| import gradio as gr | |
| from transformers import pipeline, set_seed | |
| generator = pipeline("text-generation", model="gpt2") | |
| set_seed(42) | |
| def ask_librarian(user_input): | |
| prompt = f"μ¬μ©μ: {user_input}\nμ¬μ:" | |
| result = generator(prompt, max_new_tokens=60, do_sample=True, temperature=0.8)[0]["generated_text"] | |
| response = result.split("μ¬μ:")[-1].strip() | |
| return response | |
| demo = gr.Interface( | |
| fn=ask_librarian, | |
| inputs=gr.Textbox(lines=3, placeholder="μ± μΆμ²μ΄λ κΆκΈν κ²μ μ λ ₯ν΄ λ³΄μΈμ."), | |
| outputs="text", | |
| title="π AI λμκ΄ μ¬μ", | |
| description="μ± μΆμ², λμ κ΄λ ¨ μ§λ¬Έ, κ°λ¨ν μμμ λ΅λ³νλ AI μ¬μ μ±λ΄μ λλ€." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |