```json
{
  "sym_variables": [
    ("x1", "number of commercials with famous actors"),
    ("x2", "number of commercials with regular people"),
    ("x3", "number of commercials with no people")
  ],
  "objective_function": "50000*x1 + 20000*x2 + 18000*x3",
  "constraints": [
    "10000*x1 + 3000*x2 + 2000*x3 <= 50000",
    "x2 <= 3",
    "x3 <= (x1 + x2 + x3)/3",
    "x1 >= 0.2*(x1 + x2 + x3)",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0"
  ]
}
```

```python
from gurobipy import Model, GRB

# Create a new model
model = Model("Commercial Optimization")

# Create variables
x1 = model.addVar(vtype=GRB.INTEGER, name="famous_actor_commercials")
x2 = model.addVar(vtype=GRB.INTEGER, name="regular_people_commercials")
x3 = model.addVar(vtype=GRB.INTEGER, name="no_people_commercials")

# Set objective function
model.setObjective(50000*x1 + 20000*x2 + 18000*x3, GRB.MAXIMIZE)

# Add constraints
model.addConstr(10000*x1 + 3000*x2 + 2000*x3 <= 50000, "budget_constraint")
model.addConstr(x2 <= 3, "regular_people_limit")
model.addConstr(x3 <= (x1 + x2 + x3)/3, "no_people_proportion")
model.addConstr(x1 >= 0.2*(x1 + x2 + x3), "famous_actor_proportion")
model.addConstr(x1 >= 0, "non_negativity_x1")
model.addConstr(x2 >= 0, "non_negativity_x2")
model.addConstr(x3 >= 0, "non_negativity_x3")


# Optimize model
model.optimize()

# Print results
if model.status == GRB.OPTIMAL:
    print(f"Optimal Viewership: {model.objVal}")
    print(f"Number of commercials with famous actors: {x1.x}")
    print(f"Number of commercials with regular people: {x2.x}")
    print(f"Number of commercials with no people: {x3.x}")
elif model.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {model.status}")

```
