## Problem Description and Symbolic Representation

The problem involves a bakery shop that produces strawberry and caramel brownies. The goal is to maximize revenue given certain constraints.

### Symbolic Variables
- $x_1$ : Number of strawberry brownies
- $x_2$ : Number of caramel brownies

### Objective Function
The objective is to maximize revenue. Given that the price of a strawberry brownie is $1.5 and the price of a caramel brownie is $2, the objective function can be represented as:
\[ \text{Maximize:} \quad 1.5x_1 + 2x_2 \]

### Constraints
1. At least 50 strawberry brownies: $x_1 \geq 50$
2. At least 75 caramel brownies: $x_2 \geq 75$
3. At most 100 strawberry brownies: $x_1 \leq 100$
4. At most 150 caramel brownies: $x_2 \leq 150$
5. At least twice as many strawberry brownies as caramel brownies: $x_1 \geq 2x_2$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'strawberry brownies'), ('x2', 'caramel brownies')],
    'objective_function': '1.5*x1 + 2*x2',
    'constraints': [
        'x1 >= 50',
        'x2 >= 75',
        'x1 <= 100',
        'x2 <= 150',
        'x1 >= 2*x2'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(lb=0, ub=100, name="strawberry_brownies", vtype=gp.GRB.INTEGER)
x2 = model.addVar(lb=0, ub=150, name="caramel_brownies", vtype=gp.GRB.INTEGER)

# Objective function: Maximize 1.5*x1 + 2*x2
model.setObjective(1.5 * x1 + 2 * x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 >= 50, name="min_strawberry")
model.addConstr(x2 >= 75, name="min_caramel")
model.addConstr(x1 <= 100, name="max_strawberry")
model.addConstr(x2 <= 150, name="max_caramel")
model.addConstr(x1 >= 2 * x2, name="strawberry_to_caramel_ratio")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution found.")
    print(f"Strawberry brownies: {x1.varValue}")
    print(f"Caramel brownies: {x2.varValue}")
    print(f"Revenue: {1.5 * x1.varValue + 2 * x2.varValue}")
else:
    print("No optimal solution found.")
```