From fa0f64b19457fa063e35f72f675a9372bb5667b0 Mon Sep 17 00:00:00 2001 From: Bill Glennon <1130086+billglennon@users.noreply.github.com> Date: Sun, 3 Nov 2024 10:51:56 -0500 Subject: [PATCH 1/3] add Google Gemini to the LLM Model Selections --- .../Week2_Day2_with_GeminModel.ipynb | 570 ++++++++++++++++++ 1 file changed, 570 insertions(+) create mode 100644 week2/community-contributions/Week2_Day2_with_GeminModel.ipynb diff --git a/week2/community-contributions/Week2_Day2_with_GeminModel.ipynb b/week2/community-contributions/Week2_Day2_with_GeminModel.ipynb new file mode 100644 index 0000000..891b71c --- /dev/null +++ b/week2/community-contributions/Week2_Day2_with_GeminModel.ipynb @@ -0,0 +1,570 @@ +{ + "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": "66533a93-50d9-4fad-a0d8-888d5740d209", + "metadata": {}, + "source": [ + "# Added Gemini Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ef86cf1-a0b4-4dee-bd4e-4c8c166c464f", + "metadata": {}, + "outputs": [], + "source": [ + "def stream_gemini(prompt):\n", + " gemini = google.generativeai.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": "8d07fa63-608a-42d3-9a26-1c0a28d9625d", + "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\"], label=\"Select model\", value=\"GPT\")],\n", + " outputs=[gr.Markdown(label=\"Response:\")],\n", + " flagging_mode=\"never\"\n", + ")\n", + "view.launch()" + ] + }, + { + "cell_type": "markdown", + "id": "92f82f60-bb06-4bde-a63f-7ed506f42285", + "metadata": {}, + "source": [ + "# Added Gemini Model to the Model Selection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de78191d-8ddf-4b9a-b9e1-f55de9e4f3d5", + "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": "ed8aaf37-c786-470d-aca3-d60c8e763d92", + "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\", 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": "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 +} From 8e2a11552cf4cee0d5735f0f77739555775e8ef6 Mon Sep 17 00:00:00 2001 From: Bill Glennon <1130086+billglennon@users.noreply.github.com> Date: Sun, 3 Nov 2024 12:03:04 -0500 Subject: [PATCH 2/3] removed Week2_Day2_with_GeminModel.ipynb --- .../Week2_Day2_with_GeminModel.ipynb | 570 ------------------ 1 file changed, 570 deletions(-) delete mode 100644 week2/community-contributions/Week2_Day2_with_GeminModel.ipynb diff --git a/week2/community-contributions/Week2_Day2_with_GeminModel.ipynb b/week2/community-contributions/Week2_Day2_with_GeminModel.ipynb deleted file mode 100644 index 891b71c..0000000 --- a/week2/community-contributions/Week2_Day2_with_GeminModel.ipynb +++ /dev/null @@ -1,570 +0,0 @@ -{ - "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": "66533a93-50d9-4fad-a0d8-888d5740d209", - "metadata": {}, - "source": [ - "# Added Gemini Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ef86cf1-a0b4-4dee-bd4e-4c8c166c464f", - "metadata": {}, - "outputs": [], - "source": [ - "def stream_gemini(prompt):\n", - " gemini = google.generativeai.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": "8d07fa63-608a-42d3-9a26-1c0a28d9625d", - "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\"], label=\"Select model\", value=\"GPT\")],\n", - " outputs=[gr.Markdown(label=\"Response:\")],\n", - " flagging_mode=\"never\"\n", - ")\n", - "view.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "92f82f60-bb06-4bde-a63f-7ed506f42285", - "metadata": {}, - "source": [ - "# Added Gemini Model to the Model Selection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de78191d-8ddf-4b9a-b9e1-f55de9e4f3d5", - "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": "ed8aaf37-c786-470d-aca3-d60c8e763d92", - "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\", 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": "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 -} From 8353ddd1623c3bae38b53f3047cd0329d614f082 Mon Sep 17 00:00:00 2001 From: Bill Glennon <1130086+billglennon@users.noreply.github.com> Date: Sun, 3 Nov 2024 12:03:50 -0500 Subject: [PATCH 3/3] Add Gemin Model using latest Gradio and Google API packages as of 11/3/2024 --- .../Week2_Day2_AddGeminModel.ipynb | 572 ++++++++++++++++++ 1 file changed, 572 insertions(+) create mode 100644 week2/community-contributions/Week2_Day2_AddGeminModel.ipynb diff --git a/week2/community-contributions/Week2_Day2_AddGeminModel.ipynb b/week2/community-contributions/Week2_Day2_AddGeminModel.ipynb new file mode 100644 index 0000000..3011f7b --- /dev/null +++ b/week2/community-contributions/Week2_Day2_AddGeminModel.ipynb @@ -0,0 +1,572 @@ +{ + "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 +}