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How to Create a Chatbot with Gradio

Tags: NLP, TEXT, CHAT

Introduction

Chatbots are a popular application of large language models. 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 UI.

This tutorial uses gr.ChatInterface(), which is a high-level abstraction that allows you to create your chatbot UI fast, often with a single line of code. The chatbot interface that we create will look something like this:

We’ll start with a couple of simple examples, and then show how to use gr.ChatInterface() with real language models from several popular APIs and libraries, including langchain, openai, and Hugging Face.

Prerequisites: please make sure you are using the latest version version of Gradio: 

$ pip install --upgrade gradio

Defining a chat function

When working with gr.ChatInterface(), the first thing you should do is define your chat function. Your chat function should take two arguments: message and then history (the arguments can be named anything, but must be in this order).

  • message: a str representing the user’s input.
  • history: a list of list representing the conversations up until that point. Each inner list consists of two str representing a pair: [user input, bot response].

Your function should return a single string response, which is the bot’s response to the particular user input message. Your function can take into account the history of messages, as well as the current message.

Let’s take a look at a few examples.

Example: a chatbot that responds yes or no

Let’s write a chat function that responds Yes or No randomly.

Here’s our chat function:

import random

def random_response(message, history):
    return random.choice(["Yes", "No"])

Now, we can plug this into gr.ChatInterface() and call the .launch() method to create the web interface:

import gradio as gr

gr.ChatInterface(random_response).launch()

That’s it! Here’s our running demo, try it out:

Another example using the user’s input and history

Of course, the previous example was very simplistic, it didn’t even take user input or the previous history into account! Here’s another simple example showing how to incorporate a user’s input as well as the history.

import random
import gradio as gr

def alternatingly_agree(message, history):
    if len(history) % 2 == 0:
        return f"Yes, I do think that '{message}'"
    else:
        return "I don't think so"

gr.ChatInterface(alternatingly_agree).launch()

Streaming chatbots

If in your chat function, you use yield to generate a sequence of responses, you’ll end up with a streaming chatbot. It’s that simple!

import time
import gradio as gr

def slow_echo(message, history):
    for i in range(len(message)):
        time.sleep(0.3)
        yield "You typed: " + message[: i+1]

gr.ChatInterface(slow_echo).queue().launch()

Notice that we’ve enabled queuing, which is required to use generator functions. While the response is streaming, the “Submit” button turns into a “Stop” button that can be used to stop the generator function. You can customize the appearance and behavior of the “Stop” button using the stop_btn parameter.

Customizing your chatbot

If you’re familiar with Gradio’s Interface class, the gr.ChatInterface includes many of the same arguments that you can use to customize the look and feel of your Chatbot. For example, you can:

  • add a title and description above your chatbot using title and description arguments.
  • add a theme or custom css using theme and css arguments respectively.
  • add examples and even enable cache_examples, which make it easier for users to try it out .
  • You can change the text or disable each of the buttons that appear in the chatbot interface: submit_btn, retry_btn, undo_btn, clear_btn.

If you want to customize the gr.Chatbot or gr.Textbox that compose the ChatInterface, then you can pass in your own chatbot or textbox as well. Here’s an example of how we can use these parameters:

import gradio as gr

def yes_man(message, history):
    if message.endswith("?"):
        return "Yes"
    else:
        return "Ask me anything!"

gr.ChatInterface(
    yes_man,
    chatbot=gr.Chatbot(height=300),
    textbox=gr.Textbox(placeholder="Ask me a yes or no question", container=False, scale=7),
    title="Yes Man",
    description="Ask Yes Man any question",
    theme="soft",
    examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
    cache_examples=True,
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear",
).launch()

Additional Inputs

You may want to add additional parameters to your chatbot and expose them to your users through the Chatbot UI. For example, suppose you want to add a textbox for a system prompt, or a slider that sets the number of tokens in the chatbot’s response. The ChatInterface class supports an additional_inputs parameter which can be used to add additional input components.

The additional_inputs parameters accepts a component or a list of components. You can pass the component instances directly, or use their string shortcuts (e.g. "textbox" instead of gr.Textbox()). If you pass in component instances, and they have not already been rendered, then the components will appear underneath the chatbot (and any examples) within a gr.Accordion(). You can set the label of this accordion using the additional_inputs_accordion_name parameter.

Here’s a complete example:

import gradio as gr
import time

def echo(message, history, system_prompt, tokens):
    response = f"System prompt: {system_prompt}\n Message: {message}."
    for i in range(min(len(response), int(tokens))):
        time.sleep(0.05)
        yield response[: i+1]

demo = gr.ChatInterface(echo, 
                        additional_inputs=[
                            gr.Textbox("You are helpful AI.", label="System Prompt"), 
                            gr.Slider(10, 100)
                        ]
                       )

if __name__ == "__main__":
    demo.queue().launch()

If the components you pass into the additional_inputs have already been rendered in a parent gr.Blocks(), then they will not be re-rendered in the accordion. This provides flexibility in deciding where to lay out the input components. In the example below, we position the gr.Textbox() on top of the Chatbot UI, while keeping the slider underneath.

import gradio as gr
import time

def echo(message, history, system_prompt, tokens):
    response = f"System prompt: {system_prompt}\n Message: {message}."
    for i in range(min(len(response), int(tokens))):
        time.sleep(0.05)
        yield response[: i+1]

with gr.Blocks() as demo:
    system_prompt = gr.Textbox("You are helpful AI.", label="System Prompt")
    slider = gr.Slider(10, 100, render=False)
    
    gr.ChatInterface(
        echo, additional_inputs=[system_prompt, slider]
    )

demo.queue().launch()

If you need to create something even more custom, then its best to construct the chatbot UI using the low-level gr.Blocks() API. We have a dedicated guide for that here.

Using your chatbot via an API

Once you’ve built your Gradio chatbot and are hosting it on Hugging Face Spaces or somewhere else, then you can query it with a simple API at the /chat endpoint. The endpoint just expects the user’s message (and potentially additional inputs if you have set any using the additional_inputs parameter), and will return the response, internally keeping track of the messages sent so far.

To use the endpoint, you should use either the Gradio Python Client or the Gradio JS client.

A langchain example

Now, let’s actually use the gr.ChatInterface with some real large language models. We’ll start by using langchain on top of openai to build a general-purpose streaming chatbot application in 19 lines of code. You’ll need to have an OpenAI key for this example (keep reading for the free, open-source equivalent!)

from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage
import openai
import gradio as gr

os.envrion["OPENAI_API_KEY"] = "sk-..."  # Replace with your key

llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')

def predict(message, history):
    history_langchain_format = []
    for human, ai in history:
        history_langchain_format.append(HumanMessage(content=human))
        history_langchain_format.append(AIMessage(content=ai))
    history_langchain_format.append(HumanMessage(content=message))
    gpt_response = llm(history_langchain_format)
    return gpt_response.content

gr.ChatInterface(predict).launch()

A streaming example using openai

Of course, we could also use the openai library directy. Here a similar example, but this time with streaming results as well:

import openai
import gradio as gr

openai.api_key = "sk-..."  # Replace with your key

def predict(message, history):
    history_openai_format = []
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human })
        history_openai_format.append({"role": "assistant", "content":assistant})
    history_openai_format.append({"role": "user", "content": message})

    response = openai.ChatCompletion.create(
        model='gpt-3.5-turbo',
        messages= history_openai_format,         
        temperature=1.0,
        stream=True
    )
    
    partial_message = ""
    for chunk in response:
        if len(chunk['choices'][0]['delta']) != 0:
            partial_message = partial_message + chunk['choices'][0]['delta']['content']
            yield partial_message 

gr.ChatInterface(predict).queue().launch()

Example using a local, open-source LLM with Hugging Face

Of course, in many cases you want to run a chatbot locally. Here’s the equivalent example using Together’s RedePajama model, from Hugging Face (this requires you to have a GPU with CUDA).

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", torch_dtype=torch.float16)
model = model.to('cuda:0')

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [29, 0]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

def predict(message, history): 

    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()

    messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]])  #curr_system_message + 
                for item in history_transformer_format])
    
    model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=1.0,
        num_beams=1,
        stopping_criteria=StoppingCriteriaList([stop])
        )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    partial_message  = ""
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
            yield partial_message 
            

gr.ChatInterface(predict).queue().launch()

With those examples, you should be all set to create your own Gradio Chatbot demos soon! For building even more custom Chatbot applications, check out a dedicated guide using the low-level gr.Blocks() API.