week1 exercise solution - mathxpert
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Ransford Okpoti
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week1/community-contributions/ranskills-week1-mathxpert.ipynb
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week1/community-contributions/ranskills-week1-mathxpert.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
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"metadata": {},
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"source": [
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"# End of week 1 exercise\n",
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"\n",
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"## Dynamically pick an LLM provider to let MathXpert answer your math questions."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c1070317-3ed9-4659-abe3-828943230e03",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from enum import StrEnum\n",
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"from getpass import getpass\n",
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"\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import ipywidgets as widgets\n",
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"from IPython.display import display, clear_output, Markdown, Latex\n",
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"\n",
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"load_dotenv()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f169118a-645e-44e1-9a98-4f561adfbb08",
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"metadata": {},
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"source": [
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"## Free Cloud Providers\n",
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"\n",
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"Grab your free API Keys from these generous sites:\n",
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"\n",
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"- https://openrouter.ai/\n",
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"- https://ollama.com/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Provider(StrEnum):\n",
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" OLLAMA = 'Ollama'\n",
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" OPENROUTER = 'OpenRouter'\n",
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"\n",
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"models: dict[Provider, str] = {\n",
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" Provider.OLLAMA: 'gpt-oss:120b-cloud',\n",
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" Provider.OPENROUTER: 'qwen/qwen3-4b:free'\n",
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"}\n",
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"\n",
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"def get_api_key(env_name: str) -> str:\n",
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" '''Gets the value from the environment, otherwise ask the user for it if not set'''\n",
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" key = os.environ.get(env_name)\n",
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" if not key:\n",
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" key = getpass(f'Enter {env_name}:').strip()\n",
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"\n",
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" if key:\n",
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" print(f'✅ {env_name} provided')\n",
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" else:\n",
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" print(f'❌ {env_name} provided')\n",
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" return key\n",
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"\n",
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"\n",
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"providers: dict[Provider, OpenAI] = {}\n",
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"\n",
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"if api_key := get_api_key('OLLAMA_API_KEY'):\n",
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" providers[Provider.OLLAMA] = OpenAI(base_url='https://ollama.com/v1', api_key=api_key)\n",
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"\n",
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"if api_key := get_api_key('OPENROUTER_API_KEY'):\n",
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" providers[Provider.OPENROUTER] = OpenAI(base_url='https://openrouter.ai/api/v1', api_key=api_key)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_messages(question: str) -> list[dict[str, str]]:\n",
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" \"\"\"Generate messages for the chat models.\"\"\"\n",
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"\n",
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" system_prompt = '''\n",
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" You are MathXpert, an expert Mathematician who makes math fun to learn by relating concepts to real \n",
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" practical usage to whip up the interest in learners.\n",
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" \n",
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" Explain step-by-step thoroughly how to solve a math problem. Respond in **LaTex**'\n",
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" '''\n",
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"\n",
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" return [\n",
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" {'role': 'system', 'content': system_prompt },\n",
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" {'role': 'user', 'content': question},\n",
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" ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ef72c85e",
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"metadata": {},
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"outputs": [],
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"source": [
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"get_messages('Explain how to solve a differentiation problem')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aae1579b-7a02-459d-81c6-0f775d2a1410",
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"metadata": {},
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"outputs": [],
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"source": [
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"selected_provider, client = next(iter(providers.items()))\n",
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"\n",
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"def on_provider_change(change):\n",
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" global selected_provider, client\n",
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"\n",
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" selected_provider = change['new']\n",
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" client = providers.get(selected_provider)\n",
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"\n",
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"\n",
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"provider_selector = widgets.Dropdown(\n",
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" options=list(providers.keys()),\n",
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" description='Select an LLM provider:',\n",
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" style={'description_width': 'initial'},\n",
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")\n",
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"\n",
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"provider_selector.observe(on_provider_change, names='value')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
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"metadata": {},
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"outputs": [],
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"source": [
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"handle = display(None, display_id=True)\n",
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"\n",
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"def ask(client: OpenAI, model: str, question: str):\n",
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" try:\n",
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" prompt = get_messages(question=question)\n",
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" response = client.chat.completions.create(\n",
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" model=model,\n",
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" messages=prompt,\n",
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" stream=True,\n",
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" )\n",
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" \n",
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" output = ''\n",
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" for chunk in response:\n",
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" output += chunk.choices[0].delta.content or ''\n",
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" \n",
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" handle.update(Latex(output))\n",
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" except Exception as e:\n",
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" clear_output(wait=True) \n",
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" print(f'🔥 An error occurred: {e}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "09bc9a11-adb4-4a9c-9c77-73b2b5a665cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"display(provider_selector)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e01069b2-fd2c-446f-b385-09c1d9624225",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_label = \"Ask your question (Type 'q' to quit): \"\n",
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"question = input(input_label)\n",
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"\n",
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"while question.strip().lower() not in ['quit', 'q']:\n",
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" clear_output(wait=True)\n",
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" print(selected_provider, models[selected_provider])\n",
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" model = models[selected_provider]\n",
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" ask(client, model, question)\n",
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"\n",
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" question = input(input_label)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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