app.py

import gradio as gr
from transformers import AutoModelForCausalLM, GPT2Tokenizer


def generate_text(sentence, max_length=100):
    model_path = "franzemil/bolivianlm"
    model_name = "datificate/gpt2-small-spanish"

    # Load the model and the tokenizer
    model = AutoModelForCausalLM.from_pretrained(model_path)
    tokenizer = GPT2Tokenizer.from_pretrained(model_name)

    # Generate the ids using the tokenizer
    ids = tokenizer.encode(sentence, return_tensors="pt")

    # Use the model to generate text
    outputs = model.generate(
        ids,
        do_sample=True,
        max_length=max_length,
        pad_token_id=model.config.eos_token_id,
        top_k=50,
        top_p=0.95,
    )

    # Decode the and return the string
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

demo = gr.Interface(fn=generate_text, inputs="text", outputs="text")
demo.launch()