{
 "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": [
       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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(\"<h2 style='text-align: center; color: white;'> PERL to Python Code Generator</h2>\")\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
}
