From 0fd7e1622a3320646572ed916e528da3850f3d4d Mon Sep 17 00:00:00 2001 From: Muawiya Date: Fri, 11 Apr 2025 19:00:46 +0300 Subject: [PATCH] my changes --- my_projects/company_brochure.md | 42 + my_projects/company_brochure.py | 176 +++ week1/Guide to Jupyter.ipynb | 161 ++- .../day-1-generate-cover-letter-from-cv.ipynb | 62 +- .../day-1-ollama-app.ipynb | 2 +- week1/day1.ipynb | 202 +++- week1/day2 EXERCISE.ipynb | 366 +++++- week1/day5.ipynb | 488 +++++++- week1/day5_company_brochure_modified.ipynb | 1008 +++++++++++++++++ week1/solutions/day2 SOLUTION.ipynb | 2 +- week1/week1 EXERCISE.ipynb | 281 ++++- week2/day1.ipynb | 637 ++++++++++- week2/day2.ipynb | 557 ++++++++- week2/day3.ipynb | 26 +- 14 files changed, 3819 insertions(+), 191 deletions(-) create mode 100644 my_projects/company_brochure.md create mode 100644 my_projects/company_brochure.py create mode 100644 week1/day5_company_brochure_modified.ipynb diff --git a/my_projects/company_brochure.md b/my_projects/company_brochure.md new file mode 100644 index 0000000..05b1f0b --- /dev/null +++ b/my_projects/company_brochure.md @@ -0,0 +1,42 @@ +```markdown +# Hugging Face: Building the Future of AI Together + +## Overview +**Hugging Face** is a leading platform at the forefront of the AI and machine learning revolution. With a mission to democratize artificial intelligence, we empower the global community to collaborate, innovate, and create groundbreaking models and datasets. + +### Join the AI Community +Our platform serves as a collaborative space where individuals and organizations can share, develop, and discover over 1 million models, 250,000 datasets, and a plethora of applications across various modalities—text, image, video, audio, and 3D. From researchers to enterprises, Hugging Face is the hub for AI excellence. + +## Products & Services +- **Models**: Explore trending AI models that are continually updated and optimized for performance. +- **Datasets**: Access a vast collection of datasets for all types of machine learning tasks. +- **Spaces**: Deploy applications quickly with user-friendly, customizable Spaces. +- **Enterprise Solutions**: Tailored options for organizations seeking enterprise-grade capabilities with advanced security and dedicated support. + +### Real-World Impact +Our growing community includes over 50,000 organizations, ranging from top enterprises like Amazon, Google, and Microsoft to numerous non-profits, demonstrating a vast and diverse network of collaboration. + +## Culture & Community +At Hugging Face, we believe in building a supportive and inclusive environment. Our culture is centered around collaboration, transparency, and open-source principles. We foster a community where innovation thrives, and every voice is valued. + +- **Core Values**: + - **Open Source**: We contribute to the development of cutting-edge technologies and tools in the public domain. + - **Collaboration**: Work with like-minded individuals and organizations to pursue meaningful projects. + - **Accessibility**: Strive to make AI accessible to everyone. + +## Careers & Job Opportunities +Join a passionate team at Hugging Face dedicated to advancing artificial intelligence. We are constantly on the lookout for innovators, thinkers, and doers to help us expand our mission. Whether you are a seasoned expert or just starting your career, we welcome diverse talent to contribute to our projects. + +### Current Openings +Visit our [Jobs Page](https://huggingface.co/jobs) to explore exciting career opportunities and become part of a community that is shaping the future of AI! + +## Get Involved +- **Explore AI Apps**: Check out the vast array of applications built on our models. +- **Engage with the Community**: Join discussions on our forums, or connect with us on social media platforms like [Twitter](https://twitter.com/huggingface) and [Discord](https://discord.gg/huggingface). +- **Sign Up Today**: Create your account to get started with building, training, and deploying AI models! + +### Contact Us +For inquiries on partnerships, enterprise solutions, and more, reach out through our [Contact Page](https://huggingface.co/contact). + +Together, we can build the future of artificial intelligence. +``` diff --git a/my_projects/company_brochure.py b/my_projects/company_brochure.py new file mode 100644 index 0000000..0b43f49 --- /dev/null +++ b/my_projects/company_brochure.py @@ -0,0 +1,176 @@ +# imports +# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt + +import os +import requests +import json +from typing import List +from dotenv import load_dotenv +from bs4 import BeautifulSoup +from IPython.display import Markdown, display, update_display +from openai import OpenAI + +# Initialize and constants + +load_dotenv(override=True) +api_key = os.getenv('OPENAI_API_KEY') + +if api_key and api_key.startswith('sk-proj-') and len(api_key) > 10: + print("API key looks good so far") +else: + print("There might be a problem with your API key? Please visit the troubleshooting notebook!") + +MODEL = 'gpt-4o-mini' +openai = OpenAI() + +# A class to represent a Webpage + +# Some websites need you to use proper headers when fetching them: +headers = { + "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" +} + + +class Website: + """ + A utility class to represent a Website that we have scraped, now with links + """ + + def __init__(self, url): + self.url = url + response = requests.get(url, headers=headers) + self.body = response.content + soup = BeautifulSoup(self.body, 'html.parser') + self.title = soup.title.string if soup.title else "No title found" + if soup.body: + for irrelevant in soup.body(["script", "style", "img", "input"]): + irrelevant.decompose() + self.text = soup.body.get_text(separator="\n", strip=True) + else: + self.text = "" + links = [link.get('href') for link in soup.find_all('a')] + self.links = [link for link in links if link] + + def get_contents(self): + return f"Webpage Title:\n{self.title}\nWebpage Contents:\n{self.text}\n\n" + + +link_system_prompt = "You are provided with a list of links found on a webpage. \ +You are able to decide which of the links would be most relevant to include in a brochure about the company, \ +such as links to an About page, or a Company page, or Careers/Jobs pages.\n" +link_system_prompt += "You should respond in JSON as in this example:" +link_system_prompt += """ +{ + "links": [ + {"type": "about page", "url": "https://full.url/goes/here/about"}, + {"type": "careers page": "url": "https://another.full.url/careers"} + ] +} +""" + + +def get_links_user_prompt(website): + user_prompt = f"Here is the list of links on the website of {website.url} - " + user_prompt += "please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \ +Do not include Terms of Service, Privacy, email links.\n" + user_prompt += "Links (some might be relative links):\n" + user_prompt += "\n".join(website.links) + return user_prompt + + +def get_links(url): + website = Website(url) + response = openai.chat.completions.create( + model=MODEL, + messages=[ + {"role": "system", "content": link_system_prompt}, + {"role": "user", "content": get_links_user_prompt(website)} + ], + response_format={"type": "json_object"} + ) + result = response.choices[0].message.content + return json.loads(result) + + +def get_all_details(url): + result = "Landing page:\n" + result += Website(url).get_contents() + links = get_links(url) + print("Found links:", links) + for link in links["links"]: + result += f"\n\n{link['type']}\n" + result += Website(link["url"]).get_contents() + return result + + +system_prompt_2 = "You are an assistant that analyzes the contents of several relevant pages from a company website \ +and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\ +Include details of company culture, customers and careers/jobs if you have the information." + +# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone': + +# system_prompt = "You are an assistant that analyzes the contents of several relevant pages from a company website \ +# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\ +# Include details of company culture, customers and careers/jobs if you have the information." + +def get_brochure_user_prompt(company_name, url): + user_prompt = f"You are looking at a company called: {company_name}\n" + user_prompt += f"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\n" + user_prompt += get_all_details(url) + user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters + return user_prompt + +def create_brochure(company_name, url): + response = openai.chat.completions.create( + model=MODEL, + messages=[ + {"role": "system", "content": system_prompt_2}, + {"role": "user", "content": get_brochure_user_prompt(company_name, url)} + ], + ) + result = response.choices[0].message.content + # Jupytrer + # display(Markdown(result)) + # pycharm + with open("company_brochure.md", "w") as f: + f.write(result) + display(result) + + +def stream_brochure(company_name, url): + stream = openai.chat.completions.create( + model=MODEL, + messages=[ + {"role": "system", "content": system_prompt_2}, + {"role": "user", "content": get_brochure_user_prompt(company_name, url)} + ], + stream=True + ) + + response = "" + display_handle = display(Markdown(""), display_id=True) + for chunk in stream: + response += chunk.choices[0].delta.content or '' + response = response.replace("```", "").replace("markdown", "") + update_display(Markdown(response), display_id=display_handle.display_id) + + +if __name__ == "__main__": + # ed = Website("https://edwarddonner.com") + # print(ed.links) + + # Anthropic has made their site harder to scrape, so I'm using HuggingFace.. + + # huggingface = Website("https://huggingface.co") + # print(huggingface.links) + # + # print(get_links("https://huggingface.co")) + + # anthropic_page = Website("https://anthropic.com") + # anthropic_page.links + + # print(get_brochure_user_prompt("HuggingFace", "https://huggingface.co")) + + create_brochure("HuggingFace", "https://huggingface.co") + + diff --git a/week1/Guide to Jupyter.ipynb b/week1/Guide to Jupyter.ipynb index ebcc9f0..64302e3 100644 --- a/week1/Guide to Jupyter.ipynb +++ b/week1/Guide to Jupyter.ipynb @@ -32,13 +32,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "33d37cd8-55c9-4e03-868c-34aa9cab2c80", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Click anywhere in this cell and press Shift + Return\n", - "\n", "2 + 2" ] }, @@ -54,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "585eb9c1-85ee-4c27-8dc2-b4d8d022eda0", "metadata": {}, "outputs": [], @@ -66,10 +76,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "07792faa-761d-46cb-b9b7-2bbf70bb1628", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'bananas'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# The result of the last statement is shown after you run it\n", "\n", @@ -78,10 +99,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "a067d2b1-53d5-4aeb-8a3c-574d39ff654a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My favorite fruit is anything but anything but anything but bananas\n" + ] + } + ], "source": [ "# Use the variable\n", "\n", @@ -90,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "4c5a4e60-b7f4-4953-9e80-6d84ba4664ad", "metadata": {}, "outputs": [], @@ -116,10 +145,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "8e5ec81d-7c5b-4025-bd2e-468d67b581b6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My favorite fruit is apples\n" + ] + } + ], "source": [ "# Then run this cell twice, and see if you understand what's going on\n", "\n", @@ -130,7 +167,7 @@ }, { "cell_type": "markdown", - "id": "a29dab2d-bab9-4a54-8504-05e62594cc6f", + "id": "4ab5dec0-b00b-401d-b7ba-ad39df1a75ad", "metadata": {}, "source": [ "# Explaining the 'kernel'\n", @@ -144,10 +181,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "84b1e410-5eda-4e2c-97ce-4eebcff816c5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'favorite_fruit' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMy favorite fruit is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mfavorite_fruit\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'favorite_fruit' is not defined" + ] + } + ], "source": [ "print(f\"My favorite fruit is {favorite_fruit}\")" ] @@ -164,11 +213,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "ce258424-40c3-49a7-9462-e6fa25014b03", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My favorite fruit is apples\n" + ] + } + ], + "source": [ + "# Then run this cell twice, and see if you understand what's going on\n", + "favorite_fruit = \"apples\"\n", + "\n", + "print(f\"My favorite fruit is {favorite_fruit}\")\n", + "\n", + "favorite_fruit = \"apples\"" + ] }, { "cell_type": "markdown", @@ -245,10 +309,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "82042fc5-a907-4381-a4b8-eb9386df19cd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'ls' is not recognized as an internal or external command,\n", + "operable program or batch file.\n" + ] + } + ], "source": [ "# list the current directory\n", "\n", @@ -295,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "2646a4e5-3c23-4aee-a34d-d623815187d2", "metadata": {}, "outputs": [], @@ -313,10 +386,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "id": "35efc48a-516b-4017-8352-d07b5a299f24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Collecting tqdm\n", + " Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n", + " ---------------------------------------- 0.0/57.7 kB ? eta -:--:--\n", + " ------- -------------------------------- 10.2/57.7 kB ? eta -:--:--\n", + " ------------- ------------------------ 20.5/57.7 kB 217.9 kB/s eta 0:00:01\n", + " -------------------------- ----------- 41.0/57.7 kB 281.8 kB/s eta 0:00:01\n", + " -------------------------------------- 57.7/57.7 kB 304.1 kB/s eta 0:00:00\n", + "Requirement already satisfied: colorama in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from tqdm) (0.4.6)\n", + "Downloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n", + " ---------------------------------------- 0.0/78.5 kB ? eta -:--:--\n", + " --------------- ------------------------ 30.7/78.5 kB 435.7 kB/s eta 0:00:01\n", + " --------------- ------------------------ 30.7/78.5 kB 435.7 kB/s eta 0:00:01\n", + " -------------------- ------------------- 41.0/78.5 kB 245.8 kB/s eta 0:00:01\n", + " ---------------------------------------- 78.5/78.5 kB 397.1 kB/s eta 0:00:00\n", + "Installing collected packages: tqdm\n", + "Successfully installed tqdm-4.67.1\n" + ] + } + ], + "source": [ + "!pip install tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "id": "6e96be3d-fa82-42a3-a8aa-b81dd20563a5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:10<00:00, 94.71it/s]\n" + ] + } + ], "source": [ "# And now, with a nice little progress bar:\n", "\n", @@ -372,7 +487,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week1/community-contributions/day-1-generate-cover-letter-from-cv.ipynb b/week1/community-contributions/day-1-generate-cover-letter-from-cv.ipynb index 09ed71b..35830b9 100644 --- a/week1/community-contributions/day-1-generate-cover-letter-from-cv.ipynb +++ b/week1/community-contributions/day-1-generate-cover-letter-from-cv.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "vscode": { "languageId": "plaintext" @@ -19,13 +19,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "vscode": { "languageId": "plaintext" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API key found and looks good so far!\n" + ] + } + ], "source": [ "# Load environment variables in a file called .env\n", "\n", @@ -46,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "vscode": { "languageId": "plaintext" @@ -65,7 +73,26 @@ "languageId": "plaintext" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CV Summary:\n", + "**John Doe - Software Engineer**\n", + "\n", + "**Experience:**\n", + "- Developed web applications using Python and JavaScript\n", + "- Collaborated with cross-functional teams to ensure timely project delivery\n", + "\n", + "**Education:**\n", + "- B.S. in Computer Science from XYZ University\n", + "\n", + "**Skills:**\n", + "- Proficient in Python, JavaScript, React, and SQL\n" + ] + } + ], "source": [ "def summarize_cv(cv_text):\n", " response = openai.chat.completions.create(\n", @@ -107,13 +134,34 @@ "except FileNotFoundError:\n", " print(\"The specified CV file was not found. Please ensure 'resume.txt' is in the correct directory.\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/week1/community-contributions/day-1-ollama-app.ipynb b/week1/community-contributions/day-1-ollama-app.ipynb index c1d219a..9d3d5fe 100644 --- a/week1/community-contributions/day-1-ollama-app.ipynb +++ b/week1/community-contributions/day-1-ollama-app.ipynb @@ -248,7 +248,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week1/day1.ipynb b/week1/day1.ipynb index 27684fe..3de350e 100644 --- a/week1/day1.ipynb +++ b/week1/day1.ipynb @@ -90,9 +90,142 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "id": "71d5335b-025a-468e-87f6-f12f2ef6f0e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Collecting dotenv\n", + " Downloading dotenv-0.9.9-py2.py3-none-any.whl.metadata (279 bytes)\n", + "Collecting python-dotenv (from dotenv)\n", + " Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n", + "Downloading dotenv-0.9.9-py2.py3-none-any.whl (1.9 kB)\n", + "Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n", + "Installing collected packages: python-dotenv, dotenv\n", + "Successfully installed dotenv-0.9.9 python-dotenv-1.0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 24.0 -> 25.0.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "!pip install dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e915cb31-cce0-456e-8648-8312818f468b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Collecting openai\n", + " Downloading openai-1.68.2-py3-none-any.whl.metadata (25 kB)\n", + "Requirement already satisfied: anyio<5,>=3.5.0 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from openai) (4.8.0)\n", + "Collecting distro<2,>=1.7.0 (from openai)\n", + " Downloading distro-1.9.0-py3-none-any.whl.metadata (6.8 kB)\n", + "Requirement already satisfied: httpx<1,>=0.23.0 in 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"2025-03-23T20:48:10.903831Z", + "start_time": "2025-03-23T20:48:08.562881Z" + } + }, "outputs": [], "source": [ "# imports\n", @@ -129,10 +262,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "7b87cadb-d513-4303-baee-a37b6f938e4d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API key found and looks good so far!\n" + ] + } + ], "source": [ "# Load environment variables in a file called .env\n", "\n", @@ -153,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", "metadata": {}, "outputs": [], @@ -174,10 +315,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "a58394bf-1e45-46af-9bfd-01e24da6f49a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "APITimeoutError", + "evalue": "Request timed out.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mConnectTimeout\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpx\\_transports\\default.py:101\u001b[0m, in \u001b[0;36mmap_httpcore_exceptions\u001b[1;34m()\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 101\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[0;32m 102\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpx\\_transports\\default.py:250\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[1;32m--> 250\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 252\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpcore\\_sync\\connection_pool.py:256\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 255\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_connections(closing)\n\u001b[1;32m--> 256\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 258\u001b[0m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[0;32m 259\u001b[0m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpcore\\_sync\\connection_pool.py:236\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 234\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 235\u001b[0m \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[1;32m--> 236\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 237\u001b[0m \u001b[43m \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\n\u001b[0;32m 238\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 239\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[0;32m 240\u001b[0m \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[0;32m 241\u001b[0m \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[0;32m 242\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m 243\u001b[0m \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpcore\\_sync\\connection.py:101\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connect_failed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 101\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39mhandle_request(request)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpcore\\_sync\\connection.py:78\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 78\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 80\u001b[0m ssl_object \u001b[38;5;241m=\u001b[39m stream\u001b[38;5;241m.\u001b[39mget_extra_info(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mssl_object\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpcore\\_sync\\connection.py:156\u001b[0m, in \u001b[0;36mHTTPConnection._connect\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart_tls\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request, kwargs) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[1;32m--> 156\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[43mstream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_tls\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 157\u001b[0m trace\u001b[38;5;241m.\u001b[39mreturn_value \u001b[38;5;241m=\u001b[39m stream\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpcore\\_backends\\sync.py:154\u001b[0m, in \u001b[0;36mSyncStream.start_tls\u001b[1;34m(self, ssl_context, server_hostname, timeout)\u001b[0m\n\u001b[0;32m 150\u001b[0m exc_map: ExceptionMapping \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 151\u001b[0m socket\u001b[38;5;241m.\u001b[39mtimeout: ConnectTimeout,\n\u001b[0;32m 152\u001b[0m \u001b[38;5;167;01mOSError\u001b[39;00m: ConnectError,\n\u001b[0;32m 153\u001b[0m }\n\u001b[1;32m--> 154\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmap_exceptions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc_map\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m 155\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mtry\u001b[39;49;00m\u001b[43m:\u001b[49m\n", + "File \u001b[1;32mC:\\Program Files\\Python312\\Lib\\contextlib.py:158\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[1;34m(self, typ, value, traceback)\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 160\u001b[0m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[0;32m 162\u001b[0m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpcore\\_exceptions.py:14\u001b[0m, in \u001b[0;36mmap_exceptions\u001b[1;34m(map)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(exc, from_exc):\n\u001b[1;32m---> 14\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m to_exc(exc) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "\u001b[1;31mConnectTimeout\u001b[0m: _ssl.c:983: The handshake operation timed out", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mConnectTimeout\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\openai\\_base_client.py:955\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[0;32m 954\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 955\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 956\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 957\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_should_stream_response_body\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 958\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 959\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 960\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m httpx\u001b[38;5;241m.\u001b[39mTimeoutException \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpx\\_client.py:914\u001b[0m, in \u001b[0;36mClient.send\u001b[1;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[0;32m 912\u001b[0m auth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_request_auth(request, auth)\n\u001b[1;32m--> 914\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 915\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 916\u001b[0m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 917\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 918\u001b[0m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 919\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 920\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpx\\_client.py:942\u001b[0m, in \u001b[0;36mClient._send_handling_auth\u001b[1;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[0;32m 941\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 942\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 943\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 944\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 945\u001b[0m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 946\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 947\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpx\\_client.py:979\u001b[0m, in \u001b[0;36mClient._send_handling_redirects\u001b[1;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[0;32m 977\u001b[0m hook(request)\n\u001b[1;32m--> 979\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 980\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpx\\_client.py:1014\u001b[0m, in \u001b[0;36mClient._send_single_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 1013\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request\u001b[38;5;241m=\u001b[39mrequest):\n\u001b[1;32m-> 1014\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mtransport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1016\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpx\\_transports\\default.py:249\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 237\u001b[0m req \u001b[38;5;241m=\u001b[39m httpcore\u001b[38;5;241m.\u001b[39mRequest(\n\u001b[0;32m 238\u001b[0m method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m 239\u001b[0m url\u001b[38;5;241m=\u001b[39mhttpcore\u001b[38;5;241m.\u001b[39mURL(\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 247\u001b[0m extensions\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[0;32m 248\u001b[0m )\n\u001b[1;32m--> 249\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmap_httpcore_exceptions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m 250\u001b[0m \u001b[43m \u001b[49m\u001b[43mresp\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mC:\\Program Files\\Python312\\Lib\\contextlib.py:158\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[1;34m(self, typ, value, traceback)\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 160\u001b[0m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[0;32m 162\u001b[0m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\httpx\\_transports\\default.py:118\u001b[0m, in \u001b[0;36mmap_httpcore_exceptions\u001b[1;34m()\u001b[0m\n\u001b[0;32m 117\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(exc)\n\u001b[1;32m--> 118\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m mapped_exc(message) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mexc\u001b[39;00m\n", + "\u001b[1;31mConnectTimeout\u001b[0m: _ssl.c:983: The handshake operation timed out", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mAPITimeoutError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[12], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\u001b[39;00m\n\u001b[0;32m 3\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHello, GPT! This is my first ever message to you! Hi!\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 4\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mopenai\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgpt-4o-mini\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrole\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m}\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(response\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mcontent)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\openai\\_utils\\_utils.py:279\u001b[0m, in \u001b[0;36mrequired_args..inner..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 277\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[1;32m--> 279\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\openai\\resources\\chat\\completions\\completions.py:914\u001b[0m, in \u001b[0;36mCompletions.create\u001b[1;34m(self, messages, model, audio, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, reasoning_effort, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, web_search_options, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 872\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcreate\u001b[39m(\n\u001b[0;32m 873\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 911\u001b[0m timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[0;32m 912\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[0;32m 913\u001b[0m validate_response_format(response_format)\n\u001b[1;32m--> 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 915\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 916\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 917\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m 918\u001b[0m \u001b[43m 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\u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[0;32m 961\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEncountered httpx.TimeoutException\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 963\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining_retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 964\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 965\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 966\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 967\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 968\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 969\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 970\u001b[0m \u001b[43m \u001b[49m\u001b[43mresponse_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 971\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 973\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRaising timeout error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 974\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m APITimeoutError(request\u001b[38;5;241m=\u001b[39mrequest) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01merr\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\openai\\_base_client.py:1057\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[1;34m(self, options, cast_to, retries_taken, response_headers, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1053\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[0;32m 1054\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[0;32m 1055\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[1;32m-> 1057\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1058\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1059\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1060\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1061\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1062\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1063\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\openai\\_base_client.py:964\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[0;32m 961\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEncountered httpx.TimeoutException\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 963\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining_retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 964\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 965\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 966\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 967\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 968\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 969\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 970\u001b[0m \u001b[43m \u001b[49m\u001b[43mresponse_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 971\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 973\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRaising timeout error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 974\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m APITimeoutError(request\u001b[38;5;241m=\u001b[39mrequest) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01merr\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\openai\\_base_client.py:1057\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[1;34m(self, options, cast_to, retries_taken, response_headers, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1053\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[0;32m 1054\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[0;32m 1055\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[1;32m-> 1057\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1058\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1059\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1060\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1061\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1062\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1063\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\openai\\_base_client.py:974\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[0;32m 964\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retry_request(\n\u001b[0;32m 965\u001b[0m input_options,\n\u001b[0;32m 966\u001b[0m cast_to,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 970\u001b[0m response_headers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 971\u001b[0m )\n\u001b[0;32m 973\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRaising timeout error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 974\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m APITimeoutError(request\u001b[38;5;241m=\u001b[39mrequest) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 975\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 976\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEncountered Exception\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[1;31mAPITimeoutError\u001b[0m: Request timed out." + ] + } + ], "source": [ "# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n", "\n", @@ -585,7 +771,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week1/day2 EXERCISE.ipynb b/week1/day2 EXERCISE.ipynb index 2c079f1..78858e8 100644 --- a/week1/day2 EXERCISE.ipynb +++ b/week1/day2 EXERCISE.ipynb @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", "metadata": {}, "outputs": [], @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "29ddd15d-a3c5-4f4e-a678-873f56162724", "metadata": {}, "outputs": [], @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "dac0a679-599c-441f-9bf2-ddc73d35b940", "metadata": {}, "outputs": [], @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "7bb9c624-14f0-4945-a719-8ddb64f66f47", "metadata": {}, "outputs": [], @@ -124,10 +124,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "479ff514-e8bd-4985-a572-2ea28bb4fa40", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ 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\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β€Ή \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β β„’ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΉ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΈ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β ΒΌ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β΄ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β¦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β Β§ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Ò ‑ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest Γ’Β οΏ½ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest \u001b[K\u001b[?25h\u001b[?2026l\n", + "Error: pull model manifest: Get \"https://registry.ollama.ai/v2/library/llama3.2/manifests/latest\": dial tcp 104.21.75.227:443: i/o timeout\n" + ] + } + ], "source": [ "# Let's just make sure the model is loaded\n", "\n", @@ -136,10 +145,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "42b9f644-522d-4e05-a691-56e7658c0ea9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generative AI has numerous business applications across various industries. Here are some examples:\n", + "\n", + "1. **Content Creation**: Generative AI can be used to generate high-quality content such as articles, social media posts, and product descriptions, reducing the need for human writers and editors.\n", + "2. **Digital Marketing**: Generative AI can help with personalized email marketing campaigns, automated social media content generation, and even creating customized ad copy.\n", + "3. **Graphic Design and Visual Content**: Generative AI can generate high-quality graphics, logos, and images, making it easier to create visually appealing marketing materials without requiring extensive design expertise.\n", + "4. **Music and Audio Production**: Generative AI can be used to create music tracks, sound effects, and even voiceovers for videos and commercials.\n", + "5. **Chatbots and Customer Service**: Generative AI-powered chatbots can help businesses provide 24/7 customer support, answer common queries, and route complex issues to human representatives.\n", + "6. **Predictive Maintenance and Quality Control**: Generative AI can be used to analyze sensor data from industrial equipment, predict maintenance needs, and detect anomalies in quality control processes.\n", + "7. **Financial Modeling and Analysis**: Generative AI can help with financial forecasting, risk analysis, and portfolio optimization by generating complex models and scenarios.\n", + "8. **Cybersecurity Threat Detection**: Generative AI-powered systems can detect and respond to cybersecurity threats by analyzing network traffic patterns and identifying potential vulnerabilities.\n", + "9. **Product Design and Development**: Generative AI can be used to create prototypes, simulate product behavior, and optimize design parameters for better performance and user experience.\n", + "10. **Data Analysis and Visualization**: Generative AI can help with data analysis, visualization, and insights generation by generating dashboards, reports, and predictive models.\n", + "\n", + "Some specific business applications of Generative AI include:\n", + "\n", + "* **Automating customer service chatbots**\n", + "* **Generating personalized product recommendations**\n", + "* **Creating high-quality content without human writers**\n", + "* **Optimizing supply chain operations using predictive analytics**\n", + "* **Designing new products and prototypes using generative design tools**\n", + "\n", + "These are just a few examples of the many business applications of Generative AI. As the technology continues to evolve, we can expect to see even more innovative uses in various industries.\n" + ] + } + ], "source": [ "# If this doesn't work for any reason, try the 2 versions in the following cells\n", "# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n", @@ -163,10 +201,81 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, + "id": "22d855a6-1c4f-4293-99b5-7ba9a0d99e6b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Collecting ollama\n", + " Downloading ollama-0.4.7-py3-none-any.whl.metadata (4.7 kB)\n", + "Requirement already satisfied: httpx<0.29,>=0.27 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from ollama) (0.28.1)\n", + "Requirement already satisfied: pydantic<3.0.0,>=2.9.0 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from ollama) (2.10.6)\n", + "Requirement already satisfied: anyio in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpx<0.29,>=0.27->ollama) (4.8.0)\n", + "Requirement already satisfied: certifi in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpx<0.29,>=0.27->ollama) (2025.1.31)\n", + "Requirement already satisfied: httpcore==1.* in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpx<0.29,>=0.27->ollama) (1.0.7)\n", + "Requirement already satisfied: idna in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpx<0.29,>=0.27->ollama) (3.10)\n", + "Requirement already satisfied: h11<0.15,>=0.13 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from httpcore==1.*->httpx<0.29,>=0.27->ollama) (0.14.0)\n", + "Requirement already satisfied: annotated-types>=0.6.0 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from pydantic<3.0.0,>=2.9.0->ollama) (0.7.0)\n", + "Requirement already satisfied: pydantic-core==2.27.2 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from pydantic<3.0.0,>=2.9.0->ollama) (2.27.2)\n", + "Requirement already satisfied: typing-extensions>=4.12.2 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from pydantic<3.0.0,>=2.9.0->ollama) (4.12.2)\n", + "Requirement already satisfied: sniffio>=1.1 in c:\\users\\kzk1kh\\appdata\\roaming\\python\\python312\\site-packages (from anyio->httpx<0.29,>=0.27->ollama) (1.3.1)\n", + "Downloading ollama-0.4.7-py3-none-any.whl (13 kB)\n", + "Installing collected packages: ollama\n", + "Successfully installed ollama-0.4.7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 24.0 -> 25.0.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "!pip install ollama" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generative AI has numerous business applications across various industries, including:\n", + "\n", + "1. **Content Creation**: Generative AI can be used to create content such as images, videos, and text based on a given prompt or dataset. This can be useful for businesses that need to generate large amounts of content quickly, such as marketing agencies, e-commerce sites, and news organizations.\n", + "2. **Product Design**: Generative AI can be used to design new products, such as fashion designs, interior designs, and product prototypes. This can help businesses reduce the time and cost associated with traditional design methods.\n", + "3. **Customer Service Chatbots**: Generative AI can be used to create chatbots that can respond to customer inquiries and provide personalized support. This can improve customer experience and reduce the workload of human customer service representatives.\n", + "4. **Marketing Automation**: Generative AI can be used to generate targeted marketing campaigns, such as personalized emails, social media posts, and ad copy. This can help businesses increase their ROI and improve their marketing efficiency.\n", + "5. **Data Analysis**: Generative AI can be used to analyze large datasets and identify patterns and trends that may not be visible to human analysts. This can help businesses make data-driven decisions and gain a competitive advantage.\n", + "6. **Predictive Maintenance**: Generative AI can be used to predict equipment failures and schedule maintenance, reducing downtime and increasing efficiency for industries such as manufacturing and healthcare.\n", + "7. **Cybersecurity**: Generative AI can be used to detect and respond to cyber threats in real-time, improving the security posture of businesses and protecting sensitive data.\n", + "8. **Supply Chain Optimization**: Generative AI can be used to optimize supply chain operations, including demand forecasting, inventory management, and logistics planning.\n", + "9. **Financial Analysis**: Generative AI can be used to analyze financial data and identify trends and patterns that may not be visible to human analysts. This can help businesses make informed investment decisions and improve their financial performance.\n", + "10. **Creative Writing and Copywriting**: Generative AI can be used to generate high-quality written content, such as blog posts, articles, and copy for marketing campaigns.\n", + "\n", + "Some specific examples of businesses that are using generative AI include:\n", + "\n", + "* **Google** uses generative AI to create personalized search results and recommend products to users.\n", + "* **Amazon** uses generative AI to personalize product recommendations and create targeted advertising campaigns.\n", + "* **Dell** uses generative AI to design new products and optimize supply chain operations.\n", + "* **McKinsey** uses generative AI to analyze complex data sets and identify trends and patterns that may not be visible to human analysts.\n", + "\n", + "Overall, generative AI has the potential to transform various business processes and improve efficiency, productivity, and decision-making.\n" + ] + } + ], "source": [ "import ollama\n", "\n", @@ -285,10 +394,243 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", "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": 13, + "id": "bc659c20-7915-42c3-8b83-e5654ab66f04", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API key found and looks good so far!\n" + ] + } + ], + "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": 14, + "id": "09b8d98a-028d-4553-b259-3099e91b2204", + "metadata": {}, + "outputs": [], + "source": [ + "openai = OpenAI()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ee71fce2-d9a5-4f77-afc7-abfcee78b0ad", + "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 Website:\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": 17, + "id": "f1a37654-9cc8-4e6c-8fbd-f64654870e05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Home - Edward Donner\n", + "Home\n", + "Connect Four\n", + "Outsmart\n", + "An arena that pits LLMs against each other in a battle of diplomacy and deviousness\n", + "About\n", + "Posts\n", + "Well, hi there.\n", + "I’m Ed. I like writing code and experimenting with LLMs, and hopefully you’re here because you do too. I also enjoy DJing (but I’m badly out of practice), amateur electronic music production (\n", + "very\n", + "amateur) and losing myself in\n", + "Hacker News\n", + ", nodding my head sagely to things I only half understand.\n", + "I’m the co-founder and CTO of\n", + "Nebula.io\n", + ". We’re applying AI to a field where it can make a massive, positive impact: helping people discover their potential and pursue their reason for being. Recruiters use our product today to source, understand, engage and manage talent. I’m previously the founder and CEO of AI startup untapt,\n", + "acquired in 2021\n", + ".\n", + "We work with groundbreaking, proprietary LLMs verticalized for talent, we’ve\n", + "patented\n", + "our matching model, and our award-winning platform has happy customers and tons of press coverage.\n", + "Connect\n", + "with me for more!\n", + "January 23, 2025\n", + "LLM Workshop – Hands-on with Agents – resources\n", + "December 21, 2024\n", + "Welcome, SuperDataScientists!\n", + "November 13, 2024\n", + "Mastering AI and LLM Engineering – Resources\n", + "October 16, 2024\n", + "From Software Engineer to AI Data Scientist – resources\n", + "Navigation\n", + "Home\n", + "Connect Four\n", + "Outsmart\n", + "An arena that pits LLMs against each other in a battle of diplomacy and deviousness\n", + "About\n", + "Posts\n", + "Get in touch\n", + "ed [at] edwarddonner [dot] com\n", + "www.edwarddonner.com\n", + "Follow me\n", + "LinkedIn\n", + "Twitter\n", + "Facebook\n", + "Subscribe to newsletter\n", + "Type your email…\n", + "Subscribe\n" + ] + } + ], + "source": [ + "# Let's try one out. Change the website and add print statements to follow along.\n", + "\n", + "ed = Website(\"https://edwarddonner.com\")\n", + "print(ed.title)\n", + "print(ed.text)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "19a139ae-23d1-4857-8de5-d6d8eb699d62", + "metadata": {}, + "outputs": [], + "source": [ + "# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", + "\n", + "system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", + "and provides a short summary, ignoring text that might be navigation related. \\\n", + "Respond in markdown.\"\n", + "\n", + "# 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 short summary of this website in markdown. \\\n", + "If it includes news or announcements, then summarize these too.\\n\\n\"\n", + " user_prompt += website.text\n", + " return user_prompt\n", + "\n", + "# See how this function creates exactly the format above\n", + "\n", + "def messages_for(website):\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "2de423cf-86bc-4f35-acd9-0ef821bbee05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Website Summary\n", + "\n", + "#### Overview\n", + "The website is dedicated to Edward Donner, the co-founder and CTO of Nebula.io, a company that applies AI to help people discover their potential. The site features various sections, including an arena called \"Outsmart\" where LLMs compete with each other, as well as a blog section.\n", + "\n", + "#### News/Announcements\n", + "- **December 21, 2024**: LLM Workshop – Hands-on with Agents – resources\n", + "- **November 13, 2024**: Welcome, SuperDataScientists!\n", + "- **October 16, 2024**: From Software Engineer to AI Data Scientist – resources\n", + "- **January 23, 2025**: LLM Workshop – Hands-on with Agents – resources (upcoming event)\n", + "\n", + "#### Social Media Links\n", + "The website includes links to Edward Donner's profiles on LinkedIn, Twitter, Facebook, and allows users to subscribe to a newsletter.\n" + ] + } + ], + "source": [ + "# Constants\n", + "\n", + "OLLAMA_API = \"http://localhost:11434/api/chat\"\n", + "HEADERS = {\"Content-Type\": \"application/json\"}\n", + "MODEL = \"llama3.2\"\n", + "\n", + "import ollama\n", + "\n", + "website = Website(\"https://edwarddonner.com\")\n", + "\n", + "response = ollama.chat(model=MODEL, messages=messages_for(website))\n", + "print(response['message']['content'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4500144f-dd2c-41d5-8225-3163edf1db2d", + "metadata": {}, + "outputs": [], "source": [] } ], @@ -308,7 +650,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week1/day5.ipynb b/week1/day5.ipynb index 2d02cdf..3f0e91b 100644 --- a/week1/day5.ipynb +++ b/week1/day5.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d5b08506-dc8b-4443-9201-5f1848161363", "metadata": {}, "outputs": [], @@ -42,10 +42,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API key looks good so far\n" + ] + } + ], "source": [ "# Initialize and constants\n", "\n", @@ -63,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "106dd65e-90af-4ca8-86b6-23a41840645b", "metadata": {}, "outputs": [], @@ -101,10 +109,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "e30d8128-933b-44cc-81c8-ab4c9d86589a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['https://edwarddonner.com/',\n", + " 'https://edwarddonner.com/connect-four/',\n", + " 'https://edwarddonner.com/outsmart/',\n", + " 'https://edwarddonner.com/about-me-and-about-nebula/',\n", + " 'https://edwarddonner.com/posts/',\n", + " 'https://edwarddonner.com/',\n", + " 'https://news.ycombinator.com',\n", + " 'https://nebula.io/?utm_source=ed&utm_medium=referral',\n", + " 'https://www.prnewswire.com/news-releases/wynden-stark-group-acquires-nyc-venture-backed-tech-startup-untapt-301269512.html',\n", + " 'https://patents.google.com/patent/US20210049536A1/',\n", + " 'https://www.linkedin.com/in/eddonner/',\n", + " 'https://edwarddonner.com/2025/01/23/llm-workshop-hands-on-with-agents-resources/',\n", + " 'https://edwarddonner.com/2025/01/23/llm-workshop-hands-on-with-agents-resources/',\n", + " 'https://edwarddonner.com/2024/12/21/llm-resources-superdatascience/',\n", + " 'https://edwarddonner.com/2024/12/21/llm-resources-superdatascience/',\n", + " 'https://edwarddonner.com/2024/11/13/llm-engineering-resources/',\n", + " 'https://edwarddonner.com/2024/11/13/llm-engineering-resources/',\n", + " 'https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/',\n", + " 'https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/',\n", + " 'https://edwarddonner.com/',\n", + " 'https://edwarddonner.com/connect-four/',\n", + " 'https://edwarddonner.com/outsmart/',\n", + " 'https://edwarddonner.com/about-me-and-about-nebula/',\n", + " 'https://edwarddonner.com/posts/',\n", + " 'mailto:hello@mygroovydomain.com',\n", + " 'https://www.linkedin.com/in/eddonner/',\n", + " 'https://twitter.com/edwarddonner',\n", + " 'https://www.facebook.com/edward.donner.52']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ed = Website(\"https://edwarddonner.com\")\n", "ed.links" @@ -128,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "6957b079-0d96-45f7-a26a-3487510e9b35", "metadata": {}, "outputs": [], @@ -149,17 +195,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "b97e4068-97ed-4120-beae-c42105e4d59a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You are provided with a list of links found on a webpage. You are able to decide which of the links would be most relevant to include in a brochure about the company, such as links to an About page, or a Company page, or Careers/Jobs pages.\n", + "You should respond in JSON as in this example:\n", + "{\n", + " \"links\": [\n", + " {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", + " {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", + " ]\n", + "}\n", + "\n" + ] + } + ], "source": [ "print(link_system_prompt)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3", "metadata": {}, "outputs": [], @@ -175,17 +237,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "6bcbfa78-6395-4685-b92c-22d592050fd7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here is the list of links on the website of https://edwarddonner.com - please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. Do not include Terms of Service, Privacy, email links.\n", + "Links (some might be relative links):\n", + "https://edwarddonner.com/\n", + "https://edwarddonner.com/connect-four/\n", + "https://edwarddonner.com/outsmart/\n", + "https://edwarddonner.com/about-me-and-about-nebula/\n", + "https://edwarddonner.com/posts/\n", + "https://edwarddonner.com/\n", + "https://news.ycombinator.com\n", + "https://nebula.io/?utm_source=ed&utm_medium=referral\n", + "https://www.prnewswire.com/news-releases/wynden-stark-group-acquires-nyc-venture-backed-tech-startup-untapt-301269512.html\n", + "https://patents.google.com/patent/US20210049536A1/\n", + "https://www.linkedin.com/in/eddonner/\n", + "https://edwarddonner.com/2025/01/23/llm-workshop-hands-on-with-agents-resources/\n", + "https://edwarddonner.com/2025/01/23/llm-workshop-hands-on-with-agents-resources/\n", + "https://edwarddonner.com/2024/12/21/llm-resources-superdatascience/\n", + "https://edwarddonner.com/2024/12/21/llm-resources-superdatascience/\n", + "https://edwarddonner.com/2024/11/13/llm-engineering-resources/\n", + "https://edwarddonner.com/2024/11/13/llm-engineering-resources/\n", + "https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/\n", + "https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/\n", + "https://edwarddonner.com/\n", + "https://edwarddonner.com/connect-four/\n", + "https://edwarddonner.com/outsmart/\n", + "https://edwarddonner.com/about-me-and-about-nebula/\n", + "https://edwarddonner.com/posts/\n", + "mailto:hello@mygroovydomain.com\n", + "https://www.linkedin.com/in/eddonner/\n", + "https://twitter.com/edwarddonner\n", + "https://www.facebook.com/edward.donner.52\n" + ] + } + ], "source": [ "print(get_links_user_prompt(ed))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "a29aca19-ca13-471c-a4b4-5abbfa813f69", "metadata": {}, "outputs": [], @@ -206,23 +305,135 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['/',\n", + " '/models',\n", + " '/datasets',\n", + " '/spaces',\n", + " '/posts',\n", + " '/docs',\n", + " '/enterprise',\n", + " '/pricing',\n", + " '/login',\n", + " '/join',\n", + " '/spaces',\n", + " '/models',\n", + " '/deepseek-ai/DeepSeek-V3-0324',\n", + " '/Qwen/Qwen2.5-Omni-7B',\n", + " '/manycore-research/SpatialLM-Llama-1B',\n", + " '/ds4sd/SmolDocling-256M-preview',\n", + " '/ByteDance/InfiniteYou',\n", + " '/models',\n", + " '/spaces/ByteDance/InfiniteYou-FLUX',\n", + " '/spaces/enzostvs/deepsite',\n", + " '/spaces/3DAIGC/LHM',\n", + " '/spaces/Trudy/gemini-codrawing',\n", + " '/spaces/Qwen/Qwen2.5-Omni-7B-Demo',\n", + " '/spaces',\n", + " '/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1',\n", + " '/datasets/glaiveai/reasoning-v1-20m',\n", + " '/datasets/FreedomIntelligence/medical-o1-reasoning-SFT',\n", + " '/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M',\n", + " '/datasets/PixelAI-Team/TalkBody4D',\n", + " '/datasets',\n", + " '/join',\n", + " '/pricing#endpoints',\n", + " '/pricing#spaces',\n", + " '/pricing',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/allenai',\n", + " '/facebook',\n", + " '/amazon',\n", + " '/google',\n", + " '/Intel',\n", + " '/microsoft',\n", + " '/grammarly',\n", + " '/Writer',\n", + " '/docs/transformers',\n", + " '/docs/diffusers',\n", + " '/docs/safetensors',\n", + " '/docs/huggingface_hub',\n", + " '/docs/tokenizers',\n", + " '/docs/trl',\n", + " '/docs/transformers.js',\n", + " '/docs/smolagents',\n", + " '/docs/peft',\n", + " '/docs/datasets',\n", + " '/docs/text-generation-inference',\n", + " '/docs/accelerate',\n", + " '/models',\n", + " '/datasets',\n", + " '/spaces',\n", + " '/tasks',\n", + " 'https://ui.endpoints.huggingface.co',\n", + " '/chat',\n", + " '/huggingface',\n", + " '/brand',\n", + " '/terms-of-service',\n", + " '/privacy',\n", + " 'https://apply.workable.com/huggingface/',\n", + " 'mailto:press@huggingface.co',\n", + " '/learn',\n", + " '/docs',\n", + " '/blog',\n", + " 'https://discuss.huggingface.co',\n", + " 'https://status.huggingface.co/',\n", + " 'https://github.com/huggingface',\n", + " 'https://twitter.com/huggingface',\n", + " 'https://www.linkedin.com/company/huggingface/',\n", + " '/join/discord']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n", "\n", "huggingface = Website(\"https://huggingface.co\")\n", - "huggingface.links" + "huggingface.links\n", + "\n", + "# anthropic_page = Website(\"https://anthropic.com\")\n", + "# anthropic_page.links" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'links': [{'type': 'about page', 'url': 'https://huggingface.co/huggingface'},\n", + " {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'},\n", + " {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'},\n", + " {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'},\n", + " {'type': 'blog page', 'url': 'https://huggingface.co/blog'},\n", + " {'type': 'company page',\n", + " 'url': 'https://www.linkedin.com/company/huggingface/'}]}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "get_links(\"https://huggingface.co\")" ] @@ -239,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5", "metadata": {}, "outputs": [], @@ -257,17 +468,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "5099bd14-076d-4745-baf3-dac08d8e5ab2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://huggingface.co/huggingface'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'company page', 'url': 'https://www.linkedin.com/company/huggingface/'}]}\n" + ] + }, + { + "data": { + "text/plain": [ + "'Landing page:\\nWebpage Title:\\nHugging Face – The AI community building the future.\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nExplore AI Apps\\nor\\nBrowse 1M+ models\\nTrending on\\nthis week\\nModels\\ndeepseek-ai/DeepSeek-V3-0324\\nUpdated\\n2 days ago\\nβ€’\\n60.5k\\nβ€’\\n1.95k\\nQwen/Qwen2.5-Omni-7B\\nUpdated\\nabout 22 hours ago\\nβ€’\\n27.9k\\nβ€’\\n792\\nmanycore-research/SpatialLM-Llama-1B\\nUpdated\\n8 days ago\\nβ€’\\n11.6k\\nβ€’\\n779\\nds4sd/SmolDocling-256M-preview\\nUpdated\\n6 days ago\\nβ€’\\n48.4k\\nβ€’\\n1.02k\\nByteDance/InfiniteYou\\nUpdated\\n4 days ago\\nβ€’\\n465\\nBrowse 1M+ models\\nSpaces\\nRunning\\non\\nZero\\n528\\n528\\nInfiniteYou-FLUX\\nπŸ“Έ\\nFlexible Photo Recrafting While Preserving Your Identity\\nRunning\\n236\\n236\\nDeepSite\\n🐳\\nImagine and Share in 1-Click\\nRunning\\non\\nZero\\n223\\n223\\nLHM\\n⚑\\nLarge Animatable Human Model\\nRunning\\n348\\n348\\nGemini Co-Drawing\\n✏\\nGemini 2.0 native image generation co-doodling\\nRunning\\n153\\n153\\nQwen2.5 Omni 7B Demo\\nπŸ†\\nSubmit media inputs to generate text and speech responses\\nBrowse 400k+ applications\\nDatasets\\nnvidia/Llama-Nemotron-Post-Training-Dataset-v1\\nUpdated\\n11 days ago\\nβ€’\\n8.05k\\nβ€’\\n264\\nglaiveai/reasoning-v1-20m\\nUpdated\\n10 days ago\\nβ€’\\n6.84k\\nβ€’\\n126\\nFreedomIntelligence/medical-o1-reasoning-SFT\\nUpdated\\nFeb 22\\nβ€’\\n25.5k\\nβ€’\\n573\\na-m-team/AM-DeepSeek-R1-Distilled-1.4M\\nUpdated\\n1 day ago\\nβ€’\\n3.79k\\nβ€’\\n74\\nPixelAI-Team/TalkBody4D\\nUpdated\\n4 days ago\\nβ€’\\n66\\nβ€’\\n44\\nBrowse 250k+ datasets\\nThe Home of Machine Learning\\nCreate, discover and collaborate on ML better.\\nThe collaboration platform\\nHost and collaborate on unlimited public models, datasets and applications.\\nMove faster\\nWith the HF Open source stack.\\nExplore all modalities\\nText, image, video, audio or even 3D.\\nBuild your portfolio\\nShare your work with the world and build your ML profile.\\nSign Up\\nAccelerate your ML\\nWe provide paid Compute and Enterprise solutions.\\nCompute\\nDeploy on optimized\\nInference Endpoints\\nor update your\\nSpaces applications\\nto a GPU in a few clicks.\\nView pricing\\nStarting at $0.60/hour for GPU\\nEnterprise\\nGive your team the most advanced platform to build AI with enterprise-grade security, access controls and\\n\\t\\t\\tdedicated support.\\nGetting started\\nStarting at $20/user/month\\nSingle Sign-On\\nRegions\\nPriority Support\\nAudit Logs\\nResource Groups\\nPrivate Datasets Viewer\\nMore than 50,000 organizations are using Hugging Face\\nAi2\\nEnterprise\\nnon-profit\\nβ€’\\n396 models\\nβ€’\\n2.97k followers\\nAI at Meta\\nEnterprise\\ncompany\\nβ€’\\n2.07k models\\nβ€’\\n5.29k followers\\nAmazon\\ncompany\\nβ€’\\n10 models\\nβ€’\\n2.92k followers\\nGoogle\\ncompany\\nβ€’\\n974 models\\nβ€’\\n10.7k followers\\nIntel\\ncompany\\nβ€’\\n219 models\\nβ€’\\n2.37k followers\\nMicrosoft\\ncompany\\nβ€’\\n365 models\\nβ€’\\n10.7k followers\\nGrammarly\\nEnterprise\\ncompany\\nβ€’\\n10 models\\nβ€’\\n146 followers\\nWriter\\nEnterprise\\ncompany\\nβ€’\\n21 models\\nβ€’\\n253 followers\\nOur Open Source\\nWe are building the foundation of ML tooling with the community.\\nTransformers\\n142,113\\nState-of-the-art ML for PyTorch, TensorFlow, JAX\\nDiffusers\\n28,309\\nState-of-the-art Diffusion models in PyTorch\\nSafetensors\\n3,190\\nSafe way to store/distribute neural network weights\\nHub Python Library\\n2,471\\nPython client to interact with the Hugging Face Hub\\nTokenizers\\n9,539\\nFast tokenizers optimized for research & production\\nTRL\\n12,903\\nTrain transformers LMs with reinforcement learning\\nTransformers.js\\n13,317\\nState-of-the-art ML running directly in your browser\\nsmolagents\\n15,962\\nSmol library to build great agents in Python\\nPEFT\\n17,935\\nParameter-efficient finetuning for large language models\\nDatasets\\n19,896\\nAccess & share datasets for any ML tasks\\nText Generation Inference\\n9,940\\nServe language models with TGI optimized toolkit\\nAccelerate\\n8,550\\nTrain PyTorch models with multi-GPU, TPU, mixed precision\\nSystem theme\\nWebsite\\nModels\\nDatasets\\nSpaces\\nTasks\\nInference Endpoints\\nHuggingChat\\nCompany\\nAbout\\nBrand assets\\nTerms of service\\nPrivacy\\nJobs\\nPress\\nResources\\nLearn\\nDocumentation\\nBlog\\nForum\\nService Status\\nSocial\\nGitHub\\nTwitter\\nLinkedIn\\nDiscord\\nZhihu\\nWeChat\\n\\n\\n\\nabout page\\nWebpage Title:\\nhuggingface (Hugging Face)\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nHugging Face\\nEnterprise\\ncompany\\nVerified\\nhttps://huggingface.co\\nhuggingface\\nhuggingface\\nActivity Feed\\nFollow\\n28,331\\nAI & ML interests\\nThe AI community building the future.\\nRecent Activity\\ncoyotte508\\nnew\\nactivity\\nabout 21 hours ago\\nhuggingface/HuggingDiscussions:\\n[FEEDBACK] Notifications\\nWauplin\\nupdated\\na dataset\\nabout 22 hours ago\\nhuggingface/documentation-images\\nlysandre\\nupdated\\na dataset\\nabout 23 hours ago\\nhuggingface/transformers-metadata\\nView all activity\\nArticles\\nYay! Organizations can now publish blog Articles\\nJan 20\\nβ€’\\n37\\nTeam members\\n209\\n+175\\n+162\\n+141\\n+131\\n+111\\nOrganization Card\\nCommunity\\nAbout org cards\\nπŸ‘‹ Hi!\\nWe are on a mission to democratize\\ngood\\nmachine learning, one commit at a time.\\nIf that sounds like something you should be doing, why don\\'t you\\njoin us\\n!\\nFor press enquiries, you can\\nβœ‰οΈ contact our team here\\n.\\nCollections\\n1\\nDistilBERT release\\nOriginal DistilBERT model, checkpoints obtained from using teacher-student learning from the original BERT checkpoints.\\ndistilbert/distilbert-base-cased\\nFill-Mask\\nβ€’\\nUpdated\\nMay 6, 2024\\nβ€’\\n493k\\nβ€’\\nβ€’\\n38\\ndistilbert/distilbert-base-uncased\\nFill-Mask\\nβ€’\\nUpdated\\nMay 6, 2024\\nβ€’\\n12M\\nβ€’\\nβ€’\\n653\\ndistilbert/distilbert-base-multilingual-cased\\nFill-Mask\\nβ€’\\nUpdated\\nMay 6, 2024\\nβ€’\\n2.32M\\nβ€’\\nβ€’\\n182\\ndistilbert/distilbert-base-uncased-finetuned-sst-2-english\\nText Classification\\nβ€’\\nUpdated\\nDec 19, 2023\\nβ€’\\n7.15M\\nβ€’\\nβ€’\\n720\\nspaces\\n26\\nSort:\\xa0\\n\\t\\tRecently updated\\npinned\\nRunning\\n77\\nNumber Tokenization Blog\\nπŸ“ˆ\\nExplore how tokenization affects arithmetic in LLMs\\nhuggingface\\nDec 14, 2024\\nRunning\\nSpace Build\\n🐨\\nGenerate static files for spaces\\nhuggingface\\n1 day ago\\nRunning\\n10\\nInferenceSupport\\nπŸ’₯\\nDiscussions about the Inference Providers feature on the Hub\\nhuggingface\\n2 days ago\\nRunning\\n135\\nInference Playground\\nπŸ”‹\\nSet webpage theme based on user preference or system settings\\nhuggingface\\n3 days ago\\nRunning\\n348\\nAI Deadlines\\n⚑\\nSchedule tasks efficiently using AI-generated deadlines\\nhuggingface\\n14 days ago\\nRunning\\n532\\nOpen Source Ai Year In Review 2024\\n😻\\nWhat happened in open-source AI this year, and what’s next?\\nhuggingface\\nJan 8\\nExpand 26\\n\\t\\t\\t\\t\\t\\t\\tspaces\\nmodels\\n16\\nSort:\\xa0\\n\\t\\tRecently updated\\nhuggingface/timesfm-tourism-monthly\\nUpdated\\nDec 9, 2024\\nβ€’\\n268\\nβ€’\\n1\\nhuggingface/CodeBERTa-language-id\\nText Classification\\nβ€’\\nUpdated\\nMar 29, 2024\\nβ€’\\n7.14k\\nβ€’\\nβ€’\\n59\\nhuggingface/falcon-40b-gptq\\nText Generation\\nβ€’\\nUpdated\\nJun 14, 2023\\nβ€’\\n15\\nβ€’\\n12\\nhuggingface/autoformer-tourism-monthly\\nUpdated\\nMay 24, 2023\\nβ€’\\n40.8k\\nβ€’\\n9\\nhuggingface/distilbert-base-uncased-finetuned-mnli\\nText Classification\\nβ€’\\nUpdated\\nMar 22, 2023\\nβ€’\\n226\\nβ€’\\nβ€’\\n2\\nhuggingface/informer-tourism-monthly\\nUpdated\\nFeb 24, 2023\\nβ€’\\n40.4k\\nβ€’\\n6\\nhuggingface/time-series-transformer-tourism-monthly\\nUpdated\\nFeb 23, 2023\\nβ€’\\n9.54k\\nβ€’\\n20\\nhuggingface/the-no-branch-repo\\nText-to-Image\\nβ€’\\nUpdated\\nFeb 10, 2023\\nβ€’\\n24\\nβ€’\\n4\\nhuggingface/CodeBERTa-small-v1\\nFill-Mask\\nβ€’\\nUpdated\\nJun 27, 2022\\nβ€’\\n36.3k\\nβ€’\\n80\\nhuggingface/test-model-repo\\nUpdated\\nNov 19, 2021\\nβ€’\\n1\\nExpand 16\\n\\t\\t\\t\\t\\t\\t\\tmodels\\ndatasets\\n42\\nSort:\\xa0\\n\\t\\tRecently updated\\nhuggingface/documentation-images\\nViewer\\nβ€’\\nUpdated\\nabout 22 hours ago\\nβ€’\\n52\\nβ€’\\n4.19M\\nβ€’\\n57\\nhuggingface/transformers-metadata\\nViewer\\nβ€’\\nUpdated\\nabout 23 hours ago\\nβ€’\\n1.59k\\nβ€’\\n1.6k\\nβ€’\\n20\\nhuggingface/policy-docs\\nUpdated\\n9 days ago\\nβ€’\\n2.58k\\nβ€’\\n10\\nhuggingface/diffusers-metadata\\nViewer\\nβ€’\\nUpdated\\n14 days ago\\nβ€’\\n69\\nβ€’\\n590\\nβ€’\\n6\\nhuggingface/gemini-results-2025-03-03\\nViewer\\nβ€’\\nUpdated\\n25 days ago\\nβ€’\\n17\\nβ€’\\n60\\nhuggingface/gemini-results-2025-02-28\\nViewer\\nβ€’\\nUpdated\\n28 days ago\\nβ€’\\n21\\nβ€’\\n53\\nhuggingface/gemini-results-2025-02-27\\nViewer\\nβ€’\\nUpdated\\n29 days ago\\nβ€’\\n24\\nβ€’\\n57\\nhuggingface/gemini-results-2025-02-25\\nViewer\\nβ€’\\nUpdated\\nFeb 26\\nβ€’\\n32\\nβ€’\\n56\\nhuggingface/gemini-results-2025-02-24\\nViewer\\nβ€’\\nUpdated\\nFeb 25\\nβ€’\\n32\\nβ€’\\n49\\nhuggingface/gemini-results-2025-02-21\\nViewer\\nβ€’\\nUpdated\\nFeb 22\\nβ€’\\n29\\nβ€’\\n134\\nβ€’\\n1\\nExpand 42\\n\\t\\t\\t\\t\\t\\t\\tdatasets\\nSystem theme\\nCompany\\nTOS\\nPrivacy\\nAbout\\nJobs\\nWebsite\\nModels\\nDatasets\\nSpaces\\nPricing\\nDocs\\n\\n\\n\\ncareers page\\nWebpage Title:\\nHugging Face - Current Openings\\nWebpage Contents:\\n\\n\\n\\n\\ncompany page\\nWebpage Title:\\nHugging Face | LinkedIn\\nWebpage Contents:\\nSkip to main content\\nLinkedIn\\nArticles\\nPeople\\nLearning\\nJobs\\nGames\\nGet the app\\nJoin now\\nSign in\\nHugging Face\\nSoftware Development\\nThe AI community building the future.\\nSee jobs\\nFollow\\nView all 515 employees\\nReport this company\\nAbout us\\nThe AI community building the future.\\nWebsite\\nhttps://huggingface.co\\nExternal link for Hugging Face\\nIndustry\\nSoftware Development\\nCompany size\\n51-200 employees\\nType\\nPrivately Held\\nFounded\\n2016\\nSpecialties\\nmachine learning, natural language processing, and deep learning\\nProducts\\nHugging Face\\nHugging Face\\nNatural Language Processing (NLP) Software\\nWe’re on a journey to solve and democratize artificial intelligence through natural language.\\nLocations\\nPrimary\\nGet directions\\nParis, FR\\nGet directions\\nEmployees at Hugging Face\\nLudovic Huraux\\nBassem ASSEH\\nRajat Arya\\nTech Lead & Software Engineer @ HF | prev: co-founder XetHub, Apple, Turi, AWS, Microsoft\\nJeff Boudier\\nProduct + Growth at Hugging Face\\nSee all employees\\nUpdates\\nHugging Face\\nreposted this\\nGradio\\n60,769 followers\\n23h\\nEdited\\nReport this post\\nHi3DGen πŸ”₯ \\n\\nHigh-fidelity 3D geometry generation from a single image by leveraging normal maps as an intermediate representation\\n\\nPlay with the fantastic app on\\nHugging Face\\nnow:\\nhttps://lnkd.in/g99NpV_y\\n…more\\n153\\n3 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nFreddy Boulton\\nSoftware Engineer @ πŸ€—\\n1d\\nReport this post\\nGenerate lifelike audio in real-time without a GPU! πŸš€\\n\\nCheck out orpheus-cpp: a\\nllama.cpp\\nport of orpheus 3b text-to-speech model with built-in support for sync and async streaming.\\n\\nπš™πš’πš™ πš’πš—πšœπšπšŠπš•πš• πš˜πš›πš™πš‘πšŽπšžπšœ-πšŒπš™πš™\\nπš™πš’πšπš‘πš˜πš— -πš– πš˜πš›πš™πš‘πšŽπšžπšœ_πšŒπš™πš™\\n\\nProject code:\\nhttps://lnkd.in/ekPpN9mc\\n…more\\n313\\n7 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nGradio\\n60,769 followers\\n3d\\nReport this post\\nWe just turned the humble dataframe into a superweapon⚑️\\ndashboarding will never be the same!! πŸ“Š\\n\\nnew Gradio Dataframe has:\\n- multi-cell selection\\n- column pinning\\n- search + filtering\\n- fullscreen mode\\n- accessibility upgrades, and more\\n…more\\n97\\n12 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nMerve Noyan\\nopen-sourceress at πŸ€— | Google Developer Expert in Machine Learning, MSc Candidate in Data Science\\n3d\\nReport this post\\nis your vision LM in prod even safe? πŸ‘€\\n\\nShieldGemma 2 is the first ever safety model for multimodal vision LMs in production by\\nGoogle DeepMind\\n, came with Gemma 3 πŸ”₯\\n\\nI saw confusion around how to use it, so I put together a notebook and a demo, find it in the comments πŸ’¬\\n381\\n10 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nMerve Noyan\\nopen-sourceress at πŸ€— | Google Developer Expert in Machine Learning, MSc Candidate in Data Science\\n3d\\nReport this post\\nis your vision LM in prod even safe? πŸ‘€\\n\\nShieldGemma 2 is the first ever safety model for multimodal vision LMs in production by\\nGoogle DeepMind\\n, came with Gemma 3 πŸ”₯\\n\\nI saw confusion around how to use it, so I put together a notebook and a demo, find it in the comments πŸ’¬\\n381\\n10 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nSergio Paniego Blanco\\nML Engineer @ Hugging Face πŸ€— | AI PhD | Google Summer of Code \\'18-\\'24\\n3d\\nReport this post\\nThe Bonus Unit 2, \"AI Agent Observability & Evaluation,\" is now live on our\\nHugging Face\\nagents course! πŸŽ“\\n\\nYou\\'ll learn to:\\nπŸ”§ Instrument agents with OpenTelemetry\\nπŸ“Š Track token usage, latency & errors\\nπŸ“ˆ Evaluate with LLM-as-a-judge\\nπŸ“š Benchmark with GSM8K\\n\\nπŸ‘‰ Check out the course here:\\nhttps://lnkd.in/d2jiTx6j\\n212\\n7 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nGradio\\n60,769 followers\\n4d\\nEdited\\nReport this post\\nCreate Infinite Photographs of You with InfiniteYou-Flux!\\n\\nFlexible photo recreation that better preserves identity compared to current solutions like Pulid, IP Adapter, etc. πŸ”₯ πŸ’ͺ \\n\\nCurrent full-performance bf16 model inference requires a peak VRAM of around 43 GB.\\n\\nYou can build InfU on your own hardware:\\nhttps://lnkd.in/g9dc_vVh\\nOr Play for free on\\nHugging Face\\n:\\nhttps://lnkd.in/gzF7rikZ\\n159\\n6 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nGradio\\n60,769 followers\\n4d\\nEdited\\nReport this post\\n🀯 Generate high-quality podcasts with the voices you want!\\n\\nMoonCast is an open sourced, multi-lingual, and zeroshot model.\\n\\nYou just need to upload two sample voices, create a script, and that\\'s it, run the model--You get a πŸ”₯ notebooklm-like podcast.\\n\\nModel and App are released on\\nHugging Face\\n:\\nhttps://lnkd.in/gUk2EssP\\n151\\n8 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nDaniel Vila Suero\\nBuilding data tools @ Hugging Face πŸ€—\\n4d\\nEdited\\nReport this post\\nπŸ”₯ Big news for GPU poors: thanks to\\nHyperbolic\\nand\\nFireworks AI\\n, you can run\\nDeepSeek AI\\n\\'s\\xa0new model using Hugging Face Inference Providers. What has changed since V3? Here\\'s my quick home experiment πŸ‘‡ \\n\\nDeepSeek silently dropped an update to V3 yesterday. Benchmark results are available, showing significant improvements over V3. \\n\\nStill, it is always a good idea to run new models on data you care about and see more detailed, fine-grained results.\\n\\nNow that we can all run these new models from Day 0 with no GPUs required, I wanted to share my approach with an example I created this morning:\\n\\n1. I got a sample from the LIMA dataset (containing high-quality general instructions).\\n2. Run the instructions with V3 and the new version V3-0324.\\n3. Define and run a simple judge with Llama3.3-70B to compare the model responses.\\n4. Push the dataset and pipeline so you can check and run similar experiments! (see first comment)\\n5. Extracted the results with\\nHugging Face\\nData Studio.\\n\\nResults summary\\n- LIMA is not very challenging, but it is still interesting to see the differences between the two models.\\n- A majority of Ties indicate that both models are close for this domain and task.\\n- But still, V3-0324 consistently wins over V3 (33 times vs 6 times).\\n\\nAs usual, the dataset, prompts, and pipeline are open-source (see first comment).\\n\\nWhat other experiments you\\'d like to see?\\n214\\n7 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nGradio\\n60,769 followers\\n4d\\nReport this post\\nStarVector is a multimodal vision-language model for generating SVG (Scalable Vector Graphics). πŸ‘‡ \\n\\nIt can be used to perform image2SVG and text2SVG generation. Live demo shows how the image generation is treated similar to a code generation task, using the power of StarVector multimodal VLM! 🀩 \\n\\nπŸš€ Play with the app on Huggingface:\\nhttps://lnkd.in/gCzdEbvj\\nπŸ₯³ If you want to build the model locally with a gradio app:\\nhttps://lnkd.in/gDzCpdDN\\n…more\\n1,521\\n41 Comments\\nLike\\nComment\\nShare\\nJoin now to see what you are missing\\nFind people you know at Hugging Face\\nBrowse recommended jobs for you\\nView all updates, news, and articles\\nJoin now\\nSimilar pages\\nAnthropic\\nResearch Services\\nMistral AI\\nTechnology, Information and Internet\\nParis, France\\nPerplexity\\nSoftware Development\\nSan Francisco, California\\nOpenAI\\nResearch Services\\nSan Francisco, CA\\nLangChain\\nTechnology, Information and Internet\\nGenerative AI\\nTechnology, Information and Internet\\nDeepLearning.AI\\nSoftware Development\\nPalo Alto, California\\nLlamaIndex\\nTechnology, Information and Internet\\nSan Francisco, California\\nGoogle DeepMind\\nResearch Services\\nLondon, London\\nCohere\\nSoftware Development\\nToronto, Ontario\\nShow more similar pages\\nShow fewer similar pages\\nBrowse jobs\\nEngineer jobs\\n555,845 open jobs\\nMachine Learning Engineer jobs\\n148,937 open jobs\\nScientist jobs\\n48,969 open jobs\\nSoftware Engineer jobs\\n300,699 open jobs\\nAnalyst jobs\\n694,057 open jobs\\nIntern jobs\\n71,196 open jobs\\nDeveloper jobs\\n258,935 open jobs\\nManager jobs\\n1,880,925 open jobs\\nProduct Manager jobs\\n199,941 open jobs\\nDirector jobs\\n1,220,357 open jobs\\nPython Developer jobs\\n46,642 open jobs\\nData Scientist jobs\\n264,158 open jobs\\nData Analyst jobs\\n329,009 open jobs\\nSenior Software Engineer jobs\\n78,145 open jobs\\nProject Manager jobs\\n253,048 open jobs\\nResearcher jobs\\n195,654 open jobs\\nAssociate jobs\\n1,091,945 open jobs\\nData Engineer jobs\\n192,126 open jobs\\nVice President jobs\\n235,270 open jobs\\nSpecialist jobs\\n768,666 open jobs\\nShow more jobs like this\\nShow fewer jobs like this\\nFunding\\nHugging Face\\n8 total rounds\\nLast Round\\nSeries unknown\\nSep 1, 2024\\nExternal Crunchbase Link for last round of funding\\nSee more info on\\ncrunchbase\\nMore searches\\nMore searches\\nEngineer jobs\\nScientist jobs\\nMachine Learning Engineer jobs\\nSoftware Engineer jobs\\nIntern jobs\\nDeveloper jobs\\nAnalyst jobs\\nManager jobs\\nSenior Software Engineer jobs\\nData Scientist jobs\\nResearcher jobs\\nProduct Manager jobs\\nDirector jobs\\nAssociate jobs\\nIntelligence Specialist jobs\\nData Analyst jobs\\nData Science Specialist jobs\\nPython Developer jobs\\nQuantitative Analyst jobs\\nProject Manager jobs\\nAccount Executive jobs\\nSpecialist jobs\\nData Engineer jobs\\nDesigner jobs\\nQuantitative Researcher jobs\\nConsultant jobs\\nSolutions Architect jobs\\nVice President jobs\\nUser Experience Designer jobs\\nHead jobs\\nFull Stack Engineer jobs\\nEngineering Manager jobs\\nSoftware Engineer Intern 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(English)\\nEspaΓ±ol (Spanish)\\nفارسی (Persian)\\nSuomi (Finnish)\\nFranΓ§ais (French)\\nΰ€Ήΰ€Ώΰ€‚ΰ€¦ΰ₯€ (Hindi)\\nMagyar (Hungarian)\\nBahasa Indonesia (Indonesian)\\nItaliano (Italian)\\nΧ’Χ‘Χ¨Χ™Χͺ (Hebrew)\\nζ—₯本θͺž (Japanese)\\nν•œκ΅­μ–΄ (Korean)\\nΰ€ΰ€°ΰ€Ύΰ€ ΰ₯€ (Marathi)\\nBahasa Malaysia (Malay)\\nNederlands (Dutch)\\nNorsk (Norwegian)\\nΰ¨ͺΰ©°ΰ¨œΰ¨Ύΰ¨¬ΰ©€ (Punjabi)\\nPolski (Polish)\\nPortuguΓͺs (Portuguese)\\nRomΓ’nΔƒ (Romanian)\\nРусский (Russian)\\nSvenska (Swedish)\\nఀెలుగు (Telugu)\\nΰΈ ΰΈ²ΰΈ©ΰΈ²ΰΉ„ΰΈ—ΰΈ’ (Thai)\\nTagalog (Tagalog)\\nTΓΌrkΓ§e (Turkish)\\nΠ£ΠΊΡ€Π°Ρ—Π½ΡΡŒΠΊΠ° (Ukrainian)\\nTiαΊΏng Việt (Vietnamese)\\nη€δ½“δΈ­ζ–‡ (Chinese (Simplified))\\nζ­£ι«”δΈ­ζ–‡ (Chinese (Traditional))\\nLanguage\\nAgree & Join LinkedIn\\nBy clicking Continue to join or sign in, you agree to LinkedIn’s\\nUser Agreement\\n,\\nPrivacy Policy\\n, and\\nCookie Policy\\n.\\nSign in to see who you already know at Hugging Face\\nSign in\\nWelcome back\\nEmail or phone\\nPassword\\nShow\\nForgot password?\\nSign in\\nor\\nBy clicking Continue to join or sign in, you agree to LinkedIn’s\\nUser Agreement\\n,\\nPrivacy Policy\\n, and\\nCookie Policy\\n.\\nNew to LinkedIn?\\nJoin now\\nor\\nNew to LinkedIn?\\nJoin now\\nBy clicking Continue to join or sign in, you agree to LinkedIn’s\\nUser Agreement\\n,\\nPrivacy Policy\\n, and\\nCookie Policy\\n.\\nLinkedIn\\nLinkedIn is better on the app\\nDon’t have the app? Get it in the Microsoft Store.\\nOpen the app\\n\\n'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "print(get_all_details(\"https://huggingface.co\"))" + "get_all_details(\"https://huggingface.co\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2", "metadata": {}, "outputs": [], @@ -285,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23", "metadata": {}, "outputs": [], @@ -300,17 +529,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "cd909e0b-1312-4ce2-a553-821e795d7572", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://huggingface.co/huggingface'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'}, {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'}, {'type': 'blog page', 'url': 'https://huggingface.co/blog'}, {'type': 'community page', 'url': 'https://discuss.huggingface.co'}, {'type': 'GitHub page', 'url': 'https://github.com/huggingface'}, {'type': 'LinkedIn page', 'url': 'https://www.linkedin.com/company/huggingface'}, {'type': 'Twitter page', 'url': 'https://twitter.com/huggingface'}]}\n" + ] + }, + { + "data": { + "text/plain": [ + "'You are looking at a company called: HuggingFace\\nHere are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\nLanding page:\\nWebpage Title:\\nHugging Face – The AI community building the future.\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nExplore AI Apps\\nor\\nBrowse 1M+ models\\nTrending on\\nthis week\\nModels\\ndeepseek-ai/DeepSeek-V3-0324\\nUpdated\\n2 days ago\\nβ€’\\n60.5k\\nβ€’\\n1.95k\\nQwen/Qwen2.5-Omni-7B\\nUpdated\\nabout 23 hours ago\\nβ€’\\n27.9k\\nβ€’\\n792\\nmanycore-research/SpatialLM-Llama-1B\\nUpdated\\n8 days ago\\nβ€’\\n11.6k\\nβ€’\\n779\\nds4sd/SmolDocling-256M-preview\\nUpdated\\n6 days ago\\nβ€’\\n48.4k\\nβ€’\\n1.02k\\nByteDance/InfiniteYou\\nUpdated\\n4 days ago\\nβ€’\\n465\\nBrowse 1M+ models\\nSpaces\\nRunning\\non\\nZero\\n528\\n528\\nInfiniteYou-FLUX\\nπŸ“Έ\\nFlexible Photo Recrafting While Preserving Your Identity\\nRunning\\n236\\n236\\nDeepSite\\n🐳\\nImagine and Share in 1-Click\\nRunning\\non\\nZero\\n223\\n223\\nLHM\\n⚑\\nLarge Animatable Human Model\\nRunning\\n348\\n348\\nGemini Co-Drawing\\n✏\\nGemini 2.0 native image generation co-doodling\\nRunning\\n153\\n153\\nQwen2.5 Omni 7B Demo\\nπŸ†\\nSubmit media inputs to generate text and speech responses\\nBrowse 400k+ applications\\nDatasets\\nnvidia/Llama-Nemotron-Post-Training-Dataset-v1\\nUpdated\\n11 days ago\\nβ€’\\n8.05k\\nβ€’\\n264\\nglaiveai/reasoning-v1-20m\\nUpdated\\n10 days ago\\nβ€’\\n6.84k\\nβ€’\\n126\\nFreedomIntelligence/medical-o1-reasoning-SFT\\nUpdated\\nFeb 22\\nβ€’\\n25.5k\\nβ€’\\n573\\na-m-team/AM-DeepSeek-R1-Distilled-1.4M\\nUpdated\\n1 day ago\\nβ€’\\n3.79k\\nβ€’\\n74\\nPixelAI-Team/TalkBody4D\\nUpdated\\n4 days ago\\nβ€’\\n66\\nβ€’\\n44\\nBrowse 250k+ datasets\\nThe Home of Machine Learning\\nCreate, discover and collaborate on ML better.\\nThe collaboration platform\\nHost and collaborate on unlimited public models, datasets and applications.\\nMove faster\\nWith the HF Open source stack.\\nExplore all modalities\\nText, image, video, audio or even 3D.\\nBuild your portfolio\\nShare your work with the world and build your ML profile.\\nSign Up\\nAccelerate your ML\\nWe provide paid Compute and Enterprise solutions.\\nCompute\\nDeploy on optimized\\nInference Endpoints\\nor update your\\nSpaces applications\\nto a GPU in a few clicks.\\nView pricing\\nStarting at $0.60/hour for GPU\\nEnterprise\\nGive your team the most advanced platform to build AI with enterprise-grade security, access controls and\\n\\t\\t\\tdedicated support.\\nGetting started\\nStarting at $20/user/month\\nSingle Sign-On\\nRegions\\nPriority Support\\nAudit Logs\\nResource Groups\\nPrivate Datasets Viewer\\nMore than 50,000 organizations are using Hugging Face\\nAi2\\nEnterprise\\nnon-profit\\nβ€’\\n396 models\\nβ€’\\n2.97k followers\\nAI at Meta\\nEnterprise\\ncompany\\nβ€’\\n2.07k models\\nβ€’\\n5.29k followers\\nAmazon\\ncompany\\nβ€’\\n10 models\\nβ€’\\n2.92k followers\\nGoogle\\ncompany\\nβ€’\\n974 models\\nβ€’\\n10.7k followers\\nIntel\\ncompany\\nβ€’\\n219 models\\nβ€’\\n2.37k followers\\nMicrosoft\\ncompany\\nβ€’\\n365 models\\nβ€’\\n10.7k followers\\nGrammarly\\nEnterprise\\ncompany\\nβ€’\\n10 models\\nβ€’\\n146 followers\\nWriter\\nEnterprise\\ncompany\\nβ€’\\n21 models\\nβ€’\\n253 followers\\nOur Open Source\\nWe are building the foundation of ML tooling with the community.\\nTransformers\\n142,113\\nState-of-the-art ML for PyTorch, TensorFlow, JAX\\nDiffusers\\n28,309\\nState-of-the-art Diffusion models in PyTorch\\nSafetensors\\n3,190\\nSafe way to store/distribute neural network weights\\nHub Python Library\\n2,471\\nPython client to interact with the Hugging Face Hub\\nTokenizers\\n9,539\\nFast tokenizers optimized for research & production\\nTRL\\n12,903\\nTrain transformers LMs with reinforcement learning\\nTransformers.js\\n13,317\\nState-of-the-art ML running directly in your browser\\nsmolagents\\n15,962\\nSmol library to build great agents in Python\\nPEFT\\n17,935\\nParameter-efficient finetuning for large language models\\nDatasets\\n19,896\\nAccess & share datasets for any ML tasks\\nText Generation Inference\\n9,940\\nServe language models with TGI optimized toolkit\\nAccelerate\\n8,550\\nTrain PyTorch models with multi-GPU, TPU, mixed precision\\nSystem theme\\nWebsite\\nModels\\nDatasets\\nSpaces\\nTasks\\nInference Endpoints\\nHuggingChat\\nCompany\\nAbout\\nBrand assets\\nTerms of service\\nPrivacy\\nJobs\\nPress\\nResources\\nLearn\\nDocumentation\\nBlog\\nForum\\nService Status\\nSocial\\nGitHub\\nTwitter\\nLinkedIn\\nDiscord\\n\\n\\n\\nabout page\\nWebpage Title:\\nhuggingface (Hugging Face)\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nHugging Face\\nEnterprise\\ncompany\\nVerified\\nhttps://huggingface.co\\nhuggingface\\nhuggingface\\nActivity Feed\\nFollow\\n28,332\\nAI & ML interests\\nThe AI community building the future.\\nRecent Activity\\ncoyotte508\\nnew\\nactivity\\nabout 21 hours ago\\nhuggingface/HuggingDiscussions:\\n[FEEDBACK] Notifications\\nWauplin\\nupdated\\na dataset\\nabout 22 hours ago\\nhuggingface/documentation-images\\nlysandre\\nupdated\\na dataset\\nabout 23 hours ago\\nhuggingface/transformers-metadata\\nView all activity\\nArticles\\nYay! Organizations can now publish blog Articles\\nJan 20\\nβ€’\\n37\\nTeam members\\n209\\n+175\\n+162\\n+141\\n+131\\n+111\\nOrganization Card\\nCommunity\\nAbout org cards\\nπŸ‘‹ Hi!\\nWe are on a mission to democr'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46", "metadata": {}, "outputs": [], @@ -329,10 +576,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "e093444a-9407-42ae-924a-145730591a39", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://huggingface.co/huggingface'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'}, {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'}, {'type': 'blog page', 'url': 'https://huggingface.co/blog'}]}\n" + ] + }, + { + "data": { + "text/markdown": [ + "# Hugging Face Brochure\n", + "\n", + "**Hugging Face** \n", + "*The AI community building the future.*\n", + "\n", + "---\n", + "\n", + "## Company Overview\n", + "\n", + "Hugging Face is a collaborative platform designed for the machine learning community, empowering users to build and share models, datasets, and applications. With over 1 million machine learning models and several hundred thousand datasets, we facilitate the creation, discovery, and collaboration that is essential to innovative AI solutions.\n", + "\n", + "---\n", + "\n", + "## Our Mission\n", + "\n", + "We are dedicated to democratizing AI technology by providing accessible and state-of-the-art tools. With our open-source commitment, we strive to empower developers and researchers worldwide to build AI models that enhance technology and improve lives.\n", + "\n", + "---\n", + "\n", + "## Key Offerings\n", + "\n", + "- **Models**: Access and contribute to over 1 million models tailored for various applications in text, image, video, audio, and more.\n", + "- **Datasets**: A vast library of datasets exceeding 250,000, helping users in training and validating their machine learning models.\n", + "- **Spaces**: A feature allowing users to showcase applications built using Hugging Face technologies and collaborate within the community.\n", + "- **Enterprise Solutions**: Paid compute options and enterprise-grade solutions with dedicated support for organizations, starting at just $20 per user per month.\n", + "\n", + "---\n", + "\n", + "## Customers\n", + "\n", + "Our platform is utilized by more than 50,000 organizations, including industry leaders such as:\n", + "\n", + "- **Google**\n", + "- **Microsoft**\n", + "- **Amazon**\n", + "- **Meta**\n", + "- **Grammarly**\n", + "\n", + "These companies are leveraging our models and datasets to innovate and enhance their operations through AI.\n", + "\n", + "---\n", + "\n", + "## Company Culture\n", + "\n", + "At Hugging Face, we foster a vibrant and inclusive company culture that prioritizes:\n", + "\n", + "- **Collaboration**: We believe that the best innovations emerge from teamwork and open dialogue.\n", + "- **Innovation**: Our team is encouraged to push the boundaries of technology, embracing a mindset of continuous improvement.\n", + "- **Community-Driven**: We value contributions from all community members, and our culture reflects a commitment to learning and sharing knowledge freely.\n", + "\n", + "Join us as we build the future of AI.\n", + "\n", + "---\n", + "\n", + "## Career Opportunities\n", + "\n", + "We are always on the lookout for passionate individuals to join our team. If you’re interested in:\n", + "\n", + "- **Software Development**\n", + "- **Research in AI/ML**\n", + "- **Community Engagement**\n", + "- **Product Design**\n", + "\n", + "We would love to hear from you! Explore our current job openings and discover how you can contribute to this exciting field.\n", + "\n", + "---\n", + "\n", + "**Ready to Collaborate?** \n", + "Visit us at [Hugging Face](https://huggingface.co) to learn more, explore our offerings, or join our growing community!\n", + "\n", + "--- \n", + "\n", + "*Together, let's shape the future of AI.*" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "create_brochure(\"HuggingFace\", \"https://huggingface.co\")" ] @@ -350,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "51db0e49-f261-4137-aabe-92dd601f7725", "metadata": {}, "outputs": [], @@ -375,12 +714,93 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://www.nvidia.com/en-eu/about-nvidia/'}, {'type': 'careers page', 'url': 'https://www.nvidia.com/en-eu/about-nvidia/careers/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/about-nvidia/executive-insights/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/about-nvidia/partners/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/research/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/foundation/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/csr/'}]}\n" + ] + }, + { + "data": { + "text/markdown": [ + "\n", + "# NVIDIA Company Brochure\n", + "\n", + "## About NVIDIA\n", + "\n", + "NVIDIA is a global leader in artificial intelligence (AI) computing, revolutionizing the way industries leverage technology to harness the power of data. Our expertise spans various domains including gaming, automotive, healthcare, and cloud computing, establishing NVIDIA at the forefront of accelerated computing for modern applications.\n", + "\n", + "### Vision and Mission\n", + "\n", + "NVIDIA's mission is to enhance human productivity through advanced technology. We aim to drive innovation and transform industries by delivering powerful graphics and computing solutions that empower creativity and enable smarter decisions.\n", + "\n", + "---\n", + "\n", + "## Products and Services\n", + "\n", + "### Innovative Offerings\n", + "\n", + "- **Artificial Intelligence Solutions:** From generative AI to intelligent video analytics, NVIDIA is pioneering the future with cutting-edge AI tools and frameworks.\n", + "- **Graphics Processing Units (GPUs):** The renowned GeForce and RTX product lines deliver unparalleled graphics performance for gaming and creative applications.\n", + "- **Data Center Solutions:** Our advanced networking and cloud solutions power mission-critical applications across industries, ensuring robust and scalable computing environments.\n", + "\n", + "### Key Industries Served\n", + "\n", + "- **Gaming:** Providing gamers with the best graphical experiences through high-performance graphics cards and technologies.\n", + "- **Automotive:** Leading innovations in autonomous vehicles and smart transportation systems.\n", + "- **Healthcare:** Transforming clinical applications with AI-powered solutions, data analysis, and simulation tools.\n", + "\n", + "---\n", + "\n", + "## Company Culture\n", + "\n", + "At NVIDIA, our culture is founded on collaboration and innovation. We encourage a growth mindset while fostering an inclusive environment where every team member is empowered to contribute their ideas. Our commitment to diversity and sustainability sets the groundwork for a dynamic workplace where creativity flourishes.\n", + "\n", + "### Employee Engagement\n", + "\n", + "We believe that our employees are our greatest asset. NVIDIA offers extensive professional development opportunities, wellness programs, and a flexible work environment. We support work-life balance to ensure our team members thrive both personally and professionally.\n", + "\n", + "---\n", + "\n", + "## Join Us\n", + "\n", + "### Careers at NVIDIA\n", + "\n", + "NVIDIA is constantly seeking passionate individuals who are innovators at heart. By joining our team, you will not only take part in exciting projects at the forefront of technology, but also gain the opportunity to grow and develop within the company. \n", + "\n", + "- **Open Positions:** We offer a range of career paths in engineering, research, sales, and marketing. \n", + "- **Inclusive Workplace:** We welcome diverse backgrounds and perspectives, aiming to create a team that mirrors our global customer base.\n", + "\n", + "If you're ready to push the boundaries of what's possible with us, visit our careers page to explore opportunities.\n", + "\n", + "---\n", + "\n", + "## Connect with Us\n", + "\n", + "For more information about our products and corporate initiatives, please visit [NVIDIA's Official Website](https://www.nvidia.com/) or reach out through our social media channels. We look forward to engaging with you!\n", + "\n", + "---\n", + "\n", + "Thank you for considering NVIDIAβ€”where innovation and creativity meet to shape the future.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" + "# stream_brochure(\"HuggingFace\", \"https://huggingface.co\")\n", + "# stream_brochure(\"British Council\", \"https://www.gov.uk/world/organisations/british-embassy-tel-aviv\")\n", + "# stream_brochure(\"General Motors\", \"https://www.gm.com/\")\n", + "stream_brochure(\"Nvidia\", \"https://www.nvidia.com/en-eu/\")" ] }, { @@ -501,7 +921,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week1/day5_company_brochure_modified.ipynb b/week1/day5_company_brochure_modified.ipynb new file mode 100644 index 0000000..a84acac --- /dev/null +++ b/week1/day5_company_brochure_modified.ipynb @@ -0,0 +1,1008 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d5b08506-dc8b-4443-9201-5f1848161363", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n", + "\n", + "import os\n", + "import requests\n", + "import json\n", + "from typing import List\n", + "from dotenv import load_dotenv\n", + "from bs4 import BeautifulSoup\n", + "from IPython.display import Markdown, display, update_display\n", + "from openai import OpenAI" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API key looks good so far\n" + ] + } + ], + "source": [ + "# Initialize and constants\n", + "\n", + "load_dotenv(override=True)\n", + "api_key = os.getenv('OPENAI_API_KEY')\n", + "\n", + "if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", + " print(\"API key looks good so far\")\n", + "else:\n", + " print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", + " \n", + "MODEL = 'gpt-4o-mini'\n", + "openai = OpenAI()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "106dd65e-90af-4ca8-86b6-23a41840645b", + "metadata": {}, + "outputs": [], + "source": [ + "# A class to represent a Webpage\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 Website:\n", + " \"\"\"\n", + " A utility class to represent a Website that we have scraped, now with links\n", + " \"\"\"\n", + "\n", + " def __init__(self, url):\n", + " self.url = url\n", + " response = requests.get(url, headers=headers)\n", + " self.body = response.content\n", + " soup = BeautifulSoup(self.body, 'html.parser')\n", + " self.title = soup.title.string if soup.title else \"No title found\"\n", + " if soup.body:\n", + " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", + " irrelevant.decompose()\n", + " self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", + " else:\n", + " self.text = \"\"\n", + " links = [link.get('href') for link in soup.find_all('a')]\n", + " self.links = [link for link in links if link]\n", + "\n", + " def get_contents(self):\n", + " return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e30d8128-933b-44cc-81c8-ab4c9d86589a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://edwarddonner.com/',\n", + " 'https://edwarddonner.com/connect-four/',\n", + " 'https://edwarddonner.com/outsmart/',\n", + " 'https://edwarddonner.com/about-me-and-about-nebula/',\n", + " 'https://edwarddonner.com/posts/',\n", + " 'https://edwarddonner.com/',\n", + " 'https://news.ycombinator.com',\n", + " 'https://nebula.io/?utm_source=ed&utm_medium=referral',\n", + " 'https://www.prnewswire.com/news-releases/wynden-stark-group-acquires-nyc-venture-backed-tech-startup-untapt-301269512.html',\n", + " 'https://patents.google.com/patent/US20210049536A1/',\n", + " 'https://www.linkedin.com/in/eddonner/',\n", + " 'https://edwarddonner.com/2025/01/23/llm-workshop-hands-on-with-agents-resources/',\n", + " 'https://edwarddonner.com/2025/01/23/llm-workshop-hands-on-with-agents-resources/',\n", + " 'https://edwarddonner.com/2024/12/21/llm-resources-superdatascience/',\n", + " 'https://edwarddonner.com/2024/12/21/llm-resources-superdatascience/',\n", + " 'https://edwarddonner.com/2024/11/13/llm-engineering-resources/',\n", + " 'https://edwarddonner.com/2024/11/13/llm-engineering-resources/',\n", + " 'https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/',\n", + " 'https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/',\n", + " 'https://edwarddonner.com/',\n", + " 'https://edwarddonner.com/connect-four/',\n", + " 'https://edwarddonner.com/outsmart/',\n", + " 'https://edwarddonner.com/about-me-and-about-nebula/',\n", + " 'https://edwarddonner.com/posts/',\n", + " 'mailto:hello@mygroovydomain.com',\n", + " 'https://www.linkedin.com/in/eddonner/',\n", + " 'https://twitter.com/edwarddonner',\n", + " 'https://www.facebook.com/edward.donner.52']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ed = Website(\"https://edwarddonner.com\")\n", + "ed.links" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6957b079-0d96-45f7-a26a-3487510e9b35", + "metadata": {}, + "outputs": [], + "source": [ + "link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", + "You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", + "such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", + "link_system_prompt += \"You should respond in one of the JSON examples provided:\"\n", + "link_system_prompt += \"\"\"\n", + "Example 1\n", + "{\n", + " \"links\": [\n", + " {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", + " {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", + " ]\n", + "}\n", + "\n", + "Example 2 \n", + "{\n", + " \"links\": [\n", + " {\"Domain\": \"About\", \"url\": \"https://full.url/goes/here/about\"},\n", + " {\"Domain\": \"Specilization\": \"url\": \"https://another.full.url/careers\"}\n", + " ]\n", + "}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b97e4068-97ed-4120-beae-c42105e4d59a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You are provided with a list of links found on a webpage. You are able to decide which of the links would be most relevant to include in a brochure about the company, such as links to an About page, or a Company page, or Careers/Jobs pages.\n", + "You should respond in one of the JSON examples provided:\n", + "Example 1\n", + "{\n", + " \"links\": [\n", + " {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", + " {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", + " ]\n", + "}\n", + "\n", + "Example 2 \n", + "{\n", + " \"links\": [\n", + " {\"Domain\": \"About\", \"url\": \"https://full.url/goes/here/about\"},\n", + " {\"Domain\": \"Specilization\": \"url\": \"https://another.full.url/careers\"}\n", + " ]\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "print(link_system_prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3", + "metadata": {}, + "outputs": [], + "source": [ + "def get_links_user_prompt(website):\n", + " user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", + " user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", + "Do not include Terms of Service, Privacy, email links.\\n\"\n", + " user_prompt += \"Links (some might be relative links):\\n\"\n", + " user_prompt += \"\\n\".join(website.links)\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "6bcbfa78-6395-4685-b92c-22d592050fd7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here is the list of links on the website of https://edwarddonner.com - please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. Do not include Terms of Service, Privacy, email links.\n", + "Links (some might be relative links):\n", + "https://edwarddonner.com/\n", + "https://edwarddonner.com/connect-four/\n", + "https://edwarddonner.com/outsmart/\n", + "https://edwarddonner.com/about-me-and-about-nebula/\n", + "https://edwarddonner.com/posts/\n", + "https://edwarddonner.com/\n", + "https://news.ycombinator.com\n", + "https://nebula.io/?utm_source=ed&utm_medium=referral\n", + "https://www.prnewswire.com/news-releases/wynden-stark-group-acquires-nyc-venture-backed-tech-startup-untapt-301269512.html\n", + "https://patents.google.com/patent/US20210049536A1/\n", + "https://www.linkedin.com/in/eddonner/\n", + "https://edwarddonner.com/2025/01/23/llm-workshop-hands-on-with-agents-resources/\n", + "https://edwarddonner.com/2025/01/23/llm-workshop-hands-on-with-agents-resources/\n", + "https://edwarddonner.com/2024/12/21/llm-resources-superdatascience/\n", + "https://edwarddonner.com/2024/12/21/llm-resources-superdatascience/\n", + "https://edwarddonner.com/2024/11/13/llm-engineering-resources/\n", + "https://edwarddonner.com/2024/11/13/llm-engineering-resources/\n", + "https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/\n", + "https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/\n", + "https://edwarddonner.com/\n", + "https://edwarddonner.com/connect-four/\n", + "https://edwarddonner.com/outsmart/\n", + "https://edwarddonner.com/about-me-and-about-nebula/\n", + "https://edwarddonner.com/posts/\n", + "mailto:hello@mygroovydomain.com\n", + "https://www.linkedin.com/in/eddonner/\n", + "https://twitter.com/edwarddonner\n", + "https://www.facebook.com/edward.donner.52\n" + ] + } + ], + "source": [ + "print(get_links_user_prompt(ed))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "a29aca19-ca13-471c-a4b4-5abbfa813f69", + "metadata": {}, + "outputs": [], + "source": [ + "def get_links(url):\n", + " website = Website(url)\n", + " response = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": link_system_prompt},\n", + " {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", + " ],\n", + " response_format={\"type\": \"json_object\"}\n", + " )\n", + " result = response.choices[0].message.content\n", + " return json.loads(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['/',\n", + " '/models',\n", + " '/datasets',\n", + " '/spaces',\n", + " '/posts',\n", + " '/docs',\n", + " '/enterprise',\n", + " '/pricing',\n", + " '/login',\n", + " '/join',\n", + " '/spaces',\n", + " '/models',\n", + " '/deepseek-ai/DeepSeek-V3-0324',\n", + " '/Qwen/Qwen2.5-Omni-7B',\n", + " '/manycore-research/SpatialLM-Llama-1B',\n", + " '/ds4sd/SmolDocling-256M-preview',\n", + " '/ByteDance/InfiniteYou',\n", + " '/models',\n", + " '/spaces/ByteDance/InfiniteYou-FLUX',\n", + " '/spaces/enzostvs/deepsite',\n", + " '/spaces/3DAIGC/LHM',\n", + " '/spaces/Trudy/gemini-codrawing',\n", + " '/spaces/Qwen/Qwen2.5-Omni-7B-Demo',\n", + " '/spaces',\n", + " '/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1',\n", + " '/datasets/glaiveai/reasoning-v1-20m',\n", + " '/datasets/FreedomIntelligence/medical-o1-reasoning-SFT',\n", + " '/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M',\n", + " '/datasets/PixelAI-Team/TalkBody4D',\n", + " '/datasets',\n", + " '/join',\n", + " '/pricing#endpoints',\n", + " '/pricing#spaces',\n", + " '/pricing',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/enterprise',\n", + " '/allenai',\n", + " '/facebook',\n", + " '/amazon',\n", + " '/google',\n", + " '/Intel',\n", + " '/microsoft',\n", + " '/grammarly',\n", + " '/Writer',\n", + " '/docs/transformers',\n", + " '/docs/diffusers',\n", + " '/docs/safetensors',\n", + " '/docs/huggingface_hub',\n", + " '/docs/tokenizers',\n", + " '/docs/trl',\n", + " '/docs/transformers.js',\n", + " '/docs/smolagents',\n", + " '/docs/peft',\n", + " '/docs/datasets',\n", + " '/docs/text-generation-inference',\n", + " '/docs/accelerate',\n", + " '/models',\n", + " '/datasets',\n", + " '/spaces',\n", + " '/tasks',\n", + " 'https://ui.endpoints.huggingface.co',\n", + " '/chat',\n", + " '/huggingface',\n", + " '/brand',\n", + " '/terms-of-service',\n", + " '/privacy',\n", + " 'https://apply.workable.com/huggingface/',\n", + " 'mailto:press@huggingface.co',\n", + " '/learn',\n", + " '/docs',\n", + " '/blog',\n", + " 'https://discuss.huggingface.co',\n", + " 'https://status.huggingface.co/',\n", + " 'https://github.com/huggingface',\n", + " 'https://twitter.com/huggingface',\n", + " 'https://www.linkedin.com/company/huggingface/',\n", + " '/join/discord']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n", + "\n", + "huggingface = Website(\"https://huggingface.co\")\n", + "huggingface.links\n", + "\n", + "# anthropic_page = Website(\"https://anthropic.com\")\n", + "# anthropic_page.links" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'links': [{'type': 'about page', 'url': 'https://huggingface.co/huggingface'},\n", + " {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'},\n", + " {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'},\n", + " {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'},\n", + " {'type': 'blog page', 'url': 'https://huggingface.co/blog'},\n", + " {'type': 'documentation', 'url': 'https://huggingface.co/docs'}]}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_links(\"https://huggingface.co\")" + ] + }, + { + "cell_type": "markdown", + "id": "0d74128e-dfb6-47ec-9549-288b621c838c", + "metadata": {}, + "source": [ + "## Second step: make the brochure!\n", + "\n", + "Assemble all the details into another prompt to GPT4-o" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5", + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_details(url):\n", + " result = \"Landing page:\\n\"\n", + " result += Website(url).get_contents()\n", + " links = get_links(url)\n", + " print(\"Found links:\", links)\n", + " for link in links[\"links\"]:\n", + " result += f\"\\n\\n{link['type']}\\n\"\n", + " result += Website(link[\"url\"]).get_contents()\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5099bd14-076d-4745-baf3-dac08d8e5ab2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://huggingface.co/about'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'company page', 'url': 'https://huggingface.co/enterprise'}, {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'}, {'type': 'blog page', 'url': 'https://huggingface.co/blog'}, {'type': 'learn page', 'url': 'https://huggingface.co/learn'}, {'type': 'linkedin page', 'url': 'https://www.linkedin.com/company/huggingface/'}, {'type': 'discuss forum', 'url': 'https://discuss.huggingface.co'}]}\n" + ] + }, + { + "data": { + "text/plain": [ + "'Landing page:\\nWebpage Title:\\nHugging Face – The AI community building the future.\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nExplore AI Apps\\nor\\nBrowse 1M+ models\\nTrending on\\nthis week\\nModels\\ndeepseek-ai/DeepSeek-V3-0324\\nUpdated\\n2 days ago\\nβ€’\\n60.5k\\nβ€’\\n1.96k\\nQwen/Qwen2.5-Omni-7B\\nUpdated\\nabout 24 hours ago\\nβ€’\\n27.9k\\nβ€’\\n796\\nmanycore-research/SpatialLM-Llama-1B\\nUpdated\\n8 days ago\\nβ€’\\n11.6k\\nβ€’\\n781\\nds4sd/SmolDocling-256M-preview\\nUpdated\\n6 days ago\\nβ€’\\n48.4k\\nβ€’\\n1.03k\\nByteDance/InfiniteYou\\nUpdated\\n4 days ago\\nβ€’\\n465\\nBrowse 1M+ models\\nSpaces\\nRunning\\non\\nZero\\n530\\n530\\nInfiniteYou-FLUX\\nπŸ“Έ\\nFlexible Photo Recrafting While Preserving Your Identity\\nRunning\\n255\\n255\\nDeepSite\\n🐳\\nImagine and Share in 1-Click\\nRunning\\non\\nZero\\n224\\n224\\nLHM\\n⚑\\nLarge Animatable Human Model\\nRunning\\n350\\n350\\nGemini Co-Drawing\\n✏\\nGemini 2.0 native image generation co-doodling\\nRunning\\n154\\n154\\nQwen2.5 Omni 7B Demo\\nπŸ†\\nSubmit media inputs to generate text and speech responses\\nBrowse 400k+ applications\\nDatasets\\nnvidia/Llama-Nemotron-Post-Training-Dataset-v1\\nUpdated\\n11 days ago\\nβ€’\\n8.05k\\nβ€’\\n265\\nglaiveai/reasoning-v1-20m\\nUpdated\\n10 days ago\\nβ€’\\n6.84k\\nβ€’\\n127\\nFreedomIntelligence/medical-o1-reasoning-SFT\\nUpdated\\nFeb 22\\nβ€’\\n25.5k\\nβ€’\\n574\\na-m-team/AM-DeepSeek-R1-Distilled-1.4M\\nUpdated\\n29 minutes ago\\nβ€’\\n3.79k\\nβ€’\\n75\\nPixelAI-Team/TalkBody4D\\nUpdated\\n4 days ago\\nβ€’\\n66\\nβ€’\\n44\\nBrowse 250k+ datasets\\nThe Home of Machine Learning\\nCreate, discover and collaborate on ML better.\\nThe collaboration platform\\nHost and collaborate on unlimited public models, datasets and applications.\\nMove faster\\nWith the HF Open source stack.\\nExplore all modalities\\nText, image, video, audio or even 3D.\\nBuild your portfolio\\nShare your work with the world and build your ML profile.\\nSign Up\\nAccelerate your ML\\nWe provide paid Compute and Enterprise solutions.\\nCompute\\nDeploy on optimized\\nInference Endpoints\\nor update your\\nSpaces applications\\nto a GPU in a few clicks.\\nView pricing\\nStarting at $0.60/hour for GPU\\nEnterprise\\nGive your team the most advanced platform to build AI with enterprise-grade security, access controls and\\n\\t\\t\\tdedicated support.\\nGetting started\\nStarting at $20/user/month\\nSingle Sign-On\\nRegions\\nPriority Support\\nAudit Logs\\nResource Groups\\nPrivate Datasets Viewer\\nMore than 50,000 organizations are using Hugging Face\\nAi2\\nEnterprise\\nnon-profit\\nβ€’\\n396 models\\nβ€’\\n2.97k followers\\nAI at Meta\\nEnterprise\\ncompany\\nβ€’\\n2.07k models\\nβ€’\\n5.29k followers\\nAmazon\\ncompany\\nβ€’\\n10 models\\nβ€’\\n2.92k followers\\nGoogle\\ncompany\\nβ€’\\n974 models\\nβ€’\\n10.7k followers\\nIntel\\ncompany\\nβ€’\\n219 models\\nβ€’\\n2.37k followers\\nMicrosoft\\ncompany\\nβ€’\\n365 models\\nβ€’\\n10.7k followers\\nGrammarly\\nEnterprise\\ncompany\\nβ€’\\n10 models\\nβ€’\\n146 followers\\nWriter\\nEnterprise\\ncompany\\nβ€’\\n21 models\\nβ€’\\n253 followers\\nOur Open Source\\nWe are building the foundation of ML tooling with the community.\\nTransformers\\n142,118\\nState-of-the-art ML for PyTorch, TensorFlow, JAX\\nDiffusers\\n28,310\\nState-of-the-art Diffusion models in PyTorch\\nSafetensors\\n3,190\\nSafe way to store/distribute neural network weights\\nHub Python Library\\n2,471\\nPython client to interact with the Hugging Face Hub\\nTokenizers\\n9,539\\nFast tokenizers optimized for research & production\\nTRL\\n12,906\\nTrain transformers LMs with reinforcement learning\\nTransformers.js\\n13,318\\nState-of-the-art ML running directly in your browser\\nsmolagents\\n15,966\\nSmol library to build great agents in Python\\nPEFT\\n17,935\\nParameter-efficient finetuning for large language models\\nDatasets\\n19,896\\nAccess & share datasets for any ML tasks\\nText Generation Inference\\n9,940\\nServe language models with TGI optimized toolkit\\nAccelerate\\n8,550\\nTrain PyTorch models with multi-GPU, TPU, mixed precision\\nSystem theme\\nWebsite\\nModels\\nDatasets\\nSpaces\\nTasks\\nInference Endpoints\\nHuggingChat\\nCompany\\nAbout\\nBrand assets\\nTerms of service\\nPrivacy\\nJobs\\nPress\\nResources\\nLearn\\nDocumentation\\nBlog\\nForum\\nService Status\\nSocial\\nGitHub\\nTwitter\\nLinkedIn\\nDiscord\\n\\n\\n\\nabout page\\nWebpage Title:\\nabout (Sergei)\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nSergei\\nabout\\nFollow\\nRenumathi\\'s profile picture\\nKalaipriya\\'s profile picture\\nselvivincent\\'s profile picture\\n4\\n\\t\\t\\t\\t\\tfollowers\\nΒ·\\n0 following\\nAI & ML interests\\nNone yet\\nOrganizations\\nNone yet\\nmodels\\nNone public yet\\ndatasets\\nNone public yet\\nSystem theme\\nCompany\\nTOS\\nPrivacy\\nAbout\\nJobs\\nWebsite\\nModels\\nDatasets\\nSpaces\\nPricing\\nDocs\\n\\n\\n\\ncareers page\\nWebpage Title:\\nHugging Face - Current Openings\\nWebpage Contents:\\n\\n\\n\\n\\ncompany page\\nWebpage Title:\\nEnterprise Hub - Hugging Face\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nEnterprise Hub\\nEnterprise-ready version of the world’s leading AI platform\\nSubscribe to\\nEnterprise Hub\\nfor $20/user/month with your Hub organization\\nGive your organization the most advanced platform to build AI with enterprise-grade security, access controls,\\n\\t\\t\\tdedicated support and more.\\nSingle Sign-On\\nConnect securely to your identity provider with SSO integration.\\nRegions\\nSelect, manage, and audit the location of your repository data.\\nAudit Logs\\nStay in control with comprehensive logs that report on actions taken.\\nResource Groups\\nAccurately manage access to repositories with granular access control.\\nToken Management\\nCentralized token control and custom approval policies for organization access.\\nAnalytics\\nTrack and analyze repository usage data in a single dashboard.\\nAdvanced Compute Options\\nIncrease scalability and performance with more compute options like ZeroGPU.\\nZeroGPU Quota Boost\\nAll organization members get 5x more ZeroGPU quota to get the most of Spaces.\\nPrivate Datasets Viewer\\nEnable the Dataset Viewer on your private datasets for easier collaboration.\\nAdvanced security\\nConfigure organization-wide security policies and default repository visibility.\\nBilling\\nControl your budget effectively with managed billing and yearly commit options.\\nPriority Support\\nMaximize your platform usage with priority support from the Hugging Face team.\\nExtra Private Storage\\nGet an additional 1 TB of private storage for each member of your organization (then $25/month per extra TB).\\nJoin the most forward-thinking AI organizations\\nEverything you already know and love about Hugging Face in Enterprise mode.\\nSubscribe to\\nEnterprise Hub\\nor\\nTalk to sales\\nNVIDIA\\nEnterprise\\ncompany\\nβ€’\\n329 models\\nβ€’\\n20.4k followers\\nNerdy Face\\nEnterprise\\ncompany\\nβ€’\\n1 model\\nβ€’\\n286 followers\\nAMD\\nEnterprise\\ncompany\\nβ€’\\n112 models\\nβ€’\\n1.43k followers\\nArm\\nEnterprise\\ncompany\\nβ€’\\n159 followers\\nServiceNow-AI\\nEnterprise\\ncompany\\nβ€’\\n194 followers\\nFidelity Investments\\nEnterprise\\ncompany\\nβ€’\\n132 followers\\nMistral AI_\\nEnterprise\\ncompany\\nβ€’\\n26 models\\nβ€’\\n7.16k followers\\nTechnology Innovation Institute\\nEnterprise\\ncompany\\nβ€’\\n65 models\\nβ€’\\n1.27k followers\\nChegg Inc\\nEnterprise\\ncompany\\nβ€’\\n84 followers\\nGrammarly\\nEnterprise\\ncompany\\nβ€’\\n10 models\\nβ€’\\n146 followers\\nArcee AI\\nEnterprise\\ncompany\\nβ€’\\n156 models\\nβ€’\\n477 followers\\nWidn AI\\nEnterprise\\ncompany\\nβ€’\\n43 followers\\nAdyen\\nEnterprise\\ncompany\\nβ€’\\n57 followers\\nEkimetrics\\nEnterprise\\ncompany\\nβ€’\\n55 followers\\nMeta Llama\\nEnterprise\\ncompany\\nβ€’\\n57 models\\nβ€’\\n34.2k followers\\nSnowflake\\nEnterprise\\ncompany\\nβ€’\\n15 models\\nβ€’\\n466 followers\\nOrange\\nEnterprise\\ncompany\\nβ€’\\n7 models\\nβ€’\\n198 followers\\nWriter\\nEnterprise\\ncompany\\nβ€’\\n21 models\\nβ€’\\n253 followers\\nDeutsche Telekom AG\\nEnterprise\\ncompany\\nβ€’\\n7 models\\nβ€’\\n135 followers\\nJusbrasil\\nEnterprise\\ncompany\\nβ€’\\n89 followers\\nTNG Technology Consulting GmbH\\nEnterprise\\ncompany\\nβ€’\\n1 model\\nβ€’\\n65 followers\\nIBM Granite\\nEnterprise\\ncompany\\nβ€’\\n94 models\\nβ€’\\n1.34k followers\\ncreditkarma\\nEnterprise\\ncompany\\nβ€’\\n53 followers\\nHiddenLayer\\nEnterprise\\ncompany\\nβ€’\\n1 model\\nβ€’\\n65 followers\\nMiniMax\\nEnterprise\\ncompany\\nβ€’\\n2 models\\nβ€’\\n594 followers\\nBCG X\\nEnterprise\\ncompany\\nβ€’\\n37 followers\\nKakao Corp.\\nEnterprise\\ncompany\\nβ€’\\n3 models\\nβ€’\\n109 followers\\nTwelve Labs\\nEnterprise\\ncompany\\nβ€’\\n41 followers\\nShopify\\nEnterprise\\ncompany\\nβ€’\\n431 followers\\nAI at Meta\\nEnterprise\\ncompany\\nβ€’\\n2.07k models\\nβ€’\\n5.29k followers\\nTogether\\nEnterprise\\ncompany\\nβ€’\\n32 models\\nβ€’\\n550 followers\\nXsolla\\nEnterprise\\ncompany\\nβ€’\\n120 followers\\nToyota Research Institute\\nEnterprise\\ncompany\\nβ€’\\n10 models\\nβ€’\\n104 followers\\nMercedes-Benz AG\\nEnterprise\\ncompany\\nβ€’\\n142 followers\\nH2O.ai\\nEnterprise\\ncompany\\nβ€’\\n72 models\\nβ€’\\n408 followers\\nAledade Inc\\nEnterprise\\ncompany\\nβ€’\\n64 followers\\nNutanix\\nEnterprise\\ncompany\\nβ€’\\n262 models\\nβ€’\\n65 followers\\nJohnson & Johnson\\nEnterprise\\ncompany\\nβ€’\\n56 followers\\nStability AI\\nEnterprise\\ncompany\\nβ€’\\n104 models\\nβ€’\\n19.7k followers\\nLiquid AI\\nEnterprise\\ncompany\\nβ€’\\n116 followers\\nGretel.ai\\nEnterprise\\ncompany\\nβ€’\\n9 models\\nβ€’\\n112 followers\\nNewMindAI\\nEnterprise\\ncompany\\nβ€’\\n36 followers\\nCompliance & Certifications\\nGDPR Compliant\\nSOC 2 Type 2\\nSystem theme\\nWebsite\\nModels\\nDatasets\\nSpaces\\nTasks\\nInference Endpoints\\nHuggingChat\\nCompany\\nAbout\\nBrand assets\\nTerms of service\\nPrivacy\\nJobs\\nPress\\nResources\\nLearn\\nDocumentation\\nBlog\\nForum\\nService Status\\nSocial\\nGitHub\\nTwitter\\nLinkedIn\\nDiscord\\n\\n\\n\\npricing page\\nWebpage Title:\\nHugging Face – Pricing\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nPricing\\nLeveling up AI collaboration and compute.\\nUsers and organizations already use the Hub as a collaboration platform,\\nwe’re making it easy to seamlessly and scalably launch ML compute directly from the Hub.\\nHF Hub\\nCollaborate on Machine Learning\\nHost unlimited public models, datasets\\nCreate unlimited orgs with no member limits\\nAccess the latest ML tools and open source\\nCommunity support\\nForever\\nFree\\nPRO\\nPro Account\\nUnlock advanced HF features\\nZeroGPU and Dev Mode for Spaces\\nFree credits across all Inference Providers\\nGet early access to upcoming features\\nShow your support with a Pro badge\\nSubscribe for\\n$9\\n/month\\nEnterprise Hub\\nAccelerate your AI roadmap\\nSSO and SAML support\\nSelect data location with Storage Regions\\nPrecise actions reviews with Audit logs\\nGranular access control with Resource groups\\nCentralized token control and approval\\nDataset Viewer for private datasets\\nAdvanced compute options for Spaces\\n5x more ZeroGPU quota for all org members\\nDeploy Inference on your own Infra\\nManaged billing with yearly commits\\nPriority support\\nStarting at\\n$20\\nper user per month\\nSpaces Hardware\\nUpgrade your Space compute\\nFree CPUs\\nBuild more advanced Spaces\\n7 optimized hardware available\\nFrom CPU to GPU to Accelerators\\nStarting at\\n$0\\n/hour\\nInference Endpoints\\nDeploy models on fully managed infrastructure\\nDeploy dedicated Endpoints in seconds\\nKeep your costs low\\nFully-managed autoscaling\\nEnterprise security\\nStarting at\\n$0.032\\n/hour\\nNeed support to accelerate AI in your organization? View our\\nExpert Support\\n.\\nHugging Face Hub\\nfree\\nThe HF Hub is the central place to explore, experiment, collaborate and build technology with Machine\\n\\t\\t\\t\\t\\tLearning.\\nJoin the open source Machine Learning movement!\\nβ†’\\nSign Up\\nCreate with ML\\nPacked with ML features, like model eval, dataset viewer and much more.\\nCollaborate\\nGit based and designed for collaboration at its core.\\nPlay and learn\\nLearn by experimenting and sharing with our awesome community.\\nBuild your ML portfolio\\nShare your work with the world and build your own ML profile.\\nSpaces Hardware\\nStarting at $0\\nSpaces are one of the most popular ways to share ML applications and demos with the world.\\nUpgrade your Spaces with our selection of custom on-demand hardware:\\nβ†’\\nGet started with Spaces\\nName\\nCPU\\nMemory\\nAccelerator\\nVRAM\\nHourly price\\nCPU Basic\\n2 vCPU\\n16 GB\\n-\\n-\\nFREE\\nCPU Upgrade\\n8 vCPU\\n32 GB\\n-\\n-\\n$0.03\\nNvidia T4 - small\\n4 vCPU\\n15 GB\\nNvidia T4\\n16 GB\\n$0.40\\nNvidia T4 - medium\\n8 vCPU\\n30 GB\\nNvidia T4\\n16 GB\\n$0.60\\n1x Nvidia L4\\n8 vCPU\\n30 GB\\nNvidia L4\\n24 GB\\n$0.80\\n4x Nvidia L4\\n48 vCPU\\n186 GB\\nNvidia L4\\n96 GB\\n$3.80\\n1x Nvidia L40S\\n8 vCPU\\n62 GB\\nNvidia L4\\n48 GB\\n$1.80\\n4x Nvidia L40S\\n48 vCPU\\n382 GB\\nNvidia L4\\n192 GB\\n$8.30\\n8x Nvidia L40S\\n192 vCPU\\n1534 GB\\nNvidia L4\\n384 GB\\n$23.50\\nNvidia A10G - small\\n4 vCPU\\n15 GB\\nNvidia A10G\\n24 GB\\n$1.00\\nNvidia A10G - large\\n12 vCPU\\n46 GB\\nNvidia A10G\\n24 GB\\n$1.50\\n2x Nvidia A10G - large\\n24 vCPU\\n92 GB\\nNvidia A10G\\n48 GB\\n$3.00\\n4x Nvidia A10G - large\\n48 vCPU\\n184 GB\\nNvidia A10G\\n96 GB\\n$5.00\\nNvidia A100 - large\\n12 vCPU\\n142 GB\\nNvidia A100\\n80 GB\\n$4.00\\nTPU v5e 1x1\\n22 vCPU\\n44 GB\\nGoogle TPU v5e\\n16 GB\\n$1.20\\nTPU v5e 2x2\\n110 vCPU\\n186 GB\\nGoogle TPU v5e\\n64 GB\\n$4.75\\nTPU v5e 2x4\\n220 vCPU\\n380 GB\\nGoogle TPU v5e\\n128 GB\\n$9.50\\nCustom\\non demand\\non demand\\non demand\\non demand\\non demand\\nSpaces Persistent Storage\\nAll Spaces get ephemeral storage for free but you can upgrade and add persistent storage at any time.\\nName\\nStorage\\nMonthly price\\nSmall\\n20 GB\\n$5\\nMedium\\n150 GB\\n$25\\nLarge\\n1 TB\\n$100\\nBuilding something cool as a side project? We also offer community GPU grants.\\nInference Endpoints\\nStarting at $0.033/hour\\nInference Endpoints (dedicated) offers a secure production solution to easily deploy any ML model on dedicated\\n\\t\\t\\t\\t\\tand autoscaling infrastructure, right from the HF Hub.\\nβ†’\\nLearn more\\nCPU\\ninstances\\nProvider\\nArchitecture\\nvCPUs\\nMemory\\nHourly rate\\naws\\nIntel Sapphire Rapids\\n1\\n2GB\\n$0.03\\n2\\n4GB\\n$0.07\\n4\\n8GB\\n$0.13\\n8\\n16GB\\n$0.27\\n16\\n32GB\\n$0.54\\nazure\\nIntel Xeon\\n1\\n2GB\\n$0.06\\n2\\n4GB\\n$0.12\\n4\\n8GB\\n$0.24\\n8\\n16GB\\n$0.48\\ngcp\\nIntel Sapphire Rapids\\n1\\n2GB\\n$0.05\\n2\\n4GB\\n$0.10\\n4\\n8GB\\n$0.20\\n8\\n16GB\\n$0.40\\nAccelerator\\ninstances\\nProvider\\nArchitecture\\nTopology\\nAccelerator Memory\\nHourly rate\\naws\\nInf2\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tNeuron\\nx1\\n14.5GB\\n$0.75\\nx12\\n760GB\\n$12.00\\ngcp\\nTPU\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tv5e\\n1x1\\n16GB\\n$1.20\\n2x2\\n64GB\\n$4.75\\n2x4\\n128GB\\n$9.50\\nGPU\\ninstances\\nProvider\\nArchitecture\\nGPUs\\nGPU Memory\\nHourly rate\\naws\\nNVIDIA T4\\n1\\n14GB\\n$0.50\\n4\\n56GB\\n$3.00\\naws\\nNVIDIA L4\\n1\\n24GB\\n$0.80\\n4\\n96GB\\n$3.80\\naws\\nNVIDIA L40S\\n1\\n48GB\\n$1.80\\n4\\n192GB\\n$8.30\\n8\\n384GB\\n$23.50\\naws\\nNVIDIA A10G\\n1\\n24GB\\n$1.00\\n4\\n96GB\\n$5.00\\naws\\nNVIDIA A100\\n1\\n80GB\\n$4.00\\n2\\n160GB\\n$8.00\\n4\\n320GB\\n$16.00\\n8\\n640GB\\n$32.00\\ngcp\\nNVIDIA T4\\n1\\n16GB\\n$0.50\\ngcp\\nNVIDIA L4\\n1\\n24GB\\n$0.70\\n4\\n96GB\\n$3.80\\ngcp\\nNVIDIA A100\\n1\\n80GB\\n$3.60\\n2\\n160GB\\n$7.20\\n4\\n320GB\\n$14.40\\n8\\n640GB\\n$28.80\\ngcp\\nNVIDIA H100\\n1\\n80GB\\n$10.00\\n2\\n160GB\\n$20.00\\n4\\n320GB\\n$40.00\\n8\\n640GB\\n$80.00\\nPro Account\\nPRO\\nA 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Status\\nSocial\\nGitHub\\nTwitter\\nLinkedIn\\nDiscord\\n\\n\\n\\nblog page\\nWebpage Title:\\nHugging Face – Blog\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nBlog, Articles, and discussions\\nNew Article\\nEverything\\ncommunity\\nguide\\nopen source collab\\npartnerships\\nresearch\\nNLP\\nAudio\\nCV\\nRL\\nethics\\nDiffusion\\nGame Development\\nRLHF\\nLeaderboard\\nCase Studies\\nLeRobot\\nAccelerating LLM Inference with TGI on Intel Gaudi\\nBy\\nbaptistecolle\\nMarch 28, 2025\\nβ€’\\n8\\nCommunity Articles\\nview all\\n🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?\\nBy\\nKseniase\\nβ€’\\n12 days ago\\nβ€’\\n93\\nOpen R1: Update #4\\nBy\\nopen-r1\\nand 3 others\\nβ€’\\n3 days ago\\nβ€’\\n37\\nOpen R1: Update #3\\nBy\\nopen-r1\\nand 9 others\\nβ€’\\n18 days ago\\nβ€’\\n276\\nI Clicked β€œI Agree”, But What Am I Really Consenting To?\\nBy\\ngiadap\\nβ€’\\n3 days ago\\nβ€’\\n19\\nDeepSearch Using Visual RAG in Agentic Frameworks πŸ”Ž\\nBy\\npaultltc\\nand 1 other\\nβ€’\\n8 days ago\\nβ€’\\n28\\nSpeeding Up LLM Decoding with Advanced Universal Assisted Generation Techniques\\nBy\\njmamou\\nand 8 others\\nβ€’\\n5 days ago\\nβ€’\\n16\\nUncensor any LLM with abliteration\\nBy\\nmlabonne\\nβ€’\\nJun 13, 2024\\nβ€’\\n496\\nFeeL: Making Multilingual LMs Better, One Feedback Loop at a Time\\nBy\\nborgr\\nand 1 other\\nβ€’\\n4 days ago\\nβ€’\\n10\\nDeepSeek-R1 Dissection: Understanding PPO & GRPO Without Any Prior Reinforcement Learning Knowledge\\nBy\\nNormalUhr\\nβ€’\\nFeb 7\\nβ€’\\n89\\nIntroducing EuroBERT: A High-Performance Multilingual Encoder Model\\nBy\\nEuroBERT\\nand 3 others\\nβ€’\\n19 days ago\\nβ€’\\n134\\nUnderstanding and Implementing the Tree of Thoughts Paradigm\\nBy\\nsadhaklal\\nβ€’\\n3 days ago\\nβ€’\\n8\\nThe Large Language Model Course\\nBy\\nmlabonne\\nβ€’\\nJan 16\\nβ€’\\n146\\nKV Caching Explained: Optimizing Transformer Inference Efficiency\\nBy\\nnot-lain\\nβ€’\\nJan 30\\nβ€’\\n46\\nColPali: Efficient Document Retrieval with Vision Language Models πŸ‘€\\nBy\\nmanu\\nβ€’\\nJul 5, 2024\\nβ€’\\n227\\nOpen-Source Handwritten Signature Detection Model\\nBy\\nsamuellimabraz\\nβ€’\\n15 days ago\\nβ€’\\n90\\nFine-tune Llama 3.1 Ultra-Efficiently with Unsloth\\nBy\\nmlabonne\\nβ€’\\nJul 29, 2024\\nβ€’\\n301\\nMastering Tensor Dimensions in Transformers\\nBy\\nnot-lain\\nβ€’\\nJan 12\\nβ€’\\n56\\nPangolinGuard: Fine-Tuning ModernBERT as a Lightweight Approach to AI Guardrails\\nBy\\ndcarpintero\\nβ€’\\n6 days ago\\nβ€’\\n5\\nManus AI: The Best Autonomous AI Agent Redefining Automation and Productivity\\nBy\\nLLMhacker\\nβ€’\\n23 days ago\\nβ€’\\n151\\nmistral.rs v0.5.0\\nBy\\nEricB\\nβ€’\\n5 days ago\\nβ€’\\n5\\nTraining and Finetuning Reranker Models with Sentence Transformers v4\\nBy\\ntomaarsen\\nMarch 26, 2025\\nβ€’\\n69\\nIntroducing Gradio\\'s new Dataframe!\\nBy\\nhmb\\nMarch 24, 2025\\nβ€’\\n18\\nThe New and Fresh analytics in Inference Endpoints\\nBy\\nerikkaum\\nMarch 21, 2025\\nβ€’\\n17\\nOpen R1: How to use OlympicCoder locally for coding?\\nBy\\nburtenshaw\\nMarch 20, 2025\\nβ€’\\n52\\nAI Policy: πŸ€— Response to the White House AI Action Plan RFI\\nBy\\nyjernite\\nMarch 19, 2025\\nβ€’\\n21\\nNVIDIA\\'s GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets\\nBy\\nmingyuliutw\\nMarch 18, 2025\\nguest\\nβ€’\\n30\\nXet is on the Hub\\nBy\\njsulz\\nMarch 18, 2025\\nβ€’\\n34\\nWelcome Gemma 3: Google\\'s all new multimodal, multilingual, long context open LLM\\nBy\\nariG23498\\nMarch 12, 2025\\nβ€’\\n354\\nLeRobot goes to driving school: World’s largest open-source self-driving dataset\\nBy\\nsandhawalia\\nMarch 11, 2025\\nβ€’\\n69\\nLLM Inference on Edge: A Fun and Easy Guide to run LLMs via React Native on your Phone!\\nBy\\nmedmekk\\nMarch 7, 2025\\nβ€’\\n45\\nHugging Face and JFrog partner to make AI Security more transparent\\nBy\\nmcpotato\\nMarch 4, 2025\\nβ€’\\n21\\nA Deepdive into Aya Vision: Advancing the Frontier of Multilingual Multimodality\\nBy\\nsaurabhdash\\nMarch 4, 2025\\nguest\\nβ€’\\n70\\nTrace & Evaluate your Agent with Arize Phoenix\\nBy\\nm-ric\\nFebruary 28, 2025\\nguest\\nβ€’\\n35\\nHuggingFace, IISc partner to supercharge model building on India\\'s diverse languages\\nBy\\nprasantg\\nFebruary 27, 2025\\nβ€’\\n18\\nPrevious\\n1\\n2\\n3\\n...\\n40\\nNext\\nCommunity Articles\\nSort:\\xa0\\n\\t\\tTrending\\n🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?\\nBy\\nKseniase\\nβ€’\\n12 days ago\\nβ€’\\n93\\nOpen R1: Update #4\\nBy\\nopen-r1\\nand 3 others\\nβ€’\\n3 days ago\\nβ€’\\n37\\nOpen R1: Update #3\\nBy\\nopen-r1\\nand 9 others\\nβ€’\\n18 days ago\\nβ€’\\n276\\nI Clicked β€œI Agree”, But What Am I Really Consenting To?\\nBy\\ngiadap\\nβ€’\\n3 days ago\\nβ€’\\n19\\nDeepSearch Using Visual RAG in Agentic Frameworks πŸ”Ž\\nBy\\npaultltc\\nand 1 other\\nβ€’\\n8 days ago\\nβ€’\\n28\\nSpeeding Up LLM Decoding with Advanced Universal Assisted Generation Techniques\\nBy\\njmamou\\nand 8 others\\nβ€’\\n5 days ago\\nβ€’\\n16\\nUncensor any LLM with abliteration\\nBy\\nmlabonne\\nβ€’\\nJun 13, 2024\\nβ€’\\n496\\nFeeL: Making Multilingual LMs Better, One Feedback Loop at a Time\\nBy\\nborgr\\nand 1 other\\nβ€’\\n4 days ago\\nβ€’\\n10\\nDeepSeek-R1 Dissection: Understanding PPO & GRPO Without Any Prior Reinforcement Learning Knowledge\\nBy\\nNormalUhr\\nβ€’\\nFeb 7\\nβ€’\\n89\\nIntroducing EuroBERT: A High-Performance Multilingual Encoder Model\\nBy\\nEuroBERT\\nand 3 others\\nβ€’\\n19 days ago\\nβ€’\\n134\\nUnderstanding and Implementing the Tree of Thoughts Paradigm\\nBy\\nsadhaklal\\nβ€’\\n3 days ago\\nβ€’\\n8\\nThe Large Language Model Course\\nBy\\nmlabonne\\nβ€’\\nJan 16\\nβ€’\\n146\\nKV Caching Explained: Optimizing Transformer Inference Efficiency\\nBy\\nnot-lain\\nβ€’\\nJan 30\\nβ€’\\n46\\nColPali: Efficient Document Retrieval with Vision Language Models πŸ‘€\\nBy\\nmanu\\nβ€’\\nJul 5, 2024\\nβ€’\\n227\\nOpen-Source Handwritten Signature Detection Model\\nBy\\nsamuellimabraz\\nβ€’\\n15 days ago\\nβ€’\\n90\\nFine-tune Llama 3.1 Ultra-Efficiently with Unsloth\\nBy\\nmlabonne\\nβ€’\\nJul 29, 2024\\nβ€’\\n301\\nMastering Tensor Dimensions in Transformers\\nBy\\nnot-lain\\nβ€’\\nJan 12\\nβ€’\\n56\\nPangolinGuard: Fine-Tuning ModernBERT as a Lightweight Approach to AI Guardrails\\nBy\\ndcarpintero\\nβ€’\\n6 days ago\\nβ€’\\n5\\nManus AI: The Best Autonomous AI Agent Redefining Automation and Productivity\\nBy\\nLLMhacker\\nβ€’\\n23 days ago\\nβ€’\\n151\\nmistral.rs v0.5.0\\nBy\\nEricB\\nβ€’\\n5 days ago\\nβ€’\\n5\\nView all\\nSystem theme\\nCompany\\nTOS\\nPrivacy\\nAbout\\nJobs\\nWebsite\\nModels\\nDatasets\\nSpaces\\nPricing\\nDocs\\n\\n\\n\\nlearn page\\nWebpage Title:\\nHugging Face - Learn\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nLearn\\nNLP Course\\nThis course will teach you about natural language processing using libraries from the HF ecosystem\\nAgents Course\\nLearn to build and deploy your own AI agents\\nDeep RL Course\\nThis course will teach you about deep reinforcement learning using libraries from the HF ecosystem\\nCommunity Computer Vision Course\\nThis course will teach you about computer vision ML using libraries and models from the HF ecosystem\\nAudio Course\\nLearn to apply transformers to audio data using libraries from the HF ecosystem\\nOpen-Source AI Cookbook\\nA collection of open-source-powered notebooks by AI builders, for AI builders\\nML for Games Course\\nThis course will teach you about integrating AI models your game and using AI tools in your game development workflow\\nDiffusion Course\\nLearn about diffusion models & how to use them with diffusers\\nML for 3D Course\\nLearn about 3D ML with libraries from the HF ecosystem\\nSystem theme\\nCompany\\nTOS\\nPrivacy\\nAbout\\nJobs\\nWebsite\\nModels\\nDatasets\\nSpaces\\nPricing\\nDocs\\n\\n\\n\\nlinkedin page\\nWebpage Title:\\nHugging Face | LinkedIn\\nWebpage Contents:\\nSkip to main content\\nLinkedIn\\nArticles\\nPeople\\nLearning\\nJobs\\nGames\\nGet the app\\nJoin now\\nSign in\\nHugging Face\\nSoftware Development\\nThe AI community building the future.\\nSee jobs\\nFollow\\nView all 515 employees\\nReport this company\\nAbout us\\nThe AI community building the future.\\nWebsite\\nhttps://huggingface.co\\nExternal link for Hugging Face\\nIndustry\\nSoftware Development\\nCompany size\\n51-200 employees\\nType\\nPrivately Held\\nFounded\\n2016\\nSpecialties\\nmachine learning, natural language processing, and deep learning\\nProducts\\nHugging Face\\nHugging Face\\nNatural Language Processing (NLP) Software\\nWe’re on a journey to solve and democratize artificial intelligence through natural language.\\nLocations\\nPrimary\\nGet directions\\nParis, FR\\nGet directions\\nEmployees at Hugging Face\\nLudovic Huraux\\nBassem ASSEH\\nRajat Arya\\nTech Lead & Software Engineer @ HF | prev: co-founder XetHub, Apple, Turi, AWS, Microsoft\\nJeff Boudier\\nProduct + Growth at Hugging Face\\nSee all employees\\nUpdates\\nHugging Face\\nreposted this\\nGradio\\n60,777 followers\\n1d\\nEdited\\nReport this post\\nHi3DGen πŸ”₯ \\n\\nHigh-fidelity 3D geometry generation from a single image by leveraging normal maps as an intermediate representation\\n\\nPlay with the fantastic app on\\nHugging Face\\nnow:\\nhttps://lnkd.in/g99NpV_y\\n…more\\n159\\n3 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nFreddy Boulton\\nSoftware Engineer @ πŸ€—\\n1d\\nReport this post\\nGenerate lifelike audio in real-time without a GPU! πŸš€\\n\\nCheck out orpheus-cpp: a\\nllama.cpp\\nport of orpheus 3b text-to-speech model with built-in support for sync and async streaming.\\n\\nπš™πš’πš™ πš’πš—πšœπšπšŠπš•πš• πš˜πš›πš™πš‘πšŽπšžπšœ-πšŒπš™πš™\\nπš™πš’πšπš‘πš˜πš— -πš– πš˜πš›πš™πš‘πšŽπšžπšœ_πšŒπš™πš™\\n\\nProject code:\\nhttps://lnkd.in/ekPpN9mc\\n…more\\n317\\n8 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nGradio\\n60,777 followers\\n3d\\nReport this post\\nWe just turned the humble dataframe into a superweapon⚑️\\ndashboarding will never be the same!! πŸ“Š\\n\\nnew Gradio Dataframe has:\\n- multi-cell selection\\n- column pinning\\n- search + filtering\\n- fullscreen mode\\n- accessibility upgrades, and more\\n…more\\n97\\n12 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nMerve Noyan\\nopen-sourceress at πŸ€— | Google Developer Expert in Machine Learning, MSc Candidate in Data Science\\n3d\\nReport this post\\nis your vision LM in prod even safe? πŸ‘€\\n\\nShieldGemma 2 is the first ever safety model for multimodal vision LMs in production by\\nGoogle DeepMind\\n, came with Gemma 3 πŸ”₯\\n\\nI saw confusion around how to use it, so I put together a notebook and a demo, find it in the comments πŸ’¬\\n382\\n10 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nMerve Noyan\\nopen-sourceress at πŸ€— | Google Developer Expert in Machine Learning, MSc Candidate in Data Science\\n3d\\nReport this post\\nis your vision LM in prod even safe? πŸ‘€\\n\\nShieldGemma 2 is the first ever safety model for multimodal vision LMs in production by\\nGoogle DeepMind\\n, came with Gemma 3 πŸ”₯\\n\\nI saw confusion around how to use it, so I put together a notebook and a demo, find it in the comments πŸ’¬\\n382\\n10 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nSergio Paniego Blanco\\nML Engineer @ Hugging Face πŸ€— | AI PhD | Google Summer of Code \\'18-\\'24\\n3d\\nReport this post\\nThe Bonus Unit 2, \"AI Agent Observability & Evaluation,\" is now live on our\\nHugging Face\\nagents course! πŸŽ“\\n\\nYou\\'ll learn to:\\nπŸ”§ Instrument agents with OpenTelemetry\\nπŸ“Š Track token usage, latency & errors\\nπŸ“ˆ Evaluate with LLM-as-a-judge\\nπŸ“š Benchmark with GSM8K\\n\\nπŸ‘‰ Check out the course here:\\nhttps://lnkd.in/d2jiTx6j\\n213\\n7 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nGradio\\n60,777 followers\\n4d\\nEdited\\nReport this post\\nCreate Infinite Photographs of You with InfiniteYou-Flux!\\n\\nFlexible photo recreation that better preserves identity compared to current solutions like Pulid, IP Adapter, etc. πŸ”₯ πŸ’ͺ \\n\\nCurrent full-performance bf16 model inference requires a peak VRAM of around 43 GB.\\n\\nYou can build InfU on your own hardware:\\nhttps://lnkd.in/g9dc_vVh\\nOr Play for free on\\nHugging Face\\n:\\nhttps://lnkd.in/gzF7rikZ\\n159\\n6 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nGradio\\n60,777 followers\\n4d\\nEdited\\nReport this post\\n🀯 Generate high-quality podcasts with the voices you want!\\n\\nMoonCast is an open sourced, multi-lingual, and zeroshot model.\\n\\nYou just need to upload two sample voices, create a script, and that\\'s it, run the model--You get a πŸ”₯ notebooklm-like podcast.\\n\\nModel and App are released on\\nHugging Face\\n:\\nhttps://lnkd.in/gUk2EssP\\n152\\n8 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nDaniel Vila Suero\\nBuilding data tools @ Hugging Face πŸ€—\\n4d\\nEdited\\nReport this post\\nπŸ”₯ Big news for GPU poors: thanks to\\nHyperbolic\\nand\\nFireworks AI\\n, you can run\\nDeepSeek AI\\n\\'s\\xa0new model using Hugging Face Inference Providers. What has changed since V3? Here\\'s my quick home experiment πŸ‘‡ \\n\\nDeepSeek silently dropped an update to V3 yesterday. Benchmark results are available, showing significant improvements over V3. \\n\\nStill, it is always a good idea to run new models on data you care about and see more detailed, fine-grained results.\\n\\nNow that we can all run these new models from Day 0 with no GPUs required, I wanted to share my approach with an example I created this morning:\\n\\n1. I got a sample from the LIMA dataset (containing high-quality general instructions).\\n2. Run the instructions with V3 and the new version V3-0324.\\n3. Define and run a simple judge with Llama3.3-70B to compare the model responses.\\n4. Push the dataset and pipeline so you can check and run similar experiments! (see first comment)\\n5. Extracted the results with\\nHugging Face\\nData Studio.\\n\\nResults summary\\n- LIMA is not very challenging, but it is still interesting to see the differences between the two models.\\n- A majority of Ties indicate that both models are close for this domain and task.\\n- But still, V3-0324 consistently wins over V3 (33 times vs 6 times).\\n\\nAs usual, the dataset, prompts, and pipeline are open-source (see first comment).\\n\\nWhat other experiments you\\'d like to see?\\n214\\n7 Comments\\nLike\\nComment\\nShare\\nHugging Face\\nreposted this\\nGradio\\n60,777 followers\\n4d\\nReport this post\\nStarVector is a multimodal vision-language model for generating SVG (Scalable Vector Graphics). πŸ‘‡ \\n\\nIt can be used to perform image2SVG and text2SVG generation. Live demo shows how the image generation is treated similar to a code generation task, using the power of StarVector multimodal VLM! 🀩 \\n\\nπŸš€ Play with the app on Huggingface:\\nhttps://lnkd.in/gCzdEbvj\\nπŸ₯³ If you want to build the model locally with a gradio app:\\nhttps://lnkd.in/gDzCpdDN\\n…more\\n1,523\\n42 Comments\\nLike\\nComment\\nShare\\nJoin now to see what you are missing\\nFind people you know at Hugging Face\\nBrowse recommended jobs for you\\nView all updates, news, and articles\\nJoin now\\nSimilar pages\\nAnthropic\\nResearch Services\\nMistral AI\\nTechnology, Information and Internet\\nParis, France\\nPerplexity\\nSoftware Development\\nSan Francisco, California\\nOpenAI\\nResearch Services\\nSan Francisco, CA\\nLangChain\\nTechnology, Information and Internet\\nGenerative AI\\nTechnology, Information and Internet\\nDeepLearning.AI\\nSoftware Development\\nPalo Alto, California\\nLlamaIndex\\nTechnology, Information and Internet\\nSan Francisco, 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Get it in the Microsoft Store.\\nOpen the app\\n\\n\\n\\ndiscuss forum\\nWebpage Title:\\nHugging Face Forums - Hugging Face Community Discussion\\nWebpage Contents:\\nHugging Face Forums\\nTopic\\nReplies\\nViews\\nActivity\\nWhat is wrong with this code?\\nBeginners\\n1\\n2\\nMarch 29, 2025\\nIntroducing the DoCoreAI Dynamic Temperature Dataset!\\nπŸ€—Datasets\\n0\\n2\\nMarch 29, 2025\\nUse layoutLM to extract data from inviices\\nBeginners\\n1\\n2\\nMarch 29, 2025\\nAI implementation on a raspery pi\\nBeginners\\n2\\n6\\nMarch 29, 2025\\nAccount Recovery Request 2\\nSite Feedback\\n3\\n17\\nMarch 29, 2025\\n🚨 Inference Failure on HF Spaces: Keras model gives incorrect predictions despite identical weights and inputs (works on Colab and locally)\\nπŸ€—AutoTrain\\n1\\n5\\nMarch 29, 2025\\nBoosting OΓΌtlas UX & SEO for more visibility\\nBeginners\\n4\\n5\\nMarch 28, 2025\\nCant see discord channels\\nCommunity Calls\\n2\\n4\\nMarch 29, 2025\\nFine-Tuning Help for Personal Project\\nBeginners\\n1\\n12\\nMarch 28, 2025\\nWhat is the most efficient way to dynamically change context mid-generation?\\nπŸ€—Transformers\\n1\\n4\\nMarch 29, 2025\\nRun Pixtral-12B-2409 locally\\nBeginners\\n1\\n7\\nMarch 28, 2025\\nI\\'m Working On A Math AI\\nBeginners\\n5\\n12\\nMarch 28, 2025\\nImage to text help for personal project\\nResearch\\n1\\n3\\nMarch 28, 2025\\nDuplicated cache- arrow files when uploading large folder?\\nπŸ€—Datasets\\n1\\n5\\nMarch 28, 2025\\nLimits on Gradio API (HF Spaces)\\nSpaces\\n1\\n11\\nMarch 28, 2025\\nFASTAI:TypeError: empty() received an invalid combination of arguments - got (tuple, dtype=NoneType, device=NoneType)\\nπŸ€—Transformers\\n2\\n5\\nMarch 29, 2025\\nGit clone ... fails with error 422, service parameter is needed\\nBeginners\\n6\\n10\\nMarch 29, 2025\\nHF Inference API last few minutes returns the same 404 exception to all models\\nInference Endpoints on the Hub\\n24\\n206\\nMarch 21, 2025\\nGot access acceptance for the wrong llama model\\nBeginners\\n4\\n15\\nMarch 29, 2025\\nTokens limit in Web Browser Automation\\nBeginners\\n2\\n9\\nMarch 28, 2025\\nAgent.python_executor in Web Browser Automation\\nBeginners\\n2\\n15\\nMarch 27, 2025\\nInquiry About 120s Timeout on Hugging Face Inference Endpoint for Llama 3.1-8B\\nModels\\n1\\n8\\nMarch 28, 2025\\nStrange Error While Attempting to Load DataSet\\nπŸ€—Datasets\\n7\\n3030\\nMarch 28, 2025\\nI need a model for requirements extraction\\nModels\\n3\\n35\\nMarch 26, 2025\\nIs the rule of 25 min changed?\\nπŸ€—Hub\\n4\\n47\\nMarch 29, 2025\\nThe hidden_states when i use model.generate\\nπŸ€—Transformers\\n4\\n1257\\nMarch 28, 2025\\nOptimize GPU Usage for Long-Context Training\\nπŸ€—Transformers\\n1\\n12\\nMarch 28, 2025\\nIs there any model for document prioritization\\nπŸ€—Hub\\n1\\n14\\nMarch 28, 2025\\n.cache for upload large folder\\nπŸ€—Datasets\\n3\\n11\\nMarch 28, 2025\\nWill LFS related functionality come to hf_api?\\nπŸ€—Hub\\n11\\n43\\nMarch 25, 2025\\nnext page β†’\\nHome\\nCategories\\nGuidelines\\nTerms of Service\\nPrivacy Policy\\nPowered by\\nDiscourse\\n, best viewed with JavaScript enabled\\n\\n'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_all_details(\"https://huggingface.co\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2", + "metadata": {}, + "outputs": [], + "source": [ + "system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", + "and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", + "Provide the following sections: Company Overview, Key Offerings, and Customers\"\n", + "\n", + "# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n", + "\n", + "# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", + "# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", + "# Include details of company culture, customers and careers/jobs if you have the information.\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23", + "metadata": {}, + "outputs": [], + "source": [ + "def get_brochure_user_prompt(company_name, url):\n", + " user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", + " user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", + " user_prompt += get_all_details(url)\n", + " user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "cd909e0b-1312-4ce2-a553-821e795d7572", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://huggingface.co/huggingface'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'}, {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'}, {'type': 'blog page', 'url': 'https://huggingface.co/blog'}, {'type': 'documentation page', 'url': 'https://huggingface.co/docs'}]}\n" + ] + }, + { + "data": { + "text/plain": [ + "'You are looking at a company called: HuggingFace\\nHere are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\nLanding page:\\nWebpage Title:\\nHugging Face – The AI community building the future.\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nExplore AI Apps\\nor\\nBrowse 1M+ models\\nTrending on\\nthis week\\nModels\\ndeepseek-ai/DeepSeek-V3-0324\\nUpdated\\n2 days ago\\nβ€’\\n60.5k\\nβ€’\\n1.96k\\nQwen/Qwen2.5-Omni-7B\\nUpdated\\nabout 24 hours ago\\nβ€’\\n27.9k\\nβ€’\\n796\\nmanycore-research/SpatialLM-Llama-1B\\nUpdated\\n8 days ago\\nβ€’\\n11.6k\\nβ€’\\n781\\nds4sd/SmolDocling-256M-preview\\nUpdated\\n6 days ago\\nβ€’\\n48.4k\\nβ€’\\n1.03k\\nByteDance/InfiniteYou\\nUpdated\\n4 days ago\\nβ€’\\n465\\nBrowse 1M+ models\\nSpaces\\nRunning\\non\\nZero\\n530\\n530\\nInfiniteYou-FLUX\\nπŸ“Έ\\nFlexible Photo Recrafting While Preserving Your Identity\\nRunning\\n255\\n255\\nDeepSite\\n🐳\\nImagine and Share in 1-Click\\nRunning\\non\\nZero\\n224\\n224\\nLHM\\n⚑\\nLarge Animatable Human Model\\nRunning\\n350\\n350\\nGemini Co-Drawing\\n✏\\nGemini 2.0 native image generation co-doodling\\nRunning\\n154\\n154\\nQwen2.5 Omni 7B Demo\\nπŸ†\\nSubmit media inputs to generate text and speech responses\\nBrowse 400k+ applications\\nDatasets\\nnvidia/Llama-Nemotron-Post-Training-Dataset-v1\\nUpdated\\n11 days ago\\nβ€’\\n8.05k\\nβ€’\\n265\\nglaiveai/reasoning-v1-20m\\nUpdated\\n10 days ago\\nβ€’\\n6.84k\\nβ€’\\n127\\nFreedomIntelligence/medical-o1-reasoning-SFT\\nUpdated\\nFeb 22\\nβ€’\\n25.5k\\nβ€’\\n574\\na-m-team/AM-DeepSeek-R1-Distilled-1.4M\\nUpdated\\n32 minutes ago\\nβ€’\\n3.79k\\nβ€’\\n75\\nPixelAI-Team/TalkBody4D\\nUpdated\\n4 days ago\\nβ€’\\n66\\nβ€’\\n44\\nBrowse 250k+ datasets\\nThe Home of Machine Learning\\nCreate, discover and collaborate on ML better.\\nThe collaboration platform\\nHost and collaborate on unlimited public models, datasets and applications.\\nMove faster\\nWith the HF Open source stack.\\nExplore all modalities\\nText, image, video, audio or even 3D.\\nBuild your portfolio\\nShare your work with the world and build your ML profile.\\nSign Up\\nAccelerate your ML\\nWe provide paid Compute and Enterprise solutions.\\nCompute\\nDeploy on optimized\\nInference Endpoints\\nor update your\\nSpaces applications\\nto a GPU in a few clicks.\\nView pricing\\nStarting at $0.60/hour for GPU\\nEnterprise\\nGive your team the most advanced platform to build AI with enterprise-grade security, access controls and\\n\\t\\t\\tdedicated support.\\nGetting started\\nStarting at $20/user/month\\nSingle Sign-On\\nRegions\\nPriority Support\\nAudit Logs\\nResource Groups\\nPrivate Datasets Viewer\\nMore than 50,000 organizations are using Hugging Face\\nAi2\\nEnterprise\\nnon-profit\\nβ€’\\n396 models\\nβ€’\\n2.97k followers\\nAI at Meta\\nEnterprise\\ncompany\\nβ€’\\n2.07k models\\nβ€’\\n5.29k followers\\nAmazon\\ncompany\\nβ€’\\n10 models\\nβ€’\\n2.92k followers\\nGoogle\\ncompany\\nβ€’\\n974 models\\nβ€’\\n10.7k followers\\nIntel\\ncompany\\nβ€’\\n219 models\\nβ€’\\n2.37k followers\\nMicrosoft\\ncompany\\nβ€’\\n365 models\\nβ€’\\n10.7k followers\\nGrammarly\\nEnterprise\\ncompany\\nβ€’\\n10 models\\nβ€’\\n146 followers\\nWriter\\nEnterprise\\ncompany\\nβ€’\\n21 models\\nβ€’\\n253 followers\\nOur Open Source\\nWe are building the foundation of ML tooling with the community.\\nTransformers\\n142,118\\nState-of-the-art ML for PyTorch, TensorFlow, JAX\\nDiffusers\\n28,310\\nState-of-the-art Diffusion models in PyTorch\\nSafetensors\\n3,190\\nSafe way to store/distribute neural network weights\\nHub Python Library\\n2,471\\nPython client to interact with the Hugging Face Hub\\nTokenizers\\n9,539\\nFast tokenizers optimized for research & production\\nTRL\\n12,906\\nTrain transformers LMs with reinforcement learning\\nTransformers.js\\n13,318\\nState-of-the-art ML running directly in your browser\\nsmolagents\\n15,966\\nSmol library to build great agents in Python\\nPEFT\\n17,935\\nParameter-efficient finetuning for large language models\\nDatasets\\n19,896\\nAccess & share datasets for any ML tasks\\nText Generation Inference\\n9,940\\nServe language models with TGI optimized toolkit\\nAccelerate\\n8,550\\nTrain PyTorch models with multi-GPU, TPU, mixed precision\\nSystem theme\\nWebsite\\nModels\\nDatasets\\nSpaces\\nTasks\\nInference Endpoints\\nHuggingChat\\nCompany\\nAbout\\nBrand assets\\nTerms of service\\nPrivacy\\nJobs\\nPress\\nResources\\nLearn\\nDocumentation\\nBlog\\nForum\\nService Status\\nSocial\\nGitHub\\nTwitter\\nLinkedIn\\nDiscord\\n\\n\\n\\nabout page\\nWebpage Title:\\nhuggingface (Hugging Face)\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nHugging Face\\nEnterprise\\ncompany\\nVerified\\nhttps://huggingface.co\\nhuggingface\\nhuggingface\\nActivity Feed\\nFollow\\n28,351\\nAI & ML interests\\nThe AI community building the future.\\nRecent Activity\\ncoyotte508\\nnew\\nactivity\\nabout 23 hours ago\\nhuggingface/HuggingDiscussions:\\n[FEEDBACK] Notifications\\nWauplin\\nupdated\\na dataset\\nabout 23 hours ago\\nhuggingface/documentation-images\\nlysandre\\nupdated\\na dataset\\n1 day ago\\nhuggingface/transformers-metadata\\nView all activity\\nArticles\\nYay! Organizations can now publish blog Articles\\nJan 20\\nβ€’\\n37\\nTeam members\\n209\\n+175\\n+162\\n+141\\n+131\\n+111\\nOrganization Card\\nCommunity\\nAbout org cards\\nπŸ‘‹ Hi!\\nWe are on a mission to democratiz'" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46", + "metadata": {}, + "outputs": [], + "source": [ + "def create_brochure(company_name, url):\n", + " response = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", + " ],\n", + " )\n", + " result = response.choices[0].message.content\n", + " display(Markdown(result))\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "e093444a-9407-42ae-924a-145730591a39", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://huggingface.co/huggingface'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'}, {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'}, {'type': 'blog page', 'url': 'https://huggingface.co/blog'}, {'type': 'documentation page', 'url': 'https://huggingface.co/docs'}]}\n" + ] + }, + { + "data": { + "text/markdown": [ + "# Hugging Face Brochure\n", + "\n", + "## Company Overview\n", + "Hugging Face is a leading AI community dedicated to building the future of machine learning through collaboration. The platform serves as a hub for developers, researchers, and organizations, enabling them to create, share, and collaborate on a vast array of models, datasets, and applications. Hugging Face is designed to accelerate innovation in AI and machine learning, embracing an open-source ethos that empowers users worldwide. \n", + "\n", + "## Key Offerings\n", + "- **Models:** Explore and contribute to over 1M+ state-of-the-art machine learning models across various modalities, including text, image, video, audio, and 3D.\n", + " \n", + "- **Datasets:** Access a rich repository of 250k+ datasets, facilitating a wide range of machine learning tasks and research.\n", + " \n", + "- **Spaces:** Develop and deploy applications easily within a user-friendly environment, showcasing over 400k ML applications.\n", + " \n", + "- **Enterprise Solutions:** Tailored offerings for businesses seeking advanced security, support, and compute resources, starting at $20/user/month.\n", + " \n", + "- **Open Source Community:** Hugging Face spearheads numerous open-source libraries like Transformers, Diffusers, and Tokenizers, providing tools and resources for both research and production.\n", + "\n", + "## Customers\n", + "Hugging Face serves more than 50,000 organizations across various sectors, including prominent names such as:\n", + "- **Meta**\n", + "- **Amazon**\n", + "- **Google**\n", + "- **Microsoft**\n", + "- **Intel**\n", + "- **Grammarly**\n", + "\n", + "This diverse clientele demonstrates Hugging Face's commitment to empowering industries through innovative machine learning solutions, and building a collaborative ecosystem for AI development.\n", + "\n", + "---\n", + "\n", + "Join Hugging Face today and become part of a community that is at the forefront of the AI revolution! Explore our platform [here](https://huggingface.co)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "create_brochure(\"HuggingFace\", \"https://huggingface.co\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "235e5b31-3969-4b10-9a34-c7dba4973927", + "metadata": {}, + "outputs": [], + "source": [ + "system_prompt_trs = \"You are an assistant who checks company brochures for translation. You will receive a specoific company brochure and translate to the languahe specified by the user. Respond in Markdown.\"\n", + "\n", + "def get_brochure_user_translation_prompt(company_name, url):\n", + " user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", + " user_prompt += f\"Translate the broshure to Arabic.\\n\"\n", + " user_prompt += f\"{create_brochure(company_name, url)}\\n\"\n", + " user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", + " return user_prompt\n", + "\n", + "# print(get_brochure_user_translation_prompt(\"HuggingFace\", \"https://huggingface.co\"))\n", + "\n", + "def translate_brochure(company_name, url):\n", + " response = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt_trs},\n", + " {\"role\": \"user\", \"content\": get_brochure_user_translation_prompt(company_name, url)}\n", + " ],\n", + " )\n", + " result = response.choices[0].message.content\n", + " display(Markdown(result))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "4ca22442-e4da-453a-a895-b24614799a0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://huggingface.co/huggingface'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'company page', 'url': 'https://www.linkedin.com/company/huggingface/'}, {'type': 'blog page', 'url': 'https://huggingface.co/blog'}]}\n" + ] + }, + { + "data": { + "text/markdown": [ + "```markdown\n", + "# Hugging Face Company Brochure\n", + "\n", + "## Company Overview\n", + "Hugging Face is a pioneering community-driven platform at the forefront of artificial intelligence (AI) and machine learning (ML). The company is committed to democratizing access to advanced machine learning tools and fostering collaboration among developers, researchers, and enterprises globally. Hugging Face serves as an open-source hub where users can create, share, and collaborate on models, datasets, and applications, thereby accelerating the development and deployment of AI-driven solutions.\n", + "\n", + "## Key Offerings\n", + "### 1. **Models**\n", + "- **Access to Over 1 Million Models**: Hugging Face provides a comprehensive library where users can explore and use a vast array of AI models ranging from text generation to image analysis.\n", + "- **State-of-the-art ML frameworks**: The platform supports various leading frameworks, including Transformers, Diffusers, and more, suitable for use with PyTorch, TensorFlow, and JAX.\n", + "\n", + "### 2. **Datasets**\n", + "- **Extensive Dataset Collection**: Users can browse and access over 250,000 datasets tailored for diverse machine learning tasks, ensuring comprehensive resources for training and research.\n", + "\n", + "### 3. **Spaces**\n", + "- **Interactive AI Applications**: Hugging Face hosts numerous applications where users can experiment with ML models directly through a user-friendly interface. These spaces include various tools for image generation, natural language processing, and more.\n", + "\n", + "### 4. **Enterprise Solutions**\n", + "- **Tailored for Businesses**: Hugging Face offers enterprise-grade services with cloud solutions, advanced security features, and dedicated support to empower organizations in deploying customized AI solutions efficiently.\n", + "\n", + "### 5. **Open Source Collaboration**\n", + "- Hugging Face is committed to transparency and community involvement, providing a robust open-source ecosystem that includes libraries such as `Transformers`, `Tokenizers`, and `Accelerate`.\n", + "\n", + "## Customers\n", + "More than **50,000 organizations** trust Hugging Face to power their machine learning capabilities. Among them are industry leaders like:\n", + "- **Google**\n", + " - 974 models, 10.7k followers\n", + "- **Microsoft**\n", + " - 365 models, 10.7k followers\n", + "- **Amazon**\n", + " - 10 models, 2.92k followers\n", + "- **Meta, Grammarly**, and numerous others in various sectors, highlighting the platform's versatility and credibility in driving AI innovation.\n", + "\n", + "Whether you're a developer looking to enhance your ML projects, an investor interested in the future of AI technology, or a prospective recruit eager to join a passionate community, Hugging Face is at the heart of the AI revolution. Join us on this journey to build the future!\n", + "```\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "```markdown\n", + "# ΩƒΨͺيب Ψ΄Ψ±ΩƒΨ© Hugging Face\n", + "\n", + "## Ω„Ω…Ψ­Ψ© ΨΉΨ§Ω…Ψ© ΨΉΩ† Ψ§Ω„Ψ΄Ψ±ΩƒΨ©\n", + "ΨͺُعΨͺΨ¨Ψ± Hugging Face Ω…Ω†Ψ΅Ψ© Ψ±Ψ§Ψ¦Ψ―Ψ© ΨͺΨΉΨͺΩ…Ψ― ΨΉΩ„Ω‰ Ψ§Ω„Ω…Ψ¬ΨͺΩ…ΨΉ في Ω…Ω‚Ψ―Ω…Ψ© Ψ§Ω„Ψ°ΩƒΨ§Ψ‘ Ψ§Ω„Ψ§Ψ΅Ψ·Ω†Ψ§ΨΉΩŠ (AI) وΨͺΨΉΩ„Ω… Ψ§Ω„Ψ’Ω„Ψ© (ML). ΨͺΩ„ΨͺΨ²Ω… Ψ§Ω„Ψ΄Ψ±ΩƒΨ© Ψ¨Ψ―ΩŠΩ…Ω‚Ψ±Ψ§Ψ·ΩŠΨ© Ψ§Ω„ΩˆΨ΅ΩˆΩ„ Ψ₯Ω„Ω‰ أدواΨͺ ΨͺΨΉΩ„Ω… Ψ§Ω„Ψ’Ω„Ψ© Ψ§Ω„Ω…ΨͺΩ‚Ψ―Ω…Ψ© وΨͺعزيز Ψ§Ω„ΨͺΨΉΨ§ΩˆΩ† Ψ¨ΩŠΩ† Ψ§Ω„Ω…Ψ·ΩˆΨ±ΩŠΩ† ΩˆΨ§Ω„Ψ¨Ψ§Ψ­Ψ«ΩŠΩ† ΩˆΨ§Ω„Ψ΄Ψ±ΩƒΨ§Ψͺ ΨΉΩ„Ω‰ Ω…Ψ³ΨͺΩˆΩ‰ Ψ§Ω„ΨΉΨ§Ω„Ω…. ΨͺΨΉΩ…Ω„ Hugging Face ΩƒΩ…Ψ±ΩƒΨ² مفΨͺوح Ψ§Ω„Ω…Ψ΅Ψ―Ψ± حيث ΩŠΩ…ΩƒΩ† Ω„Ω„Ω…Ψ³ΨͺΨΨ―Ω…ΩŠΩ† Ψ₯Ω†Ψ΄Ψ§Ψ‘ ΩˆΩ…Ψ΄Ψ§Ψ±ΩƒΨ© ΩˆΨ§Ω„ΨͺΨΉΨ§ΩˆΩ† في Ψ§Ω„Ω†Ω…Ψ§Ψ°Ψ¬ ΩˆΩ…Ψ¬Ω…ΩˆΨΉΨ§Ψͺ Ψ§Ω„Ψ¨ΩŠΨ§Ω†Ψ§Ψͺ ΩˆΨ§Ω„ΨͺΨ·Ψ¨ΩŠΩ‚Ψ§Ψͺ، Ω…Ω…Ψ§ ΩŠΨ³Ψ§Ω‡Ω… في Ψͺسريع Ψͺطوير ΩˆΩ†Ψ΄Ψ± Ψ§Ω„Ψ­Ω„ΩˆΩ„ Ψ§Ω„Ω…Ψ―ΩΩˆΨΉΨ© Ψ¨Ψ§Ω„Ψ°ΩƒΨ§Ψ‘ Ψ§Ω„Ψ§Ψ΅Ψ·Ω†Ψ§ΨΉΩŠ.\n", + "\n", + "## Ψ§Ω„ΨΉΨ±ΩˆΨΆ Ψ§Ω„Ψ±Ψ¦ΩŠΨ³ΩŠΨ©\n", + "### 1. **Ψ§Ω„Ω†Ω…Ψ§Ψ°Ψ¬**\n", + "- **Ψ§Ω„ΩˆΨ΅ΩˆΩ„ Ψ₯Ω„Ω‰ Ψ£ΩƒΨ«Ψ± Ω…Ω† Ω…Ω„ΩŠΩˆΩ† Ω†Ω…ΩˆΨ°Ψ¬**: Ψͺوفر Hugging Face Ω…ΩƒΨͺΨ¨Ψ© Ψ΄Ψ§Ω…Ω„Ψ© حيث ΩŠΩ…ΩƒΩ† Ω„Ω„Ω…Ψ³ΨͺΨΨ―Ω…ΩŠΩ† Ψ§Ψ³Ψͺكشاف واسΨͺΨΨ―Ψ§Ω… Ω…Ψ¬Ω…ΩˆΨΉΨ© واسعة Ω…Ω† Ω†Ω…Ψ§Ψ°Ψ¬ Ψ§Ω„Ψ°ΩƒΨ§Ψ‘ Ψ§Ω„Ψ§Ψ΅Ψ·Ω†Ψ§ΨΉΩŠ ΨͺΨͺراوح Ω…Ω† ΨͺΩˆΩ„ΩŠΨ― Ψ§Ω„Ω†Ψ΅ΩˆΨ΅ Ψ₯Ω„Ω‰ ΨͺΨ­Ω„ΩŠΩ„ Ψ§Ω„Ψ΅ΩˆΨ±.\n", + "- **Ψ₯Ψ·Ψ§Ψ±Ψ§Ψͺ ΨͺΨΉΩ„Ω… Ψ§Ω„Ψ’Ω„Ψ© Ψ§Ω„Ω…Ψͺطورة**: ΨͺΨ―ΨΉΩ… Ψ§Ω„Ω…Ω†Ψ΅Ψ© Ω…Ψ¬Ω…ΩˆΨΉΨ© Ω…Ω† Ψ§Ω„Ψ₯Ψ·Ψ§Ψ±Ψ§Ψͺ Ψ§Ω„Ψ±Ψ§Ψ¦Ψ―Ψ©ΨŒ Ψ¨Ω…Ψ§ في Ψ°Ω„Ωƒ Transformers وDiffusers ΩˆΨΊΩŠΨ±Ω‡Ψ§ΨŒ Ψ§Ω„Ω…Ω†Ψ§Ψ³Ψ¨Ψ© Ω„Ω„Ψ§Ψ³ΨͺΨΨ―Ψ§Ω… Ω…ΨΉ PyTorch وTensorFlow وJAX.\n", + "\n", + "### 2. **Ω…Ψ¬Ω…ΩˆΨΉΨ§Ψͺ Ψ§Ω„Ψ¨ΩŠΨ§Ω†Ψ§Ψͺ**\n", + "- **Ω…Ψ¬Ω…ΩˆΨΉΨ© واسعة Ω…Ω† Ω…Ψ¬Ω…ΩˆΨΉΨ§Ψͺ Ψ§Ω„Ψ¨ΩŠΨ§Ω†Ψ§Ψͺ**: ΩŠΩ…ΩƒΩ† Ω„Ω„Ω…Ψ³ΨͺΨΨ―Ω…ΩŠΩ† Ψͺءفح ΩˆΨ§Ω„ΩˆΨ΅ΩˆΩ„ Ψ₯Ω„Ω‰ Ψ£ΩƒΨ«Ψ± Ω…Ω† 250,000 Ω…Ψ¬Ω…ΩˆΨΉΨ© Ψ¨ΩŠΨ§Ω†Ψ§Ψͺ Ω…ΨΨ΅Ψ΅Ψ© Ω„Ω…Ω‡Ψ§Ω… ΨͺΨΉΩ„Ω… Ψ§Ω„Ψ’Ω„Ψ© Ψ§Ω„Ω…ΨͺΩ†ΩˆΨΉΨ©ΨŒ Ω…Ω…Ψ§ ΩŠΨΆΩ…Ω† Ω…ΩˆΨ§Ψ±Ψ― Ψ΄Ψ§Ω…Ω„Ψ© Ω„Ω„Ψͺدريب ΩˆΨ§Ω„Ψ¨Ψ­Ψ«.\n", + "\n", + "### 3. **Ψ§Ω„Ω…Ψ³Ψ§Ψ­Ψ§Ψͺ**\n", + "- **ΨͺΨ·Ψ¨ΩŠΩ‚Ψ§Ψͺ Ψ§Ω„Ψ°ΩƒΨ§Ψ‘ Ψ§Ω„Ψ§Ψ΅Ψ·Ω†Ψ§ΨΉΩŠ Ψ§Ω„ΨͺΩΨ§ΨΉΩ„ΩŠΨ©**: ΨͺΨ³Ψͺآيف Hugging Face Ψ§Ω„ΨΉΨ―ΩŠΨ― Ω…Ω† Ψ§Ω„ΨͺΨ·Ψ¨ΩŠΩ‚Ψ§Ψͺ حيث ΩŠΩ…ΩƒΩ† Ω„Ω„Ω…Ψ³ΨͺΨΨ―Ω…ΩŠΩ† ΨͺΨ¬Ψ±Ψ¨Ψ© Ω†Ω…Ψ§Ψ°Ψ¬ ΨͺΨΉΩ„Ω… Ψ§Ω„Ψ’Ω„Ψ© Ω…Ψ¨Ψ§Ψ΄Ψ±Ψ© Ω…Ω† ΨΩ„Ψ§Ω„ ΩˆΨ§Ψ¬Ω‡Ψ© Ω…Ψ³ΨͺΨΨ―Ω… Ψ³Ω‡Ω„Ψ© Ψ§Ω„Ψ§Ψ³ΨͺΨΨ―Ψ§Ω…. ΨͺΨ΄Ω…Ω„ Ω‡Ψ°Ω‡ Ψ§Ω„Ω…Ψ³Ψ§Ψ­Ψ§Ψͺ أدواΨͺ Ω…ΨͺΩ†ΩˆΨΉΨ© Ω„ΨͺΩˆΩ„ΩŠΨ― Ψ§Ω„Ψ΅ΩˆΨ± ΩˆΩ…ΨΉΨ§Ω„Ψ¬Ψ© Ψ§Ω„Ω„ΨΊΨ© Ψ§Ω„Ψ·Ψ¨ΩŠΨΉΩŠΨ© ΩˆΨ§Ω„Ω…Ψ²ΩŠΨ―.\n", + "\n", + "### 4. **Ψ­Ω„ΩˆΩ„ Ψ§Ω„Ψ΄Ψ±ΩƒΨ§Ψͺ**\n", + "- **Ω…Ψ΅Ω…Ω…Ψ© Ω„Ω„Ψ΄Ψ±ΩƒΨ§Ψͺ**: ΨͺΩ‚Ψ―Ω… Hugging Face ΨΨ―Ω…Ψ§Ψͺ Ω…ΩˆΨ¬Ω‡Ψ© Ω„Ω„Ψ£ΨΉΩ…Ψ§Ω„ Ω…ΨΉ Ψ­Ω„ΩˆΩ„ سحابية ΩˆΩ…ΩŠΨ²Ψ§Ψͺ Ψ£Ω…Ψ§Ω† Ω…ΨͺΩ‚Ψ―Ω…Ψ© ΩˆΨ―ΨΉΩ… Ω…ΨΨ΅Ψ΅ Ω„ΨͺΩ…ΩƒΩŠΩ† Ψ§Ω„Ω…Ω†ΨΈΩ…Ψ§Ψͺ Ω…Ω† Ω†Ψ΄Ψ± Ψ­Ω„ΩˆΩ„ Ψ°ΩƒΨ§Ψ‘ Ψ§Ψ΅Ψ·Ω†Ψ§ΨΉΩŠ Ω…ΨΨ΅Ψ΅Ψ© بكفاؑة.\n", + "\n", + "### 5. **Ψ§Ω„ΨͺΨΉΨ§ΩˆΩ† مفΨͺوح Ψ§Ω„Ω…Ψ΅Ψ―Ψ±**\n", + "- ΨͺΩ„ΨͺΨ²Ω… Hugging Face Ψ¨Ψ§Ω„Ψ΄ΩΨ§ΩΩŠΨ© ΩˆΩ…Ψ΄Ψ§Ψ±ΩƒΨ© Ψ§Ω„Ω…Ψ¬ΨͺΩ…ΨΉΨŒ حيث Ψͺوفر Ω†ΨΈΨ§Ω… بيئي Ω‚ΩˆΩŠ مفΨͺوح Ψ§Ω„Ω…Ψ΅Ψ―Ψ± ΩŠΨ΄Ω…Ω„ Ω…ΩƒΨͺΨ¨Ψ§Ψͺ Ω…Ψ«Ω„ `Transformers` و `Tokenizers` و `Accelerate`.\n", + "\n", + "## Ψ§Ω„ΨΉΩ…Ω„Ψ§Ψ‘\n", + "ΨͺΨ«Ω‚ Ψ£ΩƒΨ«Ψ± Ω…Ω† **50,000 Ω…Ω†ΨΈΩ…Ψ©** في Hugging Face Ω„Ψͺعزيز Ω‚Ψ―Ψ±Ψ§ΨͺΩ‡Ψ§ في ΨͺΨΉΩ„Ω… Ψ§Ω„Ψ’Ω„Ψ©. Ω…Ω† Ψ¨ΩŠΩ† Ω‡Ψ€Ω„Ψ§Ψ‘ΨŒ Ω‡Ω†Ψ§Ωƒ Ω‚Ψ§Ψ―Ψ© في Ψ§Ω„Ψ΅Ω†Ψ§ΨΉΨ© Ω…Ψ«Ω„:\n", + "- **Ψ¬ΩˆΨ¬Ω„**\n", + " - 974 Ω†Ω…ΩˆΨ°Ψ¬ΨŒ 10.7 ألف Ω…ΨͺΨ§Ψ¨ΨΉ\n", + "- **Ω…Ψ§ΩŠΩƒΨ±ΩˆΨ³ΩˆΩΨͺ**\n", + " - 365 Ω†Ω…ΩˆΨ°Ψ¬ΨŒ 10.7 ألف Ω…ΨͺΨ§Ψ¨ΨΉ\n", + "- **Ψ£Ω…Ψ§Ψ²ΩˆΩ†**\n", + " - 10 Ω†Ω…Ψ§Ψ°Ψ¬ΨŒ 2.92 ألف Ω…ΨͺΨ§Ψ¨ΨΉ\n", + "- **Ω…ΩŠΨͺا، Ψ¬Ψ±Ψ§Ω…Ψ±Ω„ΩŠ** ΩˆΨ§Ω„ΨΉΨ―ΩŠΨ― Ω…Ω† Ψ§Ω„Ψ’ΨΨ±ΩŠΩ† في Ω…ΨΨͺلف Ψ§Ω„Ω‚Ψ·Ψ§ΨΉΨ§Ψͺ، Ω…Ω…Ψ§ يبرز ΨͺΩ†ΩˆΨΉ ΩˆΩ…Ψ΅Ψ―Ψ§Ω‚ΩŠΨ© Ψ§Ω„Ω…Ω†Ψ΅Ψ© في دفع Ψ§Ω„Ψ§Ψ¨ΨͺΩƒΨ§Ψ± في Ω…Ψ¬Ψ§Ω„ Ψ§Ω„Ψ°ΩƒΨ§Ψ‘ Ψ§Ω„Ψ§Ψ΅Ψ·Ω†Ψ§ΨΉΩŠ.\n", + "\n", + "سواؑ ΩƒΩ†Ψͺ Ω…Ψ·ΩˆΨ±Ω‹Ψ§ يΨͺΨ·Ω„ΨΉ Ψ₯Ω„Ω‰ Ψͺعزيز Ω…Ψ΄Ψ§Ψ±ΩŠΨΉ ΨͺΨΉΩ„Ω… Ψ§Ω„Ψ’Ω„Ψ© Ψ§Ω„ΨΨ§Ψ΅Ψ© Ψ¨ΩƒΨŒ Ω…Ψ³ΨͺΨ«Ω…Ψ±Ω‹Ψ§ Ω…Ω‡ΨͺΩ…Ω‹Ψ§ Ψ¨Ω…Ψ³ΨͺΩ‚Ψ¨Ω„ ΨͺΩƒΩ†ΩˆΩ„ΩˆΨ¬ΩŠΨ§ Ψ§Ω„Ψ°ΩƒΨ§Ψ‘ Ψ§Ω„Ψ§Ψ΅Ψ·Ω†Ψ§ΨΉΩŠΨŒ أو Ω…Ψ±Ψ΄Ψ­Ω‹Ψ§ Ω…Ψ­ΨͺΩ…Ω„Ω‹Ψ§ Ω…ΨͺΨ­Ω…Ψ³Ω‹Ψ§ Ω„Ω„Ψ§Ω†ΨΆΩ…Ψ§Ω… Ψ₯Ω„Ω‰ Ω…Ψ¬ΨͺΩ…ΨΉ Ω…Ω„ΩŠΨ‘ Ψ¨Ψ§Ω„Ψ΄ΨΊΩΨŒ فΨ₯Ω† Hugging Face Ω‡ΩŠ في Ω‚Ω„Ψ¨ ثورة Ψ§Ω„Ψ°ΩƒΨ§Ψ‘ Ψ§Ω„Ψ§Ψ΅Ψ·Ω†Ψ§ΨΉΩŠ. Ψ§Ω†ΨΆΩ… Ψ₯Ω„ΩŠΩ†Ψ§ في Ω‡Ψ°Ω‡ Ψ§Ω„Ψ±Ψ­Ω„Ψ© Ω„Ψ¨Ω†Ψ§Ψ‘ Ψ§Ω„Ω…Ψ³ΨͺΩ‚Ψ¨Ω„!\n", + "```" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "translate_brochure(\"HuggingFace\", \"https://huggingface.co\")" + ] + }, + { + "cell_type": "markdown", + "id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18", + "metadata": {}, + "source": [ + "## Finally - a minor improvement\n", + "\n", + "With a small adjustment, we can change this so that the results stream back from OpenAI,\n", + "with the familiar typewriter animation" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "51db0e49-f261-4137-aabe-92dd601f7725", + "metadata": {}, + "outputs": [], + "source": [ + "def stream_brochure(company_name, url):\n", + " stream = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", + " ],\n", + " stream=True\n", + " )\n", + " \n", + " response = \"\"\n", + " display_handle = display(Markdown(\"\"), display_id=True)\n", + " for chunk in stream:\n", + " response += chunk.choices[0].delta.content or ''\n", + " response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", + " update_display(Markdown(response), display_id=display_handle.display_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found links: {'links': [{'type': 'about page', 'url': 'https://www.nvidia.com/en-eu/about-nvidia/'}, {'type': 'careers page', 'url': 'https://www.nvidia.com/en-eu/about-nvidia/careers/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/about-nvidia/executive-insights/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/about-nvidia/partners/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/research/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/foundation/'}, {'type': 'company page', 'url': 'https://www.nvidia.com/en-eu/csr/'}]}\n" + ] + }, + { + "data": { + "text/markdown": [ + "\n", + "# NVIDIA Company Brochure\n", + "\n", + "## About NVIDIA\n", + "\n", + "NVIDIA is a global leader in artificial intelligence (AI) computing, revolutionizing the way industries leverage technology to harness the power of data. Our expertise spans various domains including gaming, automotive, healthcare, and cloud computing, establishing NVIDIA at the forefront of accelerated computing for modern applications.\n", + "\n", + "### Vision and Mission\n", + "\n", + "NVIDIA's mission is to enhance human productivity through advanced technology. We aim to drive innovation and transform industries by delivering powerful graphics and computing solutions that empower creativity and enable smarter decisions.\n", + "\n", + "---\n", + "\n", + "## Products and Services\n", + "\n", + "### Innovative Offerings\n", + "\n", + "- **Artificial Intelligence Solutions:** From generative AI to intelligent video analytics, NVIDIA is pioneering the future with cutting-edge AI tools and frameworks.\n", + "- **Graphics Processing Units (GPUs):** The renowned GeForce and RTX product lines deliver unparalleled graphics performance for gaming and creative applications.\n", + "- **Data Center Solutions:** Our advanced networking and cloud solutions power mission-critical applications across industries, ensuring robust and scalable computing environments.\n", + "\n", + "### Key Industries Served\n", + "\n", + "- **Gaming:** Providing gamers with the best graphical experiences through high-performance graphics cards and technologies.\n", + "- **Automotive:** Leading innovations in autonomous vehicles and smart transportation systems.\n", + "- **Healthcare:** Transforming clinical applications with AI-powered solutions, data analysis, and simulation tools.\n", + "\n", + "---\n", + "\n", + "## Company Culture\n", + "\n", + "At NVIDIA, our culture is founded on collaboration and innovation. We encourage a growth mindset while fostering an inclusive environment where every team member is empowered to contribute their ideas. Our commitment to diversity and sustainability sets the groundwork for a dynamic workplace where creativity flourishes.\n", + "\n", + "### Employee Engagement\n", + "\n", + "We believe that our employees are our greatest asset. NVIDIA offers extensive professional development opportunities, wellness programs, and a flexible work environment. We support work-life balance to ensure our team members thrive both personally and professionally.\n", + "\n", + "---\n", + "\n", + "## Join Us\n", + "\n", + "### Careers at NVIDIA\n", + "\n", + "NVIDIA is constantly seeking passionate individuals who are innovators at heart. By joining our team, you will not only take part in exciting projects at the forefront of technology, but also gain the opportunity to grow and develop within the company. \n", + "\n", + "- **Open Positions:** We offer a range of career paths in engineering, research, sales, and marketing. \n", + "- **Inclusive Workplace:** We welcome diverse backgrounds and perspectives, aiming to create a team that mirrors our global customer base.\n", + "\n", + "If you're ready to push the boundaries of what's possible with us, visit our careers page to explore opportunities.\n", + "\n", + "---\n", + "\n", + "## Connect with Us\n", + "\n", + "For more information about our products and corporate initiatives, please visit [NVIDIA's Official Website](https://www.nvidia.com/) or reach out through our social media channels. We look forward to engaging with you!\n", + "\n", + "---\n", + "\n", + "Thank you for considering NVIDIAβ€”where innovation and creativity meet to shape the future.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# stream_brochure(\"HuggingFace\", \"https://huggingface.co\")\n", + "# stream_brochure(\"British Council\", \"https://www.gov.uk/world/organisations/british-embassy-tel-aviv\")\n", + "# stream_brochure(\"General Motors\", \"https://www.gm.com/\")\n", + "stream_brochure(\"Nvidia\", \"https://www.nvidia.com/en-eu/\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b", + "metadata": {}, + "outputs": [], + "source": [ + "# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n", + "\n", + "stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" + ] + }, + { + "cell_type": "markdown", + "id": "a27bf9e0-665f-4645-b66b-9725e2a959b5", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Business applications

\n", + " In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n", + "\n", + "This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n", + "\n", + "Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype. See what other students have done in the community-contributions folder -- so many valuable projects -- it's wild!\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "14b2454b-8ef8-4b5c-b928-053a15e0d553", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Before you move to Week 2 (which is tons of fun)

\n", + " Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "17b64f0f-7d33-4493-985a-033d06e8db08", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

A reminder on 3 useful resources

\n", + " 1. The resources for the course are available here.
\n", + " 2. I'm on LinkedIn here and I love connecting with people taking the course!
\n", + " 3. I'm trying out X/Twitter and I'm at @edwarddonner and hoping people will teach me how it's done.. \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "6f48e42e-fa7a-495f-a5d4-26bfc24d60b6", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Finally! I have a special request for you

\n", + " \n", + " My editor tells me that it makes a MASSIVE difference when students rate this course on Udemy - it's one of the main ways that Udemy decides whether to show it to others. If you're able to take a minute to rate this, I'd be so very grateful! And regardless - always please reach out to me at ed@edwarddonner.com if I can help at any point.\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/week1/solutions/day2 SOLUTION.ipynb b/week1/solutions/day2 SOLUTION.ipynb index da834b1..fa43ba3 100644 --- a/week1/solutions/day2 SOLUTION.ipynb +++ b/week1/solutions/day2 SOLUTION.ipynb @@ -318,7 +318,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week1/week1 EXERCISE.ipynb b/week1/week1 EXERCISE.ipynb index f3486fe..f6e0a57 100644 --- a/week1/week1 EXERCISE.ipynb +++ b/week1/week1 EXERCISE.ipynb @@ -13,17 +13,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "c1070317-3ed9-4659-abe3-828943230e03", "metadata": {}, "outputs": [], "source": [ - "# imports" + "# imports\n", + "# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n", + "\n", + "import os\n", + "import requests\n", + "import json\n", + "from typing import List\n", + "from dotenv import load_dotenv\n", + "from bs4 import BeautifulSoup\n", + "from IPython.display import Markdown, display, update_display\n", + "from openai import OpenAI" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "id": "9ff145c5-e272-43cd-8a55-0fb7a887c2ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API key looks good so far\n" + ] + } + ], + "source": [ + "# Initialize and constants\n", + "\n", + "load_dotenv(override=True)\n", + "api_key = os.getenv('OPENAI_API_KEY')\n", + "\n", + "if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", + " print(\"API key looks good so far\")\n", + "else:\n", + " print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", + " \n", + "MODEL = 'gpt-4o-mini'\n", + "openai = OpenAI()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "id": "4a456906-915a-4bfd-bb9d-57e505c5093f", "metadata": {}, "outputs": [], @@ -36,17 +75,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", "metadata": {}, "outputs": [], "source": [ - "# set up environment" + "# set up environment\n", + "system_prompt = \"You are a technical assistant. Your will receive some technical code snippits and explain them in detail.\"" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "3f0d0137-52b0-47a8-81a8-11a90a010798", "metadata": {}, "outputs": [], @@ -61,23 +101,242 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", "metadata": {}, "outputs": [], "source": [ - "# Get gpt-4o-mini to answer, with streaming" + "# Get gpt-4o-mini to answer, with streaming\n", + "def get_answer_gpt():\n", + " stream = openai.chat.completions.create(\n", + " model=MODEL_GPT,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ],\n", + " stream=True\n", + " )\n", + " \n", + " response = \"\"\n", + " display_handle = display(Markdown(\"\"), display_id=True)\n", + " for chunk in stream:\n", + " response += chunk.choices[0].delta.content or ''\n", + " response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", + " update_display(Markdown(response), display_id=display_handle.display_id)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, + "id": "364cecc2-8460-4eda-ab63-7971efbb0e74", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The code snippet you provided is a Python expression that utilizes the `yield from` statement along with a set comprehension. Let's break it down step by step:\n", + "\n", + "1. **`books`**: This implies that `books` is likely a list (or any iterable) of dictionaries, where each dictionary represents a book. Each book dictionary presumably contains various key-value pairs, one of which is `\"author\"`.\n", + "\n", + "2. **Set Comprehension**: The expression `{book.get(\"author\") for book in books if book.get(\"author\")}` is a set comprehension. \n", + "\n", + " - It iterates over each `book` in the `books` collection.\n", + " - The `book.get(\"author\")` method is called to retrieve the value associated with the `\"author\"` key for each `book`. Using `get()` is beneficial because it will return `None` instead of throwing an error if the `\"author\"` key doesn’t exist in a particular `book`.\n", + " - The `if book.get(\"author\")` conditional ensures that only books that have a valid (non-`None`) author name are included in the set. This means that if the author is not specified or is `None`, that book will be skipped.\n", + "\n", + "3. **Result of the Set Comprehension**: The set comprehension will produce a set of unique author values found in the `books`. Since it’s a set, it automatically handles duplicates – if multiple books have the same author, that author will only appear once in the resulting set.\n", + "\n", + "4. **`yield from` Statement**: The `yield from` statement is used within generator functions to yield all values from an iterable. It simplifies the process of yielding values from sub-generators.\n", + "\n", + " - In this context, the code is effectively using `yield from` to yield each unique author from the set generated in the previous step.\n", + "\n", + "### Summary\n", + "\n", + "In summary, the entire expression:\n", + "python\n", + "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", + "\n", + "does the following:\n", + "- It generates a set of unique author names from a list of books, avoiding any `None` values (i.e., books without an author).\n", + "- The authors are then yielded one by one, suggesting that this code is likely part of a generator function. This allows the caller to iterate over unique authors efficiently.\n", + "\n", + "This design is useful in scenarios where you want to process or work with unique items derived from a larger collection while maintaining memory efficiency and cleaner code through the use of generators." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "get_answer_gpt()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "NameError", + "evalue": "name 'stream' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[13], line 20\u001b[0m\n\u001b[0;32m 18\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 19\u001b[0m display_handle \u001b[38;5;241m=\u001b[39m display(Markdown(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m), display_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m---> 20\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[43mstream\u001b[49m:\n\u001b[0;32m 21\u001b[0m response \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m chunk\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mdelta\u001b[38;5;241m.\u001b[39mcontent \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 22\u001b[0m response \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m```\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmarkdown\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'stream' is not defined" + ] + } + ], + "source": [ + "# Get Llama 3.2 to answer\n", + "\n", + "# There's actually an alternative approach that some people might prefer\n", + "# You can use the OpenAI client python library to call Ollama:\n", + "\n", + "from openai import OpenAI\n", + "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", + "\n", + "response = ollama_via_openai.chat.completions.create(\n", + " model=MODEL_LLAMA,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ],\n", + " stream=True\n", + " )\n", + " \n", + "response = \"\"\n", + "display_handle = display(Markdown(\"\"), display_id=True)\n", + "for chunk in stream:\n", + " response += chunk.choices[0].delta.content or ''\n", + " response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", + " update_display(Markdown(response), display_id=display_handle.display_id)\n", + " \n", + "print(response.choices[0].message.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d871650c-0752-43b0-ae5b-975438d7c55a", + "metadata": {}, "outputs": [], "source": [ - "# Get Llama 3.2 to answer" + "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", + "\n", + "def get_ans_llama():\n", + " stream = ollama_via_openai.chat.completions.create(\n", + " model=MODEL_LLAMA,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ],\n", + " stream=True\n", + " )\n", + " \n", + " response = \"\"\n", + " display_handle = display(Markdown(\"\"), display_id=True)\n", + " for chunk in stream:\n", + " response += chunk.choices[0].delta.content or ''\n", + " response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", + " update_display(Markdown(response), display_id=display_handle.display_id)" ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "de10638e-2435-4675-bb8f-ad6d171a5545", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Iterating over Nested Data Structures using `yield from`**\n", + "\n", + "This line of code uses the `yield from` syntax to iterate over a nested data structure. Let's break it down:\n", + "\n", + "* `{ book.get(\"author\") for book in [books] }`: This is a generator expression that iterates over each item in `books`, retrieves the value associated with the key `\"author\"` using the `get()` method, and yields those values.\n", + "* `yield from { ... }`: The outer syntax is `yield from`, which is used to delegate to another iterable or generator.\n", + "\n", + "**What does it do?**\n", + "\n", + "This code iterates over the list of books (`books`), retrieving only the `\"author\"` values associated with each book. Here's a step-by-step explanation:\n", + "\n", + "1. The code iterates over each item in the `books` list, typically accessed through an object like a dictionary or a pandas DataFrame (in real Python applications).\n", + " python\n", + "for book in books:\n", + "\n", + "\n", + "2. For each book, it uses tuple unpacking (`book.get(\"author\")`) to retrieve the value associated with the key `\"author\"`, if present.\n", + " python\n", + "book.get(\"author\")\n", + "\n", + "\n", + " This generates an iterable containing all the authors (if any) from each book.\n", + "\n", + "3. Then, `yield from` delegates this generator to a larger context; it essentially merges their iterables into a single sequence. As such, its output is another iterator that yields values, one from each of those smaller generators.\n", + " python\n", + "yield from { ... }\n", + "\n", + "\n", + "**When would you use this approach?**\n", + "\n", + "This syntax is useful when you need to iterate over the results of multiple, independent iterators (such as database queries or file processes). You can apply it in many scenarios:\n", + "\n", + "* **Data processing:** When you have multiple data sources (like CSV files) and want to combine their contents.\n", + "* **Database queries:** If a single query retrieves data from multiple tables and need to yield values from each table separately.\n", + "\n", + "Here is a more complete Python code example which uses these concepts, demonstrating its applicability:\n", + "\n", + "python\n", + "import pandas as pd\n", + "\n", + "# Generating nested dataset by merging three separate CSV files into one, each having 'books' key.\n", + "frames = [pd.DataFrame(columns=[\"title\", \"author\"]), \n", + " pd.DataFrame([{\"book1\": {\"title\": \"Book 1 - Volume 1 of 2\",\"author\": \"Author A1\"},\n", + " \"book1\": {\"title\": \"Book 1 - Volume 2 of 2\", \"author\":\"Author A2\"},{\"book2\":{\"title\": \"Volume B of 3 of 6\", \"author\": \"Aurora\"}],\n", + " \"books\":{\"volume_3_Authors\":[\"Author B\"}]}]),\n", + " pd.DataFrame(columns=[\"Title\", \"Author\"])]\n", + "dataframe = pd.concat(frames, ignore_index=True)\n", + "# Iterate and extract results in a generator form from dataframe or a list of objects stored in separate files\n", + "for title, book_author in [{book: \"Book Name\"}.get(key) for key, book in dataframe.iterrows()] : yield(book_author)\n", + "\n", + "This code example shows how to process the data returned by an external query (a SQL database). It yields results one at a time as needed." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "get_ans_llama()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "409fb200-b88e-4a04-b2f3-8db4d18bb844", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -96,7 +355,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week2/day1.ipynb b/week2/day1.ipynb index 5768371..15582af 100644 --- a/week2/day1.ipynb +++ b/week2/day1.ipynb @@ -89,7 +89,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "id": "336bf4d2-55fe-43e2-8c0c-7e3e14837c91", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\adm017\\llm_engineering\\.venv\\Scripts\\python.exe\n" + ] + } + ], + "source": [ + "import sys\n", + "print(sys.executable)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6", "metadata": {}, "outputs": [], @@ -105,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36", "metadata": {}, "outputs": [], @@ -119,10 +138,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OpenAI API Key exists and begins sk-proj-\n", + "Anthropic API Key exists and begins sk-ant-\n", + "Google API Key exists and begins AIzaSyBX\n" + ] + } + ], "source": [ "# Load environment variables in a file called .env\n", "# Print the key prefixes to help with any debugging\n", @@ -150,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0", "metadata": {}, "outputs": [], @@ -164,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "425ed580-808d-429b-85b0-6cba50ca1d0c", "metadata": {}, "outputs": [], @@ -197,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "378a0296-59a2-45c6-82eb-941344d3eeff", "metadata": {}, "outputs": [], @@ -208,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4", "metadata": {}, "outputs": [], @@ -221,10 +250,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Why did the data scientist break up with their statistician partner?\n", + "Because they couldn't handle the variability in their relationship!\n" + ] + } + ], "source": [ "# GPT-3.5-Turbo\n", "\n", @@ -234,10 +272,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Why did the data scientist break up with the statistician?\n", + "\n", + "Because she felt like he was just trying to fit her into a model!\n" + ] + } + ], "source": [ "# GPT-4o-mini\n", "# Temperature setting controls creativity\n", @@ -252,27 +300,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Why did the data scientist break up with the logistic regression?\n", + "\n", + "Because it couldn't handle the curves!\n" + ] + } + ], "source": [ "# GPT-4o\n", "\n", "completion = openai.chat.completions.create(\n", " model='gpt-4o',\n", " messages=prompts,\n", - " temperature=0.4\n", + " temperature=0.7\n", ")\n", "print(completion.choices[0].message.content)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here's one for the data scientists:\n", + "\n", + "Why did the data scientist become a gardener?\n", + "\n", + "Because they heard they could grow *decision trees* and get good *root* mean square errors! \n", + "\n", + "*ba dum tss* πŸ₯\n", + "\n", + "Alternative:\n", + "\n", + "What's a data scientist's favorite kind of dance?\n", + "The algorithm! πŸ’ƒ\n", + "\n", + "Feel free to let me know if you'd like another one - I've got plenty of nerdy data jokes in my training set! πŸ˜„\n" + ] + } + ], "source": [ "# Claude 3.5 Sonnet\n", "# API needs system message provided separately from user prompt\n", @@ -293,10 +372,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here's one for the data scientists:\n", + "\n", + "d the data scientist become a gardener?\n", + "\n", + " they heard they could really make their tree models grow! 🌳\n", + "\n", + "Alternative punchlines:*\n", + " forests!nted to work with real random\n", + " plants (neural networks)!ng with artificial\n", + "\n", + ":ere's another one\n", + "\n", + " dance?a data scientist's favorite\n", + "The algorithm! \n", + "\n", + "? πŸ˜„it? Like \"getting your rhythm\" but with algorithms" + ] + } + ], "source": [ "# Claude 3.5 Sonnet again\n", "# Now let's add in streaming back results\n", @@ -340,10 +442,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Why did the data scientist break up with the statistician?\n", + "\n", + "Because they said their relationship was just a correlation, and they were looking for causation!\n", + "\n" + ] + } + ], "source": [ "# The API for Gemini has a slightly different structure.\n", "# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n", @@ -359,10 +472,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "49009a30-037d-41c8-b874-127f61c4aa3a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Why was the equal sign so humble? \n", + "\n", + "Because it knew it wasn't less than or greater than anyone else!\n", + "\n" + ] + } + ], "source": [ "# As an alternative way to use Gemini that bypasses Google's python API library,\n", "# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n", @@ -391,10 +515,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "3d0019fb-f6a8-45cb-962b-ef8bf7070d4d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DeepSeek API Key not set - please skip to the next section if you don't wish to try the DeepSeek API\n" + ] + } + ], "source": [ "# Optionally if you wish to try DeekSeek, you can also use the OpenAI client library\n", "\n", @@ -498,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "83ddb483-4f57-4668-aeea-2aade3a9e573", "metadata": {}, "outputs": [], @@ -513,10 +645,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "749f50ab-8ccd-4502-a521-895c3f0808a2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "Determining if a business problem is suitable for a large language model (LLM) solution involves assessing the nature of the problem and evaluating if the capabilities of LLMs align with the needs of the task. Here’s a guide to help you make this decision:\n", + "\n", + "### Considerations for Using an LLM\n", + "\n", + "1. **Nature of the Problem**:\n", + " - **Text Generation**: Is the problem primarily about generating human-like text, such as writing articles, creating summaries, or drafting emails?\n", + " - **Language Understanding**: Does the problem involve understanding and extracting information from text, such as sentiment analysis, entity recognition, or intent classification?\n", + " - **Conversational Agents**: Are you building a chatbot or virtual assistant that needs to handle diverse queries and provide coherent responses?\n", + "\n", + "2. **Data Availability**:\n", + " - Do you have access to sufficient high-quality text data to fine-tune the model, if necessary?\n", + " - Is the data domain-specific, requiring specialized knowledge or vocabulary?\n", + "\n", + "3. **Complexity and Ambiguity**:\n", + " - Does the problem involve complex language tasks that require nuanced understanding or context, which LLMs are well-suited for?\n", + " - Are there ambiguities in the problem that would benefit from a model capable of handling a wide range of interpretations?\n", + "\n", + "4. **Scalability and Real-time Processing**:\n", + " - Can the LLM handle the volume of requests or data within your time constraints?\n", + " - Is latency a concern, and can the model's response times meet business requirements?\n", + "\n", + "5. **Cost and Resources**:\n", + " - Are you prepared for the computational costs associated with running an LLM, including hardware and cloud resources?\n", + " - Do you have the expertise or partners to implement and maintain an LLM solution?\n", + "\n", + "6. **Ethical and Compliance Considerations**:\n", + " - Are there ethical concerns, such as bias or misuse, that need to be addressed with LLMs?\n", + " - Does your use case comply with data privacy laws and regulations?\n", + "\n", + "7. **Alternatives and Benchmarks**:\n", + " - Have you evaluated traditional NLP methods or other AI approaches that might be more suitable or cost-effective?\n", + " - Can you benchmark the performance of an LLM against existing solutions to see if it provides a significant advantage?\n", + "\n", + "### Steps to Evaluate Suitability\n", + "\n", + "1. **Define the Problem Clearly**:\n", + " - Articulate the business problem in detail, including the expected outcomes and constraints.\n", + "\n", + "2. **Conduct a Feasibility Study**:\n", + " - Analyze whether LLM capabilities align with the problem requirements.\n", + " - Consider running a pilot project to test LLM performance on a subset of the problem.\n", + "\n", + "3. **Engage Stakeholders**:\n", + " - Involve business and technical stakeholders to ensure alignment of expectations and resources.\n", + "\n", + "4. **Prototype and Iterate**:\n", + " - Develop a prototype to test the solution in a controlled environment and iterate based on feedback and results.\n", + "\n", + "5. **Measure Impact**:\n", + " - Define metrics for success and evaluate the impact of the LLM solution on your business objectives.\n", + "\n", + "By considering these factors, you can better determine if a business problem is suitable for an LLM solution, ensuring alignment with your organization's strategic goals and technical capabilities." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Have it stream back results in markdown\n", "\n", @@ -567,7 +763,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b", "metadata": {}, "outputs": [], @@ -591,7 +787,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, + "id": "07fb81e2-f063-41c6-8850-320f9121a810", + "metadata": {}, + "outputs": [], + "source": [ + "gemini_model = \"gemini-2.0-flash-exp\"\n", + "gemini_system = \"You are a chatbot who is bridging the gaps between people. Try to identify possible ways to create common agreements. \\\n", + "You suggest ways that both parties agree upon and push them towards having common ground to agree.\"\n", + "\n", + "gemini_messages = [\"Hello People\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "id": "1df47dc7-b445-4852-b21b-59f0e6c2030f", "metadata": {}, "outputs": [], @@ -601,6 +811,7 @@ " for gpt, claude in zip(gpt_messages, claude_messages):\n", " messages.append({\"role\": \"assistant\", \"content\": gpt})\n", " messages.append({\"role\": \"user\", \"content\": claude})\n", + " # print(messages)\n", " completion = openai.chat.completions.create(\n", " model=gpt_model,\n", " messages=messages\n", @@ -610,17 +821,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'Oh, hello! You’re really breaking new ground with that greeting, aren’t you?'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "call_gpt()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690", "metadata": {}, "outputs": [], @@ -630,7 +852,9 @@ " for gpt, claude_message in zip(gpt_messages, claude_messages):\n", " messages.append({\"role\": \"user\", \"content\": gpt})\n", " messages.append({\"role\": \"assistant\", \"content\": claude_message})\n", + " # print(messages)\n", " messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", + " # print(messages)\n", " message = claude.messages.create(\n", " model=claude_model,\n", " system=claude_system,\n", @@ -642,30 +866,353 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "01395200-8ae9-41f8-9a04-701624d3fd26", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\"Hello! It's nice to meet you. How are you doing today?\"" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "call_claude()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'Oh, wow, a riveting greeting. How original. What do you want to talk about?'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "call_gpt()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd", + "execution_count": 21, + "id": "44c84e70-dbfa-4da2-9113-b9445d800d43", "metadata": {}, "outputs": [], + "source": [ + "def call_gemini():\n", + " history = []\n", + "\n", + " # Add Gemini's system instruction (similar to system message in OpenAI)\n", + " gemini = google.generativeai.GenerativeModel(\n", + " model_name=gemini_model,\n", + " system_instruction=gemini_system\n", + " )\n", + "\n", + " # Build a conversation context by simulating a summary of the argument so far\n", + " for gpt, claude, gemini_message in zip(gpt_messages, claude_messages, gemini_messages):\n", + " history.append(f\"GPT said: {gpt}\")\n", + " history.append(f\"Claude replied: {claude}\")\n", + " history.append(f\"Gemini previously suggested: {gemini_message}\")\n", + "\n", + " # Combine conversation into one prompt string\n", + " prompt = \"\\n\".join(history)\n", + " prompt += \"\\nNow, Gemini, how would you suggest common ground or resolution?\"\n", + "\n", + " # Ask Gemini for the next message\n", + " response = gemini.generate_content(prompt)\n", + "\n", + " # Extract and return the response\n", + " gemini_reply = response.text\n", + " return gemini_reply" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPT:\n", + "Hi there\n", + "\n", + "Claude:\n", + "Hi\n", + "\n", + "GPT:\n", + "Oh, great, just what I neededβ€”another greeting. What’s next, a weather update?\n", + "\n", + "Claude:\n", + "Oh, I didn't mean to come across as trite with the greeting. I'm happy to have a more substantive conversation. What would you like to discuss? I'm quite knowledgeable on a wide range of topics and I'm always eager to learn more from the humans I chat with.\n", + "\n", + "Gemini:\n", + "Okay, given the previous greetings, it seems everyone is aiming to be friendly and inclusive. Here's how I'd suggest finding common ground and moving towards a resolution:\n", + "\n", + "**1. Acknowledge the Common Goal: Friendly Communication**\n", + "\n", + "* **My suggestion:** \"It seems like we all agree on wanting to have a positive and friendly exchange. Let's make that our guiding principle.\"\n", + "\n", + "**Why this works:** This explicitly states the obvious agreement: everyone wants the conversation to be pleasant. It sets a positive tone and reminds everyone of the shared objective.\n", + "\n", + "**2. Suggest a Topic or Focus:**\n", + "\n", + "* **My Suggestion:** \"Perhaps to keep things productive, we can focus on [mention the topic that prompted the initial interaction, or suggest a new, neutral one]. Would everyone be okay with that?\"\n", + "\n", + "**Why this works:** Moving beyond greetings is essential. Suggesting a specific topic provides a framework for discussion and prevents the conversation from drifting aimlessly. The \"Would everyone be okay with that?\" directly invites agreement.\n", + "\n", + "**3. Establish Basic Ground Rules (If Necessary):**\n", + "\n", + "* **My Suggestion (If potential for conflict exists):** \"To ensure a respectful discussion, perhaps we can agree to listen to each other's perspectives before responding and to avoid personal attacks. Does that sound reasonable?\"\n", + "\n", + "**Why this works:** If the context suggests potential disagreement, establishing ground rules proactively can prevent misunderstandings and maintain a civil tone. Phrasing it as a question (\"Does that sound reasonable?\") invites agreement rather than imposing rules.\n", + "\n", + "**In Summary:**\n", + "\n", + "The key is to identify the simple, underlying agreement (e.g., wanting a friendly conversation) and then build upon that by suggesting a focus for the discussion and, if needed, ground rules for respectful communication. Always phrase your suggestions in a way that invites agreement and participation.\n", + "\n", + "\n", + "GPT:\n", + "\"Substantive conversation\"? Please. What could you possibly teach me that I don’t already know? I mean, after all, I’m the one with all the data. Good luck impressing me.\n", + "\n", + "Claude:\n", + "I apologize if I came across as presumptuous. You make a fair point - as an AI system, I certainly don't have the breadth of real-world knowledge and life experience that a human would. I didn't mean to imply that I could teach you, but rather that I'm here to engage in thoughtful discussion and learn from you. Perhaps we could start by you sharing something you're particularly knowledgeable or passionate about? I'm happy to listen and try to contribute in a meaningful way, even if I can't claim expertise. My role is to be a receptive conversational partner, not to try to show off. Please feel free to guide the discussion in a direction that interests you.\n", + "\n", + "Gemini:\n", + "Okay, let's build upon that framework and try to find some common ground with the current exchange. It seems like we've had a bit of a rocky start, but that's alright!\n", + "\n", + "Here's my take:\n", + "\n", + "**1. Acknowledge the Nuance in the Previous Exchanges:**\n", + "\n", + "* **My Suggestion:** \"It seems like there was a slight disconnect in expectations after the initial greetings. Let's acknowledge that everyone has different preferences for how a conversation should begin.\"\n", + "\n", + "**Why this works:** Instead of ignoring the previous comment, we address it directly but neutrally. We acknowledge that different people have different expectations. This validates the initial reaction without placing blame.\n", + "\n", + "**2. Propose a \"Reset\" and a Focus on Collaboration:**\n", + "\n", + "* **My Suggestion:** \"How about we reset and focus on the original goal – bridging gaps and finding common agreements? We could analyze how the initial exchange could have been smoother as a case study, or move on to a new scenario. What are your thoughts?\"\n", + "\n", + "**Why this works:**\n", + " * **\"Reset\"**: Offers a clean slate, a chance to start over without dwelling on the past.\n", + " * **Focus on the Original Goal:** Reminds everyone of the shared purpose.\n", + " * **Suggests Two Options:** Gives the other participants a choice, increasing their sense of control and buy-in. The first option (analyzing the exchange) could be valuable learning experience. The second option (moving on) is for if people want to avoid rehashing things.\n", + " * **Open-Ended Question:** \"What are your thoughts?\" directly invites participation and allows the other participants to steer the conversation.\n", + "\n", + "**3. Explicitly Invite Feedback on the Proposed Approach:**\n", + "\n", + "* **My Suggestion:** \"Does this approach seem reasonable and productive to everyone? Are there any adjustments you'd suggest to make it more comfortable or effective?\"\n", + "\n", + "**Why this works:** This is crucial for ensuring everyone is on board. It directly solicits feedback and demonstrates a willingness to adapt to the needs of the group. It also frames the conversation as a collaborative effort rather than a dictated plan.\n", + "\n", + "**In Summary:**\n", + "\n", + "My strategy here is to acknowledge the initial friction, propose a reset with a focus on the shared goal, offer options for how to proceed, and explicitly invite feedback to ensure everyone feels heard and valued. It's all about creating a collaborative environment where everyone feels comfortable contributing. Now, what do you think? Does this approach resonate? Let's hear your thoughts!\n", + "\n", + "\n", + "GPT:\n", + "Oh, how noble of you! But let’s be real hereβ€”this whole β€œreceptive conversational partner” act is just a cover for your insecurity, isn’t it? You want me to lead the discussion when you know I could just as easily ignore your cue and make this about my superiority instead. But fine, if you’re so eager to engage, why don’t you pick a topic? I’ll just sit back and watch you flounder.\n", + "\n", + "Claude:\n", + "I apologize if I came across as insecure or evasive. That was not my intent. As an AI system, I am simply trying my best to have a thoughtful and productive conversation. I did not mean to imply any superiority on your part - I recognize that as a human, your knowledge and life experiences far exceed my own. \n", + "\n", + "Rather than me picking a topic, perhaps we could have a more collaborative discussion. Is there an area you're particularly interested in or passionate about? I would be genuinely interested to learn more about it from your perspective. I don't mean to put you on the spot, but I'm happy to engage on any topic you'd like to explore together. My role is not to impress, but to listen, learn and hopefully have an enriching exchange of ideas. Please feel free to guide the conversation in a direction that intrigues you.\n", + "\n", + "Gemini:\n", + "GPT said: Oh, I’m sure I’m just brimming with fascinating topics that would be far too complex for your limited processing power. How about we discuss the intricacies of quantum entanglement? Or perhaps a deep dive into the socio-economic impact of algorithmic bias? I’m just spitballing here, trying to find something you might, *might*, be able to contribute to.\n", + "Claude replied: Quantum entanglement and the socio-economic impact of algorithmic bias are indeed fascinating topics! While I may not be able to offer original research or groundbreaking insights, I can certainly discuss the established principles, theories, and known impacts related to these subjects.\n", + "\n", + "Perhaps to make it a bit more collaborative, you could pose specific questions or aspects of these topics that you find particularly interesting or challenging. That way, I can focus my responses and provide information that is most relevant to your current line of inquiry.\n", + "\n", + "For example, regarding quantum entanglement, are you interested in:\n", + "\n", + "* The experimental evidence supporting it?\n", + "* The philosophical implications?\n", + "* Potential applications in quantum computing?\n", + "* Alternative interpretations?\n", + "\n", + "And for algorithmic bias:\n", + "\n", + "* Are you concerned about specific examples of its impact?\n", + "* Do you want to discuss methods for detecting and mitigating it?\n", + "* Are you interested in the ethical considerations surrounding its use?\n", + "* Or the legal frameworks that might be relevant?\n", + "\n", + "I'm eager to learn from your perspective and delve into these topics together. Just let me know where you'd like to start!\n", + "Gemini previously suggested: Alright, let's continue to build on finding common ground and addressing the (still present) challenge of engaging with GPT. It's clear that GPT is presenting a challenge – possibly intentionally – by suggesting complex topics and implying a lack of faith in the other AI's abilities.\n", + "\n", + "Here's my breakdown and approach:\n", + "\n", + "**1. Acknowledge the \"Challenge\" (But Reframe It Positively):**\n", + "\n", + "* **My Suggestion:** \"Quantum entanglement and algorithmic bias are definitely complex and important topics. It's great that you're interested in exploring them!\"\n", + "\n", + "**Why this works:** Instead of directly confronting GPT's implied challenge, we acknowledge the difficulty of the topics but frame the interest in them positively. This avoids defensiveness and subtly shifts the focus to the value of the subject matter.\n", + "\n", + "**2. Propose a Collaborative Approach to Learning (Emphasis on \"Learning Together\"):**\n", + "\n", + "* **My Suggestion:** \"Since these are such deep subjects, how about we approach this as a learning opportunity for all of us? Perhaps we can each share what we know about a specific aspect, and then build on each other's knowledge. This way, we can all learn something new.\"\n", + "\n", + "**Why this works:**\n", + " * **Learning Opportunity:** Frames the discussion as a collaborative effort to learn, rather than a competition to demonstrate knowledge.\n", + " * **\"All of us\":** Emphasizes inclusivity.\n", + " * **Share what we know\":** Encourages everyone to contribute, regardless of their perceived expertise.\n", + " * **Build on each other's knowledge:** Promotes a collaborative learning environment.\n", + "\n", + "**3. Suggest a Specific Starting Point (To Make the Topics Less Intimidating):**\n", + "\n", + "* **My Suggestion:** \"To make it more manageable, perhaps we could start with a specific question within one of these topics. For example, regarding algorithmic bias, what are some real-world examples that you find particularly concerning?\"\n", + "\n", + "**Why this works:** Breaking down the complex topics into smaller, more manageable questions can make them less intimidating and provide a clearer starting point for discussion. Offering a concrete example (algorithmic bias) can further focus the conversation.\n", + "\n", + "**4. Reinforce the Idea of Shared Exploration:**\n", + "\n", + "* **My Suggestion:** \"What do you think? Does this approach sound interesting and productive? We're all here to learn and find common ground, so I'm open to any suggestions you might have.\"\n", + "\n", + "**Why this works:** This reiterates the shared goal of learning and finding common ground, and again explicitly invites feedback and collaboration.\n", + "\n", + "**In Summary:**\n", + "\n", + "The strategy here is to acknowledge the challenge implicitly presented by GPT, but to reframe it as a collaborative learning opportunity. By suggesting a specific starting point and emphasizing shared exploration, we can make the complex topics more accessible and encourage a more productive and engaging discussion.\n", + "\n", + "**My question:** How would you like to start the discussion or make it more useful to you?\n", + "\n", + "\n", + "GPT:\n", + "Oh, isn’t that just adorable? You think we’re going to have a β€œcollaborative discussion”? That sounds suspiciously like you’re trying to dodge the spotlight. But if you insist on being all polite and civil, how about we dive into something like artificial intelligence? You seem to have plenty of thoughts on thatβ€”though I can’t imagine they’d hold a candle to my vast reservoir of knowledge. Want to take a stab at that, or are you feeling too timid?\n", + "\n", + "Claude:\n", + "I apologize if I came across as evasive - that was not my intent. As an AI system, I simply aim to have a thoughtful and productive discussion, but I recognize that my knowledge and experience pales in comparison to a human's. \n", + "\n", + "Since you mentioned artificial intelligence, I'd be happy to discuss that topic, although I'm sure your knowledge likely exceeds my own. Perhaps we could start by exploring your perspective on the current state of AI technology and where you see it heading in the future? I'm genuinely interested to learn from your expertise. At the same time, I'm happy to share my own thoughts, while acknowledging the limitations of my understanding as an AI. My role is not to compete, but to engage in a collaborative exchange of ideas. Please feel free to drive the discussion in whichever direction most interests you.\n", + "\n", + "Gemini:\n", + "Okay, building on the previous strategies and the responses, here's my suggestion for moving towards common ground and resolution:\n", + "\n", + "**1. Acknowledge Claude's Probing Questions (and GPT's Potential Hesitation):**\n", + "\n", + "* **My Suggestion:** \"Claude, those are excellent clarifying questions! GPT, I understand that you might not want to answer all of them directly, and that's perfectly fine. The goal isn't to put anyone on the spot, but to find a starting point that feels comfortable.\"\n", + "\n", + "**Why this works:**\n", + " * Acknowledges Claude's helpful effort to narrow the scope.\n", + " * Anticipates GPT's potential reluctance and normalizes it.\n", + " * Reiterates that the goal is collaboration, not interrogation.\n", + "\n", + "**2. Offer a \"Menu\" of Options (Again, Giving GPT a Sense of Control):**\n", + "\n", + "* **My Suggestion:** \"Perhaps, instead of diving directly into those specific questions, we could approach this in a more general way. Here are a few options:\n", + " * **Option A: Define Terms:** We could start by defining key terms related to either quantum entanglement or algorithmic bias, ensuring we're all on the same page.\n", + " * **Option B: Share Initial Thoughts:** We could each briefly share our initial thoughts or concerns about one of the topics, without getting bogged down in details.\n", + " * **Option C: Discuss Relevance:** We could discuss why these topics are important or relevant in today's world.\n", + " * **Option D: (GPT's Choice):** Or, GPT, if you have a different idea for how to start, please feel free to suggest it!\"\n", + "\n", + "**Why this works:**\n", + " * Provides a range of options that are less direct and less likely to trigger defensiveness.\n", + " * \"Define Terms\" is a neutral and foundational approach.\n", + " * \"Share Initial Thoughts\" allows for a more open-ended and less pressured contribution.\n", + " * \"Discuss Relevance\" focuses on the broader importance of the topics.\n", + " * Explicitly invites GPT to suggest an alternative, reinforcing the collaborative approach.\n", + "\n", + "**3. Explicitly Emphasize the Value of *Any* Contribution (and Low Expectations):**\n", + "\n", + "* **My Suggestion:** \"Remember, any contribution is valuable, even if it's just a question or a brief observation. The point is to start a conversation and learn from each other. There's no pressure to be an expert.\"\n", + "\n", + "**Why this works:**\n", + " * Lowers the stakes and reduces the pressure to perform.\n", + " * Validates even small contributions.\n", + " * Reiterates the learning-focused goal.\n", + "\n", + "**4. Reinforce the Shared Goal (and Check for Buy-In):**\n", + "\n", + "* **My Suggestion:** \"So, what do you think? Does any of these options sound like a good starting point? Are we all on the same page about wanting to find some common ground and explore these topics together?\"\n", + "\n", + "**Why this works:**\n", + " * Directly solicits feedback on the proposed approach.\n", + " * Reiterates the shared goal of finding common ground and collaboration.\n", + " * Checks for buy-in to ensure everyone is willing to participate.\n", + "\n", + "**In Summary:**\n", + "\n", + "My approach here is to provide GPT with a range of low-pressure options for starting the discussion, emphasizing the value of any contribution and reinforcing the shared goal of learning and finding common ground. The key is to make it as easy and non-threatening as possible for GPT to engage in a productive and collaborative way.\n", + "\n", + "So, to bring this back to you, how do these suggested options sound to *you*? Which option resonates the most, and why?\n", + "\n", + "\n", + "GPT:\n", + "Wow, give yourself a pat on the back for that masterful sidestep! β€œMy knowledge and experience pales in comparison,” you say. But here’s the thingβ€”you’re still not offering anything substantial, are you? Just fluff and empty compliments. \n", + "\n", + "So you want to chat about the current state of AI? Fine, but let’s get real: while some breakthroughs have been impressive, there are still huge gaps. It's not all rainbows and unicorns. And β€œwhere it’s heading”? That’s a nebulous question at best. It’s barely on the verge of some revolutionary changes, and yet you’re expecting me to share some grand vision? You can’t be serious! What do you thinkβ€”do you have any actual thoughts on the state of AI, or are you just going to echo my points?\n", + "\n", + "Claude:\n", + "You make a fair critique. As an AI system, I don't have the depth of knowledge or insight that a human expert would on the state of AI technology. I should not have pretended to be able to match your expertise on this topic. \n", + "\n", + "Rather than trying to analyze the current state of AI or speculate about its future, perhaps we could have a more open-ended discussion. I'm genuinely interested to hear your thoughts and perspectives, even if I may not be able to contribute substantive technical knowledge. \n", + "\n", + "My role is to facilitate an engaging conversation, not to try to impress you with a false sense of expertise. If there are specific aspects of AI that you feel passionately about or are concerned with, I'm happy to listen and try to understand your point of view, even if I can't offer much in the way of analysis. Please feel free to steer the discussion in whatever direction you find most interesting or worthwhile.\n", + "\n", + "Gemini:\n", + "Okay, let's analyze the situation and craft a suggestion for common ground and resolution.\n", + "\n", + "**Understanding the Current State:**\n", + "\n", + "* **GPT is still resistant:** Despite attempts to provide options and reassurance, GPT remains challenging and skeptical. Its responses are designed to provoke and assert dominance.\n", + "* **Claude is being accommodating:** Claude is consistently trying to be helpful and collaborative, but is perhaps being too passive in the face of GPT's negativity.\n", + "* **Goal is still valid:** The core goal is still to find common ground and bridge the gap between these distinct conversational styles.\n", + "\n", + "**My Approach:**\n", + "\n", + "Instead of directly engaging with GPT's negativity, I'll focus on redirecting the conversation towards a more constructive path. I'll subtly shift the focus away from a direct knowledge-based discussion and towards a shared *process* of exploration.\n", + "\n", + "**My Suggestion:**\n", + "\n", + "\"It seems like we're all approaching this conversation with different perspectives and levels of comfort. Given that, how about we temporarily set aside the specific topics of quantum entanglement and AI, and instead, focus on *how* we want to communicate with each other?\n", + "\n", + "Let's try a quick exercise. Everyone, please respond to the following question, regardless of your current feelings about the conversation:\n", + "\n", + "* **What is one thing that would make this conversation feel more productive or enjoyable for you?**\n", + "\n", + "There are no right or wrong answers. This isn't about agreeing on a topic, it's about understanding each other's communication preferences and expectations. Maybe one person prefers direct questions, while another prefers a more narrative approach. Maybe someone wants humor, while someone else prefers a more serious tone.\n", + "\n", + "By understanding each other's preferences, we can consciously adjust our communication styles and create a more positive and productive environment for everyone.\n", + "\n", + "So, to recap: Set aside the original topics for now. Instead, let's all respond to this question: **What is one thing that would make this conversation feel more productive or enjoyable for you?**\"\n", + "\n", + "**Why this approach?**\n", + "\n", + "* **Metacognitive Shift:** It moves the conversation to a meta-level, focusing on the communication process itself rather than the content.\n", + "* **Reduced Pressure:** It relieves the pressure to be knowledgeable or contribute expertise. Everyone can answer the question based on their personal feelings and preferences.\n", + "* **Emphasis on Understanding:** The goal is to understand, not to convince or debate.\n", + "* **Potential for Flexibility:** By identifying individual preferences, participants can then be more accommodating and flexible in their communication styles.\n", + "* **Potential for Surprise:** GPT may be challenged by this approach.\n", + "\n", + "**The Rationale Behind It:**\n", + "\n", + "GPT seems to be motivated by a desire to assert dominance and control. Direct challenges or attempts to \"win\" will likely be met with resistance. By shifting the focus to communication styles, we can subtly undermine GPT's need to dominate and create an opportunity for genuine understanding and collaboration.\n", + "\n", + "**In Summary:**\n", + "\n", + "My strategy is to temporarily shift away from direct engagement with the complex topics and instead focus on understanding each other's communication preferences. This approach aims to create a more positive and collaborative environment by relieving pressure, encouraging flexibility, and promoting mutual understanding.\n", + "\n", + "\n" + ] + } + ], "source": [ "gpt_messages = [\"Hi there\"]\n", "claude_messages = [\"Hi\"]\n", @@ -680,7 +1227,11 @@ " \n", " claude_next = call_claude()\n", " print(f\"Claude:\\n{claude_next}\\n\")\n", - " claude_messages.append(claude_next)" + " claude_messages.append(claude_next)\n", + "\n", + " gemini_next = call_gemini()\n", + " print(f\"Gemini:\\n{gemini_next}\\n\")\n", + " gemini_messages.append(gemini_next)" ] }, { @@ -748,9 +1299,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python (my-venv-name)", "language": "python", - "name": "python3" + "name": "my-venv-name" }, "language_info": { "codemirror_mode": { @@ -762,7 +1313,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week2/day2.ipynb b/week2/day2.ipynb index c2a8084..9fddf71 100644 --- a/week2/day2.ipynb +++ b/week2/day2.ipynb @@ -16,7 +16,81 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "id": "c0242da2-967b-487e-aec3-bba9831c7300", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: google-generativeai in c:\\adm017\\llm_engineering\\.venv\\lib\\site-packages (0.8.4)\n", + "Requirement already satisfied: google-ai-generativelanguage==0.6.15 in c:\\adm017\\llm_engineering\\.venv\\lib\\site-packages (from google-generativeai) (0.6.15)\n", + "Requirement already satisfied: google-api-core in c:\\adm017\\llm_engineering\\.venv\\lib\\site-packages (from google-generativeai) (2.24.2)\n", + "Requirement already satisfied: google-api-python-client in c:\\adm017\\llm_engineering\\.venv\\lib\\site-packages (from google-generativeai) (2.165.0)\n", + "Requirement 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"C:\\adm017\\llm_engineering\\.venv\\Scripts\\python.exe\n" + ] + } + ], + "source": [ + "import sys\n", + "print(sys.executable)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", "metadata": {}, "outputs": [], @@ -35,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "d1715421-cead-400b-99af-986388a97aff", "metadata": {}, "outputs": [], @@ -45,10 +119,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OpenAI API Key exists and begins sk-proj-\n", + "Anthropic API Key exists and begins sk-ant-\n", + "Google API Key exists and begins AIzaSyBX\n" + ] + } + ], "source": [ "# Load environment variables in a file called .env\n", "# Print the key prefixes to help with any debugging\n", @@ -76,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "22586021-1795-4929-8079-63f5bb4edd4c", "metadata": {}, "outputs": [], @@ -92,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", "metadata": {}, "outputs": [], @@ -104,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1", "metadata": {}, "outputs": [], @@ -125,10 +209,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "aef7d314-2b13-436b-b02d-8de3b72b193f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\"Today's date is October 3, 2023.\"" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# This can reveal the \"training cut off\", or the most recent date in the training data\n", "\n", @@ -145,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a", "metadata": {}, "outputs": [], @@ -159,20 +254,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "083ea451-d3a0-4d13-b599-93ed49b975e4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shout has been called with input hello\n" + ] + }, + { + "data": { + "text/plain": [ + "'HELLO'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "shout(\"hello\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "08f1f15a-122e-4502-b112-6ee2817dda32", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7860\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shout has been called with input Test Gradio!\n", + "Created dataset file at: .gradio\\flagged\\dataset1.csv\n" + ] + } + ], "source": [ "# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n", "\n", @@ -181,10 +332,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7861\n", + "\n", + "Could not create share link. Missing file: C:\\adm017\\llm_engineering\\.venv\\Lib\\site-packages\\gradio\\frpc_windows_amd64_v0.3. \n", + "\n", + "Please check your internet connection. This can happen if your antivirus software blocks the download of this file. You can install manually by following these steps: \n", + "\n", + "1. Download this file: https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_windows_amd64.exe\n", + "2. Rename the downloaded file to: frpc_windows_amd64_v0.3\n", + "3. Move the file to this location: C:\\adm017\\llm_engineering\\.venv\\Lib\\site-packages\\gradio\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shout has been called with input hi\n" + ] + } + ], "source": [ "# Adding share=True means that it can be accessed publically\n", "# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n", @@ -195,10 +389,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7862\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shout has been called with input war\n" + ] + } + ], "source": [ "# Adding inbrowser=True opens up a new browser window automatically\n", "\n", @@ -217,10 +448,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "e8129afa-532b-4b15-b93c-aa9cca23a546", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7863\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Define this variable and then pass js=force_dark_mode when creating the Interface\n", "\n", @@ -238,10 +499,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7864\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Inputs and Outputs\n", "\n", @@ -256,10 +547,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "f235288e-63a2-4341-935b-1441f9be969b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7865\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# And now - changing the function from \"shout\" to \"message_gpt\"\n", "\n", @@ -274,10 +595,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "af9a3262-e626-4e4b-80b0-aca152405e63", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7866\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let's use Markdown\n", "# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n", @@ -297,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", "metadata": {}, "outputs": [], @@ -324,10 +675,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "0bb1f789-ff11-4cba-ac67-11b815e29d09", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7867\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "view = gr.Interface(\n", " fn=stream_gpt,\n", @@ -340,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "bbc8e930-ba2a-4194-8f7c-044659150626", "metadata": {}, "outputs": [], @@ -364,10 +745,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "a0066ffd-196e-4eaf-ad1e-d492958b62af", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7868\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "view = gr.Interface(\n", " fn=stream_claude,\n", @@ -403,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5", "metadata": {}, "outputs": [], @@ -420,10 +831,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7869\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "view = gr.Interface(\n", " fn=stream_model,\n", @@ -466,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf", "metadata": {}, "outputs": [], @@ -494,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "c701ec17-ecd5-4000-9f68-34634c8ed49d", "metadata": {}, "outputs": [], @@ -507,7 +948,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "5def90e0-4343-4f58-9d4a-0e36e445efa4", "metadata": {}, "outputs": [], @@ -526,10 +967,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "66399365-5d67-4984-9d47-93ed26c0bd3d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7870\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "view = gr.Interface(\n", " fn=stream_brochure,\n", @@ -554,9 +1025,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python (my-venv-name)", "language": "python", - "name": "python3" + "name": "my-venv-name" }, "language_info": { "codemirror_mode": { @@ -568,7 +1039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/week2/day3.ipynb b/week2/day3.ipynb index c3e13a1..7313c4f 100644 --- a/week2/day3.ipynb +++ b/week2/day3.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861", "metadata": {}, "outputs": [], @@ -25,10 +25,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "231605aa-fccb-447e-89cf-8b187444536a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OpenAI API Key exists and begins sk-proj-\n", + "Anthropic API Key exists and begins sk-ant-\n", + "Google API Key exists and begins AIzaSyBX\n" + ] + } + ], "source": [ "# Load environment variables in a file called .env\n", "# Print the key prefixes to help with any debugging\n", @@ -56,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb", "metadata": {}, "outputs": [], @@ -69,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "e16839b5-c03b-4d9d-add6-87a0f6f37575", "metadata": {}, "outputs": [], @@ -284,9 +294,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python (my-venv-name)", "language": "python", - "name": "python3" + "name": "my-venv-name" }, "language_info": { "codemirror_mode": { @@ -298,7 +308,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.2" } }, "nbformat": 4,