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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "8dee7381-2291-4202-a6e6-9eb94e896141",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import io\n",
"import sys\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import google.generativeai\n",
"import anthropic\n",
"from IPython.display import Markdown, display, update_display\n",
"import gradio as gr\n",
"import subprocess\n",
"import platform\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc145e4c-1e06-4414-aa2b-1ea1862b4600",
"metadata": {},
"outputs": [],
"source": [
"# environment\n",
"\n",
"load_dotenv(override=True)\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bfaf8584-a10f-43f0-b550-f1b2b6f07160",
"metadata": {},
"outputs": [],
"source": [
"# initialize\n",
"\n",
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()\n",
"\n",
"OPENAI_MODEL = \"gpt-4o-mini\"\n",
"CLAUDE_MODEL = \"claude-3-haiku-20240307\"\n",
"\n",
"# OPENAI_MODEL = \"gpt-4o\"\n",
"# CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b47508e-dc60-4db5-a29c-f3f0ed57d894",
"metadata": {},
"outputs": [],
"source": [
"processor = platform.machine()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ee9ec20-3b1d-4a15-9ab3-b2fbb93296b4",
"metadata": {},
"outputs": [],
"source": [
"def get_name_by_extension(extension):\n",
" for lang in programming_languages:\n",
" if lang[\"extension\"] == extension:\n",
" return lang[\"name\"]\n",
" return None "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee408ffd-fde2-4c1e-b87f-c8dce2ad49bc",
"metadata": {},
"outputs": [],
"source": [
"def get_system_message(prog_lang):\n",
" name = get_name_by_extension(prog_lang)\n",
" \n",
" system_message = f\"You are an assistant that reimplements Python code to {name} for an {processor} device. \"\n",
" system_message += f\"Respond only with code; use comments sparingly and do not provide any explanation other than occasional comments. \"\n",
" system_message += f\"The {name} response needs to produce an identical output in the fastest possible time.\"\n",
" system_message += f\"If the used function does not exists for {name} language interchange it for its compatibility and if not throw an error\"\n",
"\n",
" return system_message"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac8d5d3b-a018-4b94-8080-9b18f5634dc7",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(python, prog_lang):\n",
" name = get_name_by_extension(prog_lang)\n",
" \n",
" user_prompt = f\"Rewrite this Python code in {name} with the fastest possible implementation that produces identical output in the least time. \"\n",
" user_prompt += f\"Respond only with {name} code; do not explain your work other than a few comments. \"\n",
" user_prompt += \"Pay attention to number types to ensure no int overflows\\n\\n\"\n",
" user_prompt += python\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23c58e61-5fdd-41f5-9e60-a0847f4bf86f",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(python, prog_lang):\n",
" system_message = get_system_message(prog_lang)\n",
" \n",
" return [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(python, prog_lang)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e193cd6-16f4-440a-9376-6041672f91fc",
"metadata": {},
"outputs": [],
"source": [
"# write to a file called optimized.cpp\n",
"\n",
"def write_output(content, prog_lang):\n",
" code = content.replace(\"```cpp\",\"\").replace(\"javascript\",\"\").replace(\"```\",\"\")\n",
" \n",
" with open(f\"optimized.{prog_lang}\", \"w\") as f:\n",
" f.write(code)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "28b0be5e-73b6-49d8-8ef6-8209eace5ee6",
"metadata": {},
"outputs": [],
"source": [
"python_hard = \"\"\"# Be careful to support large number sizes\n",
"\n",
"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
" value = seed\n",
" while True:\n",
" value = (a * value + c) % m\n",
" yield value\n",
" \n",
"def max_subarray_sum(n, seed, min_val, max_val):\n",
" lcg_gen = lcg(seed)\n",
" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
" max_sum = float('-inf')\n",
" for i in range(n):\n",
" current_sum = 0\n",
" for j in range(i, n):\n",
" current_sum += random_numbers[j]\n",
" if current_sum > max_sum:\n",
" max_sum = current_sum\n",
" return max_sum\n",
"\n",
"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n",
" total_sum = 0\n",
" lcg_gen = lcg(initial_seed)\n",
" for _ in range(20):\n",
" seed = next(lcg_gen)\n",
" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
" return total_sum\n",
"\n",
"# Parameters\n",
"n = 10000 # Number of random numbers\n",
"initial_seed = 42 # Initial seed for the LCG\n",
"min_val = -10 # Minimum value of random numbers\n",
"max_val = 10 # Maximum value of random numbers\n",
"\n",
"# Timing the function\n",
"import time\n",
"start_time = time.time()\n",
"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n",
"end_time = time.time()\n",
"\n",
"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2818063c-008e-4029-851a-959f63d3f0fc",
"metadata": {},
"outputs": [],
"source": [
"def stream_gpt(python, prog_lang): \n",
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python, prog_lang), stream=True)\n",
" reply = \"\"\n",
" for chunk in stream:\n",
" fragment = chunk.choices[0].delta.content or \"\"\n",
" reply += fragment\n",
" yield reply.replace('```cpp\\n','').replace('javascript\\n','').replace('```','')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e3e0502-8550-46fe-bd2f-394078db6576",
"metadata": {},
"outputs": [],
"source": [
"def stream_claude(python, prog_lang):\n",
" system_message = get_system_message(prog_lang)\n",
" \n",
" result = claude.messages.stream(\n",
" model=CLAUDE_MODEL,\n",
" max_tokens=2000,\n",
" system=system_message,\n",
" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python, prog_lang)}],\n",
" )\n",
" reply = \"\"\n",
" with result as stream:\n",
" for text in stream.text_stream:\n",
" reply += text\n",
" yield reply.replace('```cpp\\n','').replace('javascript\\n','').replace('```','')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10accbb2-b56d-4c79-beef-928c2a3b58f0",
"metadata": {},
"outputs": [],
"source": [
"def optimize(python, model, prog_lang):\n",
" if model==\"GPT\":\n",
" result = stream_gpt(python, prog_lang)\n",
" elif model==\"Claude\":\n",
" result = stream_claude(python, prog_lang)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" for stream_so_far in result:\n",
" yield stream_so_far "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1acb130-8b5c-4199-818a-3afa89c342cb",
"metadata": {},
"outputs": [],
"source": [
"def execute_python(code):\n",
" try:\n",
" output = io.StringIO()\n",
" sys.stdout = output\n",
"\n",
" namespace = {}\n",
" exec(code, namespace)\n",
" finally:\n",
" sys.stdout = sys.__stdout__\n",
" return output.getvalue()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5e901e81-61d8-4ab2-9e16-f70c8ee6bdbe",
"metadata": {},
"outputs": [],
"source": [
"css = \"\"\"\n",
".python {background-color: #306998;}\n",
".cpp {background-color: #050;}\n",
".php {background-color: #cb7afa;}\n",
".js {background-color: #f4ff78;}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1e0dfe2e-a87d-4595-b4ef-72797bd1ad44",
"metadata": {},
"outputs": [],
"source": [
"def execute_cpp(code):\n",
" try:\n",
" compile_cmd = [\"clang++\", \"-Ofast\", \"-std=c++17\", \"-o\", \"optimized\", \"optimized.cpp\"]\n",
" compile_result = subprocess.run(compile_cmd, shell=True, text=True, capture_output=True)\n",
" run_cmd = [\"./optimized\"]\n",
" run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)\n",
" return run_result.stdout\n",
" except subprocess.CalledProcessError as e:\n",
" return f\"An error occurred:\\n{e.stderr}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91ba8a3c-8686-4636-bf21-efc861f3a2b7",
"metadata": {},
"outputs": [],
"source": [
"def execute_js(code):\n",
" try:\n",
" run_result = subprocess.run([\"node\", \"optimized.js\"], check=True, text=True, capture_output=True)\n",
" return run_result.stdout\n",
" except subprocess.CalledProcessError as e:\n",
" return f\"An error occurred:\\n{e.stderr}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b9006f67-f631-4ad4-bf45-b9366c822a04",
"metadata": {},
"outputs": [],
"source": [
"def execute_php(code):\n",
" try:\n",
" run_result = subprocess.run([\"php\", \"optimized.php\"], check=True, text=True, capture_output=True)\n",
" return run_result.stdout\n",
" except subprocess.CalledProcessError as e:\n",
" return f\"An error occurred:\\n{e.stderr}\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3991a09-f60d-448a-8e92-2561296d05cf",
"metadata": {},
"outputs": [],
"source": [
"def handle_execution(code, prog_lang):\n",
" write_output(code, prog_lang)\n",
"\n",
" index = next((i for i, lang in enumerate(programming_languages) if lang[\"extension\"] == prog_lang), -1)\n",
" return programming_languages[index][\"fn\"](code)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c127bbc9-ef4d-40e4-871a-85873fc9e406",
"metadata": {},
"outputs": [],
"source": [
"programming_languages = [\n",
" {\"name\": \"C++\", \"extension\": \"cpp\", \"fn\": execute_cpp},\n",
" {\"name\": \"Javascript\", \"extension\": \"js\", \"fn\": execute_js},\n",
" {\"name\": \"Php\", \"extension\": \"php\", \"fn\": execute_php}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "126636a1-4315-4811-9de9-61ee032effc8",
"metadata": {},
"outputs": [],
"source": [
"def create_prog_lang_ui(lang, model):\n",
" prog_name = lang[\"name\"]\n",
" extension = lang[\"extension\"]\n",
" fn = lang[\"fn\"]\n",
"\n",
" with gr.Row():\n",
" with gr.Column():\n",
" convert = gr.Button(f\"Convert to {prog_name}\")\n",
" converted_code = gr.Textbox(label=f\"Converted {prog_name} code:\", lines=10)\n",
"\n",
" with gr.Column():\n",
" prog_run = gr.Button(f\"Run {prog_name}\")\n",
" prog_out = gr.TextArea(label=f\"{prog_name} result:\", elem_classes=[extension])\n",
"\n",
" current_selected = gr.Dropdown([extension], value=extension, visible=False)\n",
" \n",
" convert.click(optimize, inputs=[python, model, current_selected], outputs=[converted_code])\n",
" prog_run.click(handle_execution, inputs=[converted_code, current_selected], outputs=[prog_out])\n",
"\n",
"with gr.Blocks(css=css) as ui:\n",
" gr.Markdown(\"# Convert code from Python to any Programming Language\")\n",
" with gr.Row():\n",
" with gr.Column():\n",
" python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n",
" with gr.Column():\n",
" python_run = gr.Button(f\"Run Python\")\n",
" python_out = gr.TextArea(label=f\"Python result:\", elem_classes=[\"python\"])\n",
" \n",
" with gr.Row():\n",
" model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n",
"\n",
" python_run.click(execute_python, inputs=[python], outputs=[python_out]) \n",
"\n",
"\n",
" for lang in programming_languages:\n",
" create_prog_lang_ui(lang, model)\n",
"\n",
"ui.launch(\n",
" inbrowser=True\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "conda-base-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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