## Problem Description and Symbolic Representation

The problem involves finding the optimal combination of chocolate and strawberry toppings for a cake that minimizes the cost while satisfying certain constraints.

### Symbolic Variables:
- $x_1$: Number of chocolate toppings
- $x_2$: Number of strawberry toppings

### Objective Function:
The objective is to minimize the cost, which is $2x_1 + 3x_2$.

### Constraints:
1. At most 5 chocolate toppings: $x_1 \leq 5$
2. At least 10 grams of sugar: $1x_1 + 0.5x_2 \geq 10$
3. At least 15 grams of butter: $2x_1 + 0.7x_2 \geq 15$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$
5. Integer constraints: $x_1 \in \mathbb{Z}, x_2 \in \mathbb{Z}$ (though Gurobi can handle these, noting them is important)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'chocolate toppings'), ('x2', 'strawberry toppings')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': [
        'x1 <= 5',
        'x1 + 0.5*x2 >= 10',
        '2*x1 + 0.7*x2 >= 15',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

def solve_bakery_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="chocolate_toppings", lb=0, ub=5, vtype=gurobi.GRB.INTEGER)  # Number of chocolate toppings
    x2 = model.addVar(name="strawberry_toppings", lb=0, vtype=gurobi.GRB.INTEGER)  # Number of strawberry toppings

    # Objective function: minimize cost
    model.setObjective(2*x1 + 3*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(x1 <= 5, name="chocolate_limit")
    model.addConstr(x1 + 0.5*x2 >= 10, name="sugar_requirement")
    model.addConstr(2*x1 + 0.7*x2 >= 15, name="butter_requirement")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("No optimal solution found")

solve_bakery_problem()
```