app.py

# app.py
import torch
import gradio as gr
import threading
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TrainingArguments,
    Trainer,
    DataCollatorForLanguageModeling
)
from datasets import load_dataset
import logging
import sys
from urllib.parse import urlparse

# Configure logging
logging.basicConfig(stream=sys.stdout, level=logging.INFO)

def parse_hf_dataset_url(url: str):
    # ... (keep previous URL parsing logic) ...

def train(dataset_url: str):
    try:
        # ... (keep previous training logic) ...
        
    except Exception as e:
        logging.error(f"Critical error: {str(e)}")
        return f"❌ Critical error: {str(e)}"

# Gradio interface
with gr.Blocks(title="Phi-2 Training") as demo:
    gr.Markdown("# 🚀 Train Phi-2 with HF Hub Data")
    
    with gr.Row():
        dataset_url = gr.Textbox(
            label="Dataset URL",
            value="https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0"
        )
    
    start_btn = gr.Button("Start Training", variant="primary")
    status_output = gr.Textbox(label="Status", interactive=False)
    
    start_btn.click(
        fn=lambda url: threading.Thread(target=train, args=(url,)).start(),
        inputs=[dataset_url],
        outputs=status_output
    )

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        enable_queue=True,
        share=False
    )