# Complete PYOMO implementation

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

def epinions_optimization():
    """Optimize review selection to maximize trust score"""
    
    # 1. MODEL CREATION
    model = pyo.ConcreteModel()
    
    # 2. DATA SETUP
    n_reviews = 5
    trust_scores = [0.7, 0.9, 0.4, 0.3, 0.1]
    ratings = [5, 2, 4, 3, 1]
    max_reviews = 3
    
    # CRITICAL: Validate array lengths before indexing
    assert len(trust_scores) == len(ratings) == n_reviews, "Array length mismatch"
    
    # 3. SETS (Pyomo way to define indices)
    model.I = pyo.RangeSet(1, n_reviews)  # 1-based indexing
    
    # 4. PARAMETERS (data containers)
    model.trust_score = pyo.Param(model.I, initialize={i+1: trust_scores[i] for i in range(n_reviews)})
    model.rating = pyo.Param(model.I, initialize={i+1: ratings[i] for i in range(n_reviews)})
    
    # 5. VARIABLES
    # Binary variables for review selection
    model.x = pyo.Var(model.I, within=pyo.Binary)
    
    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.trust_score[i] * model.rating[i] * model.x[i] for i in model.I)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.maximize)
    
    # 7. CONSTRAINTS - CORRECT SYNTAX PATTERNS
    
    # Constraint: Total number of selected reviews must not exceed max_reviews
    def max_reviews_rule(model):
        return sum(model.x[i] for i in model.I) <= max_reviews
    model.max_reviews_constraint = pyo.Constraint(rule=max_reviews_rule)
    
    # 8. SOLVING WITH GUROBI (your available solver)
    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("\nSelected reviews:")
        for i in model.I:
            x_val = pyo.value(model.x[i])
            if x_val > 0.5:  # Only print selected reviews
                print(f"Review {i} selected")
        
    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
epinions_optimization()