Unable to complete operation. Error executing tool DBSCAN_CLUSTER

[DimasPutrapangestu]

hi 

i have problem executing my tools in REST API. When I create tools and I run on the desktop, the tools work. However, when I published the service and I tried to execute it, an error appeared. Can anyone help?

import arcpy

import pandas as pd

from math import radians, cos, sin, asin, sqrt

from sklearn.cluster import DBSCAN

from scipy.spatial.distance import pdist, squareform

import matplotlib.pyplot as plt

import numpy as np

if __name__ == "__main__":

    arcpy.env.overwriteOutput = True

    in_file = arcpy.GetParameterAsText(0)

    output = arcpy.GetParameterAsText(1)

    epsilon = float(arcpy.GetParameterAsText(2))

    min_sample = int(arcpy.GetParameterAsText(3))

    #Convert the feature class to a pandas DataFrame

    array = arcpy.da.FeatureClassToNumPyArray(in_file, ["SHAPE@X", "SHAPE@Y"])

    df = pd.DataFrame(array)

    #Extract the X and Y coordinates from the DataFrame

    coords = df[["SHAPE@X", "SHAPE@Y"]].values

    #Get the spatial reference of the input feature class

    describe = arcpy.Describe(in_file)

    #create spatial object

    SR = describe.spatialReference

    #Create a spatial reference object

    SR = arcpy.SpatialReference(SR.FactoryCode)

    def haversine(lonlat1, lonlat20) :

    # convert decimal degrees to radians

        lat1, lon1 = lonlat1

        lat2, lon2 = lonlat2

        lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine formula

        dlon = lon2 - lon1

        dlat = lat2 - lat1

        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2

        b = 2 * asin(sqrt(a))

        r = 6371 # Radius of earth in kilometers. Use 3956 for miles

        return (b * r) * 1000

    distance_meter = squareform(pdist(coords, (lambda u,v: haversine(u,v))))

    arcpy.AddMessage(epsilon)

    arcpy.AddMessage(min_sample)

    #Function DBSCAN

    db = DBSCAN(eps=epsilon, min_samples=min_sample, metric='precomputed').fit(distance_meter)

    y_db = db.fit_predict(distance_meter)

    #visualization of db_scan

    plt.scatter(distance_meter[:,0],distance_meter[:,1],c= db.labels_)

    plt.xlabel("X")

    plt.ylabel("Y")

    plt.title("Grafik Data compound DBscan")

    plt.show

    print("Jumlah Kluster:{}".format(len(set(db.labels_[np.where(db.labels_!=-1)]))))

    print(db.labels_)

    labels = db.labels_

    #Export feature class

    arcpy.env.overwriteOutput = True

    out = arcpy.da.NumPyArrayToFeatureClass(array, output, ["SHAPE@X", "SHAPE@Y"],spatial_reference=SR)

    #Add field cluster into feature class output

    cluster = arcpy.AddField_management(output,"CLUSTER","Short")

    with arcpy.da.UpdateCursor(output,["CLUSTER"]) as cusor:

        for i, row in enumerate(cusor) :

            row[0] = labels[i]

            cusor.updateRow (row)