## Problem Description and Formulation

The problem is a linear programming (LP) optimization problem. Amy's bakery sells two types of bread: croissant and ficelle. The goal is to maximize revenue given the constraints on mixing time and vanilla extract.

Let's define the variables:
- \(x\): the number of croissants
- \(y\): the number of ficelles

The objective function to maximize revenue is:
\[ \text{Maximize:} \quad 4.5x + 3.5y \]

The constraints are:
1. Mixing time: \( 12x + 17y \leq 350 \)
2. Vanilla extract: \( 2x + y \leq 45 \)
3. Non-negativity: \( x \geq 0, y \geq 0 \)

## Gurobi Code

To solve this LP using Gurobi in Python, we will use the Gurobi Python interface. First, ensure you have Gurobi installed and a valid license.

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define variables
x = model.addVar(lb=0, name="croissant")  # Number of croissants
y = model.addVar(lb=0, name="ficelle")    # Number of ficelles

# Objective function: Maximize revenue
model.setObjective(4.5 * x + 3.5 * y, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(12 * x + 17 * y <= 350, name="mixing_time")
model.addConstr(2 * x + y <= 45, name="vanilla_extract")

# Optimize the model
model.optimize()

# Print the status of the optimization
print("Optimization Status:", model.status)

# If the model is optimized successfully
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal Solution:")
    print("Croissants:", x.varValue)
    print("Ficelles:", y.varValue)
    print("Max Revenue: $", 4.5 * x.varValue + 3.5 * y.varValue)
else:
    print("The model is infeasible or unbounded.")
```

This code defines the LP problem using Gurobi's Python interface, solves it, and then prints out the optimal solution if one exists. Note that you need to have a Gurobi license and the Gurobi Python package installed to run this code.