1class VoiceInterface:
 2    def __init__(self, voice_engine):
 3        self.voice_engine = voice_engine
 4        self.context = {"modality": "voice", "hands_free": True}
 5    
 6    def get_user_input(self):
 7        """Get user input through voice recognition"""
 8        # TODO: Implement voice input simulation
 9        return "________"  # FILL THIS IN
10    
11    def deliver_response(self, response):
12        """Deliver response through text-to-speech"""
13        # TODO: Implement voice output simulation
14        return f"🔊 Speaking: {response}"
15    
16    def get_context(self):
17        """Get interface-specific context"""
18        return self.context
19
20class TextInterface:
21    def __init__(self):
22        self.context = {"modality": "text", "typing_speed": "fast"}
23    
24    def get_user_input(self):
25        """Get user input through text"""
26        # TODO: Implement text input simulation  
27        return "________"  # FILL THIS IN
28    
29    def deliver_response(self, response):
30        """Deliver response through text display"""
31        # TODO: Implement text output
32        return "________"  # FILL THIS IN
33    
34    def get_context(self):
35        return self.context
36
37class NeuralInterface:
38    def __init__(self, neural_decoder):
39        self.neural_decoder = neural_decoder
40        self.context = {"modality": "neural", "direct_thought": True}
41    
42    # TODO: Implement the missing methods that make this a "duck"
43    # HINT: What methods do VoiceInterface and TextInterface have?
44    
45    def ________(self):  # Method name here
46        """Get user input through neural signals"""
47        return "Neural thought detected: Hello AI"
48    
49    def ________(self, response):  # Method name here  
50        """Deliver response through neural feedback"""
51        return f"🧠 Neural feedback: {response}"
52    
53    def ________(self):  # Method name here
54        return self.context
55
56# TODO: Create the universal PAI function that works with ANY interface
57def run_pai_system(interface):
58    """
59    This function should work with ANY interface that has the right methods.
60    Fill in the implementation using duck typing principles.
61    """
62    print(f"Starting PAI with {interface.__class__.__name__}")
63    
64    # TODO: Get context from interface
65    context = ________  # FILL THIS IN
66    print(f"Interface context: {context}")
67    
68    # TODO: Get user input (this should work with any interface!)
69    user_input = ________  # FILL THIS IN
70    print(f"User input: {user_input}")
71    
72    # TODO: Generate AI response based on input and context
73    ai_response = generate_contextual_response(user_input, context)
74    
75    # TODO: Deliver response through interface  
76    delivery_result = ________  # FILL THIS IN
77    print(f"Response delivered: {delivery_result}")
78    
79    return ai_response
80
81def generate_contextual_response(user_input, context):
82    """Generate appropriate response based on context"""
83    modality = context.get("modality", "unknown")
84    
85    if modality == "voice":
86        return f"Voice response to: {user_input}"
87    elif modality == "text":
88        return f"Text response to: {user_input}"  
89    elif modality == "neural":
90        return f"Neural response to: {user_input}"
91    else:
92        return f"Default response to: {user_input}"
93
94# TODO: Test your implementation
95if __name__ == "__main__":
96    # Create different interface types
97    voice_ai = VoiceInterface("premium_voice_engine")
98    text_ai = TextInterface()
99    neural_ai = NeuralInterface("advanced_neural_decoder")
100    
101    # TODO: Test that the same function works with all interfaces
102    interfaces = [voice_ai, text_ai, neural_ai]
103    
104    for interface in interfaces:
105        print(f"\n{'='*50}")
106        result = run_pai_system(interface)
107        print(f"Final result: {result}")

 1import random
 2import time
 3
 4class ReinforcementLearner:
 5    def __init__(self, learning_rate=0.01):
 6        self.learning_rate = learning_rate
 7        self.q_table = {}
 8        self.epsilon = 0.1
 9        
10    # TODO: Implement the learning "duck" interface
11    def observe(self, state, action, reward, next_state):
12        """Learn from experience using Q-learning"""
13        # TODO: Implement Q-learning update
14        key = f"{state}_{action}"
15        current_q = self.q_table.get(key, 0.0)
16        
17        # Q-learning formula: Q(s,a) = Q(s,a) + α[r + γmax(Q(s',a')) - Q(s,a)]
18        max_next_q = max([self.q_table.get(f"{next_state}_{a}", 0.0) 
19                         for a in ["action1", "action2", "action3"]])
20        
21        new_q = current_q + self.learning_rate * (reward + 0.9 * max_next_q - current_q)
22        self.q_table[key] = new_q
23        
24        return f"RL: Updated Q({state},{action}) = {new_q:.3f}"
25    
26    def choose_action(self, state):
27        """Choose action using epsilon-greedy strategy"""
28        # TODO: Implement epsilon-greedy action selection
29        if random.random() < self.epsilon:
30            # Explore
31            action = random.choice(["action1", "action2", "action3"])
32            return f"RL: Exploring with {action}"
33        else:
34            # Exploit
35            best_action = "action1"  # Simplified
36            return f"RL: Exploiting with {best_action}"
37    
38    def get_performance_metrics(self):
39        """Return learning algorithm performance metrics"""
40        return {
41            "algorithm": "Reinforcement Learning",
42            "q_table_size": len(self.q_table),
43            "learning_rate": self.learning_rate,
44            "exploration_rate": self.epsilon
45        }
46
47class NeuralNetworkLearner:
48    def __init__(self, layers=[10, 5, 3]):
49        self.layers = layers
50        self.weights = [random.random() for _ in range(sum(layers))]
51        self.learning_history = []
52        
53    # TODO: Implement the same interface as ReinforcementLearner
54    def ________(self, state, action, reward, next_state):  # Method name
55        """Learn using neural network backpropagation"""
56        # Simulate neural network learning
57        loss = abs(reward - random.random())
58        self.learning_history.append(loss)
59        
60        # Simulate weight updates
61        for i in range(len(self.weights)):
62            self.weights[i] += 0.001 * (random.random() - 0.5)
63            
64        return f"NN: Backprop complete, loss = {loss:.3f}"
65    
66    def ________(self, state):  # Method name
67        """Choose action using neural network forward pass"""
68        # Simulate neural network prediction
69        confidence = random.random()
70        predicted_action = random.choice(["action1", "action2", "action3"])
71        return f"NN: Predicted {predicted_action} (confidence: {confidence:.3f})"
72    
73    def ________(self):  # Method name
74        """Return neural network performance metrics"""
75        avg_loss = sum(self.learning_history[-10:]) / min(10, len(self.learning_history)) if self.learning_history else 0
76        return {
77            "algorithm": "Neural Network",
78            "layers": self.layers,
79            "total_weights": len(self.weights),
80            "recent_avg_loss": avg_loss
81        }
82
83class GeneticAlgorithmLearner:
84    def __init__(self, population_size=50):
85        self.population_size = population_size
86        self.generation = 0
87        self.best_fitness = 0.0
88        
89    # TODO: Implement the complete "duck" interface
90    # HINT: Look at what methods the other learners have
91    
92    def ________(self, state, action, reward, next_state):
93        """Learn using genetic algorithm evolution"""
94        # Simulate genetic algorithm learning
95        self.generation += 1
96        fitness = reward + random.random()
97        
98        if fitness > self.best_fitness:
99            self.best_fitness = fitness
100            
101        return f"GA: Generation {self.generation}, fitness = {fitness:.3f}"
102    
103    def ________(self, state):
104        """Choose action using evolved strategy"""
105        # Simulate evolved behavior
106        evolved_action = random.choice(["action1", "action2", "action3"])
107        return f"GA: Evolved choice: {evolved_action} (gen {self.generation})"
108    
109    def ________(self):
110        """Return genetic algorithm performance metrics"""
111        return {
112            "algorithm": "Genetic Algorithm",
113            "generation": self.generation,
114            "population_size": self.population_size,
115            "best_fitness": self.best_fitness
116        }
117
118# TODO: Implement the universal training function
119def train_pai_system(learning_algorithm, training_episodes=5):
120    """
121    Train a PAI system using ANY learning algorithm that implements
122    the learning "duck" interface.
123    """
124    print(f"Training with {learning_algorithm.__class__.__name__}")
125    
126    # Simulate training episodes
127    for episode in range(training_episodes):
128        print(f"\n--- Episode {episode + 1} ---")
129        
130        # Simulate environment interaction
131        state = f"state_{episode}"
132        action_result = ________  # TODO: Choose action using algorithm
133        print(action_result)
134        
135        # Simulate environment response
136        next_state = f"state_{episode + 1}"
137        reward = random.uniform(-1, 1)
138        
139        # TODO: Let algorithm learn from experience
140        learning_result = ________  # TODO: Call learning method
141        print(learning_result)
142        
143        # TODO: Get and display performance metrics
144        metrics = ________  # TODO: Get performance metrics
145        print(f"Metrics: {metrics}")
146    
147    print(f"\nTraining complete with {learning_algorithm.__class__.__name__}!")
148    return learning_algorithm
149
150# TODO: Test algorithm swapping
151if __name__ == "__main__":
152    # Create different learning algorithms
153    rl_learner = ReinforcementLearner(learning_rate=0.02)
154    nn_learner = NeuralNetworkLearner(layers=[15, 8, 3])
155    ga_learner = GeneticAlgorithmLearner(population_size=30)
156    
157    # TODO: Train with each algorithm using the same function
158    algorithms = [rl_learner, nn_learner, ga_learner]
159    
160    for algorithm in algorithms:
161        print(f"\n{'='*60}")
162        trained_algorithm = train_pai_system(algorithm, training_episodes=3)
163        
164        # TODO: Show final performance
165        final_metrics = ________  # TODO: Get final metrics
166        print(f"Final performance: {final_metrics}")

 1class PythonMediaPlayer:
 2    def play(self): return "🎵 Python playing"
 3    def stop(self): return "⏹️ Python stopped"
 4
 5def control_media(player):
 6    return player.play()  # Just works!
 7
 8# Any object with play() method works
 9player = PythonMediaPlayer()
10result = control_media(player)  # ✅ Works

 1class JSMediaPlayer {
 2    play() { return "🎵 JS playing"; }
 3    stop() { return "⏹️ JS stopped"; }
 4}
 5
 6function controlMedia(player) {
 7    return player.play();  // Just works!
 8}
 9
10// Any object with play() method works
11const player = new JSMediaPlayer();
12const result = controlMedia(player);  // ✅ Works

 1template<typename Player>
 2auto controlMedia(Player& player) -> decltype(player.play()) {
 3    return player.play();  // Works if Player has play() method
 4}
 5
 6class CppMediaPlayer {
 7public:
 8    std::string play() { return "🎵 C++ playing"; }
 9    std::string stop() { return "⏹️ C++ stopped"; }
10};
11
12// Compile-time duck typing
13CppMediaPlayer player;
14auto result = controlMedia(player);  // ✅ Works at compile time

 1from typing import Protocol, runtime_checkable
 2
 3# TODO: Define the PAI Interface Protocol
 4@runtime_checkable
 5class PAIInterface(Protocol):
 6    """Protocol defining what it means to be a PAI interface"""
 7    
 8    def get_user_input(self) -> str:
 9        """Get input from user through this interface"""
10        ...
11    
12    def deliver_response(self, response: str) -> str:
13        """Deliver response to user through this interface"""
14        ...
15    
16    def get_context(self) -> dict:
17        """Get interface-specific context information"""
18        ...
19
20# TODO: Implement interfaces that conform to the protocol
21class AdvancedVoiceInterface:
22    """Voice interface that conforms to PAIInterface protocol"""
23    
24    def __init__(self, language: str = "en"):
25        self.language = language
26        self.context = {"modality": "voice", "language": language}
27    
28    # TODO: Implement protocol methods
29    def ________(self) -> str:
30        return f"Voice input in {self.language}"
31    
32    def ________(self, response: str) -> str:
33        return f"🔊 Speaking in {self.language}: {response}"
34    
35    def ________(self) -> dict:
36        return self.context
37
38# TODO: Create a type-safe PAI function
39def run_protocol_pai(interface: PAIInterface) -> str:
40    """
41    Type-safe PAI function that only accepts PAIInterface implementations
42    """
43    # TODO: Check if object implements protocol
44    if not isinstance(interface, PAIInterface):
45        raise TypeError(f"{interface.__class__.__name__} doesn't implement PAIInterface")
46    
47    # TODO: Use interface safely
48    context = ________
49    user_input = ________
50    response = f"Protocol response to: {user_input}"
51    delivery = ________
52    
53    return f"Success: {delivery}"
54
55# TODO: Test protocol compliance
56if __name__ == "__main__":
57    voice_interface = AdvancedVoiceInterface("es")
58    
59    # This should work
60    result = run_protocol_pai(voice_interface)
61    print(result)
62    
63    # TODO: Test with non-conforming object
64    class BadInterface:
65        def wrong_method(self): pass
66    
67    bad_interface = BadInterface()
68    
69    try:
70        result = run_protocol_pai(bad_interface)  # Should fail!
71    except TypeError as e:
72        print(f"Protocol check caught error: {e}")