Cost Optimization Problem for ingredients in Python using pulp

Cost Optimization for feed management.

importing pulp modeller functionality Simplified Cost Model Python Formulation for the PuLP Modeller

from pulp import *
import re
import numpy as np





def get_total_cost(name1, dm, cp, tdn, dm_req, cp_req, tdn_req, price, qty):
    # problem defining
    # Lp minimize for minimizing function
    print("Ingredients:", name1)
    dm = {k: v for k, v in zip(name1, dm)}
    cp = {k: v for k, v in zip(name1, cp)}
    tdn = {k: v for k, v in zip(name1, tdn)}
    pri = {k: v for k, v in zip(name1, price)}
    qty1 = {k: v for k, v in zip(name1, qty)}
    prob = LpProblem("The Cost Model", LpMinimize)

    # minimize function variables
    ingredients_vars = LpVariable.dicts("", (i for i in name1), lowBound=0, cat='Continuous')

    # Minimizing Function
    prob += (
        lpSum([pri[i] * ingredients_vars[i] for i in name1]), "Total cost of Ingredients",
    )
    
    # Constraints:
    print("const1", [qty1[i] * dm[i] / 100 for i in name1])
    prob += (lpSum([qty1[i] * dm[i] * ingredients_vars[i] for i in name1]) / 100 >= dm_req, "DM Check",)

    print("const2", [qty1[i] * dm[i] * cp[i] / 10000 for i in name1])
    prob += (
        lpSum([qty1[i] * dm[i] * cp[i] * ingredients_vars[i] for i in name1]) / 100 >= cp_req,
        "CP Check",)

    print("const3", [qty1[i] * dm[i] * tdn[i] / 10000 for i in name1])
    prob += (
        lpSum([qty1[i] * dm[i] * tdn[i] * ingredients_vars[i] for i in name1]) / 100 >= tdn_req,
        "TDN Check",)

    # prob.writeLP('CostModel.lp')

    print(prob)

    solver1 = getSolver('GUROBI')
    prob.solve(GUROBI())

    # Status of solution:
    print("Status:", LpStatus[prob.status])
    final_ingredients = []
    for v in prob.variables():
        if v.varValue == None:
            return "No Solution"
        if v.varValue > 0:
            r_name = v.name
            ingredient_name = str(re.sub('_', ' ', r_name)).strip()
            quantity = np.round(v.varValue, 2)
            final_ingredients.append({"ingredientName": ingredient_name,
                                      "quantity": quantity})

    # print((ingredients_vars))
    # The optimised objective function value is printed to the screen
    # print("Total Cost of Ingredients = ", value(prob.objective))
    total_cost = np.round(value(prob.objective), 2)
    return {"finalIngredients": final_ingredients,
            "totalCost": total_cost}

OUTPUT : {'finalIngredients': [{'ingredientName': 'BREWERS GRAINS wet', 'quantity': 114.37}], 'totalCost': 130.38}

function only giving values for a single ingredient how to change or what to change the code so that it returns multiple ingredients?

also can I assign different upperbounds to each individual ingredient within the LpVariable.dicts()