app.py

import pandas_profiling as pp
from huggingface_hub.hf_api import create_repo, upload_file
from huggingface_hub.repository import Repository
import gradio as gr
import pandas as pd
import subprocess
import os
import tempfile

token = gr.Textbox(label = "Your Hugging Face Token")
username = gr.Textbox(label = "Your Hugging Face User name")
dataset_name = gr.Textbox(label = "Dataset Name")
dataset = gr.File(label = "Dataset")
output_text = gr.Textbox(label = "Status")

def profile_dataset(dataset, username, token, dataset_name):

    df = pd.read_csv(dataset.name)
    profile = pp.ProfileReport(df, title=f"{dataset_name} Report")
    
    repo_url = create_repo(f"{username}/{dataset_name}", repo_type = "space", token = token, space_sdk = "static")
    
    
    profile.to_file(f"{username}/{dataset_name}/index.html")
    upload_file(path_or_fileobj =f"{username}/{dataset_name}/index.html", repo_id =f"{username}/{dataset_name}", repo_type = "space", token=token)
    
    
    
    return f"Your dataset report will be ready at {repo_url}"

gr.Interface(profile_dataset, inputs = [dataset, username, token, dataset_name], outputs=[output_text], enable_queue = True).launch(debug=True)