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{
"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!"
]
},
{
"cell_type": "code",
"execution_count": null,
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1715421-cead-400b-99af-986388a97aff",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr # oh yeah!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv()\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "22586021-1795-4929-8079-63f5bb4edd4c",
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI, Anthropic and Google\n",
"\n",
"openai = OpenAI()\n",
"\n",
"claude = anthropic.Anthropic()\n",
"\n",
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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='gpt-4o-mini',\n",
" messages=messages,\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aef7d314-2b13-436b-b02d-8de3b72b193f",
"metadata": {},
"outputs": [],
"source": [
"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": null,
"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": null,
"id": "083ea451-d3a0-4d13-b599-93ed49b975e4",
"metadata": {},
"outputs": [],
"source": [
"shout(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08f1f15a-122e-4502-b112-6ee2817dda32",
"metadata": {},
"outputs": [],
"source": [
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426",
"metadata": {},
"outputs": [],
"source": [
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0",
"metadata": {},
"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": "f235288e-63a2-4341-935b-1441f9be969b",
"metadata": {},
"outputs": [],
"source": [
"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": [
"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": null,
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f",
"metadata": {},
"outputs": [],
"source": [
"# Let's create a call that streams back results\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='gpt-4o-mini',\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": [
"view = gr.Interface(\n",
" fn=stream_claude,\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": "72d7de50-22ba-4758-92ea-9a4820947488",
"metadata": {},
"source": [
"# Add Gemini Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "026abd83-fb9a-4c8f-9f4d-cc73f9d20779",
"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",
" system_instruction=system_message\n",
" )\n",
"\n",
" response = gemini.generate_content(prompt, stream=True)\n",
" \n",
" result = \"\"\n",
" for chunk in response:\n",
" result += chunk.text\n",
" yield result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf6fc87f-dd11-4668-9faa-19cb4f4865f1",
"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": "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`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5",
"metadata": {},
"outputs": [],
"source": [
"def stream_model(prompt, model):\n",
" print(model) #Shows what model is being used\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": "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\", \"Gemini\"], label=\"Select model\")],\n",
" outputs=[gr.Markdown(label=\"Response:\")],\n",
" flagging_mode=\"never\"\n",
")\n",
"view.launch()"
]
},
{
"cell_type": "markdown",
"id": "76211a29-e1d5-49a9-b176-bb2d50e85155",
"metadata": {},
"source": [
"# Added Gemini Model to the Model Selection"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b1eb9ab-927b-44a7-9565-180bde4453b7",
"metadata": {},
"outputs": [],
"source": [
"def stream_model(prompt, model):\n",
" print(model) #Shows what model is being used\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": "89adb706-8f6d-43c2-b99c-e4786278e7b0",
"metadata": {},
"outputs": [],
"source": [
"view = gr.Interface(\n",
" fn=stream_model,\n",
" inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\"], label=\"Select model\")],\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": "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": [
"system_prompt = \"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": "d0fc580a-dc98-48c3-9dd4-b19cd3be5a18",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3d3bf11-e02c-492b-96f1-f4dd7df6f4d7",
"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": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}