{ "cells": [ { "cell_type": "markdown", "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", "metadata": {}, "source": [ "# End of week 1 exercise\n", "\n", "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", "and responds with an explanation. This is a tool that you will be able to use yourself during the course!" ] }, { "cell_type": "code", "execution_count": null, "id": "c1070317-3ed9-4659-abe3-828943230e03", "metadata": {}, "outputs": [], "source": [ "# imports\n", "import os\n", "from dotenv import load_dotenv\n", "from openai import OpenAI\n", "import ollama\n", "import ipywidgets as widgets\n", "from IPython.display import display, Markdown" ] }, { "cell_type": "code", "execution_count": null, "id": "4a456906-915a-4bfd-bb9d-57e505c5093f", "metadata": {}, "outputs": [], "source": [ "# constants\n", "\n", "MODEL_GEMINI = \"gemini-2.5-flash\"\n", "MODEL_LLAMA = \"llama3.1:8b\"\n", "\n", "CHOICE_GEMINI = \"gemini\"\n", "CHOICE_OLLAMA = \"ollama\"\n", "\n", "SYSTEM_PROMPT = (\n", " \"You are a technical adviser. The student is learning LLM engineering \"\n", " \"and you will be asked to explain lines of code with an example, \"\n", " \"mostly in Python.\"\n", " \"You can answer other questions as well.\"\n", ")\n", "\n", "GEMINI_BASE_URL = \"https://generativelanguage.googleapis.com/v1beta/openai/\"" ] }, { "cell_type": "code", "execution_count": null, "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", "metadata": {}, "outputs": [], "source": [ "# set up environment\n", "load_dotenv(override=True)\n", "google_api_key = os.getenv(\"GOOGLE_API_KEY\")\n", "\n", "if not google_api_key:\n", " print(\"Warning: GOOGLE_API_KEY not found. Gemini calls will fail.\")\n", " print(\"Please create a .env file with GOOGLE_API_KEY=your_key\")\n", "\n", "gemini_client = OpenAI(\n", " base_url=GEMINI_BASE_URL,\n", " api_key=google_api_key,\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "3f0d0137-52b0-47a8-81a8-11a90a010798", "metadata": {}, "outputs": [], "source": [ "# here is the question; type over this to ask something new\n", "\n", "question = \"\"\"\n", "Please explain what this code does and why:\n", "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", "metadata": {}, "outputs": [], "source": [ "def make_messages(user_question: str):\n", " return [\n", " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n", " {\"role\": \"user\", \"content\": user_question},\n", " ]\n", "\n", "\n", "def stream_gemini(messages):\n", " \"\"\"Stream response chunks from Gemini.\"\"\"\n", " stream = gemini_client.chat.completions.create(\n", " model=MODEL_GEMINI,\n", " messages=messages,\n", " stream=True,\n", " )\n", "\n", " full = []\n", " for chunk in stream:\n", " piece = chunk.choices[0].delta.content or \"\"\n", " full.append(piece)\n", " return \"\".join(full)\n", "\n", "\n", "def stream_ollama(messages):\n", " \"\"\"Stream response chunks from local Ollama.\"\"\"\n", " stream = ollama.chat(\n", " model=MODEL_LLAMA,\n", " messages=messages,\n", " stream=True,\n", " )\n", "\n", " full = []\n", " for chunk in stream:\n", " piece = chunk[\"message\"][\"content\"]\n", " full.append(piece)\n", " return \"\".join(full)\n", "\n", "\n", "def get_explanation(question: str, model_choice: str):\n", " \"\"\"Gets a technical explanation from the chosen model and streams the response.\"\"\"\n", " messages = make_messages(question)\n", " try:\n", " if model_choice == CHOICE_GEMINI:\n", " return stream_gemini(messages)\n", " elif model_choice == CHOICE_OLLAMA:\n", " return stream_ollama(messages)\n", " else:\n", " print(\"Unknown model choice.\")\n", " return \"\"\n", " except Exception as e:\n", " print(f\"\\nAn error occurred: {e}\")\n", " return \"\"\n", "\n", "print(\"💡 Your personal technical tutor is ready.\\n\")\n", "\n", "# Dropdown for model selection\n", "model_dropdown = widgets.Dropdown(\n", " options=[\n", " (\"Gemini (gemini-2.5-flash)\", CHOICE_GEMINI),\n", " (\"Ollama (llama3.1:8b)\", CHOICE_OLLAMA),\n", " ],\n", " value=CHOICE_GEMINI,\n", " description=\"Model:\",\n", " style={\"description_width\": \"initial\"},\n", ")\n", "\n", "# Text input for question\n", "question_box = widgets.Textarea(\n", " placeholder=\"Type your technical question here...\",\n", " description=\"Question:\",\n", " layout=widgets.Layout(width=\"100%\", height=\"100px\"),\n", " style={\"description_width\": \"initial\"},\n", ")\n", "\n", "submit_button = widgets.Button(description=\"Ask\", button_style=\"success\", icon=\"paper-plane\")\n", "\n", "output_area = widgets.Output()\n", "loader_label = widgets.Label(value=\"\")\n", "\n", "def on_submit(_):\n", " output_area.clear_output()\n", " question = question_box.value.strip()\n", " if not question:\n", " with output_area:\n", " print(\"Please enter a question.\")\n", " return\n", "\n", " loader_label.value = \"⏳ Thinking...\"\n", " submit_button.disabled = True\n", "\n", " answer = get_explanation(question, model_dropdown.value)\n", "\n", " loader_label.value = \"\"\n", " submit_button.disabled = False\n", "\n", " with output_area:\n", " print(f\"🤖 Model: {model_dropdown.label}\")\n", " print(f\"📜 Question: {question}\\n\")\n", " display(Markdown(answer))\n", " print(\"\\n--- End of response ---\")\n", "\n", "submit_button.on_click(on_submit)\n", "\n", "# Display everything\n", "display(model_dropdown, question_box, submit_button, loader_label, output_area)" ] } ], "metadata": { "kernelspec": { "display_name": "llm-engineering (3.12.10)", "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": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 5 }