```json
{
  "sym_variables": [
    ("x1", "kilograms of oats"),
    ("x2", "kilograms of sunflower seeds")
  ],
  "objective_function": "50*x1 + 70*x2",
  "constraints": [
    "5*x1 + 10*x2 >= 250",
    "16*x1 + 22*x2 >= 400",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
oats = m.addVar(nonnegative=True, name="oats")  # Kilograms of oats
sunflower = m.addVar(nonnegative=True, name="sunflower")  # Kilograms of sunflower seeds

# Set objective function: Minimize the cost
m.setObjective(50 * oats + 70 * sunflower, GRB.MINIMIZE)

# Add constraints
m.addConstr(5 * oats + 10 * sunflower >= 250, "protein_req")  # Protein requirement
m.addConstr(16 * oats + 22 * sunflower >= 400, "fat_req")  # Fat requirement

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal cost: ${m.objVal:.2f}")
    print(f"Kilograms of oats: {oats.x:.2f}")
    print(f"Kilograms of sunflower seeds: {sunflower.x:.2f}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
