```json
{
  "sym_variables": [
    ("x1", "investment in wood industry"),
    ("x2", "investment in bamboo industry")
  ],
  "objective_function": "0.07 * x1 + 0.03 * x2",
  "constraints": [
    "x1 + x2 <= 10000",
    "x1 >= 4 * x2",
    "x1 <= 7000",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
wood_investment = m.addVar(name="wood_investment")
bamboo_investment = m.addVar(name="bamboo_investment")

# Set objective function
m.setObjective(0.07 * wood_investment + 0.03 * bamboo_investment, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(wood_investment + bamboo_investment <= 10000, "total_investment")
m.addConstr(wood_investment >= 4 * bamboo_investment, "wood_bamboo_ratio")
m.addConstr(wood_investment <= 7000, "wood_investment_limit")
m.addConstr(wood_investment >= 0, "wood_non_negative")  # Explicit non-negativity constraint
m.addConstr(bamboo_investment >= 0, "bamboo_non_negative") # Explicit non-negativity constraint


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal investment in wood industry: {wood_investment.x}")
    print(f"Optimal investment in bamboo industry: {bamboo_investment.x}")
    print(f"Maximum average return: {m.objVal}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The problem is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
