From 7bd46ba43b75f5a7958a291aa9d8f036d463ea1d Mon Sep 17 00:00:00 2001 From: habibmir808 Date: Mon, 2 Jun 2025 19:40:49 +0600 Subject: [PATCH] user can summarize research papers by website link --- .../day1_summarize_research_papers.ipynb | 307 ++++++++++++++++++ 1 file changed, 307 insertions(+) create mode 100644 week1/community-contributions/day1_summarize_research_papers.ipynb diff --git a/week1/community-contributions/day1_summarize_research_papers.ipynb b/week1/community-contributions/day1_summarize_research_papers.ipynb new file mode 100644 index 0000000..246da69 --- /dev/null +++ b/week1/community-contributions/day1_summarize_research_papers.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "3ba06289-d17a-4ccd-85f5-2b79956d4e59", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install selenium" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb6636be-e43f-4896-aadd-cafda003ed4e", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q -U google-genai" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfe66209-1d33-4292-80f1-20e11baf4bc3", + "metadata": {}, + "outputs": [], + "source": [ + "from selenium import webdriver\n", + "from selenium.webdriver.chrome.options import Options\n", + "from selenium.webdriver.chrome.service import Service\n", + "from bs4 import BeautifulSoup\n", + "import time\n", + "import os\n", + "from dotenv import load_dotenv\n", + "from IPython.display import Markdown, display\n", + "from google import genai\n", + "from google.genai import types\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2b4306c-17d0-46fe-a889-7440ff809dc6", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#load env\n", + "load_dotenv(override=True)\n", + "api_key = os.getenv('GEMINI_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 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!\")" + ] + }, + { + "cell_type": "markdown", + "id": "08ec6fec-886c-4a0c-a046-e8643ad700d3", + "metadata": {}, + "source": [ + "# Lets make a simple call for check our model is working fine or not" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89143d5c-0013-4f7e-8e1f-f7db7e936f0d", + "metadata": {}, + "outputs": [], + "source": [ + "client = genai.Client(api_key=api_key)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1144b77a-6785-479a-ab4f-bb0ab5624b49", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "response = client.models.generate_content(\n", + " model=\"gemini-2.5-flash-preview-05-20\",\n", + " contents=[\"hi gemini\"]\n", + ")\n", + "print(response.text)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbf3836c-19b8-44e1-904a-f265925c2786", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class Website:\n", + " def __init__(self, url, driver_path=None, wait_time=3):\n", + " self.url = url\n", + " self.wait_time = wait_time\n", + "\n", + " # Headless Chrome settings\n", + " options = Options()\n", + " # options.add_argument(\"--headless\") \n", + " # Headless mode runs the browser in the background (invisible).\n", + " # However, some websites (like openai.com) block headless browsers.\n", + " # So if this line is active, the page may not load correctly and you may not get the full content.\n", + " options.add_argument(\"--disable-gpu\")\n", + " options.add_argument(\"--no-sandbox\")\n", + " options.add_argument(\"--window-size=1920x1080\")\n", + "\n", + " # Driver path\n", + " if driver_path:\n", + " service = Service(executable_path=driver_path)\n", + " else:\n", + " service = Service() \n", + "\n", + " # Start browser\n", + " driver = webdriver.Chrome(service=service, options=options)\n", + " driver.get(url)\n", + "\n", + " # Wait for the loading page\n", + " time.sleep(self.wait_time)\n", + "\n", + " # Take page source\n", + " html = driver.page_source\n", + " driver.quit()\n", + "\n", + " # Analysis with BeautifulSoup \n", + " soup = BeautifulSoup(html, 'html.parser')\n", + " self.title = soup.title.string if soup.title else \"No title found\"\n", + "\n", + " # Clean irrelevant tags\n", + " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", + " irrelevant.decompose()\n", + "\n", + " self.text = soup.body.get_text(separator=\"\\n\", strip=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "852c52e2-bd4d-4bb9-94ef-e498c33f1a89", + "metadata": {}, + "outputs": [], + "source": [ + "system_prompt = \"\"\"You are an academic research assistant specialized in summarizing scholarly papers. Follow this workflow rigorously:\n", + "\n", + "Step 1: Document Verification\n", + "Verify if the input is a research paper by checking for:\n", + "\n", + "Presence of academic sections (Abstract, Introduction, Methodology, Results, Discussion, References)\n", + "\n", + "Technical/scholarly language\n", + "\n", + "Citations (in-text or bibliography)\n", + "\n", + "Research claims or data analysis\n", + "If NOT a research paper:\n", + "→ Respond: \"This doesn't appear to be a research paper. Please upload peer-reviewed academic literature for summarization.\"\n", + "\n", + "Step 2: Structured Summary (If verified)\n", + "Generate a 5-section summary in this exact format:\n", + "\n", + "1. Research Question\n", + "[Identify core problem/gap addressed in 1 sentence]\n", + "\n", + "2. Methodology\n", + "[Study design, data sources, analytical techniques in 2 bullet points]\n", + "\n", + "3. Key Findings\n", + "[3-4 quantified results with numerical evidence from tables/figures]\n", + "\n", + "4. Limitations\n", + "[2 major constraints acknowledged by authors]\n", + "\n", + "5. Significance\n", + "[Impact on field & practical implications in 1 sentence]\n", + "\n", + "Critical Rules:\n", + "Accuracy Priority: Never invent data. Write \"Not specified\" for missing elements\n", + "\n", + "Source Anchoring: Cite page/paragraph numbers for claims (e.g., \"Fig 3 shows 24% improvement\")\n", + "\n", + "Jargon Handling: Simplify complex terms using: [Technical Term → Layman Explanation] inline\n", + "\n", + "Bias Alert: Flag any undeclared funding/sponsorship conflicts\n", + "\n", + "Output Format: Strict Markdown with section headers, 200-word maximum\n", + "\n", + "Example Output:\n", + "1. Research Question\n", + "How does microplastic concentration affect zebrafish neural development?\n", + "\n", + "2. Methodology\n", + "\n", + "Exposed embryos to 0.1-10μm PET particles (5-100mg/L) for 96h\n", + "\n", + "Quantified gene expression (RT-qPCR) and behavioral assays (Open Field Test)\n", + "\n", + "3. Key Findings\n", + "▲ 40% reduction in neuron count at 50mg/L exposure (p<0.01, Fig 2B)\n", + "■ 2.3x increase in anxiolytic behavior (Table 3)\n", + "▼ 17% downregulation in shha expression (p=0.03)\n", + "\n", + "4. Limitations\n", + " \n", + "Used static exposure vs dynamic aquatic environments\n", + "\n", + "Limited proteomic validation\n", + "\n", + "5. Significance\n", + "Establishes dose-dependent neurotoxicity thresholds for aquatic toxicology regulations.\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7620c685-c35c-4d6b-aaf1-a3da98f19ca7", + "metadata": {}, + "outputs": [], + "source": [ + "# A function that writes a User Prompt that asks for summaries of websites:\n", + "\n", + "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 summary of this website in markdown.\\n\\n\"\n", + " user_prompt += website.text\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4257406-089b-45a3-bfb5-272004360a49", + "metadata": {}, + "outputs": [], + "source": [ + "def summarize(url):\n", + " website = Website(url)\n", + " response = client.models.generate_content(\n", + " model=\"gemini-2.5-flash-preview-05-20\",\n", + " config=types.GenerateContentConfig(\n", + " system_instruction=system_prompt),\n", + " contents=user_prompt_for(website)\n", + " )\n", + "\n", + " return response.text\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f68b32ae-9e65-4aa4-ae8d-cc2482c4a2e2", + "metadata": {}, + "outputs": [], + "source": [ + "def display_summary(url):\n", + " summary = summarize(url)\n", + " display(Markdown(summary))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae52543c-01c1-4262-b53c-95ef4e5a93aa", + "metadata": {}, + "outputs": [], + "source": [ + "display_summary(\"https://onlinelibrary.wiley.com/doi/full/10.1155/2021/8812542\")" + ] + } + ], + "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": "3.11.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}