gradio-spreadsheetcomponent 0.0.3

This component is used to answer questions about spreadsheets.

---

tags: [gradio-custom-component, custom-component-track, gradio-spreadsheet-custom-component] title: gradio_spreadsheetcomponent short_description: This component answers questions about spreadsheets. colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py app_link: https://huggingface.co/spaces/Mustafiz996/gradio_spreadsheetcomponent

gradio_spreadsheetcomponent

This component is used to answer questions about spreadsheets.

Installation

pip install gradio_spreadsheetcomponent

Usage

import gradio as gr
from gradio_spreadsheetcomponent import SpreadsheetComponent
from dotenv import load_dotenv
import os
import pandas as pd

def answer_question(file, question):
    if not file or not question:
        return "Please upload a file and enter a question."
    
    # Load the spreadsheet data
    df = pd.read_excel(file.name)
    
    # Create a SpreadsheetComponent instance
    spreadsheet = SpreadsheetComponent(value=df)
    
    # Use the component to answer the question
    return spreadsheet.answer_question(question)

with gr.Blocks() as demo:
    gr.Markdown("# Spreadsheet Question Answering")
    
    with gr.Row():
        file_input = gr.File(label="Upload Spreadsheet", file_types=[".xlsx"])
        question_input = gr.Textbox(label="Ask a Question")
    
    answer_output = gr.Textbox(label="Answer", interactive=False, lines=4)
    
    submit_button = gr.Button("Submit")
    submit_button.click(answer_question, inputs=[file_input, question_input], outputs=answer_output)

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

SpreadsheetComponent

Initialization

name	type	default	description
value	pandas.core.frame.DataFrame | list | dict | None	None	Default value to show in spreadsheet. Can be a pandas DataFrame, list of lists, or dictionary

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.

def predict(
    value: typing.Any
) -> Unknown:
    return value