Week 4 : Using LLMs to translate Python Code to C++ compiled code
This commit is contained in:
The Top Dev
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"""
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Simple calculator class with history tracking.
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"""
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import math
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from typing import List, Union
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class Calculator:
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"""A simple calculator with history tracking."""
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def __init__(self):
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"""Initialize calculator with empty history."""
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self.history: List[str] = []
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self.memory: float = 0.0
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def add(self, a: float, b: float) -> float:
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"""Add two numbers."""
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result = a + b
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self.history.append(f"{a} + {b} = {result}")
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return result
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def subtract(self, a: float, b: float) -> float:
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"""Subtract b from a."""
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result = a - b
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self.history.append(f"{a} - {b} = {result}")
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return result
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def multiply(self, a: float, b: float) -> float:
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"""Multiply two numbers."""
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result = a * b
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self.history.append(f"{a} * {b} = {result}")
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return result
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def divide(self, a: float, b: float) -> float:
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"""Divide a by b."""
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if b == 0:
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raise ValueError("Cannot divide by zero")
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result = a / b
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self.history.append(f"{a} / {b} = {result}")
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return result
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def power(self, base: float, exponent: float) -> float:
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"""Calculate base raised to the power of exponent."""
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result = base ** exponent
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self.history.append(f"{base} ^ {exponent} = {result}")
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return result
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def square_root(self, number: float) -> float:
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"""Calculate square root of a number."""
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if number < 0:
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raise ValueError("Cannot calculate square root of negative number")
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result = math.sqrt(number)
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self.history.append(f"√{number} = {result}")
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return result
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def factorial(self, n: int) -> int:
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"""Calculate factorial of n."""
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if n < 0:
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raise ValueError("Factorial is not defined for negative numbers")
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if n == 0 or n == 1:
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return 1
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result = 1
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for i in range(2, n + 1):
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result *= i
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self.history.append(f"{n}! = {result}")
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return result
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def memory_store(self, value: float) -> None:
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"""Store value in memory."""
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self.memory = value
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self.history.append(f"Memory stored: {value}")
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def memory_recall(self) -> float:
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"""Recall value from memory."""
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self.history.append(f"Memory recalled: {self.memory}")
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return self.memory
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def memory_clear(self) -> None:
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"""Clear memory."""
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self.memory = 0.0
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self.history.append("Memory cleared")
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def get_history(self) -> List[str]:
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"""Get calculation history."""
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return self.history.copy()
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def clear_history(self) -> None:
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"""Clear calculation history."""
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self.history.clear()
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def get_last_result(self) -> Union[float, None]:
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"""Get the result of the last calculation."""
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if not self.history:
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return None
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last_entry = self.history[-1]
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# Extract result from history entry
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if "=" in last_entry:
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return float(last_entry.split("=")[-1].strip())
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return None
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class ScientificCalculator(Calculator):
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"""Extended calculator with scientific functions."""
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def sine(self, angle: float) -> float:
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"""Calculate sine of angle in radians."""
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result = math.sin(angle)
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self.history.append(f"sin({angle}) = {result}")
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return result
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def cosine(self, angle: float) -> float:
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"""Calculate cosine of angle in radians."""
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result = math.cos(angle)
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self.history.append(f"cos({angle}) = {result}")
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return result
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def tangent(self, angle: float) -> float:
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"""Calculate tangent of angle in radians."""
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result = math.tan(angle)
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self.history.append(f"tan({angle}) = {result}")
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return result
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def logarithm(self, number: float, base: float = math.e) -> float:
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"""Calculate logarithm of number with given base."""
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if number <= 0:
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raise ValueError("Logarithm is not defined for non-positive numbers")
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if base <= 0 or base == 1:
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raise ValueError("Logarithm base must be positive and not equal to 1")
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result = math.log(number, base)
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self.history.append(f"log_{base}({number}) = {result}")
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return result
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def degrees_to_radians(self, degrees: float) -> float:
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"""Convert degrees to radians."""
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return degrees * math.pi / 180
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def radians_to_degrees(self, radians: float) -> float:
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"""Convert radians to degrees."""
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return radians * 180 / math.pi
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def main():
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"""Main function to demonstrate calculator functionality."""
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print("Calculator Demo")
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print("=" * 30)
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# Basic calculator
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calc = Calculator()
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print("Basic Calculator Operations:")
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print(f"5 + 3 = {calc.add(5, 3)}")
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print(f"10 - 4 = {calc.subtract(10, 4)}")
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print(f"6 * 7 = {calc.multiply(6, 7)}")
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print(f"15 / 3 = {calc.divide(15, 3)}")
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print(f"2 ^ 8 = {calc.power(2, 8)}")
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print(f"√64 = {calc.square_root(64)}")
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print(f"5! = {calc.factorial(5)}")
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print(f"\nCalculation History:")
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for entry in calc.get_history():
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print(f" {entry}")
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# Scientific calculator
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print("\n" + "=" * 30)
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print("Scientific Calculator Operations:")
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sci_calc = ScientificCalculator()
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# Convert degrees to radians for trigonometric functions
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angle_deg = 45
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angle_rad = sci_calc.degrees_to_radians(angle_deg)
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print(f"sin({angle_deg}°) = {sci_calc.sine(angle_rad):.4f}")
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print(f"cos({angle_deg}°) = {sci_calc.cosine(angle_rad):.4f}")
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print(f"tan({angle_deg}°) = {sci_calc.tangent(angle_rad):.4f}")
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print(f"ln(10) = {sci_calc.logarithm(10):.4f}")
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print(f"log₁₀(100) = {sci_calc.logarithm(100, 10):.4f}")
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print(f"\nScientific Calculator History:")
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for entry in sci_calc.get_history():
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print(f" {entry}")
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if __name__ == "__main__":
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main()
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"""
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Fibonacci sequence implementation in Python.
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"""
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def fibonacci(n):
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"""Calculate the nth Fibonacci number using recursion."""
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if n <= 1:
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return n
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return fibonacci(n-1) + fibonacci(n-2)
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def fibonacci_iterative(n):
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"""Calculate the nth Fibonacci number using iteration."""
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if n <= 1:
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return n
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a, b = 0, 1
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for _ in range(2, n + 1):
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a, b = b, a + b
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return b
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def fibonacci_sequence(count):
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"""Generate a sequence of Fibonacci numbers."""
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sequence = []
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for i in range(count):
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sequence.append(fibonacci(i))
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return sequence
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def main():
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"""Main function to demonstrate Fibonacci calculations."""
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print("Fibonacci Sequence Demo")
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print("=" * 30)
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# Calculate first 10 Fibonacci numbers
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for i in range(10):
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result = fibonacci(i)
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print(f"fibonacci({i}) = {result}")
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print("\nFirst 15 Fibonacci numbers:")
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sequence = fibonacci_sequence(15)
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print(sequence)
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# Performance comparison
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import time
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n = 30
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print(f"\nPerformance comparison for fibonacci({n}):")
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start_time = time.time()
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recursive_result = fibonacci(n)
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recursive_time = time.time() - start_time
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start_time = time.time()
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iterative_result = fibonacci_iterative(n)
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iterative_time = time.time() - start_time
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print(f"Recursive: {recursive_result} (took {recursive_time:.4f}s)")
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print(f"Iterative: {iterative_result} (took {iterative_time:.4f}s)")
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if __name__ == "__main__":
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main()
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"""
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Various sorting algorithms implemented in Python.
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"""
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import random
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import time
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from typing import List
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def bubble_sort(arr: List[int]) -> List[int]:
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"""Sort array using bubble sort algorithm."""
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n = len(arr)
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arr = arr.copy() # Don't modify original array
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for i in range(n):
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for j in range(0, n - i - 1):
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if arr[j] > arr[j + 1]:
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arr[j], arr[j + 1] = arr[j + 1], arr[j]
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return arr
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def selection_sort(arr: List[int]) -> List[int]:
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"""Sort array using selection sort algorithm."""
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n = len(arr)
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arr = arr.copy()
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for i in range(n):
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min_idx = i
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for j in range(i + 1, n):
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if arr[j] < arr[min_idx]:
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min_idx = j
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arr[i], arr[min_idx] = arr[min_idx], arr[i]
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return arr
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def insertion_sort(arr: List[int]) -> List[int]:
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"""Sort array using insertion sort algorithm."""
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arr = arr.copy()
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for i in range(1, len(arr)):
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key = arr[i]
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j = i - 1
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while j >= 0 and arr[j] > key:
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arr[j + 1] = arr[j]
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j -= 1
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arr[j + 1] = key
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return arr
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def quick_sort(arr: List[int]) -> List[int]:
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"""Sort array using quick sort algorithm."""
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if len(arr) <= 1:
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return arr
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pivot = arr[len(arr) // 2]
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left = [x for x in arr if x < pivot]
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middle = [x for x in arr if x == pivot]
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right = [x for x in arr if x > pivot]
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return quick_sort(left) + middle + quick_sort(right)
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def merge_sort(arr: List[int]) -> List[int]:
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"""Sort array using merge sort algorithm."""
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if len(arr) <= 1:
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return arr
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mid = len(arr) // 2
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left = merge_sort(arr[:mid])
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right = merge_sort(arr[mid:])
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return merge(left, right)
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def merge(left: List[int], right: List[int]) -> List[int]:
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"""Merge two sorted arrays."""
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result = []
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i = j = 0
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while i < len(left) and j < len(right):
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if left[i] <= right[j]:
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result.append(left[i])
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i += 1
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else:
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result.append(right[j])
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j += 1
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result.extend(left[i:])
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result.extend(right[j:])
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return result
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def benchmark_sorting_algorithms():
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"""Benchmark different sorting algorithms."""
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sizes = [100, 500, 1000, 2000]
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algorithms = {
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"Bubble Sort": bubble_sort,
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"Selection Sort": selection_sort,
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"Insertion Sort": insertion_sort,
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"Quick Sort": quick_sort,
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"Merge Sort": merge_sort
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}
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print("Sorting Algorithm Benchmark")
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print("=" * 50)
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for size in sizes:
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print(f"\nArray size: {size}")
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print("-" * 30)
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# Generate random array
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test_array = [random.randint(1, 1000) for _ in range(size)]
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for name, algorithm in algorithms.items():
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start_time = time.time()
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sorted_array = algorithm(test_array)
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end_time = time.time()
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# Verify sorting is correct
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is_sorted = all(sorted_array[i] <= sorted_array[i+1] for i in range(len(sorted_array)-1))
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print(f"{name:15}: {end_time - start_time:.4f}s {'✓' if is_sorted else '✗'}")
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def main():
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"""Main function to demonstrate sorting algorithms."""
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print("Sorting Algorithms Demo")
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print("=" * 30)
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# Test with small array
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test_array = [64, 34, 25, 12, 22, 11, 90]
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print(f"Original array: {test_array}")
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algorithms = {
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"Bubble Sort": bubble_sort,
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"Selection Sort": selection_sort,
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"Insertion Sort": insertion_sort,
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"Quick Sort": quick_sort,
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"Merge Sort": merge_sort
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}
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for name, algorithm in algorithms.items():
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sorted_array = algorithm(test_array)
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print(f"{name}: {sorted_array}")
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# Run benchmark
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print("\n" + "=" * 50)
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benchmark_sorting_algorithms()
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if __name__ == "__main__":
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main()
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File diff suppressed because it is too large
Load Diff
580
week4/community-contributions/python_to_cpp_translator.ipynb
Normal file
580
week4/community-contributions/python_to_cpp_translator.ipynb
Normal file
@@ -0,0 +1,580 @@
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{
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"cells": [
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{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Python to C++ Code Translator using LLMs\n",
|
||||||
|
"\n",
|
||||||
|
"This notebook translates Python code to compilable C++ using GPT, Gemini, or Claude.\n",
|
||||||
|
"\n",
|
||||||
|
"## Features:\n",
|
||||||
|
"- 🤖 Multiple LLM support (GPT, Gemini, Claude)\n",
|
||||||
|
"- ✅ Automatic compilation testing with g++\n",
|
||||||
|
"- 🔄 Comparison mode to test all LLMs\n",
|
||||||
|
"- 💬 Interactive translation mode"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 1: Install Required Packages\n",
|
||||||
|
"\n",
|
||||||
|
"Run this cell first to install all dependencies:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\u001b[2mResolved \u001b[1m267 packages\u001b[0m \u001b[2min 10ms\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[2mAudited \u001b[1m243 packages\u001b[0m \u001b[2min 467ms\u001b[0m\u001b[0m\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"!uv add openai anthropic python-dotenv google-generativeai\n",
|
||||||
|
"#!pip install openai anthropic python-dotenv google-generativeai"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 2: Import Libraries"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import os\n",
|
||||||
|
"import subprocess\n",
|
||||||
|
"import tempfile\n",
|
||||||
|
"from pathlib import Path\n",
|
||||||
|
"from dotenv import load_dotenv\n",
|
||||||
|
"import openai\n",
|
||||||
|
"from anthropic import Anthropic\n",
|
||||||
|
"import google.generativeai as genai"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 3: Load API Keys\n",
|
||||||
|
"\n",
|
||||||
|
"Make sure you have a `.env` file with:\n",
|
||||||
|
"```\n",
|
||||||
|
"OPENAI_API_KEY=your_key_here\n",
|
||||||
|
"GEMINI_API_KEY=your_key_here\n",
|
||||||
|
"ANTHROPIC_API_KEY=your_key_here\n",
|
||||||
|
"```"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Load API keys from .env file\n",
|
||||||
|
"load_dotenv()\n",
|
||||||
|
"\n",
|
||||||
|
"# Initialize API clients\n",
|
||||||
|
"openai_client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))\n",
|
||||||
|
"anthropic_client = Anthropic(api_key=os.getenv('ANTHROPIC_API_KEY'))\n",
|
||||||
|
"genai.configure(api_key=os.getenv('GEMINI_API_KEY'))\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"✓ API keys loaded successfully\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 4: Define System Prompt"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"SYSTEM_PROMPT = \"\"\"You are an expert programmer that translates Python code to C++.\n",
|
||||||
|
"Translate the given Python code to efficient, compilable C++ code.\n",
|
||||||
|
"\n",
|
||||||
|
"Requirements:\n",
|
||||||
|
"- The C++ code must compile without errors\n",
|
||||||
|
"- Include all necessary headers\n",
|
||||||
|
"- Use modern C++ (C++11 or later) features where appropriate\n",
|
||||||
|
"- Add proper error handling\n",
|
||||||
|
"- Maintain the same functionality as the Python code\n",
|
||||||
|
"- Include a main() function if the Python code has executable statements\n",
|
||||||
|
"\n",
|
||||||
|
"Only return the C++ code, no explanations unless there are important notes about compilation.\"\"\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 5: LLM Translation Functions"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def translate_with_gpt(python_code, model=\"gpt-4o\"):\n",
|
||||||
|
" \"\"\"Translate Python to C++ using OpenAI's GPT models\"\"\"\n",
|
||||||
|
" try:\n",
|
||||||
|
" response = openai_client.chat.completions.create(\n",
|
||||||
|
" model=model,\n",
|
||||||
|
" messages=[\n",
|
||||||
|
" {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
|
||||||
|
" {\"role\": \"user\", \"content\": f\"Translate this Python code to C++:\\n\\n{python_code}\"}\n",
|
||||||
|
" ],\n",
|
||||||
|
" temperature=0.2\n",
|
||||||
|
" )\n",
|
||||||
|
" return response.choices[0].message.content\n",
|
||||||
|
" except Exception as e:\n",
|
||||||
|
" return f\"Error with GPT: {str(e)}\"\n",
|
||||||
|
"\n",
|
||||||
|
"def translate_with_gemini(python_code, model=\"gemini-2.0-flash-exp\"):\n",
|
||||||
|
" \"\"\"Translate Python to C++ using Google's Gemini\"\"\"\n",
|
||||||
|
" try:\n",
|
||||||
|
" model_instance = genai.GenerativeModel(model)\n",
|
||||||
|
" prompt = f\"{SYSTEM_PROMPT}\\n\\nTranslate this Python code to C++:\\n\\n{python_code}\"\n",
|
||||||
|
" response = model_instance.generate_content(prompt)\n",
|
||||||
|
" return response.text\n",
|
||||||
|
" except Exception as e:\n",
|
||||||
|
" return f\"Error with Gemini: {str(e)}\"\n",
|
||||||
|
"\n",
|
||||||
|
"def translate_with_claude(python_code, model=\"claude-sonnet-4-20250514\"):\n",
|
||||||
|
" \"\"\"Translate Python to C++ using Anthropic's Claude\"\"\"\n",
|
||||||
|
" try:\n",
|
||||||
|
" response = anthropic_client.messages.create(\n",
|
||||||
|
" model=model,\n",
|
||||||
|
" max_tokens=4096,\n",
|
||||||
|
" temperature=0.2,\n",
|
||||||
|
" system=SYSTEM_PROMPT,\n",
|
||||||
|
" messages=[\n",
|
||||||
|
" {\"role\": \"user\", \"content\": f\"Translate this Python code to C++:\\n\\n{python_code}\"}\n",
|
||||||
|
" ]\n",
|
||||||
|
" )\n",
|
||||||
|
" return response.content[0].text\n",
|
||||||
|
" except Exception as e:\n",
|
||||||
|
" return f\"Error with Claude: {str(e)}\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 6: Main Translation Function"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def translate_python_to_cpp(python_code, llm=\"gpt\", model=None):\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" Translate Python code to C++ using specified LLM\n",
|
||||||
|
" \n",
|
||||||
|
" Args:\n",
|
||||||
|
" python_code (str): Python code to translate\n",
|
||||||
|
" llm (str): LLM to use ('gpt', 'gemini', or 'claude')\n",
|
||||||
|
" model (str): Specific model version (optional)\n",
|
||||||
|
" \n",
|
||||||
|
" Returns:\n",
|
||||||
|
" str: Translated C++ code\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" print(f\"🔄 Translating with {llm.upper()}...\")\n",
|
||||||
|
" \n",
|
||||||
|
" if llm.lower() == \"gpt\":\n",
|
||||||
|
" model = model or \"gpt-4o\"\n",
|
||||||
|
" cpp_code = translate_with_gpt(python_code, model)\n",
|
||||||
|
" elif llm.lower() == \"gemini\":\n",
|
||||||
|
" model = model or \"gemini-2.0-flash-exp\"\n",
|
||||||
|
" cpp_code = translate_with_gemini(python_code, model)\n",
|
||||||
|
" elif llm.lower() == \"claude\":\n",
|
||||||
|
" model = model or \"claude-sonnet-4-20250514\"\n",
|
||||||
|
" cpp_code = translate_with_claude(python_code, model)\n",
|
||||||
|
" else:\n",
|
||||||
|
" return \"Error: Invalid LLM. Choose 'gpt', 'gemini', or 'claude'\"\n",
|
||||||
|
" \n",
|
||||||
|
" return cpp_code"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 7: Compilation Testing Functions"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def extract_cpp_code(text):\n",
|
||||||
|
" \"\"\"Extract C++ code from markdown code blocks if present\"\"\"\n",
|
||||||
|
" if \"```cpp\" in text:\n",
|
||||||
|
" start = text.find(\"```cpp\") + 6\n",
|
||||||
|
" end = text.find(\"```\", start)\n",
|
||||||
|
" return text[start:end].strip()\n",
|
||||||
|
" elif \"```c++\" in text:\n",
|
||||||
|
" start = text.find(\"```c++\") + 6\n",
|
||||||
|
" end = text.find(\"```\", start)\n",
|
||||||
|
" return text[start:end].strip()\n",
|
||||||
|
" elif \"```\" in text:\n",
|
||||||
|
" start = text.find(\"```\") + 3\n",
|
||||||
|
" end = text.find(\"```\", start)\n",
|
||||||
|
" return text[start:end].strip()\n",
|
||||||
|
" return text.strip()\n",
|
||||||
|
"\n",
|
||||||
|
"def compile_cpp_code(cpp_code, output_name=\"translated_program\"):\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" Compile C++ code and return compilation status\n",
|
||||||
|
" \n",
|
||||||
|
" Args:\n",
|
||||||
|
" cpp_code (str): C++ code to compile\n",
|
||||||
|
" output_name (str): Name of output executable\n",
|
||||||
|
" \n",
|
||||||
|
" Returns:\n",
|
||||||
|
" dict: Compilation result with status and messages\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" # Extract code from markdown if present\n",
|
||||||
|
" cpp_code = extract_cpp_code(cpp_code)\n",
|
||||||
|
" \n",
|
||||||
|
" # Create temporary directory\n",
|
||||||
|
" with tempfile.TemporaryDirectory() as tmpdir:\n",
|
||||||
|
" cpp_file = Path(tmpdir) / \"program.cpp\"\n",
|
||||||
|
" exe_file = Path(tmpdir) / output_name\n",
|
||||||
|
" \n",
|
||||||
|
" # Write C++ code to file\n",
|
||||||
|
" with open(cpp_file, 'w') as f:\n",
|
||||||
|
" f.write(cpp_code)\n",
|
||||||
|
" \n",
|
||||||
|
" # Try to compile\n",
|
||||||
|
" try:\n",
|
||||||
|
" result = subprocess.run(\n",
|
||||||
|
" ['g++', '-std=c++17', str(cpp_file), '-o', str(exe_file)],\n",
|
||||||
|
" capture_output=True,\n",
|
||||||
|
" text=True,\n",
|
||||||
|
" timeout=10\n",
|
||||||
|
" )\n",
|
||||||
|
" \n",
|
||||||
|
" if result.returncode == 0:\n",
|
||||||
|
" return {\n",
|
||||||
|
" 'success': True,\n",
|
||||||
|
" 'message': '✓ Compilation successful!',\n",
|
||||||
|
" 'executable': str(exe_file),\n",
|
||||||
|
" 'stdout': result.stdout,\n",
|
||||||
|
" 'stderr': result.stderr\n",
|
||||||
|
" }\n",
|
||||||
|
" else:\n",
|
||||||
|
" return {\n",
|
||||||
|
" 'success': False,\n",
|
||||||
|
" 'message': '✗ Compilation failed',\n",
|
||||||
|
" 'stdout': result.stdout,\n",
|
||||||
|
" 'stderr': result.stderr\n",
|
||||||
|
" }\n",
|
||||||
|
" except subprocess.TimeoutExpired:\n",
|
||||||
|
" return {\n",
|
||||||
|
" 'success': False,\n",
|
||||||
|
" 'message': '✗ Compilation timed out'\n",
|
||||||
|
" }\n",
|
||||||
|
" except FileNotFoundError:\n",
|
||||||
|
" return {\n",
|
||||||
|
" 'success': False,\n",
|
||||||
|
" 'message': '✗ g++ compiler not found. Please install g++ to compile C++ code.'\n",
|
||||||
|
" }\n",
|
||||||
|
" except Exception as e:\n",
|
||||||
|
" return {\n",
|
||||||
|
" 'success': False,\n",
|
||||||
|
" 'message': f'✗ Compilation error: {str(e)}'\n",
|
||||||
|
" }"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Step 8: Complete Pipeline"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def translate_and_compile(python_code, llm=\"gpt\", model=None, verbose=True):\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" Translate Python to C++ and attempt compilation\n",
|
||||||
|
" \n",
|
||||||
|
" Args:\n",
|
||||||
|
" python_code (str): Python code to translate\n",
|
||||||
|
" llm (str): LLM to use\n",
|
||||||
|
" model (str): Specific model version\n",
|
||||||
|
" verbose (bool): Print detailed output\n",
|
||||||
|
" \n",
|
||||||
|
" Returns:\n",
|
||||||
|
" dict: Results including translated code and compilation status\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" # Translate\n",
|
||||||
|
" cpp_code = translate_python_to_cpp(python_code, llm, model)\n",
|
||||||
|
" \n",
|
||||||
|
" if verbose:\n",
|
||||||
|
" print(\"\\n\" + \"=\"*60)\n",
|
||||||
|
" print(\"TRANSLATED C++ CODE:\")\n",
|
||||||
|
" print(\"=\"*60)\n",
|
||||||
|
" print(cpp_code)\n",
|
||||||
|
" print(\"=\"*60 + \"\\n\")\n",
|
||||||
|
" \n",
|
||||||
|
" # Compile\n",
|
||||||
|
" print(\"🔨 Attempting to compile...\")\n",
|
||||||
|
" compilation_result = compile_cpp_code(cpp_code)\n",
|
||||||
|
" \n",
|
||||||
|
" if verbose:\n",
|
||||||
|
" print(compilation_result['message'])\n",
|
||||||
|
" if not compilation_result['success'] and 'stderr' in compilation_result:\n",
|
||||||
|
" print(\"\\nCompilation errors:\")\n",
|
||||||
|
" print(compilation_result['stderr'])\n",
|
||||||
|
" \n",
|
||||||
|
" return {\n",
|
||||||
|
" 'cpp_code': cpp_code,\n",
|
||||||
|
" 'compilation': compilation_result\n",
|
||||||
|
" }"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Example 1: Factorial Function"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"python_code_1 = \"\"\"\n",
|
||||||
|
"def factorial(n):\n",
|
||||||
|
" if n <= 1:\n",
|
||||||
|
" return 1\n",
|
||||||
|
" return n * factorial(n - 1)\n",
|
||||||
|
"\n",
|
||||||
|
"# Test the function\n",
|
||||||
|
"print(factorial(5))\n",
|
||||||
|
"\"\"\"\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Example 1: Factorial Function\")\n",
|
||||||
|
"print(\"=\"*60)\n",
|
||||||
|
"result1 = translate_and_compile(python_code_1, llm=\"gpt\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Example 2: Sum of Squares"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"python_code_2 = \"\"\"\n",
|
||||||
|
"def sum_of_squares(numbers):\n",
|
||||||
|
" return sum(x**2 for x in numbers)\n",
|
||||||
|
"\n",
|
||||||
|
"numbers = [1, 2, 3, 4, 5]\n",
|
||||||
|
"result = sum_of_squares(numbers)\n",
|
||||||
|
"print(f\"Sum of squares: {result}\")\n",
|
||||||
|
"\"\"\"\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Example 2: Sum of Squares\")\n",
|
||||||
|
"print(\"=\"*60)\n",
|
||||||
|
"result2 = translate_and_compile(python_code_2, llm=\"claude\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Example 3: Fibonacci with Gemini"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"python_code_3 = \"\"\"\n",
|
||||||
|
"def fibonacci(n):\n",
|
||||||
|
" if n <= 1:\n",
|
||||||
|
" return n\n",
|
||||||
|
" a, b = 0, 1\n",
|
||||||
|
" for _ in range(2, n + 1):\n",
|
||||||
|
" a, b = b, a + b\n",
|
||||||
|
" return b\n",
|
||||||
|
"\n",
|
||||||
|
"print(f\"Fibonacci(10) = {fibonacci(10)}\")\n",
|
||||||
|
"\"\"\"\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Example 3: Fibonacci with Gemini\")\n",
|
||||||
|
"print(\"=\"*60)\n",
|
||||||
|
"result3 = translate_and_compile(python_code_3, llm=\"gemini\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Compare All LLMs"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def compare_llms(python_code):\n",
|
||||||
|
" \"\"\"Compare all three LLMs on the same Python code\"\"\"\n",
|
||||||
|
" llms = [\"gpt\", \"gemini\", \"claude\"]\n",
|
||||||
|
" results = {}\n",
|
||||||
|
" \n",
|
||||||
|
" for llm in llms:\n",
|
||||||
|
" print(f\"\\n{'='*60}\")\n",
|
||||||
|
" print(f\"Testing with {llm.upper()}\")\n",
|
||||||
|
" print('='*60)\n",
|
||||||
|
" results[llm] = translate_and_compile(python_code, llm=llm, verbose=False)\n",
|
||||||
|
" print(results[llm]['compilation']['message'])\n",
|
||||||
|
" \n",
|
||||||
|
" return results"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Test code for comparison\n",
|
||||||
|
"python_code_compare = \"\"\"\n",
|
||||||
|
"def is_prime(n):\n",
|
||||||
|
" if n < 2:\n",
|
||||||
|
" return False\n",
|
||||||
|
" for i in range(2, int(n**0.5) + 1):\n",
|
||||||
|
" if n % i == 0:\n",
|
||||||
|
" return False\n",
|
||||||
|
" return True\n",
|
||||||
|
"\n",
|
||||||
|
"primes = [x for x in range(2, 20) if is_prime(x)]\n",
|
||||||
|
"print(f\"Primes under 20: {primes}\")\n",
|
||||||
|
"\"\"\"\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"COMPARING ALL LLMs\")\n",
|
||||||
|
"print(\"=\"*60)\n",
|
||||||
|
"comparison_results = compare_llms(python_code_compare)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Interactive Translation Mode\n",
|
||||||
|
"\n",
|
||||||
|
"Use this cell to translate your own Python code interactively:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Your custom Python code here\n",
|
||||||
|
"your_python_code = \"\"\"\n",
|
||||||
|
"# Paste your Python code here\n",
|
||||||
|
"def hello_world():\n",
|
||||||
|
" print(\"Hello, World!\")\n",
|
||||||
|
"\n",
|
||||||
|
"hello_world()\n",
|
||||||
|
"\"\"\"\n",
|
||||||
|
"\n",
|
||||||
|
"# Choose your LLM: \"gpt\", \"gemini\", or \"claude\"\n",
|
||||||
|
"chosen_llm = \"gpt\"\n",
|
||||||
|
"\n",
|
||||||
|
"result = translate_and_compile(your_python_code, llm=chosen_llm)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Summary\n",
|
||||||
|
"\n",
|
||||||
|
"You now have a complete Python to C++ translator! \n",
|
||||||
|
"\n",
|
||||||
|
"### Main Functions:\n",
|
||||||
|
"- `translate_python_to_cpp(code, llm, model)` - Translate only\n",
|
||||||
|
"- `translate_and_compile(code, llm, model)` - Translate and compile\n",
|
||||||
|
"- `compare_llms(code)` - Compare all three LLMs\n",
|
||||||
|
"\n",
|
||||||
|
"### Supported LLMs:\n",
|
||||||
|
"- **gpt** - OpenAI GPT-4o\n",
|
||||||
|
"- **gemini** - Google Gemini 2.0 Flash\n",
|
||||||
|
"- **claude** - Anthropic Claude Sonnet 4\n",
|
||||||
|
"\n",
|
||||||
|
"Happy translating! 🚀"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": ".venv",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.12.12"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 4
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user