Add renovate.json

Reviewed-on: remoll/fast-network-navigation#12

.gitea/workflows/backend_build-image.yaml

@@ -2,6 +2,8 @@ on:
   pull_request:
     branches:
       - main
+    paths:
+      - backend/**
 
 name: Build and push docker image
 
@@ -12,13 +14,13 @@ jobs:
     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.GITEA_TOKEN}}
+        password: ${{ secrets.PACKAGE_REGISTRY_ACCESS }}
 
     - name: Set up Docker Buildx
       uses: docker/setup-buildx-action@v3
@@ -27,5 +29,5 @@ jobs:
       uses: docker/build-push-action@v5
       with:
         context: backend
-        tags: git.kluster.moll.re/remoll/fast_network_navigation/backend:latest
+        tags: git.kluster.moll.re/anydev/anyway-backend:latest
         push: true

.gitea/workflows/frontend_build-android.yaml

@@ -42,20 +42,24 @@ jobs:
     - run: flutter pub get
       working-directory: ./frontend
 
-    - run: flutter build apk --release --split-per-abi
+    - name: Add required secrets
+      run: |
+        echo ${{ secrets.ANDROID_SECRETS_PROPERTIES }} > ./android/secrets.properties
       working-directory: ./frontend
 
-    - name: Release APK
-      uses: https://gitea.com/akkuman/gitea-release-action@v1
+    - name: Sanity check
+      run: |
+        ls
+        ls -lah android
+      working-directory: ./frontend
+
+    - run: flutter build apk --release --split-per-abi --build-number=${{ gitea.run_number }}
+      working-directory: ./frontend
+
+    - name: Upload APKs to artifacts
+      uses: https://gitea.com/actions/upload-artifact@v3
       with:
-        files: ./frontend/build/app/outputs/flutter-apk/*.apk
-        name: Testing release
-        release_name: testing
-        tag: testing
-        tag_name: testing
-        release_body: "This is a testing release."
-        prerelease: true
-        token: ${{ secrets.GITEA_TOKEN }}
-      env:
-        NODE_OPTIONS: '--experimental-fetch'
-      
+        name: app-release
+        path: frontend/build/app/outputs/flutter-apk/
+        if-no-files-found: error
+        retention-days: 15

.gitea/workflows/frontend_build-web.yaml

@@ -1,34 +1,34 @@
-on:
-  pull_request:
-    branches:
-      - main
-    paths:
-      - frontend/**
+# on:
+#   pull_request:
+#     branches:
+#       - main
+#     paths:
+#       - frontend/**
 
 
-name: Build web
+# name: Build web
 
-jobs:
-  build:
-    name: Build Web
-    runs-on: ubuntu-latest
-    steps:
+# jobs:
+#   build:
+#     name: Build Web
+#     runs-on: ubuntu-latest
+#     steps:
 
-    - name: Install prerequisites
-      run: |
-        sudo apt-get update
-        sudo apt-get install -y xz-utils
+#     - name: Install prerequisites
+#       run: |
+#         sudo apt-get update
+#         sudo apt-get install -y xz-utils
 
-    - uses: actions/checkout@v4
+#     - uses: actions/checkout@v4
 
-    - uses: https://github.com/subosito/flutter-action@v2
-      with:
-        channel: stable
-        flutter-version: 3.19.6
-        cache: true
+#     - uses: https://github.com/subosito/flutter-action@v2
+#       with:
+#         channel: stable
+#         flutter-version: 3.19.6
+#         cache: true
 
-    - run: flutter pub get
-      working-directory: ./frontend
+#     - run: flutter pub get
+#       working-directory: ./frontend
 
-    - run: flutter build web
-      working-directory: ./frontend
+#     - run: flutter build web
+#       working-directory: ./frontend

.gitignore

@@ -0,0 +1 @@
+cache/

.vscode/launch.json

@@ -4,25 +4,49 @@
     // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
     "version": "0.2.0",
     "configurations": [
+        // backend - python using fastapi
         {
-            "name": "frontend",
-            "cwd": "frontend",
+            "name": "Backend - debug",
+            "type": "debugpy",
             "request": "launch",
-            "type": "dart"
+            "module": "uvicorn",
+            "env": {
+                "DEBUG": "true"
+            },
+            "args": [
+                "--app-dir",
+                "src",
+                "main:app",
+                "--reload",
+            ],
+            "jinja": true,
+            "cwd": "${workspaceFolder}/backend"
         },
         {
-            "name": "frontend (profile mode)",
-            "cwd": "frontend",
+            "name": "Backend - tester",
+            "type": "debugpy",
             "request": "launch",
+            "program": "src/tester.py",
+            "env": {
+                "DEBUG": "true"
+            },
+            "cwd": "${workspaceFolder}/backend"
+        },
+        // frontend - flutter app
+        {
+            "name": "Frontend - debug",
             "type": "dart",
-            "flutterMode": "profile"
+            "request": "launch",
+            "program": "lib/main.dart",
+            "cwd": "${workspaceFolder}/frontend"
         },
         {
-            "name": "frontend (release mode)",
-            "cwd": "frontend",
-            "request": "launch",
+            "name": "Frontend - profile",
             "type": "dart",
-            "flutterMode": "release"
-        },
+            "request": "launch",
+            "program": "lib/main.dart",
+            "flutterMode": "profile",
+            "cwd": "${workspaceFolder}/frontend"
+        }
     ]
 }

README.md

@@ -1,16 +1,40 @@
-# fast_network_navigation
+# AnyWay - plan city trips your way
+
+AnyWay is a mobile application that helps users plan city trips. The app allows users to specify their preferences and constraints, and then generates a personalized itinerary for them. The planning follows some guiding principles:
+- **Personalization**:The user's preferences should be reflected in the choice of destinations.
+- **Efficiency**:The itinerary should be optimized for the user's constraints.
+- **Flexibility**: We aknowledge that tourism is a dynamic activity, and that users may want to change their plans on the go.
+- **Discoverability**: Tourism is an inherently exploratory activity. Once a rough itinerary is generated, detours and spontaneous decisions should be encouraged.
+
+
+## Architecture
+This project is divided into two main components: a frontend and a backend. The frontend is a Flutter application that runs on Android and iOS. The backend is a python server that runs on a cloud provider. The two components communicate via a REST API. Since both components are very interdependent and share many data structures, we opted for a monorepo approach.
+
+
+### Frontend
+See the [frontend README](frontend/README.md) for more information. The application is centered around its map view, which displays the user's itinerary. This is based on the Google Maps API. 
+
+### Backend
+See the [backend README](backend/README.md) for more information. The backend is responsible for generating the itinerary based on the user's preferences and constraints. Rather than using google maps, we use the OpenStreetMap API, which is much more flexible.
 
-A new Flutter project.
 
 ## Getting Started
+Refer to the READMEs in the `frontend` and `backend` directories for instructions on how to run the application locally. Notable prerequisites include:
+- Flutter SDK
+    - `google_maps_flutter` plugin
+- Python 3
+    - `fastapi`
+    - `OSMPythonTools`
+    - `numpy, scipy`
+- Docker
 
-This project is a starting point for a Flutter application.
 
-A few resources to get you started if this is your first Flutter project:
+## Releases
+The project is still in its early stages. We are currently working on a prototype that demonstrates the core functionality of the application.
 
-- [Lab: Write your first Flutter app](https://docs.flutter.dev/get-started/codelab)
-- [Cookbook: Useful Flutter samples](https://docs.flutter.dev/cookbook)
+Official releases will be made available on the Google Play Store and the Apple App Store.
 
-For help getting started with Flutter development, view the
-[online documentation](https://docs.flutter.dev/), which offers tutorials,
-samples, guidance on mobile development, and a full API reference.
+
+## Roadmap
+See the [project board](https://todos.kluster.moll.re/share/L8vZJGU45Vx9RzzVqTzyCPrpRybXm1OAqaW7YkWb/auth?view=list):
+<iframe src="https://todos.kluster.moll.re/share/L8vZJGU45Vx9RzzVqTzyCPrpRybXm1OAqaW7YkWb/auth?view=list" title="Todos" width="100%">

backend/.gitignore

@@ -1,5 +1,6 @@
 # osm-cache
 cache/
+apicache/
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

backend/Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3
+FROM python:3.11-slim
 
 WORKDIR /app
 COPY Pipfile Pipfile.lock .
@@ -6,6 +6,12 @@ COPY Pipfile Pipfile.lock .
 RUN pip install pipenv
 RUN pipenv install --deploy --system
 
-COPY . /src
+COPY src src
 
-CMD ["pipenv", "run", "python", "/app/src/main.py"]
+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
+
+CMD fastapi run src/main.py --port 8000 --workers $NUM_WORKERS

backend/Pipfile

@@ -3,10 +3,14 @@ url = "https://pypi.org/simple"
 verify_ssl = true
 name = "pypi"
 
+[dev-packages]
+
 [packages]
 numpy = "*"
-scipy = "*"
 fastapi = "*"
+pydantic = "*"
+geopy = "*"
+shapely = "*"
+scipy = "*"
 osmpythontools = "*"
-
-[dev-packages]
+pywikibot = "*"

backend/README.md

@@ -0,0 +1,16 @@
+# Backend
+
+This repository contains the backend code for the application. It utilizes FastAPI that allows to quickly create a RESTful API that exposes the endpoints of the route optimizer.
+
+
+## Getting Started
+- The code of the python application is located in the `src` directory.
+- Package management is handled with `pipenv` and the dependencies are listed in the `Pipfile`.
+- Since the application is aimed to be deployed in a container, the `Dockerfile` is provided to build the image.
+
+### Deployment
+To deploy the backend docker container, we use kubernetes. The deployment configuration is located under [https://git.kluster.moll.re/anydev/deployment-backend/](https://git.kluster.moll.re/anydev/deployment-backend/).
+
+
+## Development
+TBD

backend/app/dependencies.py

@@ -1 +0,0 @@
-import app.src

backend/app/main.py

@@ -1,34 +0,0 @@
-from .src.optimizer import solve_optimization
-from .src.landmarks_manager import Landmark
-from fastapi import FastAPI
-
-app = FastAPI()
-
-
-@app.get("/optimize/{max_steps}/{print_details}")
-def main(max_steps: int, print_details: bool):
-    
-    # CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
-    #max_steps = 16
-
-
-    # Initialize all landmarks (+ start and goal). Order matters here
-    landmarks = []
-    landmarks.append(Landmark("départ", -1, (0, 0)))
-    landmarks.append(Landmark("tour eiffel", 99, (0,2)))                           # PUT IN JSON
-    landmarks.append(Landmark("arc de triomphe", 99, (0,4)))
-    landmarks.append(Landmark("louvre", 99, (0,6)))
-    landmarks.append(Landmark("montmartre", 99, (0,10)))
-    landmarks.append(Landmark("concorde", 99, (0,8)))
-    landmarks.append(Landmark("arrivée", -1, (0, 0)))
-
-
-    visiting_order = solve_optimization(landmarks, max_steps, print_details)
-
-    #return visiting_order
-
-    return("max steps :", max_steps, "\n", visiting_order)
-
-
-"""if __name__ == "__main__":
-    main()"""

backend/app/src/landmarks_manager.py

@@ -1,57 +0,0 @@
-from OSMPythonTools.api import Api
-from OSMPythonTools.overpass import Overpass
-from dataclasses import dataclass
-
-
-# Defines the landmark class (aka some place there is to visit)
-@dataclass
-class Landmarkkkk :
-    name : str
-    attractiveness : int
-    id : int
-    
-@dataclass
-class Landmark :
-    name : str
-    attractiveness : int
-    loc : tuple
-
-# Converts a OSM id to a landmark
-def add_from_id(id: int, score: int) :
-
-    try :
-        s = 'way/' + str(id)           # prepare string for query
-        obj =  api.query(s)                             # object to add
-    except :
-        s = 'relation/' + str(id)           # prepare string for query
-        obj =  api.query(s)                             # object to add
-
-    return Landmarkkkk(obj.tag('name:fr'), score, id)      # create Landmark out of it
-
-
-# take a lsit of tuples (id, score) to generate a list of landmarks
-def generate_landmarks(ids_and_scores: list) :
-
-    L = []
-    for tup in ids_and_scores :
-        L.append(add_from_id(tup[0], tup[1]))
-
-    return L
-
-api = Api()
-
-
-l = (7515426, 70)
-t = (5013364, 100)
-n = (201611261, 99)
-a = (226413508, 50)
-m = (23762981, 30)
-
-
-ids_and_scores = [t, l, n, a, m]
-
-landmarks = generate_landmarks(ids_and_scores)
-
-
-for obj in landmarks :
-    print(obj)

backend/app/src/main_example.py

@@ -1,23 +0,0 @@
-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/app/src/optimizer.py

@@ -1,323 +0,0 @@
-from scipy.optimize import linprog
-import numpy as np
-from scipy.linalg import block_diag
-
-
-# landmarks = [Landmark_1, Landmark_2, ...]
-
-# Convert the solution of the optimization into the list of edges to follow. Order is taken into account
-def untangle(resx: list) :
-    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
-
-    order = []
-    nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
-
-    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
-
-    indx = nonzero_tup[0].tolist()
-    indy = nonzero_tup[1].tolist()
-
-    vert = (indx[0], indy[0])
-
-    order.append(vert[0])
-    order.append(vert[1])
-    
-    while len(order) < n_edges + 1 :
-        ind = indx.index(vert[1])
-
-        vert = (indx[ind], indy[ind])
-
-        order.append(vert[1])
-
-    return order
-
-# Just to print the result
-def print_res(res, landmarks: list, P) :
-    X = abs(res.x)
-    order = untangle(X)
-    things = []
-
-    """N = int(np.sqrt(len(X)))
-    for i in range(N):
-        print(X[i*N:i*N+N])
-    print("Optimal value:", -res.fun)  # Minimization, so we negate to get the maximum
-    print("Optimal point:", res.x)
-    for i,x in enumerate(X) : X[i] = round(x,0)
-    print(order)"""
-
-    if (X.sum()+1)**2 == len(X) : 
-        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 : ')
-
-    for idx in order : 
-        print('- ' + landmarks[idx].name)
-        things.append(landmarks[idx].name)
-
-    steps = path_length(P, abs(res.x))
-    print("\nSteps walked : " + str(steps))
-
-    return things
-
-# Checks for cases of circular symmetry in the result
-def has_circle(resx: list) :
-    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))
-
-    indx = nonzero_tup[0].tolist()
-    indy = nonzero_tup[1].tolist()
-    
-
-    verts = []
-
-    for i, x in enumerate(indx) :
-        verts.append((x, indy[i]))
-
-    
-    for vert in verts :
-        visited = []
-        visited.append(vert)
-
-        while len(visited) < n_edges + 1 :
-
-            try : 
-                ind = indx.index(vert[1])
-
-                vert = (indx[ind], indy[ind])
-
-                if vert in visited :
-                    return visited
-                else :
-                    visited.append(vert)
-            except :
-                break
-
-    return []
-
-# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
-def break_sym(landmarks, A_ub, b_ub):
-    L = len(landmarks)
-    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)
-
-            """for i in range(7):
-                print(l[i*7:i*7+7])
-            print("\n")"""
-
-    return A_ub, b_ub
-
-# Constraint to not have circular paths. Want to go from start -> finish without unconnected loops
-def break_circle(landmarks, A_ub, b_ub, circle) :
-    N = len(landmarks)
-    l = [0]*N*N
-
-    for index in circle :
-        x = index[0]
-        y = index[1]
-        l[x*N+y] = 1
-
-    A_ub = np.vstack((A_ub,l))
-    b_ub.append(len(circle)-1)
-
-    """print("\n\nPREVENT CIRCLE")
-    for i in range(7):
-        print(l[i*7:i*7+7])
-    print("\n")"""
-
-    return A_ub, b_ub
-
-# Constraint to respect max number of travels
-def respect_number(landmarks, A_ub, b_ub):
-    h = []
-    for i in range(len(landmarks)) : h.append([1]*len(landmarks))
-    T = block_diag(*h)
-    """for l in T :
-        for i in range(7):
-            print(l[i*7:i*7+7])
-        print("\n")"""
-    return np.vstack((A_ub, T)), b_ub + [1]*len(landmarks)
-
-# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
-def respect_order(landmarks: list, A_eq, b_eq): 
-    N = len(landmarks)
-    for i in range(N-1) :     # Prevent stacked ones
-        if i == 0 :
-            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)
-
-            """for i in range(7):
-                print(l[i*7:i*7+7])
-            print("\n")"""
-
-    return A_eq, b_eq
-
-# Compute manhattan distance between 2 locations
-def manhattan_distance(loc1: tuple, loc2: tuple):
-    x1, y1 = loc1
-    x2, y2 = loc2
-    return abs(x1 - x2) + abs(y1 - y2)
-
-# Constraint to not stay in position
-def init_eq_not_stay(landmarks): 
-    L = len(landmarks)
-    l = [0]*L*L
-
-
-    for i in range(L) :
-        for j in range(L) :
-            if j == i :
-                l[j + i*L] = 1
-    l[L-1] = 1      # cannot skip from start to finish
-    #A_eq = np.array([np.array(xi) for xi in A_eq])                  # Must convert A_eq into an np array
-    l = np.array(np.array(l))
-
-    """for i in range(7):
-        print(l[i*7:i*7+7])"""
-
-    return [l], [0]
-
-# 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, max_steps: int):
-    # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
-    c = []
-    # Coefficients of inequality constraints (left-hand side)
-    A = []
-    for i, spot1 in enumerate(landmarks) :
-        dist_table = [0]*len(landmarks)
-        c.append(-spot1.attractiveness)
-        for j, spot2 in enumerate(landmarks) :
-            dist_table[j] = manhattan_distance(spot1.loc, spot2.loc)
-        A.append(dist_table)
-    c = c*len(landmarks)
-    A_ub = []
-    for line in A :
-        #print(line)
-        A_ub += line
-    return c, A_ub, [max_steps]
-
-# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
-def respect_user_mustsee(landmarks: list, A_eq: list, b_eq: list) :
-    L = len(landmarks)
-    H = 0       # sort of heuristic to get an idea of the number of steps needed
-    for i in landmarks : 
-        if i.name == "départ" : elem_prev = i              # list of all matches
-    for i, elem in enumerate(landmarks) :
-        if elem.attractiveness == -1 :
-            l = [0]*L*L
-            if elem.name != "arrivée" :
-                for j in range(L) :
-                    l[j +i*L] = 1
-                    
-            else :                          # This ensures we go to goal
-                for k in range(L-1) :
-                        l[k*L+L-1] = 1  
-
-            H += manhattan_distance(elem.loc, elem_prev.loc)
-            elem_prev = elem
-
-            """for i in range(7):
-                print(l[i*7:i*7+7])
-            print("\n")"""
-
-            A_eq = np.vstack((A_eq,l))
-            b_eq.append(1)
-    return A_eq, b_eq, H
-
-# Computes the path length given path matrix (dist_table) and a result
-def path_length(P: list, resx: list) :
-    return np.dot(P, resx)
-
-# Main optimization pipeline
-def solve_optimization (landmarks, max_steps, printing_details) :
-
-    # SET CONSTRAINTS FOR INEQUALITY
-    c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)              # Add the distances from each landmark to the other
-    P = A_ub                                                        # store the paths for later. Needed to compute path length
-    A_ub, b_ub = respect_number(landmarks, A_ub, b_ub)              # Respect max number of visits. 
-
-    # TODO : Problems with circular symmetry
-    A_ub, b_ub = break_sym(landmarks, A_ub, b_ub)                  # break the symmetry. Only use the upper diagonal values
-
-    # SET CONSTRAINTS FOR EQUALITY
-    A_eq, b_eq = init_eq_not_stay(landmarks)                       # Force solution not to stay in same place
-    A_eq, b_eq, H = 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_order(landmarks, A_eq, b_eq)              # Respect order of visit (only works when max_steps is limiting factor)
-
-    # Bounds for variables (x can only be 0 or 1)
-    x_bounds = [(0, 1)] * len(c)
-
-    # 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 :
-
-        # Override the max_steps using the heuristic
-        for i, val in enumerate(b_ub) :
-            if val == max_steps : b_ub[i] = H
-
-        # Solve problem again :
-        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)
-
-        if not res.success :
-            s = "No solution could be found, even when increasing max_steps using the heuristic"
-            return s
-            #raise ValueError("No solution could be found, even when increasing max_steps using the heuristic")
-    
-    # If there is a solution, we're good to go, just check for
-    else :
-        circle = has_circle(res.x)
-        i = 0
-
-        # Break the circular symmetry if needed
-        while len(circle) != 0 :
-            A_ub, b_ub = break_circle(landmarks, A_ub, b_ub, circle)
-            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)
-            circle = has_circle(res.x)
-            i += 1
-
-        if printing_details is True :
-            if i != 0 :
-                print(f"Neded to recompute paths {i} times because of unconnected loops...")
-            X = print_res(res, landmarks, P)
-            return X
-        else :
-            return untangle(res.x)
-
-
-
-
-
-

backend/src/constants.py

@@ -0,0 +1,27 @@
+import logging.config
+from pathlib import Path
+import os
+
+LOCATION_PREFIX = Path('src')
+PARAMETERS_DIR = LOCATION_PREFIX / 'parameters'
+AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
+LANDMARK_PARAMETERS_PATH = PARAMETERS_DIR / 'landmark_parameters.yaml'
+OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
+
+
+
+cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
+OSM_CACHE_DIR = Path(cache_dir_string)
+
+
+import logging
+import yaml
+
+LOGGING_CONFIG = LOCATION_PREFIX / 'log_config.yaml'
+config = yaml.safe_load(LOGGING_CONFIG.read_text())
+
+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)

backend/src/log_config.yaml

@@ -0,0 +1,34 @@
+version: 1
+disable_existing_loggers: False
+formatters:
+  simple:
+    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+handlers:
+  console:
+    class: rich.logging.RichHandler
+    formatter: simple
+  # access:
+  #   class: logging.FileHandler
+  #   filename: logs/access.log
+  #   level: INFO
+  #   formatter: simple
+
+
+
+
+loggers:
+  uvicorn.error:
+    level: INFO
+    handlers:
+      - console
+    propagate: no
+  # uvicorn.access:
+  #   level: INFO
+  #   handlers:
+  #     - access
+  #   propagate: no
+root:
+  level: INFO
+  handlers:
+    - console
+  propagate: yes

backend/src/main.py

@@ -0,0 +1,68 @@
+import logging
+from fastapi import FastAPI, Query, Body
+
+from structs.landmark import Landmark
+from structs.preferences import Preferences
+from structs.linked_landmarks import LinkedLandmarks
+from utils.landmarks_manager import LandmarkManager
+from utils.optimizer import Optimizer
+from utils.refiner import Refiner
+
+
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+manager = LandmarkManager()
+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:
+    '''
+    Main function to call the optimizer.
+    :param preferences: the preferences specified by the user as the post body
+    :param start: the coordinates of the starting point as a tuple of floats (as url query parameters)
+    :param end: the coordinates of the finishing point as a tuple of floats (as url query parameters)
+    :return: the uuid of the first landmark in the optimized route
+    '''
+    if preferences is None:
+        raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
+    if start is None:
+        raise ValueError("Please provide the starting coordinates as a tuple of floats.")
+    if end is None:
+        end = start
+        logger.info("No end coordinates provided. Using start=end.")
+
+    start_landmark = Landmark(name='start', type='start', location=(start[0], start[1]), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    end_landmark = Landmark(name='end', type='finish', location=(end[0], end[1]), osm_type='end', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+
+    # Generate the landmarks from the start location
+    landmarks, landmarks_short = manager.generate_landmarks_list(
+        center_coordinates = start,
+        preferences = preferences
+    )
+
+    # insert start and finish to the landmarks list
+    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)
+    
+    # Second stage optimization
+    refined_tour = refiner.refine_optimization(landmarks, base_tour, max_walking_time*60, detour)
+
+    linked_tour = LinkedLandmarks(refined_tour)
+    return linked_tour[0].uuid
+
+
+
+@app.get("/landmark/{landmark_uuid}")
+def get_landmark(landmark_uuid: str) -> Landmark:
+    #cherche dans linked_tour et retourne le landmark correspondant
+    pass

backend/src/parameters/amenity_selectors.yaml

@@ -0,0 +1,32 @@
+nature:
+  leisure: park
+  geological: ''
+  natural:
+    - geyser
+    - hot_spring
+    - arch
+    - volcano
+    - stone
+  tourism:
+    - alpine_hut
+    - viewpoint
+    - zoo
+  waterway: waterfall
+
+shopping:
+  shop:
+    - department_store
+    - mall
+
+sightseeing:
+  tourism:
+    - museum
+    - attraction
+    - gallery
+  historic: ''
+  amenity:
+    - planetarium
+    - place_of_worship
+    - fountain
+  water:
+    - reflecting_pool

backend/src/parameters/landmark_parameters.yaml

@@ -0,0 +1,6 @@
+city_bbox_side: 5000 #m
+radius_close_to: 50
+church_coeff: 0.8
+park_coeff: 1.2
+tag_coeff: 10
+N_important: 40

backend/src/parameters/optimizer_parameters.yaml

@@ -0,0 +1,4 @@
+detour_factor: 1.4
+detour_corridor_width: 200
+average_walking_speed: 4.8
+max_landmarks: 7

backend/src/structs/landmark.py

@@ -0,0 +1,38 @@
+from typing import Optional, Literal
+from pydantic import BaseModel, Field
+
+from uuid import uuid4
+
+# Output to frontend
+class Landmark(BaseModel) :
+    
+    # Properties of the landmark
+    name : str
+    type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
+    location : tuple
+    osm_type : str
+    osm_id : int
+    attractiveness : int
+    n_tags : int
+    image_url : Optional[str] = None                            # TODO future
+    description : Optional[str] = None                          # TODO future
+    duration : Optional[int] = 0                                # TODO future
+
+    # Unique ID of a given landmark
+    uuid: str = Field(default_factory=uuid4)                    # TODO implement this ASAP
+    
+    # Additional properties depending on specific tour
+    must_do : Optional[bool] = False
+    must_avoid : Optional[bool] = False
+    is_secondary : Optional[bool] = False                       # TODO future   
+    
+    time_to_reach_next : Optional[int] = 0                      # TODO fix this in existing code
+    next_uuid : Optional[str] = None                            # TODO implement this ASAP
+
+    def __hash__(self) -> int:
+        return self.uuid.int
+    
+    def __str__(self) -> str:
+        time_to_next_str = f", time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
+        return f'Landmark({self.type}): [{self.name} @{self.location}, score={self.attractiveness}{time_to_next_str}]'
+    

backend/src/structs/linked_landmarks.py

@@ -0,0 +1,61 @@
+import uuid
+from .landmark import Landmark
+from utils.get_time_separation import get_time
+
+class LinkedLandmarks:
+    """
+    A list of landmarks that are linked together, e.g. in a route.
+    Each landmark serves as a node in the linked list, but since we expect these to be consumed through the rest API, a pythonic reference to the next landmark is not well suited. Instead we use the uuid of the next landmark to reference the next landmark in the list. This is not very efficient, but appropriate for the expected use case ("short" trips with onyl few landmarks).
+    """
+    
+    _landmarks = list[Landmark]
+    total_time = int
+    uuid = str
+
+    def __init__(self, data: list[Landmark] = None) -> None:
+        """
+        Initialize a new LinkedLandmarks object. This expects an ORDERED list of landmarks, where the first landmark is the starting point and the last landmark is the end point.
+        
+        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()
+
+
+    def _link_landmarks(self) -> None:
+        """
+        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)
+            landmark.time_to_reach_next = time_to_next
+            self.total_time += time_to_next
+
+        self._landmarks[-1].next_uuid = None
+        self._landmarks[-1].time_to_reach_next = 0
+
+
+    def __getitem__(self, index: int) -> Landmark:
+        return self._landmarks[index]
+    
+
+    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]
+        }

backend/src/structs/preferences.py

@@ -0,0 +1,21 @@
+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
+
+# 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
+
+    max_time_minute: Optional[int] = 6*60 
+    detour_tolerance_minute: Optional[int] = 0

backend/src/tester.py

@@ -0,0 +1,85 @@
+import logging
+import yaml
+
+from utils.landmarks_manager import LandmarkManager
+from utils.optimizer import Optimizer
+from utils.refiner import Refiner
+from structs.landmark import Landmark
+from structs.linked_landmarks import LinkedLandmarks
+from structs.preferences import Preferences, Preference
+
+
+logger = logging.getLogger(__name__)
+
+
+
+def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = None) -> list[Landmark]:
+    manager = LandmarkManager()
+    optimizer = Optimizer()
+    refiner = Refiner(optimizer=optimizer)
+
+    
+    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),
+
+                    max_time_minute=180,
+                    detour_tolerance_minute=30
+                    )
+
+    # Create start and finish 
+    if finish_coords is None :
+        finish_coords = start_coords
+    start = Landmark(name='start', type='start', location=start_coords, osm_type='', osm_id=0, attractiveness=0, n_tags = 0)
+    finish = Landmark(name='finish', type='finish', location=finish_coords, osm_type='', osm_id=0, attractiveness=0, 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 = manager.generate_landmarks_list(
+        center_coordinates = start_coords,
+        preferences = preferences
+    )
+    
+    # Store data to file for debug purposes
+    # write_data(landmarks, "landmarks_Strasbourg.txt")
+
+    # Insert start and finish to the landmarks list
+    landmarks_short.insert(0, start)
+    landmarks_short.append(finish)
+
+    # First stage optimization
+    base_tour = optimizer.solve_optimization(max_time=preferences.max_time_minute, landmarks=landmarks_short)
+
+    # Second stage using linear optimization
+    refined_tour = refiner.refine_optimization(all_landmarks=landmarks, base_tour=base_tour, max_time = preferences.max_time_minute, detour = preferences.detour_tolerance_minute)
+
+    linked_tour = LinkedLandmarks(refined_tour)
+    logger.info(f"Optimized route: {linked_tour}")
+
+    # with open('linked_tour.yaml', 'w') as f:
+    #     yaml.dump(linked_tour.asdict(), f)
+
+    return linked_tour
+
+
+#test(tuple((48.8344400, 2.3220540)))       # Café Chez César 
+#test(tuple((48.8375946, 2.2949904)))       # Point random
+#test(tuple((47.377859, 8.540585)))         # Zurich HB
+#test(tuple((45.7576485, 4.8330241)))      # Lyon Bellecour
+test(tuple((48.5848435, 7.7332974)))      # Strasbourg Gare
+#test(tuple((48.2067858, 16.3692340)))      # Vienne

backend/src/utils/get_time_separation.py

@@ -0,0 +1,39 @@
+import yaml
+from geopy.distance import geodesic
+
+import constants
+
+with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+    parameters = yaml.safe_load(f)
+    DETOUR_FACTOR = parameters['detour_factor']
+    AVERAGE_WALKING_SPEED = parameters['average_walking_speed']
+
+
+def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
+    """
+    Calculate the time in minutes to travel from one location to another.
+
+    Args:
+        p1 (Tuple[float, float]): Coordinates of the starting location.
+        p2 (Tuple[float, float]): Coordinates of the destination.
+        detour (float): Detour factor affecting the distance.
+        speed (float): Walking speed in kilometers per hour.
+
+    Returns:
+        int: Time to travel from p1 to p2 in minutes.
+    """
+
+
+    # Compute the straight-line distance in km
+    if p1 == p2 :
+        return 0
+    else: 
+        dist = geodesic(p1, p2).kilometers
+
+    # Consider the detour factor for average cityto deterline walking distance (in km)
+    walk_dist = dist*DETOUR_FACTOR
+
+    # Time to walk this distance (in minutes)
+    walk_time = walk_dist/AVERAGE_WALKING_SPEED*60
+
+    return round(walk_time)

backend/src/utils/landmarks_manager.py

@@ -0,0 +1,365 @@
+import math as m
+import yaml
+import logging
+
+from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
+from pywikibot import ItemPage, Site
+from pywikibot import config
+config.put_throttle = 0
+config.maxlag = 0
+
+from structs.preferences import Preferences, Preference
+from structs.landmark import Landmark
+from .take_most_important import take_most_important
+import constants
+
+
+SIGHTSEEING = 'sightseeing'
+NATURE = 'nature'
+SHOPPING = 'shopping'
+
+
+
+class LandmarkManager:
+
+    logger = logging.getLogger(__name__)
+
+    city_bbox_side: int     # bbox side in meters
+    radius_close_to: int    # radius in meters
+    church_coeff: float     # coeff to adjsut score of churches
+    park_coeff: float       # coeff to adjust score of parks
+    tag_coeff: float        # coeff to adjust weight of tags
+    N_important: int        # number of important landmarks to consider
+
+
+    def __init__(self) -> None:
+
+        with constants.AMENITY_SELECTORS_PATH.open('r') as f:
+            self.amenity_selectors = yaml.safe_load(f)
+
+        with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
+            parameters = yaml.safe_load(f)
+            self.city_bbox_side = parameters['city_bbox_side']
+            self.radius_close_to = parameters['radius_close_to']
+            self.church_coeff = parameters['church_coeff']
+            self.park_coeff = parameters['park_coeff']
+            self.tag_coeff = parameters['tag_coeff']
+            self.N_important = parameters['N_important']
+
+        self.overpass = Overpass()
+        CachingStrategy.use(JSON, cacheDir=constants.OSM_CACHE_DIR)
+
+
+    def generate_landmarks_list(self, center_coordinates: tuple[float, float], preferences: Preferences) -> tuple[list[Landmark], list[Landmark]]:
+        """
+        Generate and prioritize a list of landmarks based on user preferences.
+
+        This method fetches landmarks from various categories (sightseeing, nature, shopping) based on the user's preferences
+        and current location. It scores and corrects these landmarks, removes duplicates, and then selects the most important
+        landmarks based on a predefined criterion.
+
+        Parameters:
+        center_coordinates (tuple[float, float]): The latitude and longitude of the center location around which to search.
+        preferences (Preferences): The user's preference settings that influence the landmark selection.
+
+        Returns:
+            tuple[list[Landmark], list[Landmark]]:
+                - A list of all existing landmarks.
+                - A list of the most important landmarks based on the user's preferences.
+        """
+
+        L = []
+        bbox = self.create_bbox(center_coordinates)
+        # 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)
+            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)
+            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)
+            L += L3
+
+        L = self.remove_duplicates(L)
+        L_constrained = take_most_important(L, self.N_important)
+        self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
+
+        return L, L_constrained
+
+
+    def remove_duplicates(self, landmarks: list[Landmark]) -> list[Landmark]:
+        """
+        Removes duplicate landmarks based on their names from the given list. Only retains the landmark with highest score
+
+        Parameters:
+        landmarks (list[Landmark]): A list of Landmark objects.
+
+        Returns:
+        list[Landmark]: A list of unique Landmark objects based on their names.
+        """
+
+        L_clean = []
+        names = []
+
+        for landmark in landmarks:
+            if landmark.name in names: 
+                continue  
+            else:
+                names.append(landmark.name)
+                L_clean.append(landmark)
+        
+        return L_clean
+        
+
+    def correct_score(self, landmarks: list[Landmark], preference: Preference):
+        """
+        Adjust the attractiveness score of each landmark in the list based on user preferences.
+
+        This method updates the attractiveness of each landmark by scaling it according to the user's preference score.
+        The score adjustment is computed using a simple linear transformation based on the preference score.
+
+        Args:
+            landmarks (list[Landmark]): A list of landmarks whose scores need to be corrected.
+            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.
+        """
+
+        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
+
+
+    def count_elements_close_to(self, coordinates: tuple[float, float]) -> int:
+        """
+        Count the number of OpenStreetMap elements (nodes, ways, relations) within a specified radius of the given location.
+
+        This function constructs a bounding box around the specified coordinates based on the radius. It then queries
+        OpenStreetMap data to count the number of elements within that bounding box.
+
+        Args:
+            coordinates (tuple[float, float]): The latitude and longitude of the location to search around.
+
+        Returns:
+            int: The number of elements (nodes, ways, relations) within the specified radius. Returns 0 if no elements
+                are found or if an error occurs during the query.
+        """
+        
+        lat = coordinates[0]
+        lon = coordinates[1]
+
+        radius = self.radius_close_to
+
+        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: 
+            radius_result = self.overpass.query(radius_query)
+            N_elem = radius_result.countWays() + radius_result.countRelations()
+            self.logger.debug(f"There are {N_elem} ways/relations within 50m")
+            if N_elem is None:
+                return 0
+            return N_elem
+        except:
+            return 0
+
+
+    def create_bbox(self, coordinates: tuple[float, float]) -> tuple[float, float, float, float]:
+        """
+        Create a bounding box around the given coordinates.
+
+        Args:
+            coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
+
+        Returns:
+            tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
+                                                defining the bounding box.
+        """
+        
+        lat = coordinates[0]
+        lon = coordinates[1]
+
+        # Half the side length in km (since it's a square bbox)
+        half_side_length_km = self.city_bbox_side / 2 / 1000
+
+        # 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 fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
+        """
+        Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
+
+        Args:
+            bbox (tuple[float, float, float, float]): The bounding box coordinates (min_lat, min_lon, max_lat, max_lon).
+            amenity_selector (dict): The Overpass API query selector for the desired landmark type. 
+            landmarktype (str): The type of the landmark (e.g., 'sightseeing', 'nature', 'shopping').
+            score_function (callable): The function to compute the score of the landmark based on its attributes.
+
+        Returns:
+            list[Landmark]: A list of Landmark objects that were fetched and filtered based on the provided criteria.
+
+        Notes:
+            - Landmarks are fetched using Overpass API queries.
+            - Selectors are translated from the dictionary to the Overpass query format. (e.g., 'amenity'='place_of_worship')
+            - Landmarks are filtered based on various conditions including tags and type.
+            - Scores are assigned to landmarks based on their attributes and surrounding elements.
+        """
+        return_list = []
+
+        # caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
+        # we need to split the selectors into separate queries and merge the results
+        for sel in dict_to_selector_list(amenity_selector):
+            self.logger.debug(f"Current selector: {sel}")
+            query = overpassQueryBuilder(
+                bbox = bbox,
+                elementType = ['way', 'relation'],
+                selector = sel,
+                # conditions = [],
+                includeCenter = True,
+                out = 'body'
+                )
+
+            try:
+                result = self.overpass.query(query)
+            except Exception as e:
+                self.logger.error(f"Error fetching landmarks: {e}")
+                return
+            
+            for elem in result.elements():
+
+                name = elem.tag('name')                             # Add name
+                location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
+
+                # TODO: exclude these from the get go
+                # skip if unprecise location
+                if name is None or location[0] is None:
+                    continue
+
+                # skip if unused
+                if 'disused:leisure' in elem.tags().keys():
+                    continue
+                
+                # skip if part of another building
+                if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
+                    continue
+            
+                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
+
+                # remove specific tags
+                skip = False
+                for tag in elem.tags().keys():
+                    if "pay" in tag:
+                        n_tags -= 1             # discard payment options for tags
+
+                    if "disused" in tag:
+                        skip = True             # skip disused amenities
+                        break
+
+                    if "wikipedia" in tag:
+                        n_tags += 3             # wikipedia entries count more
+
+                    if tag == "wikidata":
+                        Q = elem.tag('wikidata')
+                        site = Site("wikidata", "wikidata")
+                        item = ItemPage(site, Q)
+                        item.get()
+                        n_languages = len(item.labels)
+                        n_tags += n_languages/10
+
+                    if elem_type != "nature":
+                        if "leisure" in tag and elem.tag('leisure') == "park":
+                            elem_type = "nature"
+
+                    if landmarktype != SHOPPING:
+                        if "shop" in tag:
+                            skip = True
+                            break
+
+                        if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
+                            skip = True
+                            break
+
+                if skip:
+                    continue
+
+                score = score_function(location, n_tags)
+                if score != 0:
+                    # 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=int(n_tags)
+                    )
+                    return_list.append(landmark)
+        
+        self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
+
+        return return_list
+
+
+
+def dict_to_selector_list(d: dict) -> list:
+    """
+    Convert a dictionary of key-value pairs to a list of Overpass query strings.
+
+    Args:
+        d (dict): A dictionary of key-value pairs representing the selector.
+
+    Returns:
+        list: A list of strings representing the Overpass query selectors.
+    """
+    return_list = []
+    for key, value in d.items():
+        if type(value) == list:
+            val = '|'.join(value)
+            return_list.append(f'{key}~"{val}"')
+        elif type(value) == str and len(value) == 0:
+            return_list.append(f'{key}')
+        else:
+            return_list.append(f'{key}={value}')
+    return return_list

backend/src/utils/optimizer.py

@@ -0,0 +1,519 @@
+import yaml, logging
+import numpy as np
+
+from scipy.optimize import linprog
+from collections import defaultdict, deque
+from geopy.distance import geodesic
+
+from structs.landmark import Landmark
+from .get_time_separation import get_time
+import constants
+
+    
+
+
+
+class Optimizer:
+
+    logger = logging.getLogger(__name__)
+
+    detour: int = None          # accepted max detour time (in minutes)
+    detour_factor: float        # detour factor of straight line vs real distance in cities
+    average_walking_speed: float        # average walking speed of adult
+    max_landmarks: int          # max number of landmarks to visit
+
+
+    def __init__(self) :
+
+        # load parameters from file
+        with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+            parameters = yaml.safe_load(f)
+            self.detour_factor = parameters['detour_factor']
+            self.average_walking_speed = parameters['average_walking_speed']
+            self.max_landmarks = parameters['max_landmarks']
+        
+
+
+    # Prevent the use of a particular solution
+    def prevent_config(self, resx):
+        """
+        Prevent the use of a particular solution by adding constraints to the optimization.
+
+        Args:
+            resx (list[float]): List of edge weights.
+
+        Returns:
+            Tuple[list[int], list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
+        """
+        
+        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
+
+        return h, [len(vertices_visited)-1]
+
+
+    # Prevents the creation of the same circle (both directions)
+    def prevent_circle(self, circle_vertices: list, L: int) :
+        """
+        Prevent circular paths by by adding constraints to the optimization.
+
+        Args:
+            circle_vertices (list): List of vertices forming a circle.
+            L (int): Number of landmarks.
+
+        Returns:
+            Tuple[np.ndarray, list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
+        """
+
+        l1 = [0]*L*L
+        l2 = [0]*L*L
+        for i, node in enumerate(circle_vertices[:-1]) :
+            next = circle_vertices[i+1]
+
+            l1[node*L + next] = 1
+            l2[next*L + node] = 1
+
+        s = circle_vertices[0]
+        g = circle_vertices[-1]
+
+        l1[g*L + s] = 1
+        l2[s*L + g] = 1
+
+        return np.vstack((l1, l2)), [0, 0]
+
+
+    def is_connected(self, resx) :
+        """
+        Determine the order of visits and detect any circular paths in the given configuration.
+
+        Args:
+            resx (list): List of edge weights.
+
+        Returns:
+            Tuple[list[int], Optional[list[list[int]]]]: A tuple containing the visit order and a list of any detected circles.
+        """
+
+        # 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.
+
+        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()
+
+        # Step 1: Create a graph representation
+        graph = defaultdict(list)
+        for a, b in zip(ind_a, ind_b):
+            graph[a].append(b)
+
+        # Step 2: Function to perform BFS/DFS to extract journeys
+        def get_journey(start):
+            journey_nodes = []
+            visited = set()
+            stack = deque([start])
+
+            while stack:
+                node = stack.pop()
+                if node not in visited:
+                    visited.add(node)
+                    journey_nodes.append(node)
+                    for neighbor in graph[node]:
+                        if neighbor not in visited:
+                            stack.append(neighbor)
+
+            return journey_nodes
+
+        # Step 3: Extract all journeys
+        all_journeys_nodes = []
+        visited_nodes = set()
+
+        for node in ind_a:
+            if node not in visited_nodes:
+                journey_nodes = get_journey(node)
+                all_journeys_nodes.append(journey_nodes)
+                visited_nodes.update(journey_nodes)
+
+        for l in all_journeys_nodes :
+            if 0 in l :
+                order = l
+                all_journeys_nodes.remove(l)
+                break
+
+        if len(all_journeys_nodes) == 0 :
+            return order, None
+
+        return order, all_journeys_nodes
+
+
+
+    def init_ub_dist(self, landmarks: list[Landmark], max_steps: int):
+        """
+        Initialize the objective function coefficients and inequality constraints for the optimization problem.
+
+        This function computes the distances between all landmarks and stores their attractiveness to maximize sightseeing. 
+        The goal is to maximize the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
+
+        Args:
+            landmarks (list[Landmark]): List of landmarks.
+            max_steps (int): Maximum number of steps allowed.
+
+        Returns:
+            Tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
+        """
+        
+        # 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_time(spot1.location, spot2.location)
+                dist_table[j] = t
+            closest = sorted(dist_table)[:22]
+            for i, dist in enumerate(dist_table) :
+                if dist not in closest :
+                    dist_table[i] = 32700
+            A_ub += dist_table
+        c = c*len(landmarks)
+
+        return c, A_ub, [max_steps]
+
+
+    def respect_number(self, L: int):
+        """
+        Generate constraints to ensure each landmark is visited only once and cap the total number of visited landmarks.
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+
+        ones = [1]*L
+        zeros = [0]*L
+        A = ones + zeros*(L-1)
+        b = [1]
+        for i in range(L-1) :
+            h_new = zeros*i + ones + zeros*(L-1-i)
+            A = np.vstack((A, h_new))
+            b.append(1)
+
+        A = np.vstack((A, ones*L))
+        b.append(self.max_landmarks+1)
+
+        return A, b
+
+
+    # Constraint to not have d14 and d41 simultaneously. Does not prevent cyclic paths with more elements
+    def break_sym(self, L):
+        """
+        Generate constraints to prevent simultaneous travel between two landmarks in both directions.
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+
+        upper_ind = np.triu_indices(L,0,L)
+
+        up_ind_x = upper_ind[0]
+        up_ind_y = upper_ind[1]
+
+        A = [0]*L*L
+        b = [1]
+
+        for i, _ in enumerate(up_ind_x[1:]) :
+            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 = np.vstack((A,l))
+                b.append(1)
+
+        return A, b
+
+
+    def init_eq_not_stay(self, L: int): 
+        """
+        Generate constraints to prevent staying in the same position (e.g., removing d11, d22, d33, etc.).
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            Tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
+        """
+
+        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), dtype=np.int8)
+
+        return [l], [0]
+
+
+    def respect_user_must_do(self, landmarks: list[Landmark]) :
+        """
+        Generate constraints to ensure that landmarks marked as 'must_do' are included in the optimization.
+
+        Args:
+            landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_do'.
+
+        Returns:
+            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+
+        L = len(landmarks)
+        A = [0]*L*L
+        b = [0]
+
+        for i, elem in enumerate(landmarks[1:]) :
+            if elem.must_do is True and elem.name not in ['finish', 'start']:
+                l = [0]*L*L
+                l[i*L:i*L+L] = [1]*L        # set mandatory departures from landmarks tagged as 'must_do'
+
+                A = np.vstack((A,l))
+                b.append(1)
+
+        return A, b
+    
+
+    def respect_user_must_avoid(self, landmarks: list[Landmark]) :
+        """
+        Generate constraints to ensure that landmarks marked as 'must_avoid' are skipped in the optimization.
+
+        Args:
+            landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_avoid'.
+
+        Returns:
+            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+
+        L = len(landmarks)
+        A = [0]*L*L
+        b = [0]
+
+        for i, elem in enumerate(landmarks[1:]) :
+            if elem.must_avoid is True and elem.name not in ['finish', 'start']:
+                l = [0]*L*L
+                l[i*L:i*L+L] = [1]*L        
+
+                A = np.vstack((A,l))
+                b.append(0)             # prevent departures from landmarks tagged as 'must_do'
+
+        return A, b
+
+
+    # Constraint to ensure start at start and finish at goal
+    def respect_start_finish(self, L: int):
+        """
+        Generate constraints to ensure that the optimization starts at the designated start landmark and finishes at the goal landmark.
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+
+        l_start = [1]*L + [0]*L*(L-1)   # sets departures only for start (horizontal ones)
+        l_start[L-1] = 0                # prevents the jump from start to finish
+        l_goal = [0]*L*L                # sets arrivals only for finish (vertical ones)
+        l_L = [0]*L*(L-1) + [1]*L       # prevents arrivals at start and departures from goal
+        for k in range(L-1) :           # sets only vertical ones for goal (go to)
+            l_L[k*L] = 1
+            if k != 0 :
+                l_goal[k*L+L-1] = 1     
+
+        A = np.vstack((l_start, l_goal))
+        b = [1, 1]
+        A = np.vstack((A,l_L))
+        b.append(0)
+
+        return A, b
+
+
+    def respect_order(self, L: int): 
+        """
+        Generate constraints to tie the optimization problem together and prevent stacked ones, although this does not fully prevent circles.
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+
+        A = [0]*L*L
+        b = [0]
+        for i in range(L-1) :           # Prevent stacked ones
+            if i == 0 or i == L-1:      # Don't touch start or finish
+                continue
+            else : 
+                l = [0]*L
+                l[i] = -1
+                l = l*L
+                for j in range(L) :
+                    l[i*L + j] = 1
+
+                A = np.vstack((A,l))
+                b.append(0)
+
+        return A, b
+
+
+    def link_list(self, order: list[int], landmarks: list[Landmark])->list[Landmark] :
+        """
+        Compute the time to reach from each landmark to the next and create a list of landmarks with updated travel times.
+
+        Args:
+            order (list[int]): List of indices representing the order of landmarks to visit.
+            landmarks (list[Landmark]): List of all landmarks.
+
+        Returns:
+            list[Landmark]]: The updated linked list of landmarks with travel times
+        """
+        
+        L =  []
+        j = 0
+        while j < len(order)-1 :
+            # get landmarks involved
+            elem = landmarks[order[j]]
+            next = landmarks[order[j+1]]
+
+            # get attributes
+            elem.time_to_reach_next = get_time(elem.location, next.location)
+            elem.must_do = True
+            elem.location = (round(elem.location[0], 5), round(elem.location[1], 5))
+            elem.next_uuid = next.uuid
+            L.append(elem)
+            j += 1
+
+        next.location = (round(next.location[0], 5), round(next.location[1], 5))
+        next.must_do = True   
+        L.append(next)
+        
+        return L
+
+
+    # Main optimization pipeline
+    def solve_optimization(
+            self,
+            max_time: int,
+            landmarks: list[Landmark],
+        ) -> list[Landmark]:
+        """
+        Main optimization pipeline to solve the landmark visiting problem.
+
+        This method sets up and solves a linear programming problem with constraints to find an optimal tour of landmarks,
+        considering user-defined must-visit landmarks, start and finish points, and ensuring no cycles are present.
+
+        Args:
+            max_time (int): Maximum time allowed for the tour in minutes.
+            landmarks (list[Landmark]): List of landmarks to visit.
+        Returns:
+            list[Landmark]: The optimized tour of landmarks with updated travel times, or None if no valid solution is found.
+        """
+
+        L = len(landmarks)
+
+        # SET CONSTRAINTS FOR INEQUALITY
+        c, A_ub, b_ub = self.init_ub_dist(landmarks, max_time)          # Add the distances from each landmark to the other
+        A, b = self.respect_number(L)                     # Respect max number of visits (no more possible stops than landmarks). 
+        A_ub = np.vstack((A_ub, A), dtype=np.int16)
+        b_ub += b
+        A, b = self.break_sym(L)                                         # break the 'zig-zag' symmetry
+        A_ub = np.vstack((A_ub, A), dtype=np.int16)
+        b_ub += b
+
+
+        # SET CONSTRAINTS FOR EQUALITY
+        A_eq, b_eq = self.init_eq_not_stay(L)                            # Force solution not to stay in same place
+        A, b = self.respect_user_must_do(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
+        A_eq = np.vstack((A_eq, A), dtype=np.int8)
+        b_eq += b
+        A, b = self.respect_user_must_avoid(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
+        A_eq = np.vstack((A_eq, A), dtype=np.int8)
+        b_eq += b
+        A, b = self.respect_start_finish(L)                  # Force start and finish positions
+        A_eq = np.vstack((A_eq, A), dtype=np.int8)
+        b_eq += b
+        A, b = self.respect_order(L)                         # Respect order of visit (only works when max_steps is limiting factor)
+        A_eq = np.vstack((A_eq, A), dtype=np.int8)
+        b_eq += b
+        
+        # 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
+        order, circles = self.is_connected(res.x)
+        #nodes, edges = is_connected(res.x)
+        i = 0
+        timeout = 80
+        while circles is not None and i < timeout:
+            A, b = self.prevent_config(res.x)
+            A_ub = np.vstack((A_ub, A))
+            b_ub += b
+            #A_ub, b_ub = prevent_circle(order, len(landmarks), A_ub, b_ub)
+            for circle in circles :
+                A, b = self.prevent_circle(circle, L)
+                A_eq = np.vstack((A_eq, A))
+                b_eq += b
+            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)
+            if not res.success :
+                raise ArithmeticError("Solving failed because of overconstrained problem")
+                return None
+            order, circles = self.is_connected(res.x)
+            #nodes, edges = is_connected(res.x)
+            if circles is None :
+                break
+            # print(i)
+            i += 1
+        
+        if i == timeout :
+            raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
+
+        #sort the landmarks in the order of the solution
+        tour =  [landmarks[i] for i in order] 
+        
+        self.logger.debug(f"Re-optimized {i} times, score: {int(-res.fun)}")
+        return tour

backend/src/utils/refiner.py

@@ -0,0 +1,340 @@
+import yaml, logging
+
+from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
+from math import pi
+
+from structs.landmark import Landmark
+from . import take_most_important, get_time_separation
+from .optimizer import Optimizer
+import constants
+
+
+
+class Refiner :
+
+    logger = logging.getLogger(__name__)
+    
+    detour_factor: float            # detour factor of straight line vs real distance in cities
+    detour_corridor_width: float    # width of the corridor around the path
+    average_walking_speed: float    # average walking speed of adult
+    max_landmarks: int              # max number of landmarks to visit
+    optimizer: Optimizer            # optimizer object
+
+    def __init__(self, optimizer: Optimizer) :
+        self.optimizer = optimizer
+
+        # load parameters from file
+        with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+            parameters = yaml.safe_load(f)
+            self.detour_factor = parameters['detour_factor']
+            self.detour_corridor_width = parameters['detour_corridor_width']
+            self.average_walking_speed = parameters['average_walking_speed']
+            self.max_landmarks = parameters['max_landmarks'] + 4
+
+
+    def create_corridor(self, landmarks: list[Landmark], width: float) :
+        """
+        Create a corridor around the path connecting the landmarks.
+
+        Args:
+            landmarks (list[Landmark]): the landmark path around which to create the corridor
+            width (float): Width of the corridor in meters.
+
+        Returns:
+            Geometry: A buffered geometry object representing the corridor around the path.
+        """
+
+        corrected_width = (180*width)/(6371000*pi)
+        
+        path = self.create_linestring(landmarks)
+        obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2)
+
+        return obj
+
+
+    def create_linestring(self, tour: list[Landmark]) -> LineString :
+        """
+        Create a `LineString` object from a tour.
+
+        Args:
+            tour (list[Landmark]): An ordered sequence of landmarks that represents the visiting order.
+
+        Returns:
+            LineString: A `LineString` object representing the path through the landmarks.
+        """
+
+        points = []
+        for landmark in tour :
+            points.append(Point(landmark.location))
+
+        return LineString(points)
+
+
+    # Check if some coordinates are in area. Used for the corridor 
+    def is_in_area(self, area: Polygon, coordinates) -> bool :
+        """
+        Check if a given point is within a specified area.
+
+        Args:
+            area (Polygon): The polygon defining the area.
+            coordinates (tuple[float, float]): The coordinates of the point to check.
+
+        Returns:
+            bool: True if the point is within the area, otherwise False.
+        """
+        point = Point(coordinates)
+        return point.within(area)
+
+
+    # Function to determine if two landmarks are close to each other 
+    def is_close_to(self, location1: tuple[float], location2: tuple[float]):
+        """
+        Determine if two locations are close to each other by rounding their coordinates to 3 decimal places.
+
+        Args:
+            location1 (tuple[float, float]): The coordinates of the first location.
+            location2 (tuple[float, float]): The coordinates of the second location.
+
+        Returns:
+            bool: True if the locations are within 0.001 degrees of each other, otherwise False.
+        """
+
+        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(self, tour: list[Landmark]) -> list[Landmark]:
+        """
+        Rearrange landmarks to group nearby visits together.
+
+        This function reorders landmarks so that nearby landmarks are adjacent to each other in the list,
+        while keeping 'start' and 'finish' landmarks in their original positions.
+
+        Args:
+            tour (list[Landmark]): Ordered list of landmarks to be rearranged.
+
+        Returns:
+            list[Landmark]: The rearranged list of landmarks with grouped nearby visits.
+        """
+    
+        i = 1
+        while i < len(tour):
+            j = i+1
+            while j < len(tour):
+                if self.is_close_to(tour[i].location, tour[j].location) and tour[i].name not in ['start', 'finish'] and tour[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:
+                        tour.insert(i + 1, tour.pop(j))
+                        break  # Move to the next i-th element after rearrangement
+            j += 1
+            i += 1
+    
+        return tour
+
+
+    def find_shortest_path_through_all_landmarks(self, landmarks: list[Landmark]) -> tuple[list[Landmark], Polygon]:
+        """
+        Find the shortest path through all landmarks using a nearest neighbor heuristic.
+
+        This function constructs a path that starts from the 'start' landmark, visits all other landmarks in the order
+        of their proximity, and ends at the 'finish' landmark. It returns both the ordered list of landmarks and a 
+        polygon representing the path.
+
+        Args:
+            landmarks (list[Landmark]): list of all landmarks including 'start' and 'finish'.
+
+        Returns:
+            tuple[list[Landmark], Polygon]: A tuple where the first element is the list of landmarks in the order they 
+                                             should be visited, and the second element is a `Polygon` representing 
+                                             the path connecting all landmarks.
+        """
+            
+        # Step 1: Find 'start' and 'finish' landmarks
+        start_idx = next(i for i, lm in enumerate(landmarks) if lm.type == 'start')
+        finish_idx = next(i for i, lm in enumerate(landmarks) if lm.type == '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_time_separation.get_time(current_landmark.location, lm.location))
+            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
+
+
+    # Returns a list of minor landmarks around the planned path to enhance experience
+    def get_minor_landmarks(self, all_landmarks: list[Landmark], visited_landmarks: list[Landmark], width: float) -> list[Landmark] :
+        """
+        Identify landmarks within a specified corridor that have not been visited yet.
+
+        This function creates a corridor around the path defined by visited landmarks and then finds landmarks that fall
+        within this corridor. It returns a list of these landmarks, excluding those already visited, sorted by their importance.
+
+        Args:
+            all_landmarks (list[Landmark]): list of all available landmarks.
+            visited_landmarks (list[Landmark]): list of landmarks that have already been visited.
+            width (float): Width of the corridor around the visited landmarks.
+
+        Returns:
+            list[Landmark]: list of important landmarks within the corridor that have not been visited yet.
+        """
+
+        second_order_landmarks = []
+        visited_names = []
+        area = self.create_corridor(visited_landmarks, width)
+
+        for visited in visited_landmarks :
+            visited_names.append(visited.name)
+        
+        for landmark in all_landmarks :
+            if self.is_in_area(area, landmark.location) and landmark.name not in visited_names:
+                second_order_landmarks.append(landmark)
+
+        return take_most_important.take_most_important(second_order_landmarks, len(visited_landmarks))
+
+
+    # Try fix the shortest path using shapely
+    def fix_using_polygon(self, tour: list[Landmark])-> list[Landmark] :
+        """
+        Improve the tour path using geometric methods to ensure it follows a more optimal shape.
+
+        This function creates a polygon from the given tour and attempts to refine it using a concave hull. It reorders
+        the landmarks to fit within this refined polygon and adjusts the tour to ensure the 'start' landmark is at the
+        beginning. It also checks if the final polygon is simple and rearranges the tour if necessary.
+
+        Args:
+            tour (list[Landmark]): list of landmarks representing the current tour path.
+
+        Returns:
+            list[Landmark]: Refined list of landmarks in the order of visit to produce a better tour path.
+        """
+
+        coords = []
+        coords_dict = {}
+        for landmark in tour :
+            coords.append(landmark.location)
+            if landmark.name != 'finish' :
+                coords_dict[landmark.location] = landmark
+
+        tour_poly = Polygon(coords)
+        
+        better_tour_poly = tour_poly.buffer(0)
+        try :
+            xs, ys = better_tour_poly.exterior.xy
+
+            if len(xs) != len(tour) :
+                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
+
+        except :
+            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]) :
+            y = ys[i]
+            better_tour.append(coords_dict[tuple((x,y))])
+            name_index[coords_dict[tuple((x,y))].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(tour[-1])
+
+        # Rearrange only if polygon still not simple
+        if not better_tour_poly.is_simple :
+            better_tour = self.rearrange(better_tour)
+        
+        return better_tour
+
+
+    def refine_optimization(
+            self,
+            all_landmarks: list[Landmark],
+            base_tour: list[Landmark],
+            max_time: int,
+            detour: int
+        ) -> list[Landmark]:
+        """
+        This is the second stage of the optimization. It refines the initial tour path by considering additional minor landmarks and optimizes the path.
+
+        This method evaluates the need for further optimization based on the initial tour. If a detour is required
+        it adds minor landmarks around the initial predicted path and solves a new optimization problem to find a potentially better
+        tour. It then links the new tour and adjusts it using a nearest neighbor heuristic and polygon-based methods to
+        ensure a valid path. The final tour is chosen based on the shortest distance.
+
+        Args:
+            all_landmarks (list[Landmark]): The full list of landmarks available for the optimization.
+            base_tour (list[Landmark]): The initial tour path to be refined.
+            max_time (int): The maximum time available for the tour in minutes.
+            detour (int): The maximum detour time allowed for the tour in minutes.
+        Returns:
+            list[Landmark]: The refined list of landmarks representing the optimized tour path.
+        """
+
+        # No need to refine if no detour is taken
+        if detour == 0:
+            return base_tour
+        
+        minor_landmarks = self.get_minor_landmarks(all_landmarks, base_tour, self.detour_corridor_width)
+
+        self.logger.info(f"Using {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 = self.optimizer.solve_optimization(
+            max_time = max_time + detour,
+            landmarks = full_set
+        )
+
+        if new_tour is None:
+            self.logger.warning("No solution found for the refined tour. Returning the initial tour.")
+            new_tour = base_tour
+
+
+        # Find shortest path using the nearest neighbor heuristic
+        better_tour, better_poly = self.find_shortest_path_through_all_landmarks(new_tour)
+
+        # Fix the tour using Polygons if the path looks weird
+        if base_tour[0].location == base_tour[-1].location and not better_poly.is_valid :
+            better_tour = self.fix_using_polygon(better_tour)
+
+        return better_tour

backend/src/utils/take_most_important.py

@@ -0,0 +1,38 @@
+from structs.landmark import Landmark
+
+def take_most_important(landmarks: list[Landmark], N_important) -> list[Landmark] :
+    L = len(landmarks)
+    L_copy = []
+    L_clean = []
+    scores = [0]*len(landmarks)
+    names = []
+    name_id = {}
+
+    for i, elem in enumerate(landmarks) :
+        if elem.name not in names :
+            names.append(elem.name)
+            name_id[elem.name] = [i]
+            L_copy.append(elem)
+        else :
+            name_id[elem.name] += [i]
+            scores = []
+            for j in name_id[elem.name] :
+                scores.append(L[j].attractiveness)
+            best_id = max(range(len(scores)), key=scores.__getitem__)
+            t = name_id[elem.name][best_id]
+            if t == i :
+                for old in L_copy :
+                    if old.name == elem.name :
+                        old.attractiveness = L[t].attractiveness
+    
+    scores = [0]*len(L_copy)
+    for i, elem in enumerate(L_copy) :
+        scores[i] = elem.attractiveness
+
+    res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-L):]
+
+    for i, elem in enumerate(L_copy) :
+        if i in res :
+            L_clean.append(elem)
+
+    return L_clean

frontend/README.md

@@ -0,0 +1,17 @@
+# Frontend
+
+This is the frontend of the project. It is a Flutter application that is designed to run on both Android and iOS devices. The frontend is responsible for displaying the user interface and handling user input. It communicates with the backend to retrieve and send data.
+
+
+## Getting Started
+The flutter application is divided into multiple chunks of code.
+- the `lib` directory contains the main code of the application.
+- the `android` and `ios` directories contain platform-specific code.
+- the root directory contains configuration files and metadata.
+
+To run the application, you need to have the Flutter SDK installed. You can find instructions on how to do this [here](https://flutter.dev/docs/get-started/install).
+
+Once you have the Flutter SDK installed, you can locally install the dependencies by running:
+```bash
+flutter pub get
+```

frontend/android/.gitignore

@@ -4,6 +4,7 @@ gradle-wrapper.jar
 /gradlew
 /gradlew.bat
 /local.properties
+/secrets.properties
 GeneratedPluginRegistrant.java
 
 # Remember to never publicly share your keystore.

frontend/android/README.md

@@ -0,0 +1,48 @@
+## Android Setup
+
+### Keystore setup
+```bash
+keytool -genkey -v -keystore release.keystore -keyalg RSA -keysize 2048 -validity 10000 -alias release
+```
+- This is required to store local credentials securely (not used for now).
+- But necesseary in order to restrict the particular api key to a particular app (through the sha1 of the associated keystore).
+
+
+### Building and secret credentials
+Following the guide under [https://developers.google.com/maps/flutter-package/config#android_1](https://developers.google.com/maps/flutter-package/config#android_1).
+- Add the following to `android/build.gradle`:
+    ```gradle
+    buildscript {
+        dependencies {
+            classpath "com.google.android.libraries.mapsplatform.secrets-gradle-plugin:secrets-gradle-plugin:2.0.1"
+        }
+    }
+    ```
+- Add the following to `android/app/build.gradle`:
+    ```gradle
+    plugins {
+        // ...
+        id 'com.google.android.libraries.mapsplatform.secrets-gradle-plugin'
+    }
+    ```
+- Add the credentials to `android/secrets.properties`:
+    ```properties
+    MAPS_API_KEY=YOUR_API_KEY
+    ```
+- Reference the credentials in `android/app/src/main/AndroidManifest.xml`:
+    ```xml
+    <meta-data
+        android:name="com.google.android.geo.API_KEY"
+        android:value="${MAPS_API_KEY}" />
+    ```
+
+
+### Using the credentials in CI
+- Add the base64 encoded credentials to the repository secrets (e.g. `ANDROID_SECRETS`).
+    ```bash
+    base64 -i android/secrets.properties
+    ```
+- Use the following in the CI script:
+    ```bash
+    echo {{ secrets.ANDROID_SECRETS }} | base64 -d > android/secrets.properties
+    ```

frontend/android/app/build.gradle

@@ -2,14 +2,19 @@ plugins {
     id "com.android.application"
     id "kotlin-android"
     id "dev.flutter.flutter-gradle-plugin"
+    id 'com.google.android.libraries.mapsplatform.secrets-gradle-plugin'
+    // last is probably not needed
 }
 
-def localProperties = new Properties()
 def localPropertiesFile = rootProject.file('local.properties')
+def localProperties = new Properties()
+
 if (localPropertiesFile.exists()) {
     localPropertiesFile.withReader('UTF-8') { reader ->
         localProperties.load(reader)
     }
+} else {
+    throw new GradleException("local.properties not found")
 }
 
 def flutterVersionCode = localProperties.getProperty('flutter.versionCode')
@@ -22,6 +27,20 @@ if (flutterVersionName == null) {
     flutterVersionName = '1.0'
 }
 
+
+
+def secretPropertiesFile = rootProject.file('secrets.properties')
+def secretProperties = new Properties()
+
+if (secretPropertiesFile.exists()) {
+    secretPropertiesFile.withReader('UTF-8') { reader ->
+        secretProperties.load(reader)
+    }
+} else {
+    throw new GradleException("Secrets file secrets.properties not found")
+}
+
+
 android {
     namespace "com.example.fast_network_navigation"
     compileSdk flutter.compileSdkVersion
@@ -52,6 +71,9 @@ android {
         targetSdkVersion flutter.targetSdkVersion
         versionCode flutterVersionCode.toInteger()
         versionName flutterVersionName
+        // // Placeholders of keys that are replaced by the build system.
+        manifestPlaceholders += ['MAPS_API_KEY': secretProperties.getProperty('MAPS_API_KEY')]
+
     }
 
     buildTypes {

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

@@ -1,4 +1,7 @@
 <manifest xmlns:android="http://schemas.android.com/apk/res/android">
+    <!-- Required to fetch data from the internet. -->
+    <uses-permission android:name="android.permission.INTERNET"/>
+
     <application
         android:label="fast_network_navigation"
         android:name="${applicationName}"
@@ -32,11 +35,10 @@
         />
         <meta-data
             android:name="com.google.android.geo.API_KEY"
-            android:value="AIzaSyCeWk_D2xvfOHLidvV56EZeQCUybypEntw"
+            android:value="${MAPS_API_KEY}"
         />
-
-
     </application>
+    
     <!-- Required to query activities that can process text, see:
          https://developer.android.com/training/package-visibility?hl=en and
          https://developer.android.com/reference/android/content/Intent#ACTION_PROCESS_TEXT.
@@ -48,7 +50,4 @@
             <data android:mimeType="text/plain"/>
         </intent>
     </queries>
-
-    <!-- Required to fetch data from the internet. -->
-    <uses-permission android:name="android.permission.INTERNET"/>
 </manifest>

frontend/android/build.gradle

@@ -16,3 +16,14 @@ subprojects {
 tasks.register("clean", Delete) {
     delete rootProject.buildDir
 }
+
+buildscript {
+    repositories {
+        google()
+        mavenCentral()
+    }
+
+    dependencies {
+        classpath "com.google.android.libraries.mapsplatform.secrets-gradle-plugin:secrets-gradle-plugin:2.0.1"
+    }
+}

frontend/android/fallback.properties

@@ -0,0 +1 @@
+MAPS_API_KEY=Key

frontend/lib/layout.dart

@@ -2,6 +2,7 @@ import 'package:fast_network_navigation/modules/trips_overview.dart';
 import 'package:fast_network_navigation/pages/new_trip.dart';
 import 'package:fast_network_navigation/pages/tutorial.dart';
 import 'package:fast_network_navigation/structs/trip.dart';
+import 'package:fast_network_navigation/utils/load_trips.dart';
 import 'package:flutter/material.dart';
 
 import 'package:fast_network_navigation/pages/overview.dart';
@@ -11,10 +12,13 @@ import 'package:fast_network_navigation/pages/profile.dart';
 // A side drawer is used to switch between pages
 class BasePage extends StatefulWidget {
   final String mainScreen;
-  final String currentMap;
-  final List<Trip> trips;
+  final Future<Trip>? trip;
   
-  const BasePage({super.key, required this.mainScreen, this.currentMap = "map", this.trips = const []});
+  const BasePage({
+    super.key,
+    required this.mainScreen,
+    this.trip
+  });
 
   @override
   State<BasePage> createState() => _BasePageState();
@@ -22,12 +26,14 @@ class BasePage extends StatefulWidget {
 
 class _BasePageState extends State<BasePage> {
 
-
   @override
   Widget build(BuildContext context) {
     Widget currentView = const Text("loading...");
+    Future<List<Trip>> trips = loadTrips();
+    
+    
     if (widget.mainScreen == "map") {
-      currentView = NavigationOverview();
+      currentView = NavigationOverview(trip: widget.trip ?? getFirstTrip(trips));
     } else if (widget.mainScreen == "tutorial") {
       currentView = TutorialPage();
     } else if (widget.mainScreen == "profile") {
@@ -40,9 +46,6 @@ class _BasePageState extends State<BasePage> {
       appBar: AppBar(title: Text("City Nav")),
       body: Center(child: currentView),
       drawer: Drawer(
-        // Add a ListView to the drawer. This ensures the user can scroll
-        // through the options in the drawer if there isn't enough vertical
-        // space to fit everything.
         child: Column(
           children: [
             DrawerHeader(
@@ -61,20 +64,10 @@ class _BasePageState extends State<BasePage> {
               ),
             ),
             ListTile(
-              title: const Text('Start'),
+              title: const Text('Your Trips'),
               leading: const Icon(Icons.map),
               selected: widget.mainScreen == "map",
-              onTap: () {
-                Navigator.of(context).push(
-                  MaterialPageRoute(
-                    builder: (context) => BasePage(mainScreen: "map")
-                  )
-                );
-              },
-            ),
-            ListTile(
-              title: const Text('Trip Overview'),
-              leading: const Icon(Icons.list),
+              onTap: () {},
               trailing: ElevatedButton(
                 onPressed: () {
                   Navigator.of(context).push(
@@ -86,7 +79,19 @@ class _BasePageState extends State<BasePage> {
                 child: const Text('New'),
               ),
             ),
-            Expanded(child: TripsOverview()),
+
+            // Adds a ListView to the drawer. This ensures the user can scroll
+            // through the options in the drawer if there isn't enough vertical
+            // space to fit everything.
+            Expanded(
+              child: TripsOverview(trips: trips),
+            ),
+            ElevatedButton(
+              onPressed: () async {
+                removeAllTripsFromPrefs();
+              },
+              child: const Text('Clear trips'),
+            ),
             const Divider(),
             ListTile(
               title: const Text('How to use'),
@@ -101,6 +106,7 @@ class _BasePageState extends State<BasePage> {
               },
             ),
 
+            // settings in the bottom of the drawer
             ListTile(
               title: const Text('Settings'),
               leading: const Icon(Icons.settings),
@@ -113,7 +119,6 @@ class _BasePageState extends State<BasePage> {
                 );
               },
             ),
-            // settings in the bottom of the drawer
           ],
         ),
       ),
@@ -121,3 +126,9 @@ class _BasePageState extends State<BasePage> {
   }
 }
 
+
+
+Future<Trip> getFirstTrip (Future<List<Trip>> trips) async {
+  List<Trip> tripsf = await trips;
+  return tripsf[0];
+}

frontend/lib/main.dart

@@ -17,4 +17,3 @@ class App extends StatelessWidget {
     );
   }
 }
-

frontend/lib/modules/greeter.dart

@@ -1,44 +1,73 @@
+import 'package:fast_network_navigation/structs/trip.dart';
+
 import 'package:flutter/material.dart';
 
-Widget Greeter (ThemeData theme, {bool full = false}) {
-  String greeterText = "";
-  try {
-    String cityName = getCityName();
-    greeterText = "Welcome to $cityName!";
-  } catch (e) {
-    greeterText = "Welcome ...";
-  }
+class Greeter extends StatefulWidget {
+  final Future<Trip> trip;
+  final bool standalone;
 
-  Widget topGreeter = Text(
-    greeterText,
-    style: TextStyle(color: theme.primaryColor, fontSize: 24.0, fontWeight: FontWeight.bold),
-    maxLines: 1,
-  );
+  Greeter({
+    required this.standalone,
+    required this.trip
+  });
 
-  Widget bottomGreeter = Container();
-  if (full) {
-    bottomGreeter = Text(
-      "Busy day ahead? Here is how to make the most of it!",
-      style: TextStyle(color: Colors.black, fontSize: 18),
-      textAlign: TextAlign.center,
-      );
-  }
-  Widget greeter = Center(
-    child: Column(
-      children: [
-        if (!full) Padding(padding: EdgeInsets.only(top: 24.0)),
-        topGreeter,
-        if (full) bottomGreeter,
-        Padding(padding: EdgeInsets.only(bottom: 24.0)),
-      ],
-    ),
-  );
-
-  return greeter;
+  @override
+  State<Greeter> createState() => _GreeterState();
 }
 
 
 
-String getCityName() {
-  return "Paris";
+class _GreeterState extends State<Greeter> {
+  Widget greeterBuild (BuildContext context, AsyncSnapshot<Trip> snapshot) {
+    ThemeData theme = Theme.of(context);
+    String cityName = "";
+    if (snapshot.hasData) {
+      cityName = snapshot.data?.cityName ?? '...';
+    } else if (snapshot.hasError) {
+      cityName = "error";
+    } else { // still awaiting the cityname
+      cityName = "...";
+    }
+
+    Widget topGreeter = Text(
+      'Welcome to $cityName!',
+      style: TextStyle(color: theme.primaryColor, fontWeight: FontWeight.bold, fontSize: 24),
+    );
+
+    if (widget.standalone) {
+      return Center(
+        child: Padding(
+          padding: EdgeInsets.only(top: 24.0),
+          child: topGreeter,
+        ),
+      );
+    } else {
+      return Center(
+        child: Column(
+          children: [
+            Padding(padding: EdgeInsets.only(top: 24.0)),
+            topGreeter,
+            bottomGreeter,
+            Padding(padding: EdgeInsets.only(bottom: 24.0)),
+          ],
+        )
+      );
+    }
+  }
+
+  Widget bottomGreeter = const Text(
+    "Busy day ahead? Here is how to make the most of it!",
+    style: TextStyle(color: Colors.black, fontSize: 18),
+    textAlign: TextAlign.center,
+  );
+
+
+
+  @override
+  Widget build(BuildContext context) {
+    return FutureBuilder(
+      future: widget.trip,
+      builder: greeterBuild,
+    );
+  }
 }

frontend/lib/modules/landmark_card.dart

@@ -15,59 +15,66 @@ class _LandmarkCardState extends State<LandmarkCard> {
   @override
   Widget build(BuildContext context) {
     ThemeData theme = Theme.of(context);
-    return Card(
-      shape: RoundedRectangleBorder(
-        borderRadius: BorderRadius.circular(15.0),
-      ),
-      child: Row(
-        children: [
-          Container(
-            width: 160,
-            height: 160,
-            decoration: BoxDecoration(
-              borderRadius: BorderRadius.only(
-                topLeft: Radius.circular(15.0),
-                bottomLeft: Radius.circular(15.0),
-              ),
-              image: DecorationImage(
-                image: NetworkImage('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg/1037px-Tour_Eiffel_Wikimedia_Commons.jpg'),
+    return Container(
+      height: 160,
+      child: Card(
+        shape: RoundedRectangleBorder(
+          borderRadius: BorderRadius.circular(15.0),
+        ),
+        elevation: 5,
+        clipBehavior: Clip.antiAliasWithSaveLayer,
+        child: Row(
+          crossAxisAlignment: CrossAxisAlignment.start,
+          children: [
+            Container( // the image on the left
+              // inherit the height of the parent container
+              height: double.infinity,
+              // force a fixed width
+              width: 160,
+              child: Image.network(
+                widget.landmark.imageURL ?? '',
+                errorBuilder: (context, error, stackTrace) => Icon(Icons.question_mark_outlined),
+                // TODO: make this a switch statement to load a placeholder if null
+                // cover the whole container meaning the image will be cropped
                 fit: BoxFit.cover,
               ),
             ),
-          ),
-          Padding(
-            padding: EdgeInsets.all(10),
-            child: Expanded(
-              child: Column(
-                crossAxisAlignment: CrossAxisAlignment.start,
-                children: [
-                  Row(
-                    mainAxisAlignment: MainAxisAlignment.spaceBetween,
-                    children: [
-                      Text(
-                        widget.landmark.name,
-                        style: TextStyle(
-                          fontSize: 18,
-                          fontWeight: FontWeight.bold,
-                        ),
-                      ),
-                    ],
-                  ),
-                  SizedBox(height: 5),
-                  Text(
-                    "${widget.landmark.name} (${widget.landmark.type.name})",
-                    style: TextStyle(fontSize: 14),
-                  ),
-                ],
+            Flexible(
+              child: Padding(
+                padding: EdgeInsets.all(10),
+                child: Column(
+                  children: [
+                    Row(
+                      children: [
+                        Flexible(
+                          child: Text(
+                            widget.landmark.name,
+                            style: const TextStyle(
+                              fontSize: 18,
+                              fontWeight: FontWeight.bold,
+                            ),
+                            maxLines: 2,
+                          ),
+                        )
+                      ],
+                    ),
+                    Row(
+                      children: [
+                        Flexible(
+                          child: Text(
+                            "${widget.landmark.name} (${widget.landmark.type.name})",
+                            style: const TextStyle(fontSize: 14),
+                          ),
+                        )
+                      ]
+                    ),
+                  ],
+                ),
               ),
             ),
-          ),
-          // Align(
-          //   alignment: Alignment.topRight,
-          //   child: Icon(Icons.push_pin, color: theme.primaryColor),
-          // ),
-        ],
+          ],
+        ),
       ),
     );
   }
-}
+}

frontend/lib/modules/landmarks_overview.dart

@@ -1,32 +1,38 @@
+import 'dart:collection';
+
 import 'package:fast_network_navigation/modules/landmark_card.dart';
 import 'package:fast_network_navigation/structs/landmark.dart';
-import 'package:fast_network_navigation/utils/get_landmarks.dart';
+
+import 'package:fast_network_navigation/structs/trip.dart';
 import 'package:flutter/material.dart';
+import 'package:shared_preferences/shared_preferences.dart';
 
 
 
 
-class loadLandmarksOverview extends StatefulWidget {
-  const loadLandmarksOverview({super.key});
+class LandmarksOverview extends StatefulWidget {
+  final Future<Trip>? trip;
+  const LandmarksOverview({super.key, this.trip});
 
   @override
-  State<loadLandmarksOverview> createState() => _loadLandmarksOverviewState();
+  State<LandmarksOverview> createState() => _LandmarksOverviewState();
 }
 
-class _loadLandmarksOverviewState extends State<loadLandmarksOverview> {
-  final Future<List<Landmark>> _landmarks = fetchLandmarks();
+class _LandmarksOverviewState extends State<LandmarksOverview> {
+  // final Future<List<Landmark>> _landmarks = fetchLandmarks();
 
   @override
   Widget build(BuildContext context) {
+    final Future<LinkedList<Landmark>> _landmarks = getLandmarks(widget.trip);
     return DefaultTextStyle(
       style: Theme.of(context).textTheme.displayMedium!,
       textAlign: TextAlign.center,
-      child: FutureBuilder<List<Landmark>>(
+      child: FutureBuilder<LinkedList<Landmark>>(
         future: _landmarks,
-        builder: (BuildContext context, AsyncSnapshot<List<Landmark>> snapshot) {
+        builder: (BuildContext context, AsyncSnapshot<LinkedList<Landmark>> snapshot) {
           List<Widget> children;
           if (snapshot.hasData) {
-            children = [landmarksWithSteps(snapshot.data!)];
+            children = [landmarksWithSteps(snapshot.data!), saveButton()];
           } else if (snapshot.hasError) {
             children = <Widget>[
               const Icon(
@@ -36,7 +42,7 @@ class _loadLandmarksOverviewState extends State<loadLandmarksOverview> {
               ),
               Padding(
                 padding: const EdgeInsets.only(top: 16),
-                child: Text('Error: ${snapshot.error}'),
+                child: Text('Error: ${snapshot.error}', style: TextStyle(fontSize: 12)),
               ),
             ];
           } else {
@@ -52,50 +58,73 @@ class _loadLandmarksOverviewState extends State<loadLandmarksOverview> {
       ),
     );
   }
+  Widget saveButton() => ElevatedButton(
+    onPressed: () async {
+      Trip? trip = await widget.trip;
+      SharedPreferences prefs = await SharedPreferences.getInstance();
+      trip?.toPrefs(prefs);
+    },
+    child: const Text('Save'),
+  );
+
 }
 
-Widget landmarksWithSteps(List<Landmark> landmarks) {
+Widget landmarksWithSteps(LinkedList<Landmark> landmarks) {
   List<Widget> children = [];
   for (Landmark landmark in landmarks) {
     children.add(LandmarkCard(landmark));
-    children.add(stepBetweenLandmarks());
+    if (landmark.next != null) {
+      Widget step = stepBetweenLandmarks(landmark, landmark.next!);
+      children.add(step);
+    }
   }
 
   return Column(
-    children: children.sublist(0, children.length - 1)
+    children: children
   );
 }
 
 
-Widget stepBetweenLandmarks() {
+Widget stepBetweenLandmarks(Landmark before, Landmark after) {
   // This is a simple widget that draws a line between landmark-cards
   // It's a vertical dotted line
   // Next to the line is the icon for the mode of transport (walking for now) and the estimated time
   // There is also a button to open the navigation instructions as a new intent
-  return Row(
-    children: [
-      Container(
-        width: 50,
-        height: 50,
-        decoration: BoxDecoration(
-          border: Border(
-            left: BorderSide(width: 1.0, color: Colors.black),
-          ),
-        ),
-        child: Column(
+  return Container(
+    margin: EdgeInsets.all(10),
+    padding: EdgeInsets.all(10),
+    decoration: BoxDecoration(
+      border: Border(
+        left: BorderSide(width: 3.0, color: Colors.black),
+      ),
+      // gradient: LinearGradient(
+      //   begin: Alignment.topLeft,
+      //   end: Alignment.bottomRight,
+      //   colors: [Colors.grey, Colors.white, Colors.white],
+      // ),
+    ),
+    child: Row( 
+      children: [
+        Column(
           children: [
             Icon(Icons.directions_walk),
             Text("5 min", style: TextStyle(fontSize: 10)),
           ],
         ),
-      ),
-      Spacer(),
-      ElevatedButton(
-        onPressed: () {
-          // Open navigation instructions
-        },
-        child: Text("Navigate"),
-      ),
-    ],
+        Spacer(),
+        ElevatedButton(
+          onPressed: () {
+            // Open navigation instructions
+          },
+          child: Text("Navigate"),
+        ),
+      ],
+    ),
   );
-}
+}
+
+Future<LinkedList<Landmark>> getLandmarks (Future<Trip>? trip) async {
+  Trip tripf = await trip!;
+  return tripf.landmarks;
+}
+

frontend/lib/modules/map.dart

@@ -1,9 +1,18 @@
+import 'dart:collection';
+
 import 'package:fast_network_navigation/structs/landmark.dart';
-import 'package:fast_network_navigation/utils/get_landmarks.dart';
+import 'package:fast_network_navigation/structs/trip.dart';
 import 'package:flutter/material.dart';
 import 'package:google_maps_flutter/google_maps_flutter.dart';
 
 class MapWidget extends StatefulWidget {
+
+  final Future<Trip>? trip;
+
+  MapWidget({
+    this.trip
+  });
+
   @override
   State<MapWidget> createState() => _MapWidgetState();
 }
@@ -18,12 +27,18 @@ class _MapWidgetState extends State<MapWidget> {
   Set<Marker> markers = <Marker>{};
   
 
-  void _onMapCreated(GoogleMapController controller) {
+  void _onMapCreated(GoogleMapController controller) async {
     mapController = controller;
+    Trip? trip = await widget.trip;
+    List<double>? newLocation = trip?.landmarks.first.location;
+    if (newLocation != null) {
+      CameraUpdate update = CameraUpdate.newLatLng(LatLng(newLocation[0], newLocation[1]));
+      controller.moveCamera(update);
+    }
     drawLandmarks();
   }
 
-  
+
   void _onCameraIdle() {
     // print(mapController.getLatLng(ScreenCoordinate(x: 0, y: 0)));
   }
@@ -31,16 +46,19 @@ class _MapWidgetState extends State<MapWidget> {
 
   void drawLandmarks() async {
     // (re)draws landmarks on the map
-    List<Landmark> landmarks = await fetchLandmarks();
-    setState(() {
-      for (Landmark landmark in landmarks) {
-        markers.add(Marker(
-          markerId: MarkerId(landmark.name),
-          position: LatLng(landmark.location[0], landmark.location[1]),
-          infoWindow: InfoWindow(title: landmark.name, snippet: landmark.type.name),
-        ));
-      }
-    });
+    Trip? trip = await widget.trip;
+    LinkedList<Landmark>? landmarks = trip?.landmarks;
+    if (landmarks != null){
+      setState(() {
+        for (Landmark landmark in landmarks) {
+          markers.add(Marker(
+            markerId: MarkerId(landmark.name),
+            position: LatLng(landmark.location[0], landmark.location[1]),
+            infoWindow: InfoWindow(title: landmark.name, snippet: landmark.type.name),
+          ));
+        }
+      });
+    }
   }
 
 
@@ -52,7 +70,7 @@ class _MapWidgetState extends State<MapWidget> {
       onCameraIdle: _onCameraIdle,
       // onLongPress: ,
       markers: markers,
+      cloudMapId: '41c21ac9b81dbfd8',
     );
   }
 }
-

frontend/lib/modules/trips_overview.dart

@@ -2,19 +2,21 @@ import 'package:flutter/material.dart';
 
 import 'package:fast_network_navigation/layout.dart';
 import 'package:fast_network_navigation/structs/trip.dart';
-import 'package:fast_network_navigation/utils/get_trips.dart';
 
 
 class TripsOverview extends StatefulWidget {
-  
-  const TripsOverview({super.key});
+  final Future<List<Trip>> trips;
+  const TripsOverview({
+    super.key,
+    required this.trips,
+    });
 
   @override
   State<TripsOverview> createState() => _TripsOverviewState();
 }
 
 class _TripsOverviewState extends State<TripsOverview> {
-  final Future<List<Trip>> _trips = loadTrips();
+  // final Future<List<Trip>> _trips = loadTrips();
 
 
   Widget listBuild (BuildContext context, AsyncSnapshot<List<Trip>> snapshot) {
@@ -23,12 +25,12 @@ class _TripsOverviewState extends State<TripsOverview> {
       children = List<Widget>.generate(snapshot.data!.length, (index) {
         Trip trip = snapshot.data![index];
         return ListTile(
-          title: Text("Trip to ${trip.cityName} (${trip.landmarks.length} stops)"),
+          title: Text("Trip to ${trip.cityName}"),
           leading: Icon(Icons.pin_drop),
           onTap: () {
             Navigator.of(context).push(
               MaterialPageRoute(
-                builder: (context) => BasePage(mainScreen: "map") //, trip: trip)
+                builder: (context) => BasePage(mainScreen: "map", trip: Future.value(trip))
               )
             );
           },
@@ -58,7 +60,7 @@ class _TripsOverviewState extends State<TripsOverview> {
   @override
   Widget build(BuildContext context) {
     return FutureBuilder(
-      future: _trips,
+      future: widget.trips,
       builder: listBuild,
     );
   }

frontend/lib/pages/overview.dart

@@ -1,13 +1,21 @@
-import 'package:fast_network_navigation/modules/greeter.dart';
 import 'package:flutter/material.dart';
 import 'package:sliding_up_panel/sliding_up_panel.dart';
 
+import 'package:fast_network_navigation/structs/trip.dart';
+
 import 'package:fast_network_navigation/modules/landmarks_overview.dart';
 import 'package:fast_network_navigation/modules/map.dart';
+import 'package:fast_network_navigation/modules/greeter.dart';
 
 
 
 class NavigationOverview extends StatefulWidget {
+  final Future<Trip> trip;
+
+  NavigationOverview({
+    required this.trip
+  });
+
   @override
   State<NavigationOverview> createState() => _NavigationOverviewState();
 }
@@ -18,16 +26,16 @@ 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()
+        panel: _floatingPanel(),
+        collapsed: _floatingCollapsed(),
+        body: MapWidget(trip: widget.trip)
     );
   }
 
-  Widget _floatingCollapsed(ThemeData theme){
+  Widget _floatingCollapsed(){
+    final ThemeData theme = Theme.of(context);
     return Container(
       decoration: BoxDecoration(
         color: theme.canvasColor,
@@ -35,11 +43,12 @@ class _NavigationOverviewState extends State<NavigationOverview> {
         boxShadow: []
       ),
       
-      child: Greeter(theme)
+      child: Greeter(standalone: true, trip: widget.trip)
     );
   }
 
-  Widget _floatingPanel(ThemeData theme){
+  Widget _floatingPanel(){
+    final ThemeData theme = Theme.of(context);
     return Container(
       decoration: BoxDecoration(
         color: Colors.white,
@@ -57,8 +66,8 @@ class _NavigationOverviewState extends State<NavigationOverview> {
         child: SingleChildScrollView(
           child: Column(
             children: <Widget>[
-              Greeter(theme, full: true),
-              loadLandmarksOverview(),
+              Greeter(standalone: false, trip: widget.trip),
+              LandmarksOverview(trip: widget.trip),
             ],
           ),
         ),

frontend/lib/pages/profile.dart

@@ -53,12 +53,6 @@ class _ImportanceSlidersState extends State<ImportanceSliders> {
 
   UserPreferences _prefs = UserPreferences();
 
-  @override
-  void initState() {
-    _prefs.load();
-    super.initState();
-  }
-
   List<Card> _createSliders() {
     List<Card> sliders = [];
     for (SinglePreference pref in _prefs.preferences) {
@@ -80,7 +74,7 @@ class _ImportanceSlidersState extends State<ImportanceSliders> {
             },
           )
         ),
-        margin: EdgeInsets.only(left: 10, right: 10, top: 10, bottom: 0),
+        margin: const EdgeInsets.only(left: 10, right: 10, top: 10, bottom: 0),
         shadowColor: Colors.grey,
       ));
     }
@@ -89,6 +83,7 @@ class _ImportanceSlidersState extends State<ImportanceSliders> {
 
   @override
   Widget build(BuildContext context) {
+
     return Column(children: _createSliders());
   }
 }

frontend/lib/structs/landmark.dart

@@ -1,54 +1,88 @@
-class Landmark {
-  final String name;
-  final List location;
-  final LandmarkType type;
-  // final String description;
-  // final Duration duration;
-  // final bool visited;
+import 'dart:collection';
+import 'dart:convert';
 
-  const Landmark({
+import 'package:shared_preferences/shared_preferences.dart';
+
+final class Landmark extends LinkedListEntry<Landmark>{
+  // A linked node of a list of Landmarks
+  final String uuid;
+  final String name;
+  final List<double> location;
+  final LandmarkType type;
+  final bool? isSecondary;
+
+  // description to be shown in the overview
+  final String? imageURL;
+  final String? description;
+  final Duration? duration;
+  final bool? visited;
+
+  // Next node
+  // final Landmark? next;
+  final Duration? tripTime;
+
+
+  Landmark({
+    required this.uuid,
     required this.name,
     required this.location,
     required this.type,
-    // required this.description,
-    // required this.duration,
-    // required this.visited,
+    this.isSecondary,
+
+    this.imageURL,
+    this.description,
+    this.duration,
+    this.visited,
+
+    // this.next,
+    this.tripTime,
   });
 
-  factory Landmark.fromJson(Map<String, dynamic> json) {
-    return switch (json) {
-      {
-        'loc': List location,
-        'name': String name,
-        'type': String type,
-        // 'description': String description,
-        // 'duration': int duration,
-        // 'visited': bool visited
 
-      } =>
-        Landmark(
-          name: name,
-          location: location,
-          type: LandmarkType(name: type)
-          // description: description,
-          // duration: Duration(minutes: duration),
-          // visited: visited
-        ),
-      _ => throw const FormatException('Failed to load destination.'),
-    };
+  factory Landmark.fromJson(Map<String, dynamic> json) {
+    if (json
+      case { // automatically match all the non-optionals and cast them to the right type
+        'uuid': String uuid,
+        'name': String name,
+        'location': List<dynamic> location,
+        'type': String type,
+      }) {
+      // refine the parsing on a few
+      List<double> locationFixed = List<double>.from(location);
+      // parse the rest separately, they could be missing
+      LandmarkType typeFixed = LandmarkType(name: type);
+      final isSecondary = json['is_secondary'] as bool?;
+      final imageURL = json['image_url'] as String?;
+      final description = json['description'] as String?;
+      var duration = Duration(minutes: json['duration'] ?? 0) as Duration?;
+      if (duration == const Duration()) {duration = null;};
+      final visited = json['visited'] as bool?;
+      
+      return Landmark(
+        uuid: uuid, name: name, location: locationFixed, type: typeFixed, isSecondary: isSecondary, imageURL: imageURL, description: description, duration: duration, visited: visited);
+    } else {
+      throw FormatException('Invalid JSON: $json');
+    }
   }
 
 
-    Map<String, dynamic> toJson() {
-      return {
-        'name': name,
-        'location': location,
-        'type': type.name,
-        // 'description': description,
-        // 'duration': duration.inMinutes,
-        // 'visited': visited
-      };
-    }
+  @override
+  bool operator ==(Object other) {
+    return other is Landmark && uuid == other.uuid;
+  }
+
+
+  Map<String, dynamic> toJson() => {
+    'uuid': uuid,
+    'name': name,
+    'location': location,
+    'type': type.name,
+    'is_secondary': isSecondary,
+    'image_url': imageURL,
+    'description': description,
+    'duration': duration?.inMinutes,
+    'visited': visited
+  };
 }
 
 
@@ -65,3 +99,18 @@ class LandmarkType {
 }
 
 
+// Helpers
+// Handling the landmarks requires a little bit of special care because the linked list is not directly representable in json
+(Landmark, String?) getLandmarkFromPrefs(SharedPreferences prefs, String uuid) {
+  String? content = prefs.getString('landmark_$uuid');
+  Map<String, dynamic> json = jsonDecode(content!);
+  String? nextUUID = json['next_uuid'];
+  return (Landmark.fromJson(json), nextUUID);
+}
+
+
+void landmarkToPrefs(SharedPreferences prefs, Landmark current, Landmark? next) {
+  Map<String, dynamic> json = current.toJson();
+  json['next_uuid'] = next?.uuid;
+  prefs.setString('landmark_${current.uuid}', jsonEncode(json));
+}

frontend/lib/structs/linked_landmarks.dart

@@ -0,0 +1,46 @@
+// import "package:fast_network_navigation/structs/landmark.dart";
+
+// class Linked<Landmark> {
+//   Landmark? head;
+  
+//   Linked();
+
+//   // class methods
+//   bool get isEmpty => head == null;
+
+//   // Add a new node to the end of the list
+//   void add(Landmark value) {
+//     if (isEmpty) {
+//       // If the list is empty, set the new node as the head
+//       head = value;
+//     } else {
+//       Landmark? current = head;
+//       while (current!.next != null) {
+//         // Traverse the list to find the last node
+//         current = current.next;
+//       }
+//       current.next = value; // Set the new node as the next node of the last node
+//     }
+//   }
+
+//   // Remove the first node with the given value
+//   void remove(Landmark value) {
+//     if (isEmpty) return;
+
+//     // If the value is in the head node, update the head to the next node
+//     if (head! == value) {
+//       head = head.next;
+//       return;
+//     }
+
+//     var current = head;
+//     while (current!.next != null) {
+//       if (current.next! == value) {
+//         // If the value is found in the next node, skip the next node
+//         current.next = current.next.next;
+//         return;
+//       }
+//       current = current.next;
+//     }
+//   }
+// }

frontend/lib/structs/preferences.dart

@@ -67,16 +67,16 @@ class UserPreferences {
 
 
   void save() async {
-    SharedPreferences prefs = await SharedPreferences.getInstance();
+    SharedPreferences sharedPrefs = await SharedPreferences.getInstance();
     for (SinglePreference pref in preferences) {
-      prefs.setInt(pref.key, pref.value);
+      sharedPrefs.setInt(pref.key, pref.value);
     }
   }
 
   void load() async {
-    SharedPreferences prefs = await SharedPreferences.getInstance();
+    SharedPreferences sharedPrefs = await SharedPreferences.getInstance();
     for (SinglePreference pref in preferences) {
-      pref.value = prefs.getInt(pref.key) ?? 0;
+      pref.value = sharedPrefs.getInt(pref.key) ?? 0;
     }
   }
 }

frontend/lib/structs/route.dart

@@ -5,7 +5,7 @@ class Route {
   final String name;
   final Duration duration;
   final List<Landmark> landmarks;
-  
+
   Route({
     required this.name,
     required this.duration,

frontend/lib/structs/trip.dart

@@ -1,25 +1,75 @@
 // Represents a collection of landmarks that represent a journey
 // Different instances of a Trip can be saved and loaded by the user
 
+import 'dart:collection';
+import 'dart:convert';
+
 import 'package:fast_network_navigation/structs/landmark.dart';
+import 'package:shared_preferences/shared_preferences.dart';
 
 class Trip {
   final String uuid;
   final String cityName;
-  final List<Landmark> landmarks;
+  // TODO: cityName should be inferred from coordinates of the Landmarks
+  final LinkedList<Landmark> landmarks;
+  // could be empty as well
 
 
-  Trip({required this.uuid, required this.cityName, required this.landmarks});
+  Trip({
+    required this.uuid,
+    required this.cityName,
+    required this.landmarks,
+  });
+
 
   factory Trip.fromJson(Map<String, dynamic> json) {
-    List<Landmark> landmarks = [];
-    for (var landmark in json['landmarks']) {
-      landmarks.add(Landmark.fromJson(landmark));
-    }
     return Trip(
       uuid: json['uuid'],
-      cityName: json['cityName'],
-      landmarks: landmarks,
+      cityName: json['city_name'],
+      landmarks: LinkedList()
     );
   }
-}
+
+
+  factory Trip.fromPrefs(SharedPreferences prefs, String uuid) {
+    String? content = prefs.getString('trip_$uuid');
+    Map<String, dynamic> json = jsonDecode(content!);
+    Trip trip = Trip.fromJson(json);
+    String? firstUUID = json['entry_uuid'];
+    readLandmarks(trip.landmarks, prefs, firstUUID);
+    return trip;
+  }
+
+
+  Map<String, dynamic> toJson() => {
+    'uuid': uuid,
+    'city_name': cityName,
+    'entry_uuid': landmarks.first?.uuid ?? ''
+  };
+
+
+  void toPrefs(SharedPreferences prefs){
+    Map<String, dynamic> json = toJson();
+    prefs.setString('trip_$uuid', jsonEncode(json));
+    for (Landmark landmark in landmarks) {
+      landmarkToPrefs(prefs, landmark, landmark.next);
+    }
+  }
+}
+
+
+// Helper
+readLandmarks(LinkedList<Landmark> landmarks, SharedPreferences prefs, String? firstUUID) {
+  while (firstUUID != null) {
+    var (head, nextUUID) = getLandmarkFromPrefs(prefs, firstUUID);
+    landmarks.add(head);
+    firstUUID = nextUUID;
+  }
+}
+
+
+
+void removeAllTripsFromPrefs () async {
+  SharedPreferences prefs = await SharedPreferences.getInstance();
+  prefs.clear();
+}

frontend/lib/utils/fetch_landmarks.dart

@@ -0,0 +1,52 @@
+// import "package:fast_network_navigation/structs/landmark.dart";
+// import 'package:http/http.dart' as http;
+
+
+// Future<List<Landmark>> fetchLandmarks() 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.
+//     List<Landmark> landmarks = [
+//       // 48°51′29.6″N 2°17′40.2″E
+//       Landmark(
+//         name: "Eiffel Tower",
+//         location: [48.51296, 2.17402],
+//         type: LandmarkType(name: "Tower"),
+//         imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg/1037px-Tour_Eiffel_Wikimedia_Commons.jpg"
+//         ),
+//       Landmark(
+//         name: "Notre Dame Cathedral",
+//         location: [48.8530, 2.3498],
+//         type: LandmarkType(name: "Monument"),
+//         imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/f/f7/Notre-Dame_de_Paris%2C_4_October_2017.jpg/440px-Notre-Dame_de_Paris%2C_4_October_2017.jpg"
+//         ),
+//       Landmark(
+//         name: "Louvre palace",
+//         location: [48.8606, 2.3376],
+//         type: LandmarkType(name: "Museum"),
+//         imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/6/66/Louvre_Museum_Wikimedia_Commons.jpg/540px-Louvre_Museum_Wikimedia_Commons.jpg"
+//         ),
+//       Landmark(
+//         name: "Pont-des-arts",
+//         location: [48.5130, 2.2015],
+//         type: LandmarkType(name: "Bridge"),
+//         imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d1/Pont_des_Arts%2C_6e_Arrondissement%2C_Paris_%28HDR%29_20140320_1.jpg/560px-Pont_des_Arts%2C_6e_Arrondissement%2C_Paris_%28HDR%29_20140320_1.jpg"),
+//       Landmark(
+//         name: "Panthéon",
+//         location: [48.5046, 2.2046],
+//         type: LandmarkType(name: "Monument"),
+//         imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/8/80/Pantheon_of_Paris_007.JPG/1280px-Pantheon_of_Paris_007.JPG"
+//         ),
+//     ];
+//     // sleep 10 seconds
+//     await Future.delayed(Duration(seconds: 5));
+//     return landmarks;
+//   // } else {
+//   //   // If the server did not return a 200 OK response,
+//   //   // then throw an exception.
+//   //   throw Exception('Failed to load destination');
+//   // }
+// }

frontend/lib/utils/get_landmarks.dart

@@ -1,27 +0,0 @@
-import "package:fast_network_navigation/structs/landmark.dart";
-import 'package:http/http.dart' as http;
-
-
-Future<List<Landmark>> fetchLandmarks() 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.
-    List<Landmark> landmarks = [
-      Landmark(name: "Landmark 1", location: [48.85, 2.35], type: LandmarkType(name: "Type 1")),
-      Landmark(name: "Landmark 2", location: [48.86, 2.36], type: LandmarkType(name: "Type 2")),
-      Landmark(name: "Landmark 3", location: [48.75, 2.3], type: LandmarkType(name: "Type 3")),
-      Landmark(name: "Landmark 4", location: [48.9, 2.4], type: LandmarkType(name: "Type 4")),
-      Landmark(name: "Landmark 5", location: [48.91, 2.45], type: LandmarkType(name: "Type 5")),
-    ];
-    // sleep 10 seconds
-    await Future.delayed(Duration(seconds: 10));
-    return landmarks;
-  // } else {
-  //   // If the server did not return a 200 OK response,
-  //   // then throw an exception.
-  //   throw Exception('Failed to load destination');
-  // }
-}

frontend/lib/utils/get_trips.dart

@@ -1,37 +0,0 @@
-import 'dart:convert';
-
-import 'package:fast_network_navigation/structs/trip.dart';
-import 'package:fast_network_navigation/structs/landmark.dart';
-import 'package:shared_preferences/shared_preferences.dart';
-
-Future<List<Trip>> loadTrips() async {
-  SharedPreferences prefs = await SharedPreferences.getInstance();
-
-  Set<String> keys = prefs.getKeys();
-  List<Trip> itineraries = [];
-  for (String key in keys) {
-    if (key.startsWith("itinerary_")) {
-      String json = prefs.getString(key)!;
-      itineraries.add(Trip.fromJson(jsonDecode(json)));
-    }
-  }
-  itineraries.add(Trip(uuid: "1", cityName: "Paris", landmarks: [
-      Landmark(name: "Landmark 1", location: [48.85, 2.35], type: LandmarkType(name: "Type 1")),
-      Landmark(name: "Landmark 2", location: [48.86, 2.36], type: LandmarkType(name: "Type 2")),
-      Landmark(name: "Landmark 3", location: [48.75, 2.3], type: LandmarkType(name: "Type 3")),
-      Landmark(name: "Landmark 4", location: [48.9, 2.4], type: LandmarkType(name: "Type 4")),
-      Landmark(name: "Landmark 5", location: [48.91, 2.45], type: LandmarkType(name: "Type 5")),
-    ]));
-  itineraries.add(Trip(uuid: "2", cityName: "Vienna", landmarks: []));
-  itineraries.add(Trip(uuid: "3", cityName: "London", landmarks: []));
-  itineraries.add(Trip(uuid: "4", cityName: "Madrid", landmarks: []));
-  itineraries.add(Trip(uuid: "5", cityName: "Tokyo", landmarks: []));
-  itineraries.add(Trip(uuid: "6", cityName: "New York", landmarks: []));
-  itineraries.add(Trip(uuid: "7", cityName: "Los Angeles", landmarks: []));
-  itineraries.add(Trip(uuid: "8", cityName: "Zurich", landmarks: []));
-  itineraries.add(Trip(uuid: "9", cityName: "Orschwiller", landmarks: []));
-
-  await Future.delayed(Duration(seconds: 3));
-
-  return itineraries;
-}

frontend/lib/utils/load_trips.dart

@@ -0,0 +1,119 @@
+import 'dart:collection';
+
+import 'package:fast_network_navigation/structs/trip.dart';
+import 'package:fast_network_navigation/structs/landmark.dart';
+import 'package:shared_preferences/shared_preferences.dart';
+
+Future<List<Trip>> loadTrips() async {
+  SharedPreferences prefs = await SharedPreferences.getInstance();
+
+  List<Trip> trips = [];
+  Set<String> keys = prefs.getKeys();
+  for (String key in keys) {
+    if (key.startsWith('trip_')) {
+      String uuid = key.replaceFirst('trip_', '');
+      trips.add(Trip.fromPrefs(prefs, uuid));
+    }
+  }
+
+  if (trips.isEmpty) {
+    Trip t1 = Trip(uuid: '1', cityName: 'Paris', landmarks: LinkedList<Landmark>());
+    t1.landmarks.add(
+      Landmark(
+        uuid: '1',
+        name: "Eiffel Tower",
+        location: [48.859, 2.295],
+        type: LandmarkType(name: "Tower"),
+        imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg/1037px-Tour_Eiffel_Wikimedia_Commons.jpg"
+      ),
+    );
+    t1.landmarks.add(
+      Landmark(
+        uuid: "2",
+        name: "Notre Dame Cathedral",
+        location: [48.8530, 2.3498],
+        type: LandmarkType(name: "Monument"),
+        imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/f/f7/Notre-Dame_de_Paris%2C_4_October_2017.jpg/440px-Notre-Dame_de_Paris%2C_4_October_2017.jpg"
+      ),
+    );
+    t1.landmarks.add(
+      Landmark(
+        uuid: "3",
+        name: "Louvre palace",
+        location: [48.8606, 2.3376],
+        type: LandmarkType(name: "Museum"),
+        imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/6/66/Louvre_Museum_Wikimedia_Commons.jpg/540px-Louvre_Museum_Wikimedia_Commons.jpg"
+      ),
+    );
+    t1.landmarks.add(
+      Landmark(
+        uuid: "4",
+        name: "Pont-des-arts",
+        location: [48.8585, 2.3376],
+        type: LandmarkType(name: "Bridge"),
+        imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d1/Pont_des_Arts%2C_6e_Arrondissement%2C_Paris_%28HDR%29_20140320_1.jpg/560px-Pont_des_Arts%2C_6e_Arrondissement%2C_Paris_%28HDR%29_20140320_1.jpg"
+      ),
+    );
+    t1.landmarks.add(
+      Landmark(
+        uuid: "5",
+        name: "Panthéon",
+        location: [48.847, 2.347],
+        type: LandmarkType(name: "Monument"),
+        imageURL: "https://upload.wikimedia.org/wikipedia/commons/thumb/8/80/Pantheon_of_Paris_007.JPG/1280px-Pantheon_of_Paris_007.JPG"
+      ),
+    );
+    trips.add(t1);
+
+
+    Trip t2 = Trip(uuid: '2', cityName: 'Vienna', landmarks: LinkedList<Landmark>());
+
+    t2.landmarks.add(
+      Landmark(
+        uuid: '21',
+        name: "St. Charles's Church",
+        location: [48.1924563,16.3334399],
+        type: LandmarkType(name: "Monument"),
+        imageURL: "https://lh5.googleusercontent.com/p/AF1QipNNmA76Ps71NCL9rOOFoyheCEOyXWdHcUgQx9jd=w408-h305-k-no"
+      ),
+    );
+    t2.landmarks.add(
+      Landmark(
+        uuid: "22",
+        name: "Vienna State Opera",
+        location: [48.1949124,16.3483292],
+        type: LandmarkType(name: "Culture"),
+        imageURL: "https://lh5.googleusercontent.com/p/AF1QipMOx398kcoeDXFruSHNsb4lmZtdT8vibtK0cLi-=w408-h306-k-no"
+      ),
+    );
+    t2.landmarks.add(
+      Landmark(
+        uuid: "23",
+        name: "Belvedere-Schlossgarten",
+        location: [48.1956427,16.3711521],
+        type: LandmarkType(name: "Nature"),
+        imageURL: "https://lh5.googleusercontent.com/p/AF1QipNcI5LImH2Qdzx0GmF-5CY1wRKINFZ7HkahPEy1=w408-h306-k-no"
+      ),
+    );
+    t2.landmarks.add(
+      Landmark(
+        uuid: "24",
+        name: "Kunsthistorisches Museum Wien",
+        location: [48.2047501,16.3581904],
+        type: LandmarkType(name: "Museum"),
+        imageURL: "https://lh5.googleusercontent.com/p/AF1QipPuDu-kCCowO4TcawjziE8AhDVAANagVtRYBjlv=w408-h450-k-no"
+      ),
+    );
+    t2.landmarks.add(
+      Landmark(
+        uuid: "25",
+        name: "Salztorbrücke",
+        location: [48.2132382,16.369051],
+        type: LandmarkType(name: "Bridge"),
+      ),
+    );
+    trips.add(t2);
+
+  }
+  return trips;
+}

renovate.json

@@ -0,0 +1,3 @@
+{
+  "$schema": "https://docs.renovatebot.com/renovate-schema.json"
+}