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| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| # Load GPT-2 once when the app starts | |
| generator = pipeline( | |
| "text-generation", | |
| model="gpt2", | |
| device=-1 # use CPU on free Spaces | |
| ) | |
| def generate_text(prompt, max_new_tokens, temperature, top_p, top_k, repetition_penalty): | |
| prompt = prompt.strip() | |
| if not prompt: | |
| return "Please enter a prompt." | |
| try: | |
| result = generator( | |
| prompt, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| temperature=float(temperature), | |
| top_p=float(top_p), | |
| top_k=int(top_k), | |
| repetition_penalty=float(repetition_penalty), | |
| num_return_sequences=1, | |
| return_full_text=False, | |
| pad_token_id=generator.tokenizer.eos_token_id, | |
| ) | |
| return result[0]["generated_text"].strip() | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# GPT-2 Text Generator") | |
| gr.Markdown("Enter a prompt and generate text using the pretrained GPT-2 model.") | |
| with gr.Row(): | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| placeholder="Type your prompt here...", | |
| lines=4 | |
| ) | |
| with gr.Row(): | |
| max_new_tokens = gr.Slider(20, 200, value=80, step=1, label="Max New Tokens") | |
| temperature = gr.Slider(0.1, 1.5, value=0.8, step=0.1, label="Temperature") | |
| with gr.Row(): | |
| top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.01, label="Top-p") | |
| top_k = gr.Slider(0, 100, value=50, step=1, label="Top-k") | |
| repetition_penalty = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repetition Penalty") | |
| submit = gr.Button("Generate") | |
| output = gr.Textbox(label="Generated Text", lines=10) | |
| submit.click( | |
| fn=generate_text, | |
| inputs=[prompt, max_new_tokens, temperature, top_p, top_k, repetition_penalty], | |
| outputs=output | |
| ) | |
| demo.launch() |