app.py

import os
import gradio as gr

HF_TOKEN = os.getenv('HW_TOKEN')
#print (HF_TOKEN)
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "save_audio")

def calculator(num1, operation, num2):
    if operation == "add":
        return num1 + num2
    elif operation == "subtract":
        return num1 - num2
    elif operation == "multiply":
        return num1 * num2
    elif operation == "divide":
        return num1 / num2


iface = gr.Interface(
    calculator,
    ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
    "number",
    #description="Check out the crowd-sourced dataset at: [https://huggingface.co/datasets/aliabd/crowdsourced-calculator-demo](https://huggingface.co/datasets/aliabd/crowdsourced-calculator-demo)",
    allow_flagging="manual",
    flagging_options=["wrong sign", "off by one", "other"],
    flagging_callback=hf_writer
)

iface.launch()