gradio-awsbr-mmchatbot 0.0.5

This component enables multi-modal input for the Anthropic Claude v3 suite of models available from Amazon Bedrock

gradio_awsbr_mmchatbot

This component enables multi-modal input for the Anthropic Claude v3 suite of models available from Amazon Bedrock

Installation

pip install gradio_awsbr_mmchatbot

Usage

import gradio as gr
from gradio_awsbr_mmchatbot import MultiModalChatbot
from gradio.data_classes import FileData
from bedrock_utils import MultimodalInputHandler


# A function to call the multi-modal input for Anthropic Claude v3 sonnet using Bedrock boto3
async def get_response(text, file):
    # If there is a file uploaded, then we will send it to Anthropic Claude v3 sonnet.
    # If there is no file uploaded, then we will send the text to Anthropic Claude v3 sonnet.
    try:
        userMsg = {
            "text": text,
            "files": [{"file": FileData(path=file)}]
        }
    except:
        userMsg = {
            "text": text,
            "files": []
        }
    # Define a variable to store the response from the Anthropic Claude v3 sonnet
    llmResponse = ""
    handler = MultimodalInputHandler(text, file)
    # Loop through the response from Anthropic Claude v3 sonnet, and append it to our llmResponse variable.
    async for x in handler.handleInput():
        llmResponse += x
        yield [[userMsg, {"text": llmResponse, "files": []}]]
    # Yield the response from Anthropic Claude v3 sonnet. This is unecessary as we can just yield the llmResponse variable in an iterative fashion as above.
    # But just in case.... let's yield the entire response object as well and overwrite the messages in the Chatbot.
    response = {
        "text": llmResponse,
        "files": []
    }
    yield [[userMsg, response]]

# Defining Gradio Interface using Blocks Structure
with gr.Blocks() as demo:
    # Give it a Title
    gr.Markdown("## Gradio - MultiModal Chatbot")
    # Define the Chat Tab
    with gr.Tab(label="Chat"):
        with gr.Row():
            with gr.Column(scale=3):
                # Set a variable equal to our MultiModalChatBot class
                chatBot = MultiModalChatbot(height=700, render_markdown=True, bubble_full_width=True)
        with gr.Row():
            with gr.Column(scale=3):
                # Set a variable equal to our user message
                msg = gr.Textbox(placeholder='What is the meaning of life?', show_label=False)
            with gr.Column(scale=1):
                # Set a variable equal to our file upload
                fileInput = gr.File(label="Upload Files")
            with gr.Column(scale=1):
                # Define our submit button and invoke our 'get_response' function when it's clicked.
                gr.Button('Submit', variant='primary').click(get_response, inputs=[msg,fileInput], outputs=chatBot)
                # Same function as above, but with the 'enter' key being pressed inside the gr.Textbox() component instead of the 'submit' button.
                msg.submit(get_response, inputs=[msg, fileInput], outputs=chatBot)


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

MultiModalChatbot

Initialization

name	type	default	description
value	list[ list[ str | tuple[str] | tuple[str | pathlib.Path, str] | None ] ] | Callable | None	None	Default value to show in chatbot. If callable, the function will be called whenever the app loads to set the initial value of the component.
label	str | None	None	The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
every	float | None	None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
show_label	bool | None	None	if True, will display label.
container	bool	True	If True, will place the component in a container - providing some extra padding around the border.
scale	int | None	None	relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
min_width	int	160	minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
visible	bool	True	If False, component will be hidden.
elem_id	str | None	None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
elem_classes	list[str] | str | None	None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
render	bool	True	If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
height	int | str | None	None	The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed.
latex_delimiters	list[dict[str, str | bool]] | None	None	A list of dicts of the form {"left": open delimiter (str), "right": close delimiter (str), "display": whether to display in newline (bool)} that will be used to render LaTeX expressions. If not provided, `latex_delimiters` is set to `[{ "left": "$$", "right": "$$", "display": True }]`, so only expressions enclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass in an empty list to disable LaTeX rendering. For more information, see the [KaTeX documentation](https://katex.org/docs/autorender.html).
rtl	bool	False	If True, sets the direction of the rendered text to right-to-left. Default is False, which renders text left-to-right.
show_share_button	bool | None	None	If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.
show_copy_button	bool	False	If True, will show a copy button for each chatbot message.
avatar_images	tuple[ str | pathlib.Path | None, str | pathlib.Path | None ] | None	None	Tuple of two avatar image paths or URLs for user and bot (in that order). Pass None for either the user or bot image to skip. Must be within the working directory of the Gradio app or an external URL.
sanitize_html	bool	True	If False, will disable HTML sanitization for chatbot messages. This is not recommended, as it can lead to security vulnerabilities.
render_markdown	bool	True	If False, will disable Markdown rendering for chatbot messages.
bubble_full_width	bool	True	If False, the chat bubble will fit to the content of the message. If True (default), the chat bubble will be the full width of the component.
line_breaks	bool	True	If True (default), will enable Github-flavored Markdown line breaks in chatbot messages. If False, single new lines will be ignored. Only applies if `render_markdown` is True.
likeable	bool	False	Whether the chat messages display a like or dislike button. Set automatically by the .like method but has to be present in the signature for it to show up in the config.
layout	"panel" | "bubble" | None	None	If "panel", will display the chatbot in a llm style layout. If "bubble", will display the chatbot with message bubbles, with the user and bot messages on alterating sides. Will default to "bubble".

Events

name	description
change	Triggered when the value of the MultiModalChatbot changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See .input() for a listener that is only triggered by user input.
select	Event listener for when the user selects or deselects the MultiModalChatbot. Uses event data gradio.SelectData to carry value referring to the label of the MultiModalChatbot, and selected to refer to state of the MultiModalChatbot. See EventData documentation on how to use this event data
like	This listener is triggered when the user likes/dislikes from within the MultiModalChatbot. This event has EventData of type gradio.LikeData that carries information, accessible through LikeData.index and LikeData.value. See EventData documentation on how to use this event data.

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

• As output: Is passed, the preprocessed input data sent to the user's function in the backend.
• As input: Should return, expects a list[list[str | None | tuple]], i.e. a list of lists. The inner list should have 2 elements: the user message and the response message. The individual messages can be (1) strings in valid Markdown, (2) tuples if sending files: (a filepath or URL to a file, [optional string alt text]) -- if the file is image/video/audio, it is displayed in the MultiModalChatbot, or (3) None, in which case the message is not displayed.

def predict(
    value: list[MultimodalMessage] | None
) -> list[
       list[str | tuple[str] | tuple[str, str] | None]
       | tuple
   ]
   | None:
    return value

MultimodalMessage

class MultimodalMessage(GradioModel):
    text: Optional[str] = None
    files: Optional[List[FileMessage]] = None

FileMessage

class FileMessage(GradioModel):
    file: FileData
    alt_text: Optional[str] = None