## Problem Description and Formulation

Robert wants to mix a white cocktail and a green cocktail to create a super cocktail with specific requirements. The white cocktail has 7% alcohol and 10% sugar, while the green cocktail has 2% alcohol and 25% sugar. The objective is to minimize the cost of the mixture while meeting the requirements of at least 5 kilograms of alcohol and 25 kilograms of sugar.

Let's denote:
- \(W\) as the amount of white cocktail in kilograms
- \(G\) as the amount of green cocktail in kilograms

The white cocktail costs $5.5 per kilogram, and the green cocktail costs $12 per kilogram. The requirements are:
- Alcohol: \(0.07W + 0.02G \geq 5\)
- Sugar: \(0.10W + 0.25G \geq 25\)

The objective function to minimize the total cost is: \(5.5W + 12G\)

## Converting to Gurobi Code

We will use the Gurobi Python API to model and solve this linear programming problem.

```python
import gurobi

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

    # Define variables
    W = model.addVar(lb=0, name="White_Cocktail")  # Amount of white cocktail
    G = model.addVar(lb=0, name="Green_Cocktail")  # Amount of green cocktail

    # Objective function: Minimize cost
    model.setObjective(5.5 * W + 12 * G, gurobi.GRB.MINIMIZE)

    # Constraints
    # Alcohol requirement
    model.addConstr(0.07 * W + 0.02 * G >= 5, name="Alcohol_Requirement")
    # Sugar requirement
    model.addConstr(0.10 * W + 0.25 * G >= 25, name="Sugar_Requirement")

    # Solve the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Amount of white cocktail: {W.varValue} kg")
        print(f"Amount of green cocktail: {G.varValue} kg")
        print(f"Minimum cost: ${model.objVal:.2f}")
    else:
        print("The model is infeasible or no solution exists.")

# Run the function
solve_cocktail_mixing_problem()
```