day-2 exercise with ollama
This commit is contained in:
Rohit Nain
290
week1/community-contributions/Day-2_exercise_with_ollama3.ipynb
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290
week1/community-contributions/Day-2_exercise_with_ollama3.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "135717e7",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import requests\n",
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"from dotenv import load_dotenv\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display\n",
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"from openai import OpenAI\n",
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"import ollama"
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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": 2,
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"id": "29a9e634",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# OPTION 1\n",
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"# using openai\n",
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"\n",
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"# message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
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"# client = OpenAI(base_url=\"http://localhost:11434/v1\", api_key=\"not-needed\")\n",
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"# response = openai.chat.completions.create(model=`<name of model>`, messages=[{\"role\":\"user\", \"content\":message}])\n",
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"# print(response.choices[0].message.content)"
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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": "306993ed",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# OPTION 2\n",
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"# using Ollama\n",
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"\n",
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"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
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"model=\"llama3\"\n",
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"response=ollama.chat(model=model,messages=[{\"role\":\"user\",\"content\":message}])\n",
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"print(response[\"message\"][\"content\"])\n"
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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": 4,
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"id": "856f767b",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# A class to represent a Webpage\n",
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"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
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"\n",
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"# Some websites need you to use proper headers when fetching them:\n",
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"headers = {\n",
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" \"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",
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"}\n",
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"\n",
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"class Website:\n",
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"\n",
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" def __init__(self, url):\n",
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" \"\"\"\n",
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" Create this Website object from the given url using the BeautifulSoup library\n",
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" \"\"\"\n",
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" self.url = url\n",
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" response = requests.get(url, headers=headers)\n",
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" soup = BeautifulSoup(response.content, 'html.parser')\n",
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" self.title = soup.title.string if soup.title else \"No title found\"\n",
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
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" irrelevant.decompose()\n",
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
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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": 5,
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"id": "4ce558dc",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# Let's try one out. Change the website and add print statements to follow along.\n",
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"\n",
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"ed = Website(\"https://edwarddonner.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": 6,
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"id": "5e3956f8",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
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"\n",
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"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
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"and provides a short summary, ignoring text that might be navigation related. \\\n",
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"Respond in markdown.\""
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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": 7,
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"id": "99d791b4",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# A function that writes a User Prompt that asks for summaries of websites:\n",
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"\n",
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"def user_prompt_for(website):\n",
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" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
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" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
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"please provide a short summary of this website in markdown. \\\n",
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"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
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" user_prompt += website.text\n",
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" return user_prompt"
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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": 8,
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"id": "5d89b748",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# See how this function creates exactly the format above\n",
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"\n",
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"def messages_for(website):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(website)}\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": 9,
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"id": "9a97d3e2",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# And now: call the OpenAI API. You will get very familiar with this!\n",
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"\n",
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"def summarize(url):\n",
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" website = Website(url)\n",
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" response=ollama.chat(model=model,messages=messages_for(website))\n",
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" return(response[\"message\"][\"content\"])"
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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": "ec13fe0a",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"summarize(\"https://edwarddonner.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": 11,
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"id": "e3ade092",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# A function to display this nicely in the Jupyter output, using markdown\n",
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"\n",
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"def display_summary(url):\n",
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" summary = summarize(url)\n",
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" display(Markdown(summary))"
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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": "be2d49e6",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"display_summary(\"https://edwarddonner.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": "1ccbf33b",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"display_summary(\"https://cnn.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": "ae3d0eae",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"display_summary(\"https://anthropic.com\")"
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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": "3.11.13"
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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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