{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41136d6f-07bc-4f6f-acba-784b8e5707b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8612b4f7-5c31-48f3-8423-261914509617",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "\n",
    "OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
    "HEADERS = {\"Content-Type\": \"application/json\"}\n",
    "MODEL = \"llama3.2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508bd442-7860-4215-b0f2-57f7adefd807",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a messages list using the same format that we used for OpenAI\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc7e8ada-4f8d-4090-be64-4aa72e03ac58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's just make sure the model is loaded\n",
    "\n",
    "!ollama pull llama3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4afd2e56-191a-4e31-949e-9b9376a39b5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# There's actually an alternative approach that some people might prefer\n",
    "# You can use the OpenAI client python library to call Ollama:\n",
    "\n",
    "from openai import OpenAI\n",
    "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
    "\n",
    "response = ollama_via_openai.chat.completions.create(\n",
    "    model=MODEL,\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "365f3d83-2601-42fb-89cc-98a4e1f79e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
    "response = ollama_via_openai.chat.completions.create(model=MODEL, messages=[{\"role\":\"user\", \"content\":message}])\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29c383ae-bf5b-41bc-b5af-a22f851745dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A class to represent a Webpage\n",
    "# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
    "\n",
    "# Some websites need you to use proper headers when fetching them:\n",
    "headers = {\n",
    " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
    "}\n",
    "\n",
    "class Website:\n",
    "\n",
    "    def __init__(self, url):\n",
    "        \"\"\"\n",
    "        Create this Website object from the given url using the BeautifulSoup library\n",
    "        \"\"\"\n",
    "        self.url = url\n",
    "        response = requests.get(url, headers=headers)\n",
    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc61e30f-653f-4554-b1cd-6e61a0e2430a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ed = Website(\"https://edwarddonner.com\")\n",
    "print(ed.title)\n",
    "print(ed.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db2066fb-3079-4775-832a-dcc0f19beb6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
    "and provides a short summary, ignoring text that might be navigation related. \\\n",
    "Respond in markdown.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af81b070-b6fe-4b18-aa0b-c03cd76a0adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_prompt_for(website):\n",
    "    user_prompt = f\"You are looking at a website titled {website.title}\"\n",
    "    user_prompt += \"\\nThe contents of this website is as follows; \\\n",
    "please provide a short summary of this website in markdown. \\\n",
    "If it includes news or announcements, then summarize these too.\\n\\n\"\n",
    "    user_prompt += website.text\n",
    "    return user_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e66291b-23b1-4915-b6a3-11a4b6a4db66",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
    "    {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67c92f47-4a3b-491f-af00-07fda470087e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def messages_for(website):\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db1b9085-e5e7-4ec9-a264-acc389085ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages_for(ed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "677bfc2f-19ac-46a0-b67e-a2b2ddf9cf6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize(url):\n",
    "    website = Website(url)\n",
    "    response = ollama_via_openai.chat.completions.create(\n",
    "        model = MODEL,\n",
    "        messages = messages_for(website)\n",
    "    )\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee3242ba-b695-4b1e-8a91-2fdeb536c2e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "summarize(\"https://edwarddonner.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85142cb8-ce0c-4c31-8b26-bb1744cf99ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_summary(url):\n",
    "    summary = summarize(url)\n",
    "    display(Markdown(summary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63db51a7-dd03-4514-8954-57156967f82c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "display_summary(\"https://app.daily.dev/posts/bregman-arie-devops-exercises-linux-jenkins-aws-sre-prometheus-docker-python-ansible-git-k-yli9wthnf\")"
   ]
  }
 ],
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