# Complete PYOMO implementation

import pyomo.environ as pyo
from pyomo.opt import SolverFactory

def music_storage_optimization():
    """Optimize music storage for a streaming platform."""
    
    # 1. MODEL CREATION
    model = pyo.ConcreteModel()
    
    # 2. DATA SETUP
    # Example data (replace with actual data from database)
    song_ids = [1, 2, 3]
    file_sizes = {1: 8, 2: 12, 3: 6}
    ratings = {1: 4, 2: 5, 3: 3}
    artists = {1: 'A', 2: 'B', 3: 'A'}
    genres = {1: 'Pop', 2: 'Rock', 3: 'Pop'}
    
    # Constraints
    min_total_songs = 2
    min_avg_rating = 4
    max_songs_per_artist = 1
    min_songs_per_genre = 1
    
    # Validate array lengths
    assert len(song_ids) == len(file_sizes) == len(ratings) == len(artists) == len(genres), "Array length mismatch"
    
    # 3. SETS
    model.I = pyo.Set(initialize=song_ids)  # Set of songs
    
    # 4. PARAMETERS
    model.file_size = pyo.Param(model.I, initialize=file_sizes)
    model.rating = pyo.Param(model.I, initialize=ratings)
    model.artist = pyo.Param(model.I, initialize=artists)
    model.genre = pyo.Param(model.I, initialize=genres)
    
    # 5. VARIABLES
    model.x = pyo.Var(model.I, within=pyo.Binary)  # Binary decision variable for song storage
    
    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.file_size[i] * model.x[i] for i in model.I)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.minimize)
    
    # 7. CONSTRAINTS
    
    # Minimum Total Songs Stored
    def min_total_songs_rule(model):
        return sum(model.x[i] for i in model.I) >= min_total_songs
    model.min_total_songs_constraint = pyo.Constraint(rule=min_total_songs_rule)
    
    # Minimum Average Rating
    def min_avg_rating_rule(model):
        return sum((model.rating[i] - min_avg_rating) * model.x[i] for i in model.I) >= 0
    model.min_avg_rating_constraint = pyo.Constraint(rule=min_avg_rating_rule)
    
    # Maximum Songs per Artist
    def max_songs_per_artist_rule(model, a):
        return sum(model.x[i] for i in model.I if model.artist[i] == a) <= max_songs_per_artist
    model.max_songs_per_artist_constraint = pyo.Constraint(['A', 'B'], rule=max_songs_per_artist_rule)
    
    # Minimum Songs per Genre
    def min_songs_per_genre_rule(model, g):
        return sum(model.x[i] for i in model.I if model.genre[i] == g) >= min_songs_per_genre
    model.min_songs_per_genre_constraint = pyo.Constraint(['Pop', 'Rock'], rule=min_songs_per_genre_rule)
    
    # 8. SOLVING WITH GUROBI
    solver = SolverFactory('gurobi')
    
    # Optional: Set solver options
    solver.options['TimeLimit'] = 300  # 5 minutes
    solver.options['MIPGap'] = 0.01    # 1% gap
    
    # Solve the model
    results = solver.solve(model, tee=True)  # tee=True shows solver output
    
    # 9. RESULT PROCESSING
    # Check solver status
    if results.solver.termination_condition == pyo.TerminationCondition.optimal:
        print("Optimal solution found!")
        print(f"Optimal value: {pyo.value(model.objective)}")
        
        # Extract variable values
        print("\nVariable values:")
        for i in model.I:
            x_val = pyo.value(model.x[i])
            if x_val > 1e-6:  # Only print non-zero values
                print(f"x[{i}] = {int(x_val)}")
        
    elif results.solver.termination_condition == pyo.TerminationCondition.infeasible:
        print("Problem is infeasible")
    elif results.solver.termination_condition == pyo.TerminationCondition.unbounded:
        print("Problem is unbounded")
    else:
        print(f"Solver terminated with condition: {results.solver.termination_condition}")
    
    return model

# Run the optimization
if __name__ == "__main__":
    music_storage_optimization()