app.py

import gradio as gr
import pandas as pd
from datasets import Dataset, DatasetDict
from huggingface_hub import notebook_login, HfApi

def process_and_upload(json_file_path, hf_username, dataset_name):
    # Load your combined JSON file
    df = pd.read_json(json_file_path.name)
    print(f"DataFrame shape: {df.shape}")
    print(f"DataFrame columns: {df.columns.tolist()}")

    # Create HuggingFace Dataset
    dataset = Dataset.from_pandas(df)
    print("Dataset created successfully!")

    # Create a DatasetDict with a 'train' split
    dataset_dict = DatasetDict({"train": dataset})

    # Log in to Hugging Face (or use existing token if available)
    try:
        notebook_login()
    except:
        # Assume user has already logged in or token is set
        pass

    # Push to the Hugging Face Hub
    api = HfApi()
    api.create_repo(repo_id=f"{hf_username}/{dataset_name}", repo_type="dataset", private=False, exist_ok=True)
    dataset_dict.push_to_hub(f"{hf_username}/{dataset_name}", private=False)

    return f"Dataset '{dataset_name}' uploaded to Hugging Face Hub under user '{hf_username}'"

with gr.Blocks() as demo:
    gr.Markdown("## Upload and Process JSON to Hugging Face Dataset")
    with gr.Row():
        json_file_input = gr.File(label="Select JSON file")
        hf_username_input = gr.Textbox(label="Hugging Face Username", placeholder="Your HF username")
        dataset_name_input = gr.Textbox(label="Dataset Name", placeholder="Name for your dataset")
    submit_button = gr.Button("Upload to Hugging Face")
    output_label = gr.Label(label="Output")

    submit_button.click(
        process_and_upload,
        inputs=[json_file_input, hf_username_input, dataset_name_input],
        outputs=output_label
    )

demo.launch()