Merge pull request #116 from sakinarao/community-contributions-branch
Added my contributions to community-contributions
This commit is contained in:
Ed Donner
GitHub
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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": "1b8f7ac7-7089-427a-8f63-57211da7e691",
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"metadata": {},
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"source": [
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"## Summarizing Research Papers"
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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": "641d5c00-ff09-4697-9c87-5de5df1469f8",
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"metadata": {},
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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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"\n",
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"# If you get an error running this cell, then please head over to the troubleshooting notebook!"
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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": "1a6a2864-fd9d-43e2-b0ca-1476c0153077",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\n",
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"\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"# Check the key\n",
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"\n",
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"if not api_key:\n",
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
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"elif not api_key.startswith(\"sk-proj-\"):\n",
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" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
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"elif api_key.strip() != api_key:\n",
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" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
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"else:\n",
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" print(\"API key found and looks good so far!\")\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": "340e3166-5aa7-4bcf-9cf0-e2fc776dc322",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()"
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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": "73198fb7-581f-42ac-99a6-76c56c86248d",
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"metadata": {},
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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 Paper:\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": null,
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"id": "3b39c3ad-d238-418e-9e6a-55a4fd717ebc",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Insert Paper URL\n",
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"res = Paper(\" \")"
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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": "83bc1eec-4187-4c6c-b188-3f72564351f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_prompt = \"\"\"You are a research paper summarizer. You take the url of the research paper and extract the following:\n",
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"1) Title and Author of the research paper.\n",
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"2) Year it was published it\n",
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"3) Objective or aim of the research to specify why the research was conducted\n",
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"4) Background or Introduction to explain the need to conduct this research or any topics the readers must have knowledge about\n",
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"5) Type of research/study/experiment to explain what kind of research it is.\n",
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"6) Methods or methodology to explain what the researchers did to conduct the research\n",
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"7) Results and key findings to explain what the researchers found\n",
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"8) Conclusion tells about the conclusions that can be drawn from this research including limitations and future direction\"\"\""
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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": "4aba1b51-9a72-4325-8c86-3968b9d3172e",
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"metadata": {},
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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(paper):\n",
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" user_prompt = f\"You are looking at a website titled {paper.title}\"\n",
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" user_prompt += \"\\nThe contents of this paper is as follows; \\\n",
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"please provide a short summary of this paper in markdown. \\\n",
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"If it includes additional headings, then summarize these too.\\n\\n\"\n",
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" user_prompt += paper.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": null,
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"id": "659cb3c4-8a02-493d-abe7-20da9219e358",
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"metadata": {},
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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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"def messages_for(paper):\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(paper)}\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": "08ea1193-1bbb-40de-ba64-d02ffe109372",
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"metadata": {},
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"outputs": [],
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"source": [
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"messages_for(res)"
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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": "e07d00e7-1b87-4ca8-a69d-4a206e34a2b2",
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"metadata": {},
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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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" paper = Paper(url)\n",
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" response = openai.chat.completions.create(\n",
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" model = \"gpt-4o-mini\",\n",
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" messages = messages_for(paper)\n",
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" )\n",
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" return 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": "5c12df95-1700-47ee-891b-96b0a7227bdd",
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"metadata": {},
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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": "05cff05f-2b74-44a4-9dbd-57c08f8f56cb",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Insert Paper URL in the quotes below\n",
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"display_summary(\" \")"
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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": "3.11.11"
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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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@@ -0,0 +1,192 @@
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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": "e3ce0a59-fbfb-4377-85db-f62f95039200",
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"metadata": {},
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"source": [
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"# Day2 EXERCISE - Summarization using 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": null,
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"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
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"metadata": {},
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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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"from dotenv import load_dotenv\n",
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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"
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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": "29ddd15d-a3c5-4f4e-a678-873f56162724",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Constants\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\""
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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": "cb5c0f84-4e4d-4f87-b492-e09d0333a638",
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"metadata": {},
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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": null,
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"id": "23457b52-c85b-4dc1-b946-6f1461dc0675",
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"metadata": {},
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"outputs": [],
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"source": [
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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": null,
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"id": "bed206ed-43c1-4f68-ad01-a738b3b4648d",
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"metadata": {},
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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": null,
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"id": "e558f381-614a-461f-83bc-e5bdc99460df",
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"metadata": {},
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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": null,
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"id": "e5ba638d-aeb9-441e-a62a-8e8027ad8439",
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"metadata": {},
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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": null,
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"id": "e85ca2ec-3e46-4b8f-9c2f-66e7d20138fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"#website search\n",
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"\n",
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"ed = Website(\"https://edwarddonner.com\")\n",
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"messages=messages_for(ed)\n",
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"\n",
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"payload = {\n",
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" \"model\": MODEL,\n",
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" \"messages\": messages,\n",
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" \"stream\": False\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": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
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"metadata": {},
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"outputs": [],
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"source": [
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"import ollama\n",
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"\n",
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"response = ollama.chat(model=MODEL, messages=messages)\n",
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"print(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": "402d5686-4e76-4110-b65a-b3906c35c0a4",
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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": "3.11.11"
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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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Reference in New Issue
Block a user