## Problem Description and Formulation

The problem requires finding the optimal number of pills of Klun and Tao that Emma needs to take to fulfill her daily medicine requirements at the lowest cost. The requirements are:

- At least 6 grams of AX7
- At least 8 grams of BY5

The characteristics of the pills are:

- Klun: 1.5 grams of AX7, 1.8 grams of BY5, and costs $2.6
- Tao: 1.3 grams of AX7, 2 grams of BY5, and costs $3.2

## Mathematical Formulation

Let's denote:
- \(K\) as the number of Klun pills
- \(T\) as the number of Tao pills

The objective is to minimize the total cost:
\[ \text{Minimize:} \quad 2.6K + 3.2T \]

Subject to:
\[ 1.5K + 1.3T \geq 6 \] (AX7 requirement)
\[ 1.8K + 2T \geq 8 \] (BY5 requirement)
\[ K \geq 0, T \geq 0 \] (Non-negativity constraints)

## Gurobi Code

```python
import gurobi

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

    # Define variables
    K = model.addVar(name="Klun", lb=0, ub=gurobi.GRB.INFINITY)
    T = model.addVar(name="Tao", lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: minimize cost
    model.setObjective(2.6 * K + 3.2 * T, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(1.5 * K + 1.3 * T >= 6, name="AX7_requirement")
    model.addConstr(1.8 * K + 2 * T >= 8, name="BY5_requirement")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal cost: ${model.objVal:.2f}")
        print(f"Klun pills: {K.varValue:.2f}")
        print(f"Tao pills: {T.varValue:.2f}")
    else:
        print("No optimal solution found.")

if __name__ == "__main__":
    solve_medicine_problem()
```