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Merge pull request #69 from serpentile-c137/my-feature-branch

Week 2 Gemini Codes
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Ed Donner 4 months ago committed by GitHub
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  1. 130
      week2/community-contributions/Gemini-api.ipynb
  2. 393
      week2/community-contributions/day2-gemini.ipynb
  3. 310
      week2/community-contributions/day3-gemini.ipynb

130
week2/community-contributions/Gemini-api.ipynb

@ -0,0 +1,130 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"id": "147ce61d-b10e-478e-8300-2fb3101f617c",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import anthropic\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2dab29c1-3a8d-45bc-9f45-407419449ba9",
"metadata": {},
"outputs": [],
"source": [
"import google.generativeai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5fb5b749-d84b-4f8c-bfb9-2f5c4e8a2daa",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv()\n",
"# openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"# anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d34ee171-2647-47cf-9336-2d016480656f",
"metadata": {},
"outputs": [],
"source": [
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "856212a1-d07a-400b-9cef-a198e22f26ac",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are an assistant that is great at telling jokes\"\n",
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\""
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "47289056-cc2b-4d2d-9e18-ecd65c0f3232",
"metadata": {},
"outputs": [],
"source": [
"prompts = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6d331aaf-162b-499e-af7e-5e097e84f1bd",
"metadata": {},
"outputs": [],
"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",
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n",
"\n",
"gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-1.5-flash',\n",
" system_instruction=system_message\n",
")\n",
"response = gemini.generate_content(user_prompt)\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b727ee91-92b8-4d62-9a03-1b85a76b905c",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

393
week2/community-contributions/day2-gemini.ipynb

@ -0,0 +1,393 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1b89f103-fc49-487e-930e-14abff8bfab1",
"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"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1a40e64b-14c6-4589-a671-6817f9cb09f0",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c0990b15-313d-4cf8-bc5b-fc14d263ba27",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv()\n",
"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "66a38e1f-db7e-4697-aa9c-a303f9828531",
"metadata": {},
"outputs": [],
"source": [
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "beb9606e-9be9-4f2e-adfe-4e41fb99566e",
"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": 10,
"id": "19ab23bc-59cf-48a3-8651-f7b1c52874db",
"metadata": {},
"outputs": [],
"source": [
"def message_gemini(prompt):\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
" gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-1.5-flash',\n",
" system_instruction=system_message\n",
")\n",
" response = gemini.generate_content(prompt)\n",
" return response.text\n",
"\n",
"\n",
"# gemini = google.generativeai.GenerativeModel(\n",
"# model_name='gemini-1.5-flash',\n",
"# system_instruction=system_message\n",
"# )\n",
"# response = gemini.generate_content(user_prompt)\n",
"# print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8fe3c66c-d25d-4627-a401-d84c7d6613e7",
"metadata": {},
"outputs": [],
"source": [
"message_gemini(\"What is today's date?\")\n",
"# message_gemini(\"tell me a funny machine learning joke\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b27027ed-4bff-493c-a41e-8318003e0387",
"metadata": {},
"outputs": [],
"source": [
"import google.generativeai as genai\n",
"for model in genai.list_models():\n",
" print(model.name)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2f82d61b-a7cd-4bee-994d-2e83d0a01bfc",
"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": null,
"id": "5941fe3f-aab9-47ba-b29f-d99aa3b40aed",
"metadata": {},
"outputs": [],
"source": [
"shout(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d6470847-1cce-4bf0-8364-199504a5335f",
"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": "69715604-cc64-4563-967f-b5720462ac69",
"metadata": {},
"outputs": [],
"source": [
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", js=force_dark_mode).launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dede1d8c-fb7a-456a-923b-e221eaa30bd9",
"metadata": {},
"outputs": [],
"source": [
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55ae11b9-e7af-449f-b737-48dd7dc1a5b2",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"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": "cba667cf-d270-426e-b940-a01083352ecb",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=message_gemini,\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": "b8bb7885-740f-41f0-95e3-dabe864cea14",
"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_gemini,\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": 29,
"id": "43d17b00-f4bc-45ad-a679-3112a170f5fb",
"metadata": {},
"outputs": [],
"source": [
"import google.generativeai as genai\n",
"\n",
"def stream_gemini(prompt):\n",
" gemini = genai.GenerativeModel(\n",
" model_name='gemini-1.5-flash',\n",
" safety_settings=None,\n",
" system_instruction=system_message\n",
" )\n",
"\n",
" response = gemini.generate_content(prompt, safety_settings=[\n",
" {\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_HATE_SPEECH\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_HARASSMENT\", \"threshold\": \"BLOCK_NONE\"}], stream=True)\n",
" \n",
" result = \"\"\n",
" for chunk in response:\n",
" result += chunk.text\n",
" yield result\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "840f3d11-e66b-4b6b-9b98-70e0f02be9e6",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_gemini,\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": "ea8a0081-8d2e-4960-b479-7c1ef346f524",
"metadata": {},
"source": [
"# Building a company brochure generator\n",
"\n",
"Now you know how - it's simple!"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "2d43360a-515e-4008-9eef-7a3c4e47cfba",
"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": 34,
"id": "08a07e55-b05d-4360-8e05-61dd39cc019b",
"metadata": {},
"outputs": [],
"source": [
"def stream_brochure(company_name, url, model, response_tone):\n",
" prompt = f\"Please generate a {response_tone} 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",
" elif model==\"Gemini\":\n",
" result = stream_gemini(prompt)\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" yield from result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d9554211-c832-4558-90c8-fceab95fd23c",
"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\", \"Gemini\"], label=\"Select model\"),\n",
" gr.Dropdown([\"Informational\", \"Promotional\", \"Humorous\"], label=\"Select tone\")],\n",
" outputs=[gr.Markdown(label=\"Brochure:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d4e6efd-66e8-4388-bfc3-782bde4babfb",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

310
week2/community-contributions/day3-gemini.ipynb

@ -0,0 +1,310 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2",
"metadata": {},
"source": [
"# Day 3 - Conversational AI - aka Chatbot!"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861",
"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",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "231605aa-fccb-447e-89cf-8b187444536a",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv()\n",
"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb",
"metadata": {},
"outputs": [],
"source": [
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba2123e7-77ed-43b4-8c37-03658fb42b78",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are an assistant that is great at telling jokes\"\n",
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"\n",
"\n",
"prompts = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
"\n",
"# 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",
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n",
"\n",
"gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-1.5-flash',\n",
" system_instruction=system_message\n",
")\n",
"response = gemini.generate_content(user_prompt)\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7b933ff3",
"metadata": {},
"outputs": [],
"source": [
"import google.generativeai as genai\n",
"\n",
"model = genai.GenerativeModel('gemini-1.5-flash')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91578b16",
"metadata": {},
"outputs": [],
"source": [
"chat = model.start_chat(history=[])\n",
"response = chat.send_message('Hello! My name is Shardul.')\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c4bc38f",
"metadata": {},
"outputs": [],
"source": [
"response = chat.send_message('Can you tell something interesting about star wars?')\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "337bee91",
"metadata": {},
"outputs": [],
"source": [
"response = chat.send_message('Do you remember what my name is?')\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcaf4d95",
"metadata": {},
"outputs": [],
"source": [
"chat.history"
]
},
{
"cell_type": "markdown",
"id": "98e97227-f162-4d1a-a0b2-345ff248cbe7",
"metadata": {},
"source": [
"# Please read this! A change from the video:\n",
"\n",
"In the video, I explain how we now need to write a function called:\n",
"\n",
"`chat(message, history)`\n",
"\n",
"Which expects to receive `history` in a particular format, which we need to map to the OpenAI format before we call OpenAI:\n",
"\n",
"```\n",
"[\n",
" {\"role\": \"system\", \"content\": \"system message here\"},\n",
" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n",
" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n",
" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n",
"]\n",
"```\n",
"\n",
"But Gradio has been upgraded! Now it will pass in `history` in the exact OpenAI format, perfect for us to send straight to OpenAI.\n",
"\n",
"So our work just got easier!\n",
"\n",
"We will write a function `chat(message, history)` where: \n",
"**message** is the prompt to use \n",
"**history** is the past conversation, in OpenAI format \n",
"\n",
"We will combine the system message, history and latest message, then call OpenAI."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" relevant_system_message = system_message\n",
" if 'belt' in message:\n",
" relevant_system_message += \" The store does not sell belts; if you are asked for belts, be sure to point out other items on sale.\"\n",
" \n",
" messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" stream = gemini.generate_content(message, safety_settings=[\n",
" {\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_HATE_SPEECH\", \"threshold\": \"BLOCK_NONE\"},\n",
" {\"category\": \"HARM_CATEGORY_HARASSMENT\", \"threshold\": \"BLOCK_NONE\"}], stream=True)\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" print(chunk) # Print the chunk to understand its structure\n",
" # Adjust the following line based on the actual structure of the chunk\n",
" response += chunk.get('content', '') or ''\n",
" yield response"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6e745e1",
"metadata": {},
"outputs": [],
"source": [
"chat_model = genai.GenerativeModel('gemini-1.5-flash')\n",
"chat = chat_model.start_chat()\n",
"\n",
"msg = \"what is gen ai\"\n",
"stream = chat.send_message(msg, stream=True)\n",
"# print(\"Response:\", stream.text)\n",
"for chunk in stream:\n",
" print(chunk.text)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dce941ee",
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"\n",
"chat = model.start_chat(history=[])\n",
"\n",
"# Transform Gradio history to Gemini format\n",
"def transform_history(history):\n",
" new_history = []\n",
" for chat in history:\n",
" new_history.append({\"parts\": [{\"text\": chat[0]}], \"role\": \"user\"})\n",
" new_history.append({\"parts\": [{\"text\": chat[1]}], \"role\": \"model\"})\n",
" return new_history\n",
"\n",
"def response(message, history):\n",
" global chat\n",
" # The history will be the same as in Gradio, the 'Undo' and 'Clear' buttons will work correctly.\n",
" chat.history = transform_history(history)\n",
" response = chat.send_message(message)\n",
" response.resolve()\n",
"\n",
" # Each character of the answer is displayed\n",
" for i in range(len(response.text)):\n",
" time.sleep(0.01)\n",
" yield response.text[: i+1]\n",
"\n",
"gr.ChatInterface(response,\n",
" textbox=gr.Textbox(placeholder=\"Question to Gemini\")).launch(debug=True)"
]
},
{
"cell_type": "markdown",
"id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business Applications</h2>\n",
" <span style=\"color:#181;\">Conversational Assistants are of course a hugely common use case for Gen AI, and the latest frontier models are remarkably good at nuanced conversation. And Gradio makes it easy to have a user interface. Another crucial skill we covered is how to use prompting to provide context, information and examples.\n",
"<br/><br/>\n",
"Consider how you could apply an AI Assistant to your business, and make yourself a prototype. Use the system prompt to give context on your business, and set the tone for the LLM.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d",
"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
}
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