import gradio as gr from app import demo as app import os _docs = {'SpreadsheetComponent': {'description': 'Creates a spreadsheet component that can display and edit tabular data with question answering capabilities.', 'members': {'__init__': {'value': {'type': 'pandas.core.frame.DataFrame | list | dict | None', 'default': 'None', 'description': 'Default value to show in spreadsheet. Can be a pandas DataFrame, list of lists, or dictionary'}}, 'postprocess': {}, 'preprocess': {'return': {'type': 'typing.Any', 'description': "The preprocessed input data sent to the user's function in the backend."}, 'value': None}}, 'events': {}}, '__meta__': {'additional_interfaces': {}, 'user_fn_refs': {'SpreadsheetComponent': []}}} abs_path = os.path.join(os.path.dirname(__file__), "css.css") with gr.Blocks( css=abs_path, theme=gr.themes.Default( font_mono=[ gr.themes.GoogleFont("Inconsolata"), "monospace", ], ), ) as demo: gr.Markdown( """ # `gradio_spreadsheetcomponent`
This component is used to answer questions about spreadsheets. """, elem_classes=["md-custom"], header_links=True) app.render() gr.Markdown( """ ## Installation ```bash pip install gradio_spreadsheetcomponent ``` ## Usage ```python 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() ``` """, elem_classes=["md-custom"], header_links=True) gr.Markdown(""" ## `SpreadsheetComponent` ### Initialization """, elem_classes=["md-custom"], header_links=True) gr.ParamViewer(value=_docs["SpreadsheetComponent"]["members"]["__init__"], linkify=[]) gr.Markdown(""" ### 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 input:** Is passed, the preprocessed input data sent to the user's function in the backend. ```python def predict( value: typing.Any ) -> Unknown: return value ``` """, elem_classes=["md-custom", "SpreadsheetComponent-user-fn"], header_links=True) demo.load(None, js=r"""function() { const refs = {}; const user_fn_refs = { SpreadsheetComponent: [], }; requestAnimationFrame(() => { Object.entries(user_fn_refs).forEach(([key, refs]) => { if (refs.length > 0) { const el = document.querySelector(`.${key}-user-fn`); if (!el) return; refs.forEach(ref => { el.innerHTML = el.innerHTML.replace( new RegExp("\\b"+ref+"\\b", "g"), `${ref}` ); }) } }) Object.entries(refs).forEach(([key, refs]) => { if (refs.length > 0) { const el = document.querySelector(`.${key}`); if (!el) return; refs.forEach(ref => { el.innerHTML = el.innerHTML.replace( new RegExp("\\b"+ref+"\\b", "g"), `${ref}` ); }) } }) }) } """) demo.launch()