import pandas as pd

from typing import List
from landmarks_manager import LandmarkManager
from fastapi.encoders import jsonable_encoder

from optimizer import solve_optimization
# from refiner import refine_optimization
from structs.landmarks import Landmark
from structs.landmarktype import LandmarkType
from structs.preferences import Preferences, Preference


# Helper function to create a .txt file with results
def write_data(L: List[Landmark], file_name: str): 

    data = pd.DataFrame()
    i = 0

    for landmark in L :
        data[i] = jsonable_encoder(landmark)
        i += 1

    data.to_json(file_name, indent = 2, force_ascii=False)

def main(coordinates: tuple[float, float]) -> List[Landmark]:

    manager = LandmarkManager()

    preferences = Preferences(
        sightseeing=Preference(
            name='sightseeing', 
            type=LandmarkType(landmark_type='sightseeing'),
            score = 5
            ),
        nature=Preference(
            name='nature', 
            type=LandmarkType(landmark_type='nature'),
            score = 5
            ),
        shopping=Preference(
            name='shopping', 
            type=LandmarkType(landmark_type='shopping'),
            score = 5
            )
        )


    # Create start and finish 
    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
    
    # Generate the landmarks from the start location
    landmarks, landmarks_short = manager.get_landmark_lists(preferences=preferences, center_coordinates=start.location)
    print([l.name for l in landmarks_short])

    #write_data(landmarks, "landmarks.txt")

    # Insert start and finish to the landmarks list
    landmarks_short.insert(0, start)
    landmarks_short.append(finish)

    # TODO use these parameters in another way
    max_walking_time = 3    # hours
    detour = 30             # minutes

    # First stage optimization
    base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
    
    # Second stage optimization
    # refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
    
    return base_tour



if __name__ == '__main__':
    start = (48.847132, 2.312359) # Café Chez César
    # start = (47.377859, 8.540585) # Zurich HB
    main(start)