To solve Cindy's problem, we need to formulate a linear programming model that captures her requirements and constraints. Let's denote the number of glasses of orange juice as $x$ and the number of glasses of apple juice as $y$. The objective is to minimize the total amount of Glycine consumed.

The amounts of Lysine, Alanine, Methionine, and Glycine in each type of juice are given. We can set up the following inequalities based on Cindy's requirements:

- For Lysine: $2x + 5y \geq 30$
- For Alanine: $3x + 5y \geq 40$
- For Methionine: $5x + 2y \leq 50$

The objective function, which aims to minimize the total amount of Glycine consumed, can be represented as $7x + y$.

Here is how we can translate this problem into Gurobi code in Python:

```python
from gurobipy import *

# Create a model
m = Model("Cindy_Juice")

# Define variables
orange_juice = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="orange_juice")
apple_juice = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="apple_juice")

# Add constraints
m.addConstr(2*orange_juice + 5*apple_juice >= 30, "lysine_constraint")
m.addConstr(3*orange_juice + 5*apple_juice >= 40, "alanine_constraint")
m.addConstr(5*orange_juice + 2*apple_juice <= 50, "methionine_constraint")

# Set objective function
m.setObjective(7*orange_juice + apple_juice, GRB.MINIMIZE)

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Orange juice: {orange_juice.x}")
    print(f"Apple juice: {apple_juice.x}")
    print(f"Glycine intake: {7*orange_juice.x + apple_juice.x}")
else:
    print("No optimal solution found.")
```