Spaces:
Sleeping
Sleeping
File size: 7,294 Bytes
5fdb69e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 |
{
"cells": [
{
"cell_type": "markdown",
"id": "1b8f7ac7-7089-427a-8f63-57211da7e691",
"metadata": {},
"source": [
"## Summarizing Research Papers"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "641d5c00-ff09-4697-9c87-5de5df1469f8",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"\n",
"# If you get an error running this cell, then please head over to the troubleshooting notebook!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1a6a2864-fd9d-43e2-b0ca-1476c0153077",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" 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",
"elif api_key.strip() != api_key:\n",
" 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",
"else:\n",
" print(\"API key found and looks good so far!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "340e3166-5aa7-4bcf-9cf0-e2fc776dc322",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73198fb7-581f-42ac-99a6-76c56c86248d",
"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 Paper:\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": "3b39c3ad-d238-418e-9e6a-55a4fd717ebc",
"metadata": {},
"outputs": [],
"source": [
"#Insert Paper URL\n",
"res = Paper(\" \")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "83bc1eec-4187-4c6c-b188-3f72564351f1",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"\"\"You are a research paper summarizer. You take the url of the research paper and extract the following:\n",
"1) Title and Author of the research paper.\n",
"2) Year it was published it\n",
"3) Objective or aim of the research to specify why the research was conducted\n",
"4) Background or Introduction to explain the need to conduct this research or any topics the readers must have knowledge about\n",
"5) Type of research/study/experiment to explain what kind of research it is.\n",
"6) Methods or methodology to explain what the researchers did to conduct the research\n",
"7) Results and key findings to explain what the researchers found\n",
"8) Conclusion tells about the conclusions that can be drawn from this research including limitations and future direction\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4aba1b51-9a72-4325-8c86-3968b9d3172e",
"metadata": {},
"outputs": [],
"source": [
"# A function that writes a User Prompt that asks for summaries of websites:\n",
"\n",
"def user_prompt_for(paper):\n",
" user_prompt = f\"You are looking at a website titled {paper.title}\"\n",
" user_prompt += \"\\nThe contents of this paper is as follows; \\\n",
"please provide a short summary of this paper in markdown. \\\n",
"If it includes additional headings, then summarize these too.\\n\\n\"\n",
" user_prompt += paper.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "659cb3c4-8a02-493d-abe7-20da9219e358",
"metadata": {},
"outputs": [],
"source": [
"# See how this function creates exactly the format above\n",
"def messages_for(paper):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(paper)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08ea1193-1bbb-40de-ba64-d02ffe109372",
"metadata": {},
"outputs": [],
"source": [
"messages_for(res)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e07d00e7-1b87-4ca8-a69d-4a206e34a2b2",
"metadata": {},
"outputs": [],
"source": [
"# And now: call the OpenAI API. You will get very familiar with this!\n",
"\n",
"def summarize(url):\n",
" paper = Paper(url)\n",
" response = openai.chat.completions.create(\n",
" model = \"gpt-4o-mini\",\n",
" messages = messages_for(paper)\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c12df95-1700-47ee-891b-96b0a7227bdd",
"metadata": {},
"outputs": [],
"source": [
"# A function to display this nicely in the Jupyter output, using markdown\n",
"\n",
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05cff05f-2b74-44a4-9dbd-57c08f8f56cb",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Insert Paper URL in the quotes below\n",
"display_summary(\" \")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|