| import gradio as gr | |
| from transformers import pipeline | |
| # Create a text-generation pipeline using GPT-2 | |
| generator = pipeline('text-generation', model='gpt2') | |
| def generate_text(prompt): | |
| generated = generator( | |
| prompt, | |
| max_length=30, # Limit the output length | |
| do_sample=True, # Enable sampling for more natural responses | |
| temperature=0.3, # Lower temperature for less randomness | |
| repetition_penalty=1.5, # Penalize repeated tokens | |
| no_repeat_ngram_size=2 # Avoid repeating any 2-word sequences | |
| ) | |
| return generated[0]['generated_text'] | |
| iface = gr.Interface( | |
| fn=generate_text, | |
| inputs="text", | |
| outputs="text", | |
| title="Simple LLM with Hugging Face & Gradio", | |
| description="Enter a prompt and get a concise, factual answer." | |
| ) | |
| iface.launch() | |