{ "cells": [ { "cell_type": "markdown", "id": "de352746-564c-4b33-b1ad-0b449988c448", "metadata": {}, "source": [ "# Perl to Python Code Generator\n", "\n", "The requirement: use a Frontier model to generate high performance Python code from Perl code\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3", "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 requests\n", "import json\n", "#for Hugging face end points\n", "from huggingface_hub import login, InferenceClient\n", "from transformers import AutoTokenizer" ] }, { "cell_type": "code", "execution_count": 3, "id": "4f672e1c-87e9-4865-b760-370fa605e614", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n" ] } ], "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')\n", "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')\n", "##for connecting to HF End point\n", "hf_token = os.environ['HF_TOKEN']\n", "login(hf_token, add_to_git_credential=True)" ] }, { "cell_type": "code", "execution_count": 4, "id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da", "metadata": {}, "outputs": [], "source": [ "# initialize\n", "# NOTE - option to use ultra-low cost models by uncommenting last 2 lines\n", "\n", "openai = OpenAI()\n", "claude = anthropic.Anthropic()\n", "OPENAI_MODEL = \"gpt-4o\"\n", "CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"\n", "\n", "# Want to keep costs ultra-low? Uncomment these lines:\n", "OPENAI_MODEL = \"gpt-4o-mini\"\n", "CLAUDE_MODEL = \"claude-3-haiku-20240307\"\n", "\n", "#To access open source models from Hugging face end points\n", "code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n", "code_gemma = \"google/codegemma-7b-it\"\n", "CODE_QWEN_URL = \"https://h1vdol7jxhje3mpn.us-east-1.aws.endpoints.huggingface.cloud\"\n", "CODE_GEMMA_URL = \"https://c5hggiyqachmgnqg.us-east-1.aws.endpoints.huggingface.cloud\"" ] }, { "cell_type": "code", "execution_count": 27, "id": "6896636f-923e-4a2c-9d6c-fac07828a201", "metadata": {}, "outputs": [], "source": [ "system_message = \"You are an assistant that reimplements Perl scripts code into a high performance Python for a Windows 11 PC. \"\n", "system_message += \"Respond only with Python code; use comments sparingly and do not provide any explanation other than occasional # comments. \"\n", "system_message += \"The Python response needs to produce an identical output in the fastest possible time.\"" ] }, { "cell_type": "code", "execution_count": 6, "id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb", "metadata": {}, "outputs": [], "source": [ "def user_prompt_for(perl):\n", " user_prompt = \"Rewrite this Perl scripts code in C++ with the fastest possible implementation that produces identical output in the least time. \"\n", " user_prompt += \"Respond only with Python code; do not explain your work other than a few comments. \"\n", " user_prompt += \"Pay attention to number types to ensure no int overflows. Remember to #include all necessary python libraries as needed,\\\n", " such as requests, os, json etc.\\n\\n\"\n", " user_prompt += perl\n", " return user_prompt" ] }, { "cell_type": "code", "execution_count": 7, "id": "c6190659-f54c-4951-bef4-4960f8e51cc4", "metadata": {}, "outputs": [], "source": [ "def messages_for(perl):\n", " return [\n", " {\"role\": \"system\", \"content\": system_message},\n", " {\"role\": \"user\", \"content\": user_prompt_for(perl)}\n", " ]" ] }, { "cell_type": "code", "execution_count": 8, "id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b", "metadata": {}, "outputs": [], "source": [ "# write to a file called script.py\n", "\n", "def write_output(python):\n", " code = python.replace(\"```python\",\"\").replace(\"```\",\"\")\n", " output_file = \"script.py\"\n", " with open(output_file, \"w\") as f:\n", " f.write(code)\n", " return output_file" ] }, { "cell_type": "code", "execution_count": 9, "id": "0be9f47d-5213-4700-b0e2-d444c7c738c0", "metadata": {}, "outputs": [], "source": [ "def stream_gpt(perl): \n", " stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(perl), stream=True)\n", " reply = \"\"\n", " for chunk in stream:\n", " fragment = chunk.choices[0].delta.content or \"\"\n", " reply += fragment\n", " cleaned_reply = reply.replace('```python\\n','').replace('```','')\n", " yield cleaned_reply, None\n", " yield cleaned_reply, write_output(cleaned_reply)\n", " " ] }, { "cell_type": "code", "execution_count": 10, "id": "8669f56b-8314-4582-a167-78842caea131", "metadata": {}, "outputs": [], "source": [ "def stream_claude(perl):\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(perl)}],\n", " )\n", " reply = \"\"\n", " with result as stream:\n", " for text in stream.text_stream:\n", " reply += text\n", " cleaned_reply = reply.replace('```python\\n','').replace('```','')\n", " yield cleaned_reply, None\n", " yield cleaned_reply, write_output(cleaned_reply)" ] }, { "cell_type": "code", "execution_count": 11, "id": "5b166afe-741a-4711-bc38-626de3538ea2", "metadata": {}, "outputs": [], "source": [ "def stream_code_qwen(python):\n", " tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", " messages = messages_for(python)\n", " text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", " client = InferenceClient(CODE_QWEN_URL, token=hf_token)\n", " stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)\n", " result = \"\"\n", " for r in stream:\n", " result += r.token.text\n", " cleaned_reply = result.replace('```python\\n','').replace('```','')\n", " yield cleaned_reply, None\n", " yield cleaned_reply, write_output(cleaned_reply) " ] }, { "cell_type": "code", "execution_count": 12, "id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d", "metadata": {}, "outputs": [], "source": [ "def generate(perl_script, model):\n", " if model==\"GPT\":\n", " for result, file in stream_gpt(perl_script):\n", " yield result, file\n", " yield result, file\n", " elif model==\"Claude\":\n", " for result, file in stream_claude(perl_script):\n", " yield result, file\n", " yield result, file\n", " elif model==\"CodeQwen\":\n", " for result, file in stream_code_qwen(perl_script):\n", " yield result, file\n", " yield result, file\n", " else:\n", " raise ValueError(\"Unknown model\")\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "aa8e9a1c-9509-4056-bd0b-2578f3cc3335", "metadata": {}, "outputs": [], "source": [ "def execute_perl(perl_code):\n", "\n", " import subprocess\n", " #print(perl_file)\n", " perl_path = r\"E:\\Softwares\\Perl\\perl\\bin\\perl.exe\"\n", " # Run Perl script from Jupyter Lab\n", " result = subprocess.run([perl_path, '-e', perl_code], capture_output=True, text=True)\n", "\n", " # Return the output of the Perl script\n", " return result.stdout\n", " " ] }, { "cell_type": "code", "execution_count": 14, "id": "01e9d980-8830-4421-8753-a065dcbea1ed", "metadata": {}, "outputs": [], "source": [ "def execute_python(code):\n", " try:\n", " output = io.StringIO()\n", " sys.stdout = output\n", " exec(code)\n", " finally:\n", " sys.stdout = sys.__stdout__\n", " return output.getvalue()" ] }, { "cell_type": "code", "execution_count": 22, "id": "ed4e0aff-bfde-440e-8e6b-eb3c7143837e", "metadata": {}, "outputs": [], "source": [ "css = \"\"\"\n", ".perl {background-color: #093645;}\n", ".python {background-color: #0948;}\n", "\"\"\"\n", "\n", "force_dark_mode = \"\"\"\n", "function refresh() {\n", " const url = new URL(window.location);\n", " if (url.searchParams.get('__theme') !== 'dark') {\n", " url.searchParams.set('__theme', 'dark');\n", " window.location.href = url.href;\n", " }\n", "}\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 28, "id": "caaee54d-79db-4db3-87df-2e7d2eba197c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with gr.Blocks(css=css, js=force_dark_mode) as ui:\n", "\n", " gr.HTML(\"

PERL to Python Code Generator

\")\n", " with gr.Row(scale=0, equal_height=True):\n", " model = gr.Dropdown([\"GPT\", \"Claude\", \"CodeQwen\"], label=\"Select model\", value=\"GPT\")\n", " perl_file = gr.File(label=\"Upload Perl Script:\")\n", " convert = gr.Button(\"Convert to Python\")\n", " file_output = gr.File(label=\"Download Python script\", visible=False)\n", " with gr.Row():\n", " perl_script = gr.Textbox(label=\"Perl Script:\")\n", " python_script = gr.Textbox(label=\"Converted Python Script:\") \n", " with gr.Row():\n", " perl_run = gr.Button(\"Run PERL\")\n", " python_run = gr.Button(\"Run Python\")\n", " with gr.Row():\n", " perl_out = gr.TextArea(label=\"PERL result:\", elem_classes=[\"perl\"])\n", " python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n", " with gr.Row(): \n", " clear_button = gr.Button(\"Clear\")\n", " \n", " def extract_perl_code(file):\n", " if file is None:\n", " return \"No file uploaded.\", None \n", " with open(file.name, \"r\", encoding=\"utf-8\") as f:\n", " perl_code = f.read()\n", " return perl_code\n", "\n", " convert.click(extract_perl_code, inputs=[perl_file], outputs=[perl_script]).then(\n", " generate, inputs=[perl_script, model], outputs=[python_script, file_output]).then(\n", " lambda file_output: gr.update(visible=True), inputs=[file_output], outputs=[file_output]\n", " )\n", "\n", " perl_run.click(execute_perl, inputs=[perl_script], outputs=[perl_out])\n", " python_run.click(execute_python, inputs=[python_script], outputs=[python_out]) \n", "\n", " def clear_all():\n", " return None, \"\", \"\", gr.update(visible=False), \"\", \"\"\n", "\n", " clear_button.click(\n", " clear_all,\n", " outputs=[perl_file, perl_script, python_script, file_output, perl_out, python_out]\n", " )\n", " \n", "\n", "ui.launch(inbrowser=True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.13.1" } }, "nbformat": 4, "nbformat_minor": 5 }