## Symbolic Representation

Let's denote:
- $x_1$ as the number of paintings
- $x_2$ as the number of photo prints

The objective is to maximize profit: $330x_1 + 170x_2$

Constraints:
1. Wall space: $7x_1 + 4x_2 \leq 200$
2. Budget: $400x_1 + 200x_2 \leq 7000$
3. At least 20% of items must be photo prints: $x_2 \geq 0.2(x_1 + x_2)$
4. Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Algebraic Representation

```json
{
    'sym_variables': [('x1', 'paintings'), ('x2', 'photo prints')],
    'objective_function': '330*x1 + 170*x2',
    'constraints': [
        '7*x1 + 4*x2 <= 200',
        '400*x1 + 200*x2 <= 7000',
        'x2 >= 0.2*(x1 + x2)',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name='paintings', vtype=gurobi.GRB.INTEGER, lb=0)
    x2 = model.addVar(name='photo_prints', vtype=gurobi.GRB.INTEGER, lb=0)

    # Objective function
    model.setObjective(330*x1 + 170*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(7*x1 + 4*x2 <= 200, name='wall_space')
    model.addConstr(400*x1 + 200*x2 <= 7000, name='budget')
    model.addConstr(x2 >= 0.2*(x1 + x2), name='photo_prints_percentage')

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    else:
        print("No optimal solution found")

solve_gallery_problem()
```