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"source": [
"# Prototyping a Basic Chatbot UI\n",
"* **Created by:** Eric Martinez\n",
"* **For:** Software Engineering 2\n",
"* **At:** University of Texas Rio-Grande Valley"
]
},
{
"cell_type": "markdown",
"id": "e989bbe3",
"metadata": {
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"source": [
"## Tools and Concepts"
]
},
{
"cell_type": "markdown",
"id": "9f764b43",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"#### Jupyter Notebook\n",
"Jupyter is a great tool for writing and interacting with code. It lets you embed code and text to write and document code exploration.\n",
"\n",
"It is a great tool for prototyping and rapid development on top of being a great way to distribute understandable code to others.\n",
"\n",
"In many areas, such as machine learning and data science, it is extremely common for developers to spend most of their time in a notebook environment."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ed3e93b",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"!pip install jupyter-notebook"
]
},
{
"cell_type": "markdown",
"id": "368cda1a",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"#### Gradio\n",
"\n",
"Gradio is an open-source Python library that allows developers to create and prototype user interfaces (UIs) and APIs for machine learning applications and LLMs quickly and easily. Three reasons it is a great tool are:\n",
"\n",
"* Simple and intuitive: Gradio makes it easy to create UIs with minimal code, allowing developers to focus on their models.\n",
"* Versatile: Gradio supports a wide range of input and output types, making it suitable for various machine learning tasks.\n",
"* Shareable: Gradio interfaces can be shared with others, enabling collaboration and easy access to your models."
]
},
{
"cell_type": "markdown",
"id": "e2888a24",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"#### Chatbots\n",
"\n",
"Chatbots are AI-powered conversational agents designed to interact with users through natural language. LLMs have the potential to revolutionize chatbots by providing more human-like, accurate, and context-aware responses, leading to more engaging and useful interactions."
]
},
{
"cell_type": "markdown",
"id": "6f24bac1",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"#### Example: Gradio Interface\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ecdb50a",
"metadata": {
"slideshow": {
"slide_type": "fragment"
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"outputs": [],
"source": [
"!pip -q install --upgrade gradio"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0b28a4e7",
"metadata": {
"slideshow": {
"slide_type": "fragment"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7882\n",
"Running on public URL: https://d807b00726b10425a4.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
]
},
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"text/html": [
""
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""
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"source": [
"import gradio as gr\n",
" \n",
"def do_something_cool(first_name, last_name):\n",
" return f\"{first_name} {last_name} is so cool\"\n",
" \n",
"with gr.Blocks() as app:\n",
" first_name_box = gr.Textbox(label=\"First Name\")\n",
" last_name_box = gr.Textbox(label=\"Last Name\")\n",
" output_box = gr.Textbox(label=\"Output\", interactive=False)\n",
" btn = gr.Button(value =\"Send\")\n",
" btn.click(do_something_cool, inputs = [first_name_box, last_name_box], outputs = [output_box])\n",
" app.launch(share=True)\n"
]
},
{
"cell_type": "markdown",
"id": "c1b1b69e",
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"source": [
"#### Example: Basic Gradio Chatbot Interface / Just the look"
]
},
{
"cell_type": "markdown",
"id": "ea2878bb",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Version with minimal comments"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6f1cca3",
"metadata": {
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"slide_type": "fragment"
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"outputs": [],
"source": [
"import gradio as gr\n",
"\n",
"def chat(message, history):\n",
" history = history or []\n",
" # Set a simple AI reply (replace this with a call to an LLM for a more sophisticated response)\n",
" ai_reply = \"hello\" \n",
" history.append((message, ai_reply))\n",
" return None, history, history\n",
" \n",
"with gr.Blocks() as app:\n",
" with gr.Tab(\"Conversation\"):\n",
" with gr.Row():\n",
" with gr.Column():\n",
" chatbot = gr.Chatbot(label=\"Conversation\")\n",
" message = gr.Textbox(label=\"Message\")\n",
" history_state = gr.State()\n",
" btn = gr.Button(value =\"Send\")\n",
" btn.click(chat, inputs = [message, history_state], outputs = [message, chatbot, history_state])\n",
" app.launch(share=True)\n"
]
},
{
"cell_type": "markdown",
"id": "5cc72efb",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"Version with more comments"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "44debca6",
"metadata": {
"slideshow": {
"slide_type": "fragment"
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},
"outputs": [],
"source": [
"import gradio as gr\n",
"\n",
"# Define the chat function that processes user input and generates an AI response\n",
"def chat(message, history):\n",
" # If history is empty, initialize it as an empty list\n",
" history = history or []\n",
" # Set a simple AI reply (replace this with a call to an LLM for a more sophisticated response)\n",
" ai_reply = \"hello\"\n",
" # Append the user message and AI reply to the conversation history\n",
" history.append((message, ai_reply))\n",
" # Return the updated history and display it in the chatbot interface\n",
" return None, history, history\n",
"\n",
"# Create a Gradio Blocks interface\n",
"with gr.Blocks() as app:\n",
" # Create a tab for the conversation\n",
" with gr.Tab(\"Conversation\"):\n",
" # Create a row for the input components\n",
" with gr.Row():\n",
" # Create a column for the input components\n",
" with gr.Column():\n",
" # Create a chatbot component to display the conversation\n",
" chatbot = gr.Chatbot(label=\"Conversation\")\n",
" # Create a textbox for user input\n",
" message = gr.Textbox(label=\"Message\")\n",
" # Create a state variable to store the conversation history\n",
" history_state = gr.State()\n",
" # Create a button to send the user's message\n",
" btn = gr.Button(value=\"Send\")\n",
" # Connect the button click event to the chat function, passing in the input components and updating the output components\n",
" btn.click(chat, inputs=[message, history_state], outputs=[message, chatbot, history_state])\n",
" # Launch the Gradio interface and make it shareable\n",
" app.launch(share=True)"
]
},
{
"cell_type": "markdown",
"id": "3b660170",
"metadata": {
"slideshow": {
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},
"source": [
"Sweet! That wasn't too bad, not much code to make a cool little UI!"
]
}
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