Merge pull request #259 from Hazperera/main
Multimodel Python-to-C/C++/Rust converter
This commit is contained in:
Ed Donner
GitHub
346
week4/community-contributions/w4_lang_converter.py
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346
week4/community-contributions/w4_lang_converter.py
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import os
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import io
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import sys
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import re
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import subprocess
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from dotenv import load_dotenv
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from openai import OpenAI
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from anthropic import Anthropic
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import gradio as gr
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# Load environment variables and initialize APIs
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load_dotenv(override=True)
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openai = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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anthropic = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
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MACHINE_SPEC = "MacbookPro, Apple M1 Chip"
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# Define global variables for HF integration
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# For HF chat-based CodeQwen model
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code_qwen = "Qwen/CodeQwen1.5-7B-Chat"
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CODE_QWEN_URL = ""
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def clean_code(code, target_language):
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"""
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Remove markdown code fences and stray language indicators.
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Also apply language-specific replacements.
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"""
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raw_lines = code.splitlines()
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cleaned_lines = []
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for line in raw_lines:
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if "```" in line:
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continue
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if line.strip().lower() in ["c", "cpp", "c++", "rust"]:
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continue
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cleaned_lines.append(line)
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cleaned = "\n".join(cleaned_lines)
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if target_language == "C":
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cleaned = cleaned.replace("1U << 32", "(1ULL << 32)")
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if target_language == "Rust":
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cleaned = process_rust_code(cleaned)
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return cleaned
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# Conversion prompt functions (target language-aware)
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def user_prompt_for(python_code, target_language):
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return (
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f"Rewrite this Python code in {target_language} with the fastest possible implementation that produces identical output. "
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f"Respond only with {target_language} code; do not explain your work. "
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"Pay attention to number types to ensure no int overflows. Remember to #include all necessary C++ packages such as iomanip.\n\n"
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+ python_code
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)
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def messages_for(python_code, target_language):
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system_message = (
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f"You are an assistant that reimplements Python code in high performance {target_language} for an {MACHINE_SPEC}. "
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f"Respond only with {target_language} code; use comments sparingly. "
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f"The {target_language} response needs to produce an identical output in the fastest possible time."
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)
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return [
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{"role": "system", "content": system_message},
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{"role": "user", "content": user_prompt_for(python_code, target_language)},
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]
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def write_output(code, target_language):
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"""Write the converted code to a file based on target language."""
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tag = target_language.lower() if target_language is not None else ""
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if target_language == "C++":
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filename = "optimized.cpp"
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elif target_language == "C":
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filename = "optimized.c"
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elif target_language == "Rust":
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filename = "optimized.rs"
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else:
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filename = "optimized.txt"
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cleaned = code.replace(f"```{tag}\n", "").replace("```", "")
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lines = cleaned.splitlines()
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if lines and lines[0].strip().lower() in ["cpp", "c++", "c", "rust"]:
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lines = lines[1:]
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cleaned = "\n".join(lines)
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cleaned = clean_code(cleaned, target_language)
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with open(filename, "w") as f:
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f.write(cleaned)
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return filename
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# GPT integration for conversion
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def stream_gpt(python_code, target_language, model_version):
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stream = openai.chat.completions.create(
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model=model_version, # Use selected GPT model version
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messages=messages_for(python_code, target_language),
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stream=True,
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)
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reply = ""
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for chunk in stream:
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if not hasattr(chunk, "choices") or not chunk.choices:
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continue
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fragment = chunk.choices[0].delta.content or ""
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reply += fragment
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yield reply.replace(f"```{target_language}\n", "").replace("```", "")
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# Claude integration for conversion
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def stream_claude(python_code, target_language, model_version):
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prompt = user_prompt_for(python_code, target_language)
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response = anthropic.completions.create(
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prompt=prompt,
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model=model_version,
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stream=True,
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)
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reply = ""
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for chunk in response:
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fragment = chunk.get("completion", "")
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reply += fragment
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yield reply.replace(f"```{target_language}\n", "").replace("```", "")
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# Hugging Face integration functions
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def stream_code_qwen(python_code, target_language, model_version):
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"""
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HF chat-based model using CodeQwen.
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"""
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(code_qwen)
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messages = messages_for(python_code, target_language)
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# Convert messages to chat format as expected by Qwen.
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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from huggingface_hub import InferenceClient
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client = InferenceClient(CODE_QWEN_URL, token=os.getenv("HF_TOKEN"))
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stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)
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result = ""
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for r in stream:
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result += r.token.text
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yield result.replace(f"```{target_language}\n", "").replace("```", "")
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def stream_huggingface(python_code, target_language, model_version):
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"""
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HF single-prompt model integration.
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"""
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prompt = user_prompt_for(python_code, target_language)
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from huggingface_hub import InferenceClient
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client = InferenceClient(model_name=model_version, token=os.getenv("HF_TOKEN"))
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stream = client.text_generation(prompt, stream=True, details=True, max_new_tokens=3000)
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reply = ""
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for chunk in stream:
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reply += chunk.token.text
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yield reply.replace(f"```{target_language}\n", "").replace("```", "")
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def optimize(python_code, combined_model, target_language):
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"""
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combined_model is a string like "GPT: gpt-4o", "CLAUDE: claude-3-5-sonnet-20240620" or "HF: model_name"
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"""
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provider, model_version = [x.strip() for x in combined_model.split(":")]
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if provider == "GPT":
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for partial in stream_gpt(python_code, target_language, model_version):
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yield partial
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elif provider == "CLAUDE":
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for partial in stream_claude(python_code, target_language, model_version):
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yield partial
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elif provider == "HF":
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if "CodeQwen" in model_version:
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for partial in stream_code_qwen(python_code, target_language, model_version):
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yield partial
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else:
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for partial in stream_huggingface(python_code, target_language, model_version):
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yield partial
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else:
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raise ValueError("Unknown model provider")
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def execute_python(code):
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"""Execute Python code and return its output."""
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env = {} # Dedicated global namespace
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try:
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output = io.StringIO()
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sys.stdout = output
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exec(code, env)
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finally:
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sys.stdout = sys.__stdout__
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return output.getvalue()
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def execute_cpp(code):
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write_output(code, target_language="C++")
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try:
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compile_cmd = [
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"clang++", "-Ofast", "-std=c++17", "-march=armv8.5-a",
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"-mtune=apple-m1", "-mcpu=apple-m1", "-o", "optimized", "optimized.cpp"
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]
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subprocess.run(compile_cmd, check=True, text=True, capture_output=True)
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run_cmd = ["./optimized"]
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run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)
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return run_result.stdout
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except subprocess.CalledProcessError as e:
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return f"Error:\n{e.stderr}"
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def execute_c(code):
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cleaned_code = clean_code(code, "C")
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with open("optimized.c", "w") as f:
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f.write(cleaned_code)
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try:
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compile_cmd = ["clang", "-O2", "-std=c11", "-o", "optimized_c", "optimized.c"]
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subprocess.run(compile_cmd, check=True, text=True, capture_output=True)
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run_cmd = ["./optimized_c"]
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run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)
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return run_result.stdout
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except subprocess.CalledProcessError as e:
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return f"Error:\n{e.stderr}"
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def process_rust_code(code):
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code = code.replace("{:.6f}", "{:.6}")
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code = re.sub(
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r'(println!$begin:math:text$"Execution Time: \\{\\:\\.6\\} seconds", duration\\.as_secs_f64)(\\s*)$',
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r'\\1())',
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code,
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flags=re.MULTILINE,
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)
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code = code.replace("max_val - min_val as u32 + 1", "((max_val - min_val + 1) as u32)")
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code = code.replace("1 << 32", "1u64 << 32")
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code = re.sub(r'($end:math:text$\s*as i64)\)', r'\1', code)
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return code
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def execute_rust(code):
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code = code.replace("```rust\n", "").replace("```", "")
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lines = code.split('\n', 1)
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if lines and lines[0].strip().lower() == "rust":
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code = lines[1] if len(lines) > 1 else ""
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code = process_rust_code(code)
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with open("optimized.rs", "w") as f:
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f.write(code)
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try:
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compile_cmd = ["rustc", "optimized.rs", "-O", "-o", "optimized_rust"]
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subprocess.run(compile_cmd, check=True, text=True, capture_output=True)
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run_cmd = ["./optimized_rust"]
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run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)
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return run_result.stdout
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except subprocess.CalledProcessError as e:
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return f"Error:\n{e.stderr}"
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def execute_target_code(code, target_language):
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"""Select the appropriate execution function based on target language."""
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if target_language == "C++":
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return execute_cpp(code)
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elif target_language == "C":
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return execute_c(code)
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elif target_language == "Rust":
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return execute_rust(code)
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else:
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return "Unsupported language"
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# Gradio UI setup
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css = """
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.python {background-color: #306998;}
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.code {background-color: #050;}
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"""
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def launch_ui():
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with gr.Blocks(css=css) as ui:
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gr.Markdown("## Convert Python Code to C/C++/Rust")
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with gr.Row():
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python_box = gr.Textbox(label="Python code:", value=PYTHON_HARD, lines=10)
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converted_box = gr.Textbox(label="Converted Code:", lines=10)
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with gr.Row():
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model_dropdown = gr.Dropdown(
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["GPT: gpt-4o", "GPT: gpt-4o-mini", "CLAUDE: claude-3-5-sonnet-20240620", "CLAUDE: claude-3-haiku-20240307", "HF: CodeQwen1.5-7B-Chat", "HF: bigcode/starcoder"],
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label="Select Model",
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value="GPT: gpt-4o"
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)
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target_lang_dropdown = gr.Dropdown(
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["C++", "C", "Rust"],
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label="Select target language",
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value="C++"
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)
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with gr.Row():
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convert_btn = gr.Button("Convert code")
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with gr.Row():
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python_run_btn = gr.Button("Run Python")
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run_converted_btn = gr.Button("Run Converted Code")
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with gr.Row():
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python_out = gr.TextArea(label="Python result:", elem_classes=["python"])
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converted_out = gr.TextArea(label="Converted Code result:", elem_classes=["code"])
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convert_btn.click(
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optimize,
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inputs=[python_box, model_dropdown, target_lang_dropdown],
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outputs=[converted_box],
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)
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python_run_btn.click(execute_python, inputs=[python_box], outputs=[python_out])
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run_converted_btn.click(
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execute_target_code,
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inputs=[converted_box, target_lang_dropdown],
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outputs=[converted_out],
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)
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ui.launch()
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# Example Python code blocks
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PYTHON_HARD = """
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# Support large number sizes
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def lcg(seed, a=1664525, c=1013904223, m=2**32):
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value = seed
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while True:
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value = (a * value + c) % m
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yield value
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def max_subarray_sum(n, seed, min_val, max_val):
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lcg_gen = lcg(seed)
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random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]
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max_sum = float('-inf')
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for i in range(n):
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current_sum = 0
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for j in range(i, n):
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current_sum += random_numbers[j]
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if current_sum > max_sum:
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max_sum = current_sum
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return max_sum
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def total_max_subarray_sum(n, initial_seed, min_val, max_val):
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total_sum = 0
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lcg_gen = lcg(initial_seed)
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for _ in range(20):
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seed = next(lcg_gen)
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total_sum += max_subarray_sum(n, seed, min_val, max_val)
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return total_sum
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n = 10000
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initial_seed = 42
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min_val = -10
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max_val = 10
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import time
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start_time = time.time()
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result = total_max_subarray_sum(n, initial_seed, min_val, max_val)
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end_time = time.time()
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print("Total Maximum Subarray Sum (20 runs):", result)
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print("Execution Time: {:.6f} seconds".format(end_time - start_time))
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"""
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(
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description="Single script with multiple executable sections and target language support"
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)
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parser.add_argument(
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"--mode",
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choices=["direct", "ui"],
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default="ui",
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help="Run direct conversion or launch Gradio UI",
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)
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args = parser.parse_args()
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if args.mode == "direct":
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print("\nExecuting Python code (PYTHON_HARD)...")
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exec(PYTHON_HARD)
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for partial in optimize(PYTHON_HARD, "GPT: gpt-4o", "C++"):
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print(partial, end="")
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elif args.mode == "ui":
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launch_ui()
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