Cost Optimization Problem for ingredients in Python using pulp

Question

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()