app.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr

# Load smaller GPT-2 model
model_name = "gpt2"  # smaller and faster
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Create generator pipeline for CPU
generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=-1)

def generate_data(prompt, amount):
    # Generate multiple samples in batch
    responses = generator(
        prompt,
        max_length=50,  # keep short for speed
        num_return_sequences=amount,
        do_sample=False,  # greedy for speed
        temperature=0.7,
        top_k=50,
        top_p=0.95,
        pad_token_id=tokenizer.eos_token_id,
        num_beams=1  # greedy
    )
    return [resp['generated_text'].strip() for resp in responses]

with gr.Blocks() as demo:
    gr.Markdown("### Faster Data Generator with GPT-2\nDescribe what data you want to generate.")
    prompt_input = gr.Textbox(label="Prompt / Data Type", placeholder="Describe the data you want")
    amount_input = gr.Slider(1, 10, value=3, step=1, label="Number of Data Items")
    output_box = gr.Textbox(label="Generated Data", lines=15)

    generate_btn = gr.Button("Generate")
    generate_btn.click(generate_data, inputs=[prompt_input, amount_input], outputs=output_box)

demo.launch()