The optimisation function in streamlit return the initial guess

hey i’m using streamlit to developp my application, i work with package scipy.optimise function minimize that optimize an objective function with constraints . this function is working in python an it returns a correct results while when i use this function into streamlit code i receive as a result the initial guess that i gived him . here is my code (info : i tried to remove 2 constraints and keep 1 and the function works but the results isn’t those i want )

code of the optimisation function

import numpy as np
from scipy.optimize import fsolve
import scipy.optimize as sci_opt
from scipy.stats import norm
from scipy import optimize


def BS_CALL(s, k, T1, rd , rf, sigma1):
    d1 = (np.log(s/k) + ((rd-rf) + sigma1**2/2)*T1) / (sigma1*np.sqrt(T1))
    d2 = d1 - sigma1 * np.sqrt(T1)
    Call = s * n(d1) *np.exp(-rf*T1)- k* np.exp(-rd * T1)* n(d2)
    return Call

def BS_PUT(s, k, T1, rd , rf, sigma1):

    d1 = (np.log(s/k) + ((rd-rf) + sigma1**2/2)*T1) / (sigma1 *np.sqrt(T1))
    d2 = d1 - sigma1* np.sqrt(T1)
    Put = k*np.exp(-rd * T1)*n(-d2) - s*n(-d1)*np.exp(-rf*T1)
    return Put

def opti(s,T1,rd,rf,sigma1) :

    def constrainte(k):
        k1 = k[0]
        k2 = k[1]
        return k2 - k1 - 0.0000000000000000000000000000000000000000000001

    def constrainte1(k):
        k2 = k[1]
        return k2 - s - 0.0000000000000000000000000000000000000000000001
    def constrainte2(k):
        k1 = k[0]
        return s - k1 - 0.0000000000000000000000000000000000000000000001

    def objective(k):
        k1 = k[0]
        k2 = k[1]
        return (BS_PUT(s, k2, T1, rd, rf, sigma1) - BS_CALL(s, k1, T1, rd, rf, sigma1)) ** 2
    cons=({'type':'ineq','fun':  constrainte} , {'type':'ineq','fun':  constrainte1 }, {'type':'ineq','fun':  constrainte2})
    opt= sci_opt.minimize(objective,k0,method='BFGS',constraints=cons,options={'disp':True})
    return opt

>calling the function into stremlit 

                    v= j.message

                    p = j.x[0]
                    m = j.x[1]

the message i received

fun: 0.015634769628704602 jac: array([-1.68501236e-03, -4.65661287e-10]) message: 'Optimization terminated successfully' nfev: 3 nit: 1 njev: 1 status: 0 success: True x: array([10.8, 10.8])