Add Day 2 Ollama Solution notebook
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Kunal Brahma
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week1/community-contributions/day2_Ollama_Solution.ipynb
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week1/community-contributions/day2_Ollama_Solution.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": "663695bd-d1f2-4acf-8669-02d9f75f1bf4",
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"metadata": {},
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"source": [
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"# Day 2: Ollama Solution for Website Summarization\n",
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"### Building and Deploying Website Summarization Tools with 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": 14,
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"id": "112ef04a-136e-4e65-b94e-8674a64606ed",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"**Summary of Website**\n",
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"=========================\n",
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"### Overview\n",
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"\n",
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"The \"Home - Edward Donner\" website is a personal blog hosted by Edward Donner, the co-founder and CTO of Nebula.io. The website shares insights on his professional experiences with AI, particularly in applying LLMs (Large Language Models) to help people discover their potential.\n",
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"\n",
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"### Recent Developments\n",
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"------------------------\n",
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"\n",
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"* **Courses**: Upcoming online courses, including \"Connecting my courses – become an LLM expert and leader\" and \"The Complete Agentic AI Engineering Course\"\n",
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"* **Events**:\n",
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"\t+ May 28, 2025: Launch connecting his courses (LLM Expert and Leader)\n",
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"\t+ May 18, 2025: 2025 AI Executive Briefing\n",
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"\t+ April 21, 2025: AI Executive Briefing\n",
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"\t+ January 23, 2025: The Complete Agentic AI Engineering Course\n",
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"\n",
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"### About the Creator\n",
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"-------------------------\n",
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"\n",
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"* Edward Donner is the co-founder and CTO of Nebula.io.\n",
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"* He has experience as a founder and CEO of an AI startup that was acquired in 2021.\n",
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"* His interests include DJing, amateur electronic music production, and reading Hacker News.\n",
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"\n",
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"### Contact Information\n",
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"-------------------------\n",
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"\n",
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"* Email: [ed @ edwarddonner . com](mailto:ed@edwarddonner.com)\n",
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"* Website: www.edwarddonner.com\n",
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"* Social media links:\n",
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"\t+ LinkedIn\n",
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"\t+ Twitter\n",
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"\t+ Facebook"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import requests\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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"\n",
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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
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"HEADERS = {\"Content-Type\": \"application/json\"}\n",
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"MODEL = \"llama3.2\"\n",
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"\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)\n",
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"\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.\"\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\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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" ]\n",
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"\n",
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"url = \"https://sitemakerlab.com/\" \n",
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"site = Website(url)\n",
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"messages = messages_for(site)\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_via_openai.chat.completions.create(\n",
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" model = MODEL,\n",
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" messages = messages_for(website)\n",
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" )\n",
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" return response.choices[0].message.content\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))\n",
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"\n",
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"display_summary(\"https://edwarddonner.com\")\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": null,
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"id": "566373e7-8612-4c39-a432-7795cb7d2e6c",
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"metadata": {},
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"outputs": [],
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"source": []
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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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