## Problem Description and Formulation

The street vendor's problem can be formulated as a linear programming (LP) optimization problem. The goal is to maximize revenue given the constraints on batter and oil availability.

Let's define the decision variables:
- \(x\): the number of pieces of fried fish to sell
- \(y\): the number of pieces of fried chicken to sell

The objective function to maximize revenue is:
\[ \text{Maximize:} \quad 4x + 5y \]

The constraints based on the availability of batter and oil are:
\[ 3x + 4y \leq 400 \] (batter constraint)
\[ 5x + 6y \leq 500 \] (oil constraint)

Additionally, \(x \geq 0\) and \(y \geq 0\) because the vendor cannot sell a negative number of pieces of fried fish or chicken.

## Gurobi Code

To solve this problem using Gurobi in Python, we will use the Gurobi Python API. First, ensure you have Gurobi installed in your Python environment. You can install it via pip:
```bash
pip install gurobi
```

Here's the Gurobi code to solve the problem:

```python
import gurobi as gp

# Create a new model
model = gp.Model("street_vendor")

# Define the decision variables
x = model.addVar(lb=0, name="fried_fish")
y = model.addVar(lb=0, name="fried_chicken")

# Define the objective function
model.setObjective(4*x + 5*y, gp.GRB.MAXIMIZE)

# Add the batter constraint
model.addConstr(3*x + 4*y <= 400, name="batter_constraint")

# Add the oil constraint
model.addConstr(5*x + 6*y <= 500, name="oil_constraint")

# Optimize the model
model.optimize()

# Check if the model is optimized
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Fried Fish: {x.varValue}")
    print(f"Fried Chicken: {y.varValue}")
    print(f"Max Revenue: {model.objVal}")
else:
    print("The model is infeasible or has no solution.")
```