## Problem Description and Formulation

The hiker needs to purchase two types of trail mix, A and B, to meet the requirements of at least 20 units of almonds and 15 units of chocolate chips while minimizing the cost. 

Let's define the decision variables:
- \(x_A\): The number of bags of trail mix A to purchase.
- \(x_B\): The number of bags of trail mix B to purchase.

The objective is to minimize the total cost, given by \(5x_A + 8x_B\).

The constraints based on the nutritional requirements are:
- Almonds: \(2x_A + 2x_B \geq 20\)
- Chocolate chips: \(x_A + 3x_B \geq 15\)

Also, \(x_A \geq 0\) and \(x_B \geq 0\) since the number of bags cannot be negative.

## Conversion to Gurobi Code

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

```python
import gurobi

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

    # Define the decision variables
    x_A = model.addVar(lb=0, name="Trail_Mix_A")
    x_B = model.addVar(lb=0, name="Trail_Mix_B")

    # Define the objective function: minimize cost
    model.setObjective(5 * x_A + 8 * x_B, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2 * x_A + 2 * x_B >= 20, name="Almonds_Constraint")
    model.addConstr(x_A + 3 * x_B >= 15, name="Chocolate_Constraint")

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Trail Mix A: {x_A.varValue} bags")
        print(f"Trail Mix B: {x_B.varValue} bags")
        print(f"Total Cost: ${model.objVal:.2f}")
    else:
        print("The model is infeasible or no solution exists.")

# Run the function
solve_trail_mix_problem()
```