Week2 exercise bootcamp feb2026 nikhilsuresh

week1/community-contributions/Andela_Nikhil/week1 EXERCISE.ipynb

@@ -0,0 +1,295 @@
+{
+ "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",
+<<<<<<< HEAD
+   "execution_count": 37,
+=======
+<<<<<<< Updated upstream
+   "execution_count": 34,
+=======
+   "execution_count": 37,
+>>>>>>> Stashed changes
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "c1070317-3ed9-4659-abe3-828943230e03",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'scraper'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[37]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjson\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdotenv\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_dotenv\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscraper\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m fetch_website_contents, fetch_website_links\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mopenai\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m OpenAI\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mIPython\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdisplay\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Markdown, display, update_display\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'scraper'"
+     ]
+    }
+   ],
+   "source": [
+    "# imports\n",
+    "import os \n",
+    "import json\n",
+    "from dotenv import load_dotenv\n",
+    "from scraper import fetch_website_contents, fetch_website_links\n",
+    "from openai import OpenAI\n",
+    "from IPython.display import Markdown, display, update_display\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59db502b",
+   "metadata": {},
+   "source": [
+    "So let's look at what this week one exercise is.\n",
+    "\n",
+    "I want to see your familiarity with OpenAI and Olama to build a tool that takes a technical question,\n",
+    "\n",
+    "which you can just type right here and responds with an explanation of that question.\n",
+    "\n",
+    "So I've got a little couple of hints down below, but use this as a way to call a model so that you\n",
+    "\n",
+    "could retype a question in here, and the model will explain it."
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": null,
+=======
+   "execution_count": 36,
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# constants\n",
+    "\n",
+    "MODEL_GPT = 'openai/gpt-4o-mini'\n",
+    "MODEL_LLAMA = 'meta-llama/llama-3.2-3b-instruct:free'\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": null,
+=======
+   "execution_count": 37,
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up environment\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENROUTER_API_KEY')\n",
+    "\n",
+    "if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
+    "    print(\"API key looks good so far\")\n",
+    "elif api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n",
+    "    print(\"You are bang on...dude , because you're using OpenRouter!\\n\") \n",
+    "else:\n",
+    "    print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": null,
+=======
+   "execution_count": 38,
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "63ed7c72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "openai = OpenAI(\n",
+    "  base_url=\"https://openrouter.ai/api/v1\",\n",
+    "  api_key=os.getenv('OPENROUTER_API_KEY'), \n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": null,
+=======
+   "execution_count": 40,
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# here is the question; type over this to ask something new\n",
+    "\n",
+    "question = \"\"\"\n",
+    "You are a technical assistant, and you are extremely good in python. \n",
+    "You will take a technical question and respond with an explanation of that 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",
+    "\"\"\"\n",
+    "messages = [{\"role\": \"user\", \"content\": question}]\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f352e71",
+   "metadata": {},
+   "source": [
+    "#  Streaming `without` using display and Markdown class from IPython.display "
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": null,
+=======
+   "execution_count": 24,
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get gpt-4o-mini to answer, with streaming\n",
+    "# Streaming with out using display and Markdown class from IPython.display \n",
+    "def call_gpt_4o():\n",
+    "    response = []\n",
+    "    stream = openai.chat.completions.create( model= MODEL_GPT,messages= messages , stream=True) \n",
+    "    for chunk in stream:\n",
+    "        delta = chunk.choices[0].delta\n",
+    "        if delta.content:\n",
+    "            print(delta.content, end=\"\", flush=True)\n",
+    "            response.append(delta.content) \n",
+    "    return response \n",
+    "    \n",
+    "\n",
+    "print(call_gpt_4o())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d84c027",
+   "metadata": {},
+   "source": [
+    "#  Streaming `WITH` using display and Markdown class from IPython.display "
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": null,
+=======
+   "execution_count": 25,
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "93ac3f7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def call_gpt_4o():\n",
+    "    response = ''\n",
+    "    stream = openai.chat.completions.create( model= MODEL_GPT,messages= messages , stream=True) \n",
+    "    display_handle = display(Markdown(\"\"), display_id=True)\n",
+    "    for chunk in stream:\n",
+    "        response += chunk.choices[0].delta.content or ''\n",
+    "        update_display(Markdown(response), display_id=display_handle.display_id) \n",
+    "      \n",
+    "    \n",
+    "\n",
+    "print(call_gpt_4o())"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": null,
+=======
+   "execution_count": 41,
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "008fe5dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def call_gpt_4o():\n",
+    "    response = ''\n",
+    "    stream = openai.chat.completions.create( model= MODEL_GPT,messages= messages , stream=True) \n",
+    "    display_handle = display(Markdown(\"\"), display_id=True)\n",
+    "    for chunk in stream:\n",
+    "        response += chunk.choices[0].delta.content or ''\n",
+    "        update_display(Markdown(response), display_id=display_handle.display_id) \n",
+    "      \n",
+    "    \n",
+    "\n",
+    "print(call_gpt_4o())"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": null,
+=======
+   "execution_count": 42,
+>>>>>>> week2_exercise_bootcamp_feb2026_nikhilsuresh
+   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get Llama 3.2 to answer\n",
+    "\n",
+    "def call_llama_3_3():\n",
+    "    #   \n",
+    "    try:\n",
+    "        response = ''\n",
+    "        stream = openai.chat.completions.create(model= MODEL_LLAMA,messages= messages , stream=True)\n",
+    "        display_handle = display(Markdown(\"\"), display_id=True)\n",
+    "        for chunk in stream:\n",
+    "            response += chunk.choices[0].delta.content or ''\n",
+    "            update_display(Markdown(response), display_id=display_handle.display_id) \n",
+    "    except Exception as e:\n",
+    "        print(f\"Error: {e}\") \n",
+    "    \n",
+    "    \n",
+    "print(call_llama_3_3()) "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.12.12)",
+   "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": "3.12.12"
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+ "nbformat": 4,
+ "nbformat_minor": 5
+}