Scipy.optimize.minimize with constraints

I need to minimize a two variables function and I have a constraint to respect.

I wrote the following code

def deflection_constraint(inputs):
    #return value must come back as 0 to be accepted
    #if return value is anything other than 0 it's rejected
    #as not a valid answer.
    
    inputs = [a,b]
    mass_propeller_motor =0.0075
    weight_propeller_motor = mass_propeller_motor * 9.81
    L = 0.035
    density_material = 1200 #material dependent
    density_air = 1.2
    mass_propeller_motor_battery = 0.150
    C_d = 0.7
    v_v = 3
    v_l = 7
    alpha = math.pi/6
    beta = math.pi/3
    I = (a*(b**3))/12
    M = weight_propeller_motor * L
    sigma_0 = (M*b)/(2*I)

    V_arm = a*b*L
    V_box = 1.045 * 10**(-5)
    V_frame = 4*V_arm + V_box
    M_frame = density_material*V_frame
    A_r = 0.0184
    F_w = (M_frame + mass_propeller_motor_battery)*9.81
    F_d = 0.5*density_air*A_r*C_d*v_v**2
    F_v = (F_w+F_d)/4
    M_v = F_v*L
    sigma_v = (M_v*b)/(2*I)

    A_rl = A_r*math.cos(beta)
    F_dl = 0.5*density_air*A_rl*C_d*v_l**2
    F_parallel = F_dl * math.sin(alpha)+F_w*math.cos(alpha)
    F_perpendicular = F_dl * math.cos(alpha)+F_w*math.sin(alpha) 
    F_l = math.sqrt(F_parallel**2+F_perpendicular**2)*0.25
    M_l = F_l*L
    sigma_l = (M_l*b)/(2*I)
    
    t1 = 13.33/3600
    t2 = 7186.67/3600
    t3 = 2
    t4 = 6
    t5 = 21613.33/3600
    t6 = 28786.67/3600
    t7 = 8
    t8 = 12
    t9 = 43213.33/3600
    t10 = 50386.67/3600
    t11 = 14
    t12 = 18
    
    f_t10minus = (M_v * D(t10) + (M_l-M_v)*D(t10-t1)-(M_l-M_v)*D(t10-t2)-M_v*D(t10-t3)+M_v*D(t10-t4)+(M_l-M_v)*D(t10-t5)-(M_l-M_v)*D(t10-t6)-M_v*D(t10-t7)+M_v*D(t10-t8)+M_v*D(t10-t9))*L**2/(2*I)

    total = 0.0031 - f_t10minus
    return total

This is the constraint to be respected while the function is the following:

def g(parameters):
    a,b = parameters
    return a*b

So basically I have to minimize a cross sectional area but I have the constraint about the maximum deflection of a beam. Then I tried to minimize the function by using optimize.minimize

first_guess =np.array([0.01,0.005]) 

my_constraints = ({'type': 'ineq', "fun": deflection_constraint})
result = optimize.minimize(g, 
                      first_guess, 
                      method='SLSQP', 
                      #args=(a, b),
                      bounds=((0.005, 0.1), (0.001, 0.005)),
                      options={'disp': True},
                      tol = 10**(-5),
                      constraints=my_constraints)

result.x

but what I obtained is that the final result does not satisfy the constraint and the values of the variables are the lower bounds I put. How can I make it work properly?