TL; DR

Let's learn how to build a simple chatbot using the Taipy GUI library and the Mistral-7B-Instruct-v0.1-GGUF language model from the ctransformers library.

The walkthrough

• loading the language model
• generating responses to user prompts
• updating & clearing conversation history
• application styling

By the end of this article, we will have a basic understanding of how to build a chatbot using these tools.

Loading the Mistral-7B-Instruct-v0.1-GGUF model

Mistral 7B is a super-smart language model with 7 billion parameters!
It beats the best 13B model, Llama 2, in all tests and even outperforms the powerful 34B model, Llama 1, in reasoning, math, and code generation.
How?
Mistral 7B uses smart tricks like grouped-query attention (GQA) for quick thinking and sliding window attention (SWA) to handle all sorts of text lengths without slowing down.

Source: Mistral.AI Docs

And there's more! Mistral AI Team fine-tuned Mistral 7B for specific tasks with Mistral 7B – Instruct.
It outshines Llama 2 13B in chat and rocks both human and automated tests.
The best part? Mistral 7B – was released under the Apache 2.0 license.

Download GGUF files using ctransformers

Step 1: Install ctransformers

With no GPU acceleration

pip install ctransformers

or install with ctransformers with CUDA GPU acceleration

pip install ctransformers[cuda]

or install with ctransformers with AMD ROCm GPU acceleration (Linux only)

CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers

or install with ctransformers with Metal GPU acceleration for macOS systems only

CT_METAL=1 pip install ctransformers --no-binary ctransformers

Load the model

All set? Let's run the code below to download and send a prompt to the model. Make sure to free up space on your computer and connect to a good internet connection.

# import the AutoModelForCausalLM class from the ctransformers library
from ctransformers import AutoModelForCausalLM

# load Mistral-7B-Instruct-v0.1-GGUF, Set gpu_layers to the number of layers to offload to GPU. The value is set to 0 because no GPU acceleration is available on my current system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GGUF", model_file="mistral-7b-instruct-v0.1.Q4_K_M.gguf", model_type="mistral", gpu_layers=0)

# call the model to generate text.
ask = 1000 
turn = 0
while turn < ask:
    user = input("Enter your message: ")
    print(llm(user))

The model will continue the statement as follows,

Say Hello Taipy!

Taipy is an open-source Python library that makes it simple to create data-driven web applications.
It takes care of the visible part (Frontend) and the behind-the-scenes (Backend) operations.
Its goal is to speed up the process of developing applications, from the early design stages to having a fully functional product ready for use.

Source: Taipy Docs

Requirement: Python 3.8 or later on Linux, Windows, and Mac.

Star ⭐ the Taipy repository

Your support means a lot🌱, and helps us in many ways, like writing articles! 🙏

Installing Taipy:

Open up a terminal and run the following command, which will install Taipy with all its dependencies.

pip install taipy

We're set!
Let's say hello to Taipy!

# import the library
from taipy import Gui

hello = "# Hello Taipy!" 

# run the gui
Gui(hello).run()

Save the code as a Python file: e.g., hi_taipy.py.
Run the code and wait for the client link http://127.0.0.1:5000 to display and pop up in your browser.
You can change the port if you want to run multiple servers at the same time with Gui(...).run(port=xxxx).

Create a chat interface with Taipy

Now we are familiar with Taipy, let's get our hands dirty and build our chat interface.

Step 1: Import the AutoModelForCausalLM class from the ctransformers library

In this step, we import the AutoModelForCausalLM class from the ctransformers library, which is used to generate text using pre-trained language models.

from ctransformers import AutoModelForCausalLM

Step 2: Import the Taipy library
In this step, we import the Taipy GUI library, which is used to build the user interface for our chatbot.

from taipy.gui import Gui, notify

Step 3: Load the Mistral-7B-Instruct-v0.1-GGUF model

llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GGUF", model_file="mistral-7b-instruct-v0.1.Q4_K_M.gguf", model_type="mistral", gpu_layers=0)

Step 4: Initialize the prompt and response variables

In this step, we initialize the prompt and response variables as empty strings.

prompt = ""
response = ""

Step 5: Define the chat function

In this step, we define the chat function, which is called when the user clicks the "Chat" button in the user interface. This function takes the current state of the GUI as an input, generates text using the pre-trained language model based on the user's prompt, and updates the response variable in the state.

def chat(state):
    notify(state, 'info', 'Thinking...')
    state.response = llm(state.prompt)

Step 6: Define the user interface

Time to define the user interface for our chatbot using the Taipy GUI library. The user interface consists of an input field where the user can enter a prompt, a "Chat" button that triggers the chat function, and a display area where the chatbot's response is shown.

page = """
# Chatbot Wizard! {: .color-primary}
Enter Prompt: <|{prompt}|input|> <br />
<|Chat|button|class_name=plain mt1|on_action=chat|> <br />
MistralAI: <br /> <|{response}|>
"""

Step 7: Run the Taipy GUI application

Now let's run the Taipy GUI application using the run method.

Gui(page).run(debug=True)

Full Code

# import the AutoModelForCausalLM class from the ctransformers library
from ctransformers import AutoModelForCausalLM

# import taipy library
from taipy.gui import Gui, notify

# load Mistral-7B-Instruct-v0.1-GGUF, Set gpu_layers to the number of layers to offload to GPU. The value is set to 0 because no GPU acceleration is available on my current system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GGUF", model_file="mistral-7b-instruct-v0.1.Q4_K_M.gguf", model_type="mistral", gpu_layers=0)

# initialize the `prompt` and `response` variables as empty strings.
prompt = ""
response = ""

def chat(state):
    notify(state, 'info', 'Thinking...')
    state.response = llm(state.prompt)

page = """
# Chatbot Wizard! {: .color-primary}
Enter Prompt: <|{prompt}|input|>
<|Send Prompt|button|class_name=plain mt1|on_action=chat|> <br />
MistralAI: <br /> <|{response}|>
""" 

Gui(page).run(debug=True)

Here it is, a simple chat interface!

Let's level up our application to become a chatbot, as we imagine.

Mistral AI Chatbot

Step 1: In this step, we initialize the prompt and response and the conversation

prompt = ""
response = ""
conversation = {
    "Conversation": ["Hello", "Hi there!   What would you like to talk about today?"]
}

Step 2: Update the chat function

def chat(state):
    # Notify the user that the chatbot is thinking
    notify(state, 'info', 'Thinking...')

    # Generate a response using the loaded language model
    response = llm(state.prompt)

    # Add the user's prompt and the bot's response to the conversation history
    state.conversation["Conversation"].append(state.prompt)
    state.conversation["Conversation"].append(response)

    # Update the conversation object to contain the entire conversation history
    state.conversation = {"Conversation": state.conversation["Conversation"]}

    # Clear the user's input prompt
    state.prompt = ""

    # Notify the user that the bot has generated a response
    notify(state, 'info', 'Response received!')

Step 3: Add clear_conversation function to clear the conversation history

The function sets the state.conversation object to a new dictionary with a single key-value pair, where the key is Conversation and the value is an empty list.
This effectively clears the conversation history, as the state.conversation object is now an empty dictionary with a single key-value pair containing an empty list.
The updated state.conversation object will be reflected in the chatbot UI, showing an empty conversation history.

def clear_conversation(state):
    state.conversation = {"Conversation": []}

Step 4: ZHUZH it up
Let's define the layout of the user interface for the Chatbot.
Let's add a logo by downloading and saving it in the same directory as the script.
Then attach clear_conversation to the New chat button.

Styling with CSS

Now, let's style our chat ui by floating the response the left and the prompt to the right hand side.

Step 1: Create a CSS file with the same title as the python file and save it in the same directory.

.mistral_mssg td {
    position: relative;
    display: inline-block;
    margin: 10px 10px;
    padding: 15px;
    background-color: #ff8c00;
    border-radius: 20px;
    max-width: 80%;
    box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);
    font-size: medium;
  }

  .user_mssg td {
    position: relative;
    display: inline-block;
    float: right;
    margin: 10px 10px;
    padding: 15px;
    background-color: #9400D3;
    border-radius: 20px;
    max-width: 80%;
    box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);
    font-size: medium;
  }

.flexy{
    display: flex;
    justify-content: center;
    max-width: 50vw;
    margin: 4em auto;
    align-items: center;
}

Step 2: Create the style_conv function
The style_conv function is a callback function that is used to apply styles to the conversation history table in the Taipy GUI. It takes three arguments: state, idx, and row.

The state argument is a dictionary containing the GUI's current state, including the conversation history.
The idx argument is the index of the current row in the table, and the row argument is the index of the current column in the table.

The function checks the value of the idx argument to determine which style to apply to the current row. If idx is None, the function returns None, indicating no style should be applied.

If idx is an even number, the function returns the string user_mssg, corresponding to the CSS class for the user's prompts. If idx is an odd number, the function returns the string mistral_mssg, corresponding to the CSS class for the chatbot's responses.

Here is the code for the style_conv function:

def style_conv(state, idx: int, row: int) -> str:
    if idx is None:
        return None
    elif idx % 2 == 0:
        return "user_mssg"  # return user_mssg style
    else:
        return "mistral_mssg"  # return mistral_mssg style

To use the style_conv function in the Taipy GUI, we need to pass it as the value of the style attribute in the table element. For example:

<|{conversation}|table|style=style_conv|show_all|width=100%|rebuild|>

Step 3: Add a sidebar

Redefine the page to add the sidebar.

page = """
<|layout|columns=300px 1|
<|part|render=True|class_name=sidebar bg_black|
# Chat **Wizard**{: .color-primary} # {: .logo-text}
<|New Chat|button|class_name=fullwidth plain|on_action=clear_conversation|>

### History
<|{history}|table|show_all|>
|>

<|part|render=True|class_name=p2 align-item-bottom table|
<|{conversation}|table|style=style_conv|show_all|width=100%|rebuild|>

<|part|class_name=card mt1|
<|{prompt}|input|label=Ask anything...|class_name=fullwidth|on_action=chat|>
<|Send Prompt|button|class_name=plain mt1 fullwidth|on_action=chat|>
|>
|>
|>
"""

Final thoughts

In conclusion, this article demonstrated how to build a simple chatbot using the Taipy GUI library and the Mistral-7B-Instruct-v0.1-GGUF language model from the ctransformers library. The code provided shows how to load the language model, generate responses to user prompts, update the conversation history, and clear the conversation history. The chatbot's UI, built using the Taipy GUI library, provides a user-friendly interface for interacting with the chatbot. Overall, this article provides a useful starting point for building more sophisticated chatbots using these Taipy.

Resources:

HuggingFace: Mistral-7B-Instruct-v0.1-GGUF

Taipy: Taipy Docs