Code not working and do not know how to proceed. Any one know what I am doing wrong?

Hi there, I have created an LP optimization model for optimizing the optimal buffer allocation between machines. I believe the model itself to be good enough and need to translate it to code for use with Gurobi. I have come quite far but there are just a few essential things I am missing. Could someone maybe take a look at my code and help?

import pandas as pd
import gurobipy as gp
from gurobipy import GRB

df_dict = {'Machine 1': {1: 3, 2: 2, 3: 2, 4: 3, 5: 0, 6: 1, 7: 5, 8: 5, 9: 5, 10: 5},
 'Machine 2': {1: 1, 2: 6, 3: 5, 4: 1, 5: 3, 6: 3, 7: 5, 8: 3, 9: 2, 10: 5},
 'Machine 3': {1: 1, 2: 5, 3: 0, 4: 4, 5: 2, 6: 6, 7: 2, 8: 2, 9: 2, 10: 5},
 'Machine 4': {1: 3, 2: 2, 3: 1, 4: 4, 5: 2, 6: 4, 7: 0, 8: 0, 9: 2, 10: 2},
 'Machine 5': {1: 1, 2: 4, 3: 4, 4: 4, 5: 7, 6: 7, 7: 7, 8: 0, 9: 3, 10: 8}}

df= pd.DataFrame(df_dict)

#------------------------------------------------------------------------------------------

# Data for the parameters
# Machines
machine5 = ['Machine 5']
machines = ['Machine 1', 'Machine 2', 'Machine 3', 'Machine 4']
# Buffers
buffer5 = 'b5'
buffers = ['b1', 'b2', 'b3', 'b4']
# Running time
time = df.index.to_list()
# Speed
# Machines 1 - 4
speeds = df[df.columns[:-1]].stack().to_dict()
# Machine 5
speed_M5 = df[df.columns[-1:]].stack().to_dict()
# Max total buffer capacity
k_max = 40

#------------------------------------------------------------------------------------------


# Model
model = gp.Model()
# Parameters
th5 = model.addVars(time, name = 'Throughput 5')  # The throughputs of the last machine
thi = model.addVars(machines, time, name = 'Throughputs')  # The throughputs of the machines
b5 = model.addVars(time, name ='Buffer 5')  # The amount of units in the last buffer
bj = model.addVars(buffers, time, ub = k_max, name = "Buffers")  # Actual amount of units in buffer i
kj = model.addVars(buffers, time, ub = k_max, name = "Max cap buffers")  # Max capacity in the buffers

# Constraints
# Throughput Machine 5 1
th5_1 = model.addConstrs((th5[t] <= speed_M5[t, m] for t in time for m in machine5)) 
# Throughput Machine 5 2
# th5_2 = model.addConstrs((th5[t] for t in time) <= 3)  # <-- Not sure how to do this
# Amount buffer 5
amount_b5 = (th5[t] for t in time)  # The amount that enters the final buffer
# Buffer max
buff_k = model.addConstrs((bj[b, t] <= kj[b, t] for t in time for b in buffers))
# Amount Buffers 1
amount_bj_1 = model.addConstrs((bj[b, t] <= bj[b, time[time[t]-1]] 
                            - thi[machines[machines[m]-1, t]] + speeds[t, m] 
                            for t in time for m in machines for b in buffers))
# Amount Buffers 2
amount_bj_1 = add.Constrs((bj[b, t] for t in time for b in buffers) 
                     <= (bj[b, time[time[t]-1]] - thi[machines[m]-1, t] 
                            + bj[[buffers[buffers[b]-1], time[time[t]-1]]]))
# Throughput machines
th_all = add.Constrs((thi[m, t] == bj[b, t] - bj[b, time[time[t]-1]] - thj[machines[machines[m]-1, t]]
                      for t in time for m in machines for b in buffers))
# Max Total Buffer Capacity
kmax_all = addConstrs() # <-- not sure how to do this

# Objective function
obj = gp.quicksum(b5[t] for t in time)

model.setObjective(obj, GRB.MAXIMIZE)

# Start optimization
model.optimize()
if not model.status == gp.GRB.OPTIMAL:
    print("something went wrong")
print("optimal value",model.objval)
model.printAttr("X")

I feel like I am very close but it just does not want to work. I have added a small df as for use as small reproducible sample. Any help would be greatly appreciated!