PLumber + Future + Fastsam

Hey community,
I have found some problems to run python script in future, all wrapped in plumber.

This is my Plumber endpoint

library(tidyverse)
library(plumber)

reticulate::use_python("C:/Program Files/Python310")

library(reticulate)
library(jsonlite)
library(DBI)
library(sp)
library(readxl)
library(rgeos)
library(raster)
library(RPostgreSQL)
library(sf)
library(rpostgis)
library(Rook)
library(rgdal)
library(rapidoc)
library(NbClust)
library(future)
library(planetR)
library(data.table)
library(nasapower)
library(owmr)
library(gptstudio)
library(RPostgres)
library(httr)
library(lubridate)
library(promises)
library(sen2r)
library(shinyjs)
library(emayili)
library(geojsonsf)
library(SPEI)
library(RColorBrewer)
library(mapview)
library(echarts4r)
library(base64enc)
library(reshape2)
library(soilDB)

serializer_geojson <- function(type = "application/geo+json") {
  serializer_write_file(
    fileext = ".GeoJSON",
    type = type,
    write_fn = function(val, tmpfile) {
      rgdal::writeOGR(val, dsn = tmpfile, layer = "geojson", driver = "GeoJSON", overwrite_layer = TRUE)
    }
  )
}

register_serializer("geojson", serializer_geojson)

future::plan("multisession", workers = 3)

source_python('predict_sam.py')

#* @tag FastSam
#* @param lat:double 
#* @param longi:double 
#* @post /AIBoundaries
#* @serializer geojson
function(req, res, lat, longi){
  
  future::future({
    
      predict_sam(lat, longi)

      AOI<-st_read(dsn = ".",
                 layer = "trees")

      AOI_sp<-as_Spatial(AOI)

     as_attachment(AOI_sp, "response.GeoJSON")
    
  })
}

This is the python code that i am using. It is related to SAM facebook model https://segment-anything.com/

import os
import leafmap
from samgeo import SamGeo, show_image, download_file, overlay_images, tms_to_geotiff

def predict_sam(lat, longi):
    m = leafmap.Map(center=[longi ,lat], zoom=16, height="800px", widht="800px")
    m.add_basemap("SATELLITE")
    zoom_level = m.zoom
    map_center = m.center
    lat_span = 360 / (2 ** zoom_level)
    long_span = 360 / (2 ** zoom_level)
    sw_lat = float(map_center[0]) - lat_span
    sw_long = float(map_center[1]) - long_span
    ne_lat = float(map_center[0]) + lat_span
    ne_long = float(map_center[1]) + long_span
    converted_bbox = [sw_lat, sw_long, ne_lat, ne_long]
    image = "Image.tif"
    tms_to_geotiff(output=image, bbox=converted_bbox, source="SATELLITE", overwrite=True, quiet=True, resolution=1)
    sam = SamGeo(model_type="vit_h",sam_kwargs=None)
    sam.generate(image, output="masks.tif", foreground=True, unique=True)
    sam.tiff_to_vector("masks.tif", "trees.shp")

and the follow is the rcode to perform the post request

api<-"http://127.0.0.1:3000/AIBoundaries?lat=-6.332384&longi=-35.43151"
r <- httr::POST(
  api_url
)

and this is the output of my docker container

Lazy evaluation: FALSE
Asynchronous evaluation: TRUE
Local evaluation: TRUE
Environment: 0x55fc943f0020
Capture standard output: TRUE
Capture condition classes: ‘condition’ (excluding ‘nothing’)
Globals: 18 objects totaling 1.65 MiB (environment ‘req’ of 1.11 MiB, standardGeneric ‘dbConnect’ of 10.29 KiB, character ‘vegetationindex’ of 112 bytes, python.builtin.function ‘predict_sam’ of 6.23 KiB, character ‘lat’ of 120 bytes, ...)
Packages: 9 packages (‘sf’, ‘stringr’, ‘rpostgis’, ‘reticulate’, ‘sen2r’, ‘raster’, ‘nasapower’, ‘rgeos’, ‘tidyr’)
L'Ecuyer-CMRG RNG seed: <none> (seed = FALSE)
Resolved: TRUE
Value: <not collected>
Conditions captured: <none>
Early signaling: FALSE
Owner process: 6245bd0b-a722-dcdb-650f-5ecd1a83976c
Class: ‘MultisessionFuture’, ‘ClusterFuture’, ‘MultiprocessFuture’, ‘Future’, ‘environment’

DEBUG: END TROUBLESHOOTING HELP

what can i do to solve this issue?
i have done the same thing but without the future and all works as excepted.