LLM_Engineering_OLD/week1/community-contributions/CoolCodeSummarizer.ipynb

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    "# Python Code Summarizer\n",
    "\n",
    "The Below code will summarize the python code and example it in details which can help codes better understand a forigen code."
   ]
  },
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   "cell_type": "code",
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   "id": "8dcf353c-e4f2-4ce7-a3b5-71b29700a148",
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   "outputs": [],
   "source": [
    "# Imports\n",
    "from IPython.display import Markdown, display\n",
    "import os\n",
    "import openai\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
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   "source": [
    "load_dotenv(override=True)\n",
    "api_key = os.getenv('OPENAI_API_KEY')"
   ]
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    "def read_code(**kwargs):\n",
    "    \"\"\"\n",
    "    You can pass two types of key word arguments to this function.\n",
    "    code_path= Path to your complex python code.\n",
    "    code= Passing raw python code.\n",
    "    \"\"\"\n",
    "    code_path = kwargs.get('code_path',None)\n",
    "    code_raw = kwargs.get('code',None)\n",
    "    \n",
    "    if code_path:\n",
    "        with open(code_path, 'r') as code_file:\n",
    "            code = code_file.read()\n",
    "        return (True, code)\n",
    "\n",
    "    if code_raw:\n",
    "        return (True, code_raw)\n",
    "\n",
    "    return (False, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00743dac-0e70-45b7-879a-d7293a6f68a6",
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    "# Model Prompt\n",
    "system_prompt = (\n",
    "                \"You are a helpful assistant. The following input will be a Python code snippet. \"\n",
    "                \"Your task is to:\\n\\n\"\n",
    "                \"1. Summarize the overall purpose of the code.\\n\"\n",
    "                \"2. Explain the code line by line, describing what each line does and why it's written that way.\\n\"\n",
    "                \"3. Provide reasoning behind the code structure and logic to help novice Python developers understand the concepts better.\\n\\n\"\n",
    "                \"Use Markdown format in your response. Make the explanation beginner-friendly, using code blocks, bullet points, and headings where helpful.\"\n",
    "            ) \n",
    "# In a plot twist worthy of sci-fi, this prompt was written by ChatGPT...\n",
    "# to tell ChatGPT how to respond. We’ve officially entered the Matrix. 🤖🌀"
   ]
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   "cell_type": "code",
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   "source": [
    "\n",
    "# Guess where I got this code from :)\n",
    "code_line = \"\"\"yeild from set(book.get(\"author)) for book  in books if book.get(\"author\"))\"\"\"\n",
    "is_code, raw_code = read_code(code=code_line)\n",
    "\n",
    "if is_code:\n",
    "    user_prompt = raw_code\n",
    "else:\n",
    "    print(\"Invalid Arguments\")"
   ]
  },
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   "execution_count": null,
   "id": "d74a1a39-1c24-4d4b-bd49-0ca416377a93",
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   "source": [
    "def messages_for():\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df6c2726-d0fb-4ab6-b13b-d047e8807558",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize():\n",
    "    \n",
    "    response = openai.chat.completions.create(\n",
    "        model = \"gpt-4o-mini\",\n",
    "        messages = messages_for()\n",
    "    )\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8425144c-595e-4ad6-9801-3e8778d285c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_summary():\n",
    "    summary = summarize()\n",
    "    display(Markdown(summary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "744bffdd-ec3c-4b27-b126-81bf3e8c8295",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_summary()"
   ]
  }
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