192 lines
6.4 KiB
Plaintext
192 lines
6.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4dabb31c-a584-4715-9714-9fc9978c3cb5",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Get IPL best team"
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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": "3bb88086-ea9c-4766-9baf-a57bb69c3202",
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"metadata": {},
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"outputs": [],
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"source": [
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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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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "9dc24243-d20a-48aa-b90b-26ef90233e22",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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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"source": [
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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": 14,
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"id": "cb35e3d1-8733-4931-8744-9c3754793161",
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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": 15,
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"id": "63d62eb3-3255-4046-863e-d866a833d1a6",
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"metadata": {},
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"outputs": [],
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"source": [
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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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" def __init__(self, url):\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": 17,
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"id": "409a70a6-331a-4ea4-ab8d-7a46fffc70d7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"How about \"The A-Team 2.0\"? Because clearly, you’re aiming for a sequel that’s already better than the original. Or maybe \"The Not-So-Secret League of Awesome\"? That one’s a real conversation starter! What vibe are you going for?\n",
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"[{'role': 'system', 'content': 'You are an assistant that analyzes the contents of a cric info website and provides a short summary of best team in IPL. Respond in markdown.'}, {'role': 'user', 'content': '\\n Get page title\\n'}]\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"# Best Team in IPL History\n",
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"\n",
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"The Indian Premier League (IPL) has seen various teams competing for the title since its inception in 2008. Some of the most successful teams in IPL history include:\n",
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"\n",
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"1. **Mumbai Indians**: They have clinched the IPL trophy a record five times (2013, 2015, 2017, 2019, 2020) and are known for their strong squad and strategic gameplay.\n",
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"\n",
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"2. **Chennai Super Kings**: With four titles (2010, 2011, 2018, 2021), the CSK has consistently been one of the top teams, led by the experienced MS Dhoni.\n",
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"\n",
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"3. **Kolkata Knight Riders**: They have won the championship twice (2012, 2014) and are recognized for their fan base and competitive spirit.\n",
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"\n",
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"Overall, the Mumbai Indians are often considered the best team in IPL history due to their multiple championships and consistent performance over the years."
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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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"# Step 1: Create your prompts\n",
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"system_prompt = \"You are an assistant that analyzes the contents of a cric info website \\\n",
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"and provides a short summary of best team in IPL. \\\n",
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"Respond in markdown.\"\n",
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"\n",
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"user_prompt = \"\"\"\n",
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" Get page title\n",
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"\"\"\"\n",
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"\n",
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"# Step 2: Make the messages list\n",
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"messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
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" {\"role\": \"user\", \"content\": \"Team name\"}\n",
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"]\n",
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"\n",
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"# Step 3: Call OpenAI\n",
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"\n",
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"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
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"print(response.choices[0].message.content)\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}]\n",
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"\n",
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"webUrl = \"https://www.google.com\"\n",
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"print(messages_for(webUrl))\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 = openai.chat.completions.create(\n",
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" model = \"gpt-4o-mini\",\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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"# Step 4: print the result\n",
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"summary = summarize(webUrl)\n",
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"display(Markdown(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": "3.11.12"
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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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