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    "# TechExplainAI\n",
    "---\n",
    "\n",
    "AI-driven tool that provides concise, structured explanations for technical questions and code snippets.\n",
    "\n",
    "- 🌍 Task: AI-powered technical explanation generator\n",
    "- 🧠 Model: OpenAI's `GPT-4o-mini`, Ollama's `llama3.2:3b`\n",
    "- 📌 Output Format: Markdown with real-time streaming\n",
    "- 🧑‍💻 Skill Level: Beginner\n",
    "- 🔄 Interaction Mode: User enters a technical question → AI generates a structured, concise explanation\n",
    "- 🎯 Purpose: Quickly explain technical concepts and Python code snippets\n",
    "- 🔧 Customization: Users can modify the models, prompts, and formatting as needed\n",
    "\n",
    "🛠️ Requirements\n",
    "- ⚙️ Hardware: ✅ CPU is sufficient — no GPU required\n",
    "- 🔑 OpenAI API Key\n",
    "- Install Ollama and pull llama3.2:3b or another lightweight model\n",
    "\n",
    "---\n",
    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
   ]
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   "source": [
    "import os\n",
    "import openai\n",
    "import ollama\n",
    "from dotenv import load_dotenv\n",
    "from IPython.display import display, Markdown, update_display\n",
    "\n",
    "# Load environment variables\n",
    "load_dotenv(override=True)\n",
    "\n",
    "# Set up OpenAI API key\n",
    "OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')\n",
    "if not OPENAI_API_KEY:\n",
    "    raise ValueError(\"Please set your OpenAI API key in environment variables.\")\n",
    "\n",
    "# Constants\n",
    "MODEL_GPT = \"gpt-4o-mini\"\n",
    "MODEL_LLAMA = \"llama3.2:3b\"\n",
    "\n",
    "# Prompt user for question (until input is provided)\n",
    "while True:\n",
    "    question = input(\"Hello, I am your personal technical tutor. Enter your question: \").strip()\n",
    "    if question:\n",
    "        break  # Proceed only if a valid question is entered\n",
    "    print(\"Question cannot be empty. Please enter a question.\")\n",
    "\n",
    "# Common user prompt\n",
    "user_prompt = f\"\"\"\n",
    "Please give a detailed explanation to the following question: {question}.\n",
    "Be less verbose.\n",
    "Provide a clear and concise explanation without unnecessary elaboration.\n",
    "\"\"\"\n",
    "\n",
    "# Common system prompt\n",
    "system_prompt = \"\"\"\n",
    "You are a helpful AI assistant that explains Python code in a clear and concise manner. Provide structured explanations and examples when necessary.\n",
    "Be less verbose.\n",
    "\"\"\"\n",
    "\n",
    "def ask_openai():\n",
    "    \"\"\"Gets response from OpenAI's GPT model with streaming.\"\"\"\n",
    "    print(\"\\n\\n\\n🚀🤖🚀 Response from OpenAI GPT-4o-mini 🚀🤖🚀\")\n",
    "    client = openai.OpenAI(api_key=OPENAI_API_KEY)\n",
    "    response_stream = client.chat.completions.create(\n",
    "        model=MODEL_GPT,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": user_prompt}\n",
    "        ],\n",
    "        stream=True\n",
    "    )\n",
    "    response = \"\"\n",
    "    display_handle = display(Markdown(\"\"), display_id=True)\n",
    "    for chunk in response_stream:\n",
    "        response += chunk.choices[0].delta.content or ''\n",
    "        response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
    "        update_display(Markdown(response), display_id=display_handle.display_id)\n",
    "\n",
    "def ask_ollama():\n",
    "    \"\"\"Gets response from Ollama's Llama 3.2 model with streaming.\"\"\"\n",
    "    print(\"\\n\\n\\n🔥✨🔥 Response from Llama 3.2 🔥✨🔥\\n\")\n",
    "    response = ollama.chat(\n",
    "        model=MODEL_LLAMA,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": user_prompt}\n",
    "        ],\n",
    "        stream=True\n",
    "    )\n",
    "\n",
    "    display_handle = display(Markdown(\"\"), display_id=True)\n",
    "    full_text = \"\"\n",
    "    for chunk in response:\n",
    "        if \"message\" in chunk:\n",
    "                content = chunk[\"message\"][\"content\"] or \"\"\n",
    "                full_text += content\n",
    "                update_display(Markdown(full_text), display_id=display_handle.display_id)\n",
    "\n",
    "# Call the functions\n",
    "ask_openai()\n",
    "ask_ollama()\n"
   ]
  }
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