## Problem Description and Formulation

The problem is a classic example of a linear programming problem. The dessert factory wants to maximize revenue by producing cakes and pies, given the constraints on sugar and flour availability.

Let's define the decision variables:

* `cakes`: number of cakes to produce
* `pies`: number of pies to produce

The objective function is to maximize revenue:

* Revenue per cake: $4
* Revenue per pie: $3
* Total revenue: `4 * cakes + 3 * pies`

The constraints are:

* Sugar availability: `4 * cakes + 5 * pies <= 1000`
* Flour availability: `5 * cakes + 3 * pies <= 1200`
* Non-negativity: `cakes >= 0`, `pies >= 0`

## Gurobi Code

```python
import gurobi

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

# Define the decision variables
cakes = model.addVar(lb=0, name="cakes", vtype=gurobi.GRB.CONTINUOUS)
pies = model.addVar(lb=0, name="pies", vtype=gurobi.GRB.CONTINUOUS)

# Define the objective function
model.setObjective(4 * cakes + 3 * pies, gurobi.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(4 * cakes + 5 * pies <= 1000, name="sugar_constraint")
model.addConstr(5 * cakes + 3 * pies <= 1200, name="flour_constraint")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution:")
    print(f"Cakes: {cakes.varValue:.2f}")
    print(f"Pies: {pies.varValue:.2f}")
    print(f"Revenue: {model.objVal:.2f}")
else:
    print("No optimal solution found")
```

Note that in this code, we use `gurobi.GRB.CONTINUOUS` variables, which allow for fractional solutions. If you want to restrict the solution to integer values (i.e., produce whole cakes and pies only), you can change the `vtype` to `gurobi.GRB.INTEGER`. However, this may make the problem more difficult to solve.