119 lines
4.6 KiB
Plaintext
119 lines
4.6 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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"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, clear_output\n",
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"from openai import OpenAI\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"# Day 2 Exercise with Ollama API\n",
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"api_key = os.getenv('OLLAMA_API_KEY')\n",
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"base_url = os.getenv('OLLAMA_BASE_URL')\n",
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"MODEL = \"llama3.2\"\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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"messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
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" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
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"]\n",
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" \n",
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"# Check the key\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-; Looks like you are using DeepSeek (R1) model.\")\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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" \n",
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"openai = OpenAI(api_key=api_key, base_url=base_url)\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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"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; please provide a short summary of this website in markdown. 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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"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=MODEL,\n",
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" messages=messages_for(website),\n",
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" stream=True\n",
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" )\n",
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" print(\"Streaming response:\")\n",
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" accumulated_content = \"\" # Accumulate the content here\n",
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" for chunk in response:\n",
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" if chunk.choices[0].delta.content: # Check if there's content in the chunk\n",
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" accumulated_content += chunk.choices[0].delta.content # Append the chunk to the accumulated content\n",
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" clear_output(wait=True) # Clear the previous output\n",
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" display(Markdown(accumulated_content)) # Display the updated content\n",
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" \n",
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"def display_summary():\n",
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" url = str(input(\"Enter the URL of the website you want to summarize: \"))\n",
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" summarize(url)\n",
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"\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": 4
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}
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