import gradio as gr from transformers import pipeline model_tag = "amanneo/distilgpt2-emailgen-finetuned-custom-mail" tokenizer_tag = "distilgpt2" generator = pipeline( 'text-generation', model=model_tag, do_sample=False, early_stopping=True, tokenizer=tokenizer_tag ) generation_args = { "min_length": 4, "max_length": 64, "length_penalty": 0.5, "no_repeat_ngram_size": 2, "do_sample": False, "num_beams": 4, "early_stopping": True, "repetition_penalty": 3.5, } # Main function def text_generator(seed_text,min_n,max_n): generation_args["min_length"] = min_n generation_args["max_length"] = max_n prompt = seed_text result = generator( prompt, **generation_args, ) return result[0]['generated_text'] iface = gr.Interface( fn=text_generator, inputs=["text","slider","slider"], outputs="text", title="Text Generator", live=False ) iface.launch(debug=True)