app.py

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)