## Problem Description and Symbolic Representation

The problem requires finding the optimal number of servings of each food to consume per day to meet the macronutrient requirements at minimal cost. Let's denote the number of servings of rice and beef as $x_1$ and $x_2$, respectively.

### Symbolic Representation

* Variables: $(x_1, \text{rice}), (x_2, \text{beef})$
* Objective Function: $\min 5x_1 + 30x_2$
* Constraints:
	+ Protein: $2x_1 + 20x_2 \geq 50$
	+ Carbs: $80x_1 + 200x_2 \geq 1000$
	+ Fat: $x_1 + 16x_2 \geq 40$
	+ Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Gurobi Code

```python
import gurobi

# Create a new Gurobi model
m = gurobi.Model()

# Define the variables
x1 = m.addVar(name="rice", lb=0)
x2 = m.addVar(name="beef", lb=0)

# Define the objective function
m.setObjective(5 * x1 + 30 * x2, gurobi.GRB.MINIMIZE)

# Define the constraints
m.addConstr(2 * x1 + 20 * x2 >= 50, name="protein")
m.addConstr(80 * x1 + 200 * x2 >= 1000, name="carbs")
m.addConstr(x1 + 16 * x2 >= 40, name="fat")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Servings of rice: {x1.x}")
    print(f"Servings of beef: {x2.x}")
    print(f"Total cost: ${m.objVal:.2f}")
else:
    print("No optimal solution found.")
```

## JSON Representation

```json
{
    "sym_variables": [
        ["x1", "rice"],
        ["x2", "beef"]
    ],
    "objective_function": "5*x1 + 30*x2",
    "constraints": [
        "2*x1 + 20*x2 >= 50",
        "80*x1 + 200*x2 >= 1000",
        "x1 + 16*x2 >= 40",
        "x1 >= 0",
        "x2 >= 0"
    ]
}
```