timer? _timer;
+
+  Debounce(
+    this.delay,
+  );
+
+  call(void Function() callback) {
+    _timer?.cancel();
+    _timer = Timer(delay, callback);
+  }
+
+  dispose() {
+    _timer?.cancel();
+  }
+}
+
+
+class _NavigationOverviewState extends State<NavigationOverview> {
+
+  @override
+  Widget build(BuildContext context) {
+    final ThemeData theme = Theme.of(context);
+    return SlidingUpPanel(
+        renderPanelSheet: false,
+        panel: _floatingPanel(theme),
+        collapsed: _floatingCollapsed(theme),
+        body: MapWidget()
+    );
+  }
+
+  Widget _floatingCollapsed(ThemeData theme){
+    return Container(
+      decoration: BoxDecoration(
+        color: theme.canvasColor,
+        borderRadius: BorderRadius.only(topLeft: Radius.circular(24.0), topRight: Radius.circular(24.0)),
+      ),
+      
+      child: Greeting(theme)
+    );
+  }
+
+  Widget _floatingPanel(ThemeData theme){
+    return Container(
+      decoration: BoxDecoration(
+        color: Colors.white,
+        borderRadius: BorderRadius.all(Radius.circular(24.0)),
+        boxShadow: [
+          BoxShadow(
+            blurRadius: 20.0,
+            color: theme.shadowColor,
+          ),
+        ]
+      ),
+      child: Center(
+        child: Padding(
+        padding: EdgeInsets.all(8.0),
+        child: SingleChildScrollView(
+          child: Column(
+            children: <Widget>[
+              Greeting(theme),
+              Text("Got a lot to do today! Here is a rundown:"),
+              ...loadDestinations(),
+            ],
+          ),
+        ),
+      ),
+    ),
+    );
+  }
+
+  Widget Greeting (ThemeData theme) {
+    return Center(
+        child: Text(
+            "Explore #todo",
+          style: TextStyle(color: theme.primaryColor, fontSize: 24.0, fontWeight: FontWeight.bold),
+        ),
+      );
+  }
+}

frontend/lib/modules/profile.dart

@@ -0,0 +1,91 @@
+import 'package:fast_network_navigation/structs/preferences.dart';
+import 'package:flutter/material.dart';
+
+
+
+class ProfilePage extends StatefulWidget {
+  @override
+  _ProfilePageState createState() => _ProfilePageState();
+}
+
+class _ProfilePageState extends State<ProfilePage> {
+  @override
+  Widget build(BuildContext context) {
+    return ListView(
+      children: [
+        // First a round, centered image
+        Center(
+          child: CircleAvatar(
+            radius: 100,
+            child: Icon(Icons.person, size: 100),
+          )
+        ),
+        Center(
+          child: Text('Curious traveler', style: TextStyle(fontSize: 24))
+        ),
+
+        Padding(
+          padding: EdgeInsets.all(10),
+        ),
+
+        Text('Please rate your preferences for the following activities:'),
+
+        // Now the sliders
+        ImportanceSliders()
+      ]
+    );
+  }
+}
+
+
+
+class ImportanceSliders extends StatefulWidget {
+
+  @override
+  State<ImportanceSliders> createState() => _ImportanceSlidersState();
+}
+
+
+class _ImportanceSlidersState extends State<ImportanceSliders> {
+
+  UserPreferences _prefs = UserPreferences();
+
+  @override
+  void initState() {
+    super.initState();
+    _prefs.load();
+  }
+
+  List<Card> _createSliders() {
+    List<Card> sliders = [];
+    for (SinglePreference pref in _prefs.preferences) {
+      sliders.add(Card(
+        child: ListTile(
+          leading: pref.icon,
+          title: Text(pref.name),
+          subtitle: Slider(
+            value: pref.value.toDouble(),
+            min: 0,
+            max: 10,
+            divisions: 10,
+            label: pref.value.toString(),
+            onChanged: (double newValue) {
+              setState(() {
+                pref.value = newValue.toInt();
+                _prefs.save();
+              });
+            },
+          )
+        ),
+        margin: EdgeInsets.only(left: 10, right: 10, top: 10, bottom: 0),
+        shadowColor: Colors.grey,
+      ));
+    }
+    return sliders;
+  }
+
+  @override
+  Widget build(BuildContext context) {
+    return Column(children: _createSliders());
+  }
+}

frontend/lib/structs/destination.dart

@@ -0,0 +1,62 @@
+import "package:flutter/material.dart";
+
+class Destination {
+  final double latitude;
+  final double longitude;
+  final String name;
+  final String description;
+  // final DestinationType type;
+  final Duration duration;
+  final bool visited;
+
+  const Destination({
+    required this.latitude,
+    required this.longitude,
+    required this.name,
+    required this.description,
+    // required this.type,
+    required this.duration,
+    required this.visited,
+  });
+
+  factory Destination.fromJson(Map<String, dynamic> json) {
+  return switch (json) {
+    {
+      'lat': double latitude,
+      'lon': double longitude,
+      'name': String name,
+      'description': String description,
+      // 'type': String type,
+      'duration': int duration,
+      'visited': bool visited
+
+    } =>
+      Destination(
+        latitude: latitude,
+        longitude: longitude,
+        name: name,
+        description: description,
+        // type: "DestinationType.values.firstWhere((element) => element.name == type)",
+        duration: Duration(minutes: duration),
+        visited: visited
+      ),
+    _ => throw const FormatException('Failed to load destination.'),
+  };
+}
+
+}
+
+
+class DestinationType {
+  final String name;
+  final String description;
+  final Icon icon;
+  
+  const DestinationType({
+    required this.name,
+    required this.description,
+    required this.icon,
+  });
+}
+
+

frontend/lib/structs/preferences.dart

@@ -0,0 +1,82 @@
+import 'package:flutter/material.dart';
+import 'package:shared_preferences/shared_preferences.dart';
+
+
+class SinglePreference {
+  String name;
+  String description;
+  int value;
+  Icon icon;
+  String key;
+
+  SinglePreference({
+    required this.name,
+    required this.description,
+    required this.value,
+    required this.icon,
+    required this.key,
+  });
+}
+
+
+class UserPreferences {
+  List<SinglePreference> preferences = [
+    SinglePreference(
+      name: "Sightseeing",
+      description: "How much do you like sightseeing?",
+      value: 0,
+      icon: Icon(Icons.church),
+      key: "sightseeing",
+    ),
+    SinglePreference(
+      name: "Shopping",
+      description: "How much do you like shopping?",
+      value: 0,
+      icon: Icon(Icons.shopping_bag),
+      key: "shopping",
+    ),
+    SinglePreference(
+      name: "Foods & Drinks",
+      description: "How much do you like eating?",
+      value: 0,
+      icon: Icon(Icons.restaurant),
+      key: "eating",
+    ),
+    SinglePreference(
+      name: "Nightlife",
+      description: "How much do you like nightlife?",
+      value: 0,
+      icon: Icon(Icons.wine_bar),
+      key: "nightlife",
+    ),
+    SinglePreference(
+      name: "Nature",
+      description: "How much do you like nature?",
+      value: 0,
+      icon: Icon(Icons.landscape),
+      key: "nature",
+    ),
+    SinglePreference(
+      name: "Culture",
+      description: "How much do you like culture?",
+      value: 0,
+      icon: Icon(Icons.palette),
+      key: "culture",
+    ),
+  ];
+
+
+  void save() async {
+    SharedPreferences prefs = await SharedPreferences.getInstance();
+    for (SinglePreference pref in preferences) {
+      prefs.setInt(pref.key, pref.value);
+    }
+  }
+
+  void load() async {
+    SharedPreferences prefs = await SharedPreferences.getInstance();
+    for (SinglePreference pref in preferences) {
+      pref.value = prefs.getInt(pref.key) ?? 0;
+    }
+  }
+}

frontend/lib/structs/route.dart

@@ -0,0 +1,14 @@
+import "package:fast_network_navigation/structs/destination.dart";
+
+
+class Route {
+  final String name;
+  final Duration duration;
+  final List<Destination> destinations;
+  
+  Route({
+    required this.name,
+    required this.duration,
+    required this.destinations
+  });
+}

frontend/lib/utils/get_route.dart

@@ -0,0 +1,18 @@
+import "package:fast_network_navigation/structs/destination.dart";
+import 'package:http/http.dart' as http;
+import 'dart:convert';
+
+Future<Destination> fetchDestination() async {
+  final response = await http
+      .get(Uri.parse('https://nav.kluster.moll.re/v1/destination/1'));
+
+  if (response.statusCode == 200) {
+    // If the server did return a 200 OK response,
+    // then parse the JSON.
+    return Destination.fromJson(jsonDecode(response.body) as Map<String, dynamic>);
+  } else {
+    // If the server did not return a 200 OK response,
+    // then throw an exception.
+    throw Exception('Failed to load destination');
+  }
+}