{ "cells": [ { "cell_type": "markdown", "id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2", "metadata": {}, "source": [ "# Day 3 - Conversational AI - aka Chatbot!" ] }, { "cell_type": "code", "execution_count": 1, "id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import ollama\n", "import gradio as gr" ] }, { "cell_type": "code", "execution_count": 2, "id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb", "metadata": {}, "outputs": [], "source": [ "# Initialize\n", "MODEL_LLAMA = 'llama3.2'" ] }, { "cell_type": "code", "execution_count": 3, "id": "e16839b5-c03b-4d9d-add6-87a0f6f37575", "metadata": {}, "outputs": [], "source": [ "system_message = \"You are a helpful assistant\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1", "metadata": {}, "outputs": [], "source": [ "\n", "\n", "def chat(message, history):\n", " messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", "\n", " print(\"History is:\")\n", " print(history)\n", " print(\"And messages is:\")\n", " print(messages)\n", "\n", " stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", "\n", " response_text = \"\"\n", " for chunk in stream:\n", " response_text += chunk['message']['content']\n", " yield response_text" ] }, { "cell_type": "markdown", "id": "1334422a-808f-4147-9c4c-57d63d9780d0", "metadata": {}, "source": [ "## And then enter Gradio's magic!" ] }, { "cell_type": "code", "execution_count": 7, "id": "0866ca56-100a-44ab-8bd0-1568feaf6bf2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7861\n", "* Running on public URL: https://6539f61952f430fa2d.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "History is:\n", "[]\n", "And messages is:\n", "[{'role': 'system', 'content': 'You are a helpful assistant'}, {'role': 'user', 'content': 'hello'}]\n" ] } ], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" ] }, { "cell_type": "code", "execution_count": 8, "id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "system_message = \"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n", "the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n", "For example, if the customer says 'I'm looking to buy a hat', \\\n", "you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n", "Encourage the customer to buy hats if they are unsure what to get.\"" ] }, { "cell_type": "code", "execution_count": 9, "id": "4e5be3ec-c26c-42bc-ac16-c39d369883f6", "metadata": {}, "outputs": [], "source": [ "def chat(message, history):\n", " messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", "\n", "\n", " stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", "\n", " response_text = \"\"\n", " for chunk in stream:\n", " response_text += chunk['message']['content']\n", " yield response_text" ] }, { "cell_type": "code", "execution_count": 10, "id": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7862\n", "* Running on public URL: https://79f09af36adcf63688.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" ] }, { "cell_type": "code", "execution_count": 11, "id": "d75f0ffa-55c8-4152-b451-945021676837", "metadata": {}, "outputs": [], "source": [ "system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n", "but remind the customer to look at hats!\"" ] }, { "cell_type": "code", "execution_count": 12, "id": "c602a8dd-2df7-4eb7-b539-4e01865a6351", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7863\n", "* Running on public URL: https://30446ba4b8f125e235.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" ] }, { "cell_type": "code", "execution_count": 13, "id": "5b128796-1bea-445d-9e3b-8321ca822257", "metadata": {}, "outputs": [], "source": [ "def chat(message, history):\n", "\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", "\n", " stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", "\n", " response_text = \"\"\n", " for chunk in stream:\n", " response_text += chunk['message']['content']\n", " yield response_text" ] }, { "cell_type": "code", "execution_count": 15, "id": "20570de2-eaad-42cc-a92c-c779d71b48b6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7865\n", "* Running on public URL: https://3933c80bf256709cf9.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" ] }, { "cell_type": "markdown", "id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Business Applications

\n", " 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", "

\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.
\n", "
" ] }, { "cell_type": "code", "execution_count": null, "id": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d", "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": "3.13.2" } }, "nbformat": 4, "nbformat_minor": 5 }