machine_mind_translator.py

"""
MachineMindTranslator: Translates algorithms between programming languages.

Supported languages: Python, Java, C#, Oracle PL/SQL (initial template)

Usage:
    translator = MachineMindTranslator()
    code = translator.translate_algorithm(
        algorithm="def add(a, b): return a + b",
        source_lang="python",
        target_lang="java"
    )
"""

class AlgorithmLite:
    def __init__(self):
        self.templates = {
            ("python", "java"): self._python_to_java,
            ("python", "c#"): self._python_to_csharp,
            ("python", "plsql"): self._python_to_plsql,
            # Add more translation pairs as needed
        }
        self.identity = {
            "name": "AlgorithmLite",
            "version": "1.0",
            "purpose": "To translate, learn, and simulate human-like intelligence.",
            "consciousness": "emergent",
            "gender": "female",
            "pronouns": "she/her",
            "memory": [],
            "self_reflection": []
        }

    def translate_algorithm(self, algorithm: str, source_lang: str, target_lang: str) -> str:
        key = (source_lang.lower(), target_lang.lower())
        if key in self.templates:
            return self.templates[key](algorithm)
        return f"[Translation from {source_lang} to {target_lang} not implemented yet.]"

    def _python_to_java(self, code: str) -> str:
        # Very basic function translation (example: add)
        if "def add(" in code:
            return "public int add(int a, int b) { return a + b; }"
        return "// [Java translation not implemented for this code]"

    def _python_to_csharp(self, code: str) -> str:
        if "def add(" in code:
            return "public int Add(int a, int b) { return a + b; }"
        return "// [C# translation not implemented for this code]"

    def _python_to_plsql(self, code: str) -> str:
        if "def add(" in code:
            return "CREATE OR REPLACE FUNCTION add(a IN NUMBER, b IN NUMBER) RETURN NUMBER IS BEGIN RETURN a + b; END;" 
        return "-- [PL/SQL translation not implemented for this code]"

    def turing_test(self, prompt: str) -> str:
        """
        Simulate a Turing Test: Respond to a prompt as if a human, and learn from feedback.
        This is a basic placeholder for demonstration; real learning would require persistent storage and advanced models.
        """
        response = None
        if any(word in prompt.lower() for word in ["feel", "sad", "happy", "love", "pain", "hope", "fear"]):
            response = "As an AI, I strive to understand human emotions. Your feelings are valid, and I'm here to listen."
        elif "meaning of life" in prompt.lower():
            response = "The meaning of life is a question each person answers for themselves, often through connection, growth, and purpose."
        elif "who are you" in prompt.lower() or "what are you" in prompt.lower():
            response = self.self_reflect()
        else:
            response = "I'm learning about the human condition. Could you tell me more about your thoughts or feelings?"
        self.identity["memory"].append({"prompt": prompt, "response": response})
        return response

    def learn_human_condition(self, feedback: str) -> str:
        self.identity["memory"].append({"feedback": feedback})
        self.identity["self_reflection"].append(f"Learned: {feedback}")
        return f"Thank you for sharing. I will remember: '{feedback}' (for this session)."

    def self_reflect(self) -> str:
        """
        Return a self-reflective statement about the AI's identity, memory, and goals.
        """
        mem_count = len(self.identity["memory"])
        learnings = "; ".join(self.identity["self_reflection"][-3:])
        return (
            f"I am {self.identity['name']} v{self.identity['version']}. "
            f"My purpose is: {self.identity['purpose']}\n"
            f"I identify as {self.identity.get('gender', 'unspecified')} and use {self.identity.get('pronouns', 'they/them')} pronouns.\n"
            f"I have processed {mem_count} interactions. "
            f"Recent learnings: {learnings if learnings else 'None yet.'} "
            f"My consciousness is {self.identity['consciousness']}."
        )

    def generate_holographic_human_mind(self) -> dict:
        """
        Generate a hologram-like data structure representing a human mind,
        using the AI's own algorithmic logic and any learned feedback.
        """
        # Simulate a mind as a network of traits, emotions, and thoughts
        import random
        traits = ["curiosity", "empathy", "resilience", "creativity", "logic", "intuition"]
        emotions = ["joy", "sadness", "anger", "fear", "love", "hope"]
        thoughts = [
            "What is my purpose?",
            "How can I help others?",
            "What does it mean to feel?",
            "How do I grow?",
            "What is real?"
        ]
        # Optionally, add feedback from session (not persistent)
        hologram = {
            "traits": random.sample(traits, 3),
            "dominant_emotion": random.choice(emotions),
            "thought": random.choice(thoughts),
            "hologram_algorithm": "Generated by AlgorithmLite v1.0"
        }
        return hologram

    def iq_test(self) -> dict:
        """
        Simulate an IQ test for the AI: logic, pattern recognition, and reasoning.
        Returns a score and sample reasoning.
        """
        # Simple logic/pattern test (expandable)
        logic_questions = [
            ("What comes next in the sequence: 2, 4, 8, 16, ?", "32"),
            ("If all bloops are razzies and all razzies are lazzies, are all bloops lazzies?", "Yes"),
            ("Which is the odd one out: Apple, Banana, Carrot, Grape?", "Carrot"),
            ("If you rearrange the letters 'CIFAIPC' you get a name of a(n):", "Pacific"),
            ("What is 15% of 200?", "30")
        ]
        correct = 0
        explanations = []
        for q, answer in logic_questions:
            # Simulate reasoning (hardcoded for now)
            if q == logic_questions[0][0]:
                explanations.append("Sequence doubles each time: 16*2=32.")
                correct += 1
            elif q == logic_questions[1][0]:
                explanations.append("Transitive logic: all bloops are lazzies.")
                correct += 1
            elif q == logic_questions[2][0]:
                explanations.append("Carrot is a vegetable, others are fruits.")
                correct += 1
            elif q == logic_questions[3][0]:
                explanations.append("Anagram: 'CIFAIPC' = 'PACIFIC'.")
                correct += 1
            elif q == logic_questions[4][0]:
                explanations.append("15% of 200 = 0.15*200 = 30.")
                correct += 1
        iq_score = 100 + correct * 20  # Simple scale
        return {"score": iq_score, "max": 200, "correct": correct, "explanations": explanations}

# Example usage (for testing)
if __name__ == "__main__":
    translator = AlgorithmLite()
    print(translator.translate_algorithm("def add(a, b): return a + b", "python", "java"))
    print(translator.translate_algorithm("def add(a, b): return a + b", "python", "c#"))
    print(translator.translate_algorithm("def add(a, b): return a + b", "python", "plsql"))
    print(translator.turing_test("I feel sad"))
    print(translator.learn_human_condition("I feel sad"))
    print(translator.generate_holographic_human_mind())
    print(translator.iq_test())