## Problem Description and Symbolic Representation

The problem involves a breakfast place mixing two pancake mixes, A and B, to achieve a specific consistency. The goal is to minimize costs while meeting the requirements for sugar and flour.

### Symbolic Representation:

Let's denote:
- $x_1$ as the amount of Mix A in kg,
- $x_2$ as the amount of Mix B in kg.

The objective is to minimize the cost function: $20x_1 + 25x_2$.

The constraints based on the problem description are:
1. Sugar requirement: $0.10x_1 + 0.15x_2 \geq 10$,
2. Flour requirement: $0.60x_1 + 0.50x_2 \geq 50$,
3. Non-negativity: $x_1 \geq 0, x_2 \geq 0$.

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'Mix A in kg'), ('x2', 'Mix B in kg')],
    'objective_function': '20*x1 + 25*x2',
    'constraints': [
        '0.10*x1 + 0.15*x2 >= 10',
        '0.60*x1 + 0.50*x2 >= 50',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

def solve_pancake_mix_problem():
    # Create a new model
    model = gp.Model("PancakeMix")

    # Define variables
    x1 = model.addVar(name="Mix_A", lb=0)  # Amount of Mix A
    x2 = model.addVar(name="Mix_B", lb=0)  # Amount of Mix B

    # Objective function: Minimize cost
    model.setObjective(20*x1 + 25*x2, gp.GRB.MINIMIZE)

    # Constraints
    model.addConstr(0.10*x1 + 0.15*x2 >= 10, name="Sugar_Requirement")
    model.addConstr(0.60*x1 + 0.50*x2 >= 50, name="Flour_Requirement")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gp.GRB.OPTIMAL:
        print(f"Optimal amount of Mix A: {x1.varValue} kg")
        print(f"Optimal amount of Mix B: {x2.varValue} kg")
        print(f"Optimal cost: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_pancake_mix_problem()
```