How to Create a Chatbot

Tags: NLP, TEXT, CHAT Related spaces: https://huggingface.co/spaces/gradio/chatbot_streaming, https://huggingface.co/spaces/project-baize/Baize-7B,

Introduction

Chatbots are widely used in natural language processing (NLP) research and industry. Because chatbots are designed to be used directly by customers and end users, it is important to validate that chatbots are behaving as expected when confronted with a wide variety of input prompts.

Using gradio, you can easily build a demo of your chatbot model and share that with your users, or try it yourself using an intuitive chatbot GUI.

This tutorial will show how to make several kinds of chatbot UIs with Gradio: first a simple one to display text, second one to stream text responses, and finally a chatbot that can handle media files as well. The chatbot interface that we create will look something like this:

Prerequisite: We’ll be using the gradio.Blocks class to build our Chatbot demo. You can read the Guide to Blocks first if you are not already familiar with it. Also please make sure you are using the latest version version of Gradio: pip install --upgrade gradio.

A Simple Chatbot Demo

Let’s start with recreating the simple demo above. As you may have noticed, our bot simply randomly responds “How are you?”, “I love you”, or “I’m very hungry” to any input. Here’s the code to create this with Gradio:

import gradio as gr
import random
import time

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.ClearButton([msg, chatbot])

    def respond(message, chat_history):
        bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
        chat_history.append((message, bot_message))
        time.sleep(2)
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])

demo.launch()

There are three Gradio components here:

A Chatbot, whose value stores the entire history of the conversation, as a list of response pairs between the user and bot.
A Textbox where the user can type their message, and then hit enter/submit to trigger the chatbot response
A ClearButton button to clear the Textbox and entire Chatbot history

We have a single function, respond(), which takes in the entire history of the chatbot, appends a random message, waits 1 second, and then returns the updated chat history. The respond() function also clears the textbox when it returns.

Of course, in practice, you would replace respond() with your own more complex function, which might call a pretrained model or an API, to generate a response.

Add Streaming to your Chatbot

There are several ways we can improve the user experience of the chatbot above. First, we can stream responses so the user doesn’t have to wait as long for a message to be generated. Second, we can have the user message appear immediately in the chat history, while the chatbot’s response is being generated. Here’s the code to achieve that:

import gradio as gr
import random
import time

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")

    def user(user_message, history):
        return "", history + [[user_message, None]]

    def bot(history):
        bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
        history[-1][1] = ""
        for character in bot_message:
            history[-1][1] += character
            time.sleep(0.05)
            yield history

    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, chatbot, chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)
    
demo.queue()
demo.launch()

You’ll notice that when a user submits their message, we now chain three event events with .then():

The first method user() updates the chatbot with the user message and clears the input field. This method also makes the input field non interactive so that the user can’t send another message while the chatbot is responding. Because we want this to happen instantly, we set queue=False, which would skip any queue had it been enabled. The chatbot’s history is appended with (user_message, None), the None signifying that the bot has not responded.
The second method, bot() updates the chatbot history with the bot’s response. Instead of creating a new message, we just replace the previously-created None message with the bot’s response. Finally, we construct the message character by character and yield the intermediate outputs as they are being constructed. Gradio automatically turns any function with the yield keyword into a streaming output interface.
The third method makes the input field interactive again so that users can send another message to the bot.

Of course, in practice, you would replace bot() with your own more complex function, which might call a pretrained model or an API, to generate a response.

Finally, we enable queuing by running demo.queue(), which is required for streaming intermediate outputs. You can try the improved chatbot by scrolling to the demo at the top of this page.

Adding Markdown, Images, Audio, or Videos

The gr.Chatbot component supports a subset of markdown including bold, italics, and code. For example, we could write a function that responds to a user’s message, with a bold That’s cool!, like this:

def bot(history):
    response = "**That's cool!**"
    history[-1][1] = response
    return history

In addition, it can handle media files, such as images, audio, and video. To pass in a media file, we must pass in the file as a tuple of two strings, like this: (filepath, alt_text). The alt_text is optional, so you can also just pass in a tuple with a single element (filepath,), like this:

def add_file(history, file):
    history = history + [((file.name,), None)]
    return history

Putting this together, we can create a multimodal chatbot with a textbox for a user to submit text and an file upload button to submit images / audio / video files. The rest of the code looks pretty much the same as before:

import gradio as gr
import random
import time

# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.

def add_text(history, text):
    history = history + [(text, None)]
    return history, gr.update(value="", interactive=False)


def add_file(history, file):
    history = history + [((file.name,), None)]
    return history


def bot(history):
    response = "**That's cool!**"
    history[-1][1] = ""
    for character in response:
        history[-1][1] += character
        time.sleep(0.05)
        yield history


with gr.Blocks() as demo:
    chatbot = gr.Chatbot([], elem_id="chatbot").style(height=750)

    with gr.Row():
        with gr.Column(scale=0.85):
            txt = gr.Textbox(
                show_label=False,
                placeholder="Enter text and press enter, or upload an image",
            ).style(container=False)
        with gr.Column(scale=0.15, min_width=0):
            btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])

    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
        bot, chatbot, chatbot
    )
    txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False)
    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(
        bot, chatbot, chatbot
    )

demo.queue()
demo.launch()

And you’re done! That’s all the code you need to build an interface for your chatbot model. Finally, we’ll end our Guide with some links to Chatbots that are running on Spaces so that you can get an idea of what else is possible:

project-baize/Baize-7B: A stylized chatbot that allows you to stop generation as well as regenerate responses.
MAGAer13/mPLUG-Owl: A multimodal chatbot that allows you to upvote and downvote responses.