app.py

import threading
from datasets import load_dataset
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# Global variables for dataset status
dataset_loaded = False
dataset_status_message = "Dataset is still loading..."
dataset_lock = threading.Lock()

def load_dataset_in_background():
    global dataset_loaded, dataset_status_message
    try:
        # Load dataset from Hugging Face
        dataset = load_dataset("HuggingFaceFW/fineweb", split="train")
        # Save to CSV for later use
        dataset.to_csv("data.csv")
        with dataset_lock:
            dataset_loaded = True
            dataset_status_message = "Dataset loaded successfully!"
    except Exception as e:
        with dataset_lock:
            dataset_loaded = False
            dataset_status_message = f"Error loading dataset: {e}"

# Start dataset loading in background thread
threading.Thread(target=load_dataset_in_background, daemon=True).start()

# Load GPT-2 for inference
model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=-1)

# Function to generate response
def generate_response(prompt):
    responses = generator(
        prompt,
        max_length=100,
        do_sample=True,
        temperature=0.7,
        top_k=50,
        top_p=0.95,
        num_return_sequences=1
    )
    return responses[0]['generated_text'].strip()

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## GPT-2 AI Assistant with Background Dataset Loading")
    status_box = gr.Textbox(value=dataset_status_message, label="Dataset Loading Status", interactive=False, lines=2)

    def refresh_status():
        with dataset_lock:
            return dataset_status_message

    refresh_button = gr.Button("Check Dataset Status")
    refresh_button.click(refresh_status, outputs=status_box)

    gr.Markdown("### Chat with the AI")
    prompt_input = gr.Textbox(label="Your prompt", placeholder="Ask me anything...")
    response_output = gr.Textbox(label="AI Response", lines=10)

    def chat(prompt):
        return generate_response(prompt)

    gr.Button("Ask").click(chat, inputs=prompt_input, outputs=response_output)

demo.launch()