| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| model_name = "google-t5/t5-small" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| def generate_response(prompt): | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_new_tokens=50) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| gr.Interface(fn=generate_response, inputs="text", outputs="text").launch() | |