diff --git a/week2/community-contributions/Week2_day2_openRouter_gradio_brochure.ipynb b/week2/community-contributions/Week2_day2_openRouter_gradio_brochure.ipynb new file mode 100644 index 0000000..3867bbb --- /dev/null +++ b/week2/community-contributions/Week2_day2_openRouter_gradio_brochure.ipynb @@ -0,0 +1,295 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a98030af-fcd1-4d63-a36e-38ba053498fa", + "metadata": {}, + "source": [ + "# Week 2 Practice Gradio by Creating Brochure\n", + "\n", + "- **Author**: [stoneskin](https://www.github.com/stoneskin)" + ] + }, + { + "cell_type": "markdown", + "id": "1c104f45", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "- Use OpenRouter.ai for all different types of LLM models, include many free models from Google,Meta and Deepseek\n", + "\n", + "Full code for the Week2 Gradio practice is below:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API key looks good so far\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "import requests\n", + "from bs4 import BeautifulSoup\n", + "from typing import List\n", + "from dotenv import load_dotenv\n", + "from openai import OpenAI\n", + "import gradio as gr \n", + "\n", + "load_dotenv(override=True)\n", + "\n", + "api_key = os.getenv('Open_Router_Key')\n", + "if api_key and api_key.startswith('sk-or-v1') 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", + " \n", + "openai = OpenAI(\n", + " api_key=api_key,\n", + " base_url=\"https://openrouter.ai/api/v1\"\n", + ")\n", + "\n", + "MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n", + "MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n", + "MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n", + "MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n", + "MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n", + "MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n", + "MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n", + "MODEL_OpenAi_o3mini = 'openai/o3-mini'\n", + "MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n", + "MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n", + "\n", + "\n", + "\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24866034", + "metadata": {}, + "outputs": [], + "source": [ + "MODEL=MODEL_Gemini2Pro # choice the model you want to use\n", + "\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", + "\n", + "##############################\n", + "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 JSON as in this example:\"\n", + "link_system_prompt += \"\"\"\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", + "\n", + "##############################\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", + "##############################\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\"\n", + " \n", + "##############################\n", + "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\n", + "\n", + "##############################\n", + "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", + " print(\"get_links:\", result)\n", + " return json.loads(result)\n", + "\n", + "##############################\n", + "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\n", + "\n", + "##############################\n", + "def get_all_details(url):\n", + " print(\"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": null, + "id": "82abe132", + "metadata": {}, + "outputs": [], + "source": [ + "########### modified stream brochure function for gradio ###################\n", + "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", + "\n", + " result = \"\"\n", + " for chunk in stream:\n", + " result += chunk.choices[0].delta.content or \"\"\n", + " yield result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "902f203b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7872\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" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "get_all_details: https://mlccc.herokuapp.com/\n", + "get_links: {\n", + " \"links\": [\n", + " {\"type\": \"about page\", \"url\": \"https://mlccc.herokuapp.com/about.html\"},\n", + " {\"type\": \"programs\", \"url\": \"https://mlccc.herokuapp.com/program.html\"},\n", + " {\"type\": \"camps\", \"url\": \"https://mlccc.herokuapp.com/camps.html\"},\n", + " {\"type\": \"community\", \"url\": \"https://mlccc.herokuapp.com/community.html\"},\n", + " {\"type\": \"support\", \"url\": \"https://mlccc.herokuapp.com/support.html\"},\n", + " {\"type\": \"press\", \"url\": \"https://mlccc.herokuapp.com/press.html\"},\n", + " {\"type\": \"newsletter\", \"url\": \"https://mlccc.herokuapp.com/newsletter.html\"},\n", + " {\"type\": \"testimonials\", \"url\": \"https://mlccc.herokuapp.com/testimonial.html\"}\n", + " ]\n", + "}\n", + "Found links: {'links': [{'type': 'about page', 'url': 'https://mlccc.herokuapp.com/about.html'}, {'type': 'programs', 'url': 'https://mlccc.herokuapp.com/program.html'}, {'type': 'camps', 'url': 'https://mlccc.herokuapp.com/camps.html'}, {'type': 'community', 'url': 'https://mlccc.herokuapp.com/community.html'}, {'type': 'support', 'url': 'https://mlccc.herokuapp.com/support.html'}, {'type': 'press', 'url': 'https://mlccc.herokuapp.com/press.html'}, {'type': 'newsletter', 'url': 'https://mlccc.herokuapp.com/newsletter.html'}, {'type': 'testimonials', 'url': 'https://mlccc.herokuapp.com/testimonial.html'}]}\n" + ] + } + ], + "source": [ + "view = gr.Interface(\n", + " fn=stream_brochure,\n", + " inputs=[gr.Textbox(label=\"company Name\"), gr.Textbox(label=\"URL\")],\n", + " outputs=[gr.Markdown(label=\"Response:\")],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llms", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/week2/community-contributions/day2-openrouterAi.ipynb b/week2/community-contributions/day2-openrouterAi.ipynb new file mode 100644 index 0000000..e05517b --- /dev/null +++ b/week2/community-contributions/day2-openrouterAi.ipynb @@ -0,0 +1,614 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827", + "metadata": {}, + "source": [ + "# Gradio Day!\n", + "\n", + "Today we will build User Interfaces using the outrageously simple Gradio framework.\n", + "\n", + "Prepare for joy!\n", + "\n", + "Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "\n", + "import os\n", + "import requests\n", + "from bs4 import BeautifulSoup\n", + "from typing import List\n", + "from dotenv import load_dotenv\n", + "from openai import OpenAI\n", + "#import google.generativeai\n", + "#import anthropic\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d1715421-cead-400b-99af-986388a97aff", + "metadata": {}, + "outputs": [], + "source": [ + "import gradio as gr # oh yeah!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "22586021-1795-4929-8079-63f5bb4edd4c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API key looks good so far\n" + ] + } + ], + "source": [ + "# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n", + "\n", + "# openai = OpenAI()\n", + "\n", + "# claude = anthropic.Anthropic()\n", + "\n", + "# google.generativeai.configure()\n", + "\n", + "load_dotenv(override=True)\n", + "\n", + "api_key = os.getenv('Open_Router_Key')\n", + "if api_key and api_key.startswith('sk-or-v1') 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", + " \n", + "openai = OpenAI(\n", + " api_key=api_key,\n", + " base_url=\"https://openrouter.ai/api/v1\"\n", + ")\n", + "\n", + "MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n", + "MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n", + "MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n", + "MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n", + "MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n", + "MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n", + "MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n", + "MODEL_OpenAi_o3mini = 'openai/o3-mini'\n", + "MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n", + "MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n", + "\n", + "\n", + "Default_Model = MODEL_Deepseek_V3\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", + "metadata": {}, + "outputs": [], + "source": [ + "# A generic system message - no more snarky adversarial AIs!\n", + "\n", + "system_message = \"You are a helpful assistant\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's wrap a call to GPT-4o-mini in a simple function\n", + "\n", + "def message_gpt(prompt):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": prompt}\n", + " ]\n", + " completion = openai.chat.completions.create(\n", + " model=Default_Model,\n", + " messages=messages,\n", + " )\n", + " return completion.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "aef7d314-2b13-436b-b02d-8de3b72b193f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Today is October 26, 2023.\\n'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This can reveal the \"training cut off\", or the most recent date in the training data\n", + "\n", + "message_gpt(\"What is today's date?\")" + ] + }, + { + "cell_type": "markdown", + "id": "f94013d1-4f27-4329-97e8-8c58db93636a", + "metadata": {}, + "source": [ + "## User Interface time!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a", + "metadata": {}, + "outputs": [], + "source": [ + "# here's a simple function\n", + "\n", + "def shout(text):\n", + " print(f\"Shout has been called with input {text}\")\n", + " return text.upper()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "083ea451-d3a0-4d13-b599-93ed49b975e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shout has been called with input hello\n" + ] + }, + { + "data": { + "text/plain": [ + "'HELLO'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shout(\"hello\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08f1f15a-122e-4502-b112-6ee2817dda32", + "metadata": {}, + "outputs": [], + "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", + "gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426", + "metadata": {}, + "outputs": [], + "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", + "# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n", + "\n", + "gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3", + "metadata": {}, + "outputs": [], + "source": [ + "# Adding inbrowser=True opens up a new browser window automatically\n", + "\n", + "gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b42ec007-0314-48bf-84a4-a65943649215", + "metadata": {}, + "source": [ + "## Forcing dark mode\n", + "\n", + "Gradio appears in light mode or dark mode depending on the settings of the browser and computer. There is a way to force gradio to appear in dark mode, but Gradio recommends against this as it should be a user preference (particularly for accessibility reasons). But if you wish to force dark mode for your screens, below is how to do it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8129afa-532b-4b15-b93c-aa9cca23a546", + "metadata": {}, + "outputs": [], + "source": [ + "# Define this variable and then pass js=force_dark_mode when creating the Interface\n", + "\n", + "force_dark_mode = \"\"\"\n", + "function refresh() {\n", + " const url = new URL(window.location);\n", + " if (url.searchParams.get('__theme') !== 'dark') {\n", + " url.searchParams.set('__theme', 'dark');\n", + " window.location.href = url.href;\n", + " }\n", + "}\n", + "\"\"\"\n", + "gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0", + "metadata": {}, + "outputs": [], + "source": [ + "# Inputs and Outputs\n", + "\n", + "view = gr.Interface(\n", + " fn=shout,\n", + " inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", + " outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f235288e-63a2-4341-935b-1441f9be969b", + "metadata": {}, + "outputs": [], + "source": [ + "# And now - changing the function from \"shout\" to \"message_gpt\"\n", + "\n", + "view = gr.Interface(\n", + " fn=message_gpt,\n", + " inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", + " outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af9a3262-e626-4e4b-80b0-aca152405e63", + "metadata": {}, + "outputs": [], + "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", + "# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n", + "# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n", + "\n", + "system_message = \"You are a helpful assistant that responds in markdown\"\n", + "\n", + "view = gr.Interface(\n", + " fn=message_gpt,\n", + " inputs=[gr.Textbox(label=\"Your message:\")],\n", + " outputs=[gr.Markdown(label=\"Response:\")],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's create a call that streams back results\n", + "# If you'd like a refresher on Generators (the \"yield\" keyword),\n", + "# Please take a look at the Intermediate Python notebook in week1 folder.\n", + "\n", + "def stream_gpt(prompt):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": prompt}\n", + " ]\n", + " stream = openai.chat.completions.create(\n", + " model=Default_Model,\n", + " messages=messages,\n", + " stream=True\n", + " )\n", + " result = \"\"\n", + " for chunk in stream:\n", + " result += chunk.choices[0].delta.content or \"\"\n", + " yield result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bb1f789-ff11-4cba-ac67-11b815e29d09", + "metadata": {}, + "outputs": [], + "source": [ + "view = gr.Interface(\n", + " fn=stream_gpt,\n", + " inputs=[gr.Textbox(label=\"Your message:\")],\n", + " outputs=[gr.Markdown(label=\"Response:\")],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbc8e930-ba2a-4194-8f7c-044659150626", + "metadata": {}, + "outputs": [], + "source": [ + "# def stream_claude(prompt):\n", + "# result = claude.messages.stream(\n", + "# model=\"claude-3-haiku-20240307\",\n", + "# max_tokens=1000,\n", + "# temperature=0.7,\n", + "# system=system_message,\n", + "# messages=[\n", + "# {\"role\": \"user\", \"content\": prompt},\n", + "# ],\n", + "# )\n", + "# response = \"\"\n", + "# with result as stream:\n", + "# for text in stream.text_stream:\n", + "# response += text or \"\"\n", + "# yield response" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0066ffd-196e-4eaf-ad1e-d492958b62af", + "metadata": {}, + "outputs": [], + "source": [ + "Default_Model=MODEL_Claude_Haiku\n", + "view = gr.Interface(\n", + " fn=stream_gpt,\n", + " inputs=[gr.Textbox(label=\"Your message:\")],\n", + " outputs=[gr.Markdown(label=\"Response:\")],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + }, + { + "cell_type": "markdown", + "id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b", + "metadata": {}, + "source": [ + "## Minor improvement\n", + "\n", + "I've made a small improvement to this code.\n", + "\n", + "Previously, it had these lines:\n", + "\n", + "```\n", + "for chunk in result:\n", + " yield chunk\n", + "```\n", + "\n", + "There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n", + "\n", + "`yield from result`\n", + "\n", + "I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5", + "metadata": {}, + "outputs": [], + "source": [ + "def stream_model(prompt, model):\n", + " if model==\"GPT\":\n", + " Default_Model=MODEL_Gemini2FlashThink\n", + " result = stream_gpt(prompt)\n", + " elif model==\"Claude\":\n", + " Default_Model=MODEL_Claude_Haiku\n", + " result = stream_gpt(prompt)\n", + " else:\n", + " raise ValueError(\"Unknown model\")\n", + " yield from result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855", + "metadata": {}, + "outputs": [], + "source": [ + "view = gr.Interface(\n", + " fn=stream_model,\n", + " inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")],\n", + " outputs=[gr.Markdown(label=\"Response:\")],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + }, + { + "cell_type": "markdown", + "id": "d933865b-654c-4b92-aa45-cf389f1eda3d", + "metadata": {}, + "source": [ + "# Building a company brochure generator\n", + "\n", + "Now you know how - it's simple!" + ] + }, + { + "cell_type": "markdown", + "id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Before you read the next few cells

\n", + " \n", + " Try to do this yourself - go back to the company brochure in week1, day5 and add a Gradio UI to the end. Then come and look at the solution.\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf", + "metadata": {}, + "outputs": [], + "source": [ + "# A class to represent a Webpage\n", + "\n", + "class Website:\n", + " url: str\n", + " title: str\n", + " text: str\n", + "\n", + " def __init__(self, url):\n", + " self.url = url\n", + " response = requests.get(url)\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", + " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", + " irrelevant.decompose()\n", + " self.text = soup.body.get_text(separator=\"\\n\", strip=True)\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": null, + "id": "c701ec17-ecd5-4000-9f68-34634c8ed49d", + "metadata": {}, + "outputs": [], + "source": [ + "# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n", + "\n", + "system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n", + "and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5def90e0-4343-4f58-9d4a-0e36e445efa4", + "metadata": {}, + "outputs": [], + "source": [ + "def stream_brochure(company_name, url, model):\n", + " prompt = f\"Please generate a company brochure for {company_name}. Here is their landing page:\\n\"\n", + " prompt += Website(url).get_contents()\n", + " if model==\"GPT\":\n", + " result = stream_gpt(prompt)\n", + " elif model==\"Claude\":\n", + " result = stream_claude(prompt)\n", + " else:\n", + " raise ValueError(\"Unknown model\")\n", + " yield from result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66399365-5d67-4984-9d47-93ed26c0bd3d", + "metadata": {}, + "outputs": [], + "source": [ + "view = gr.Interface(\n", + " fn=stream_brochure,\n", + " inputs=[\n", + " gr.Textbox(label=\"Company name:\"),\n", + " gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n", + " gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\")],\n", + " outputs=[gr.Markdown(label=\"Brochure:\")],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ede97ca3-a0f8-4f6e-be17-d1de7fef9cc0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llms", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}