import transformers
import streamlit as st

from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("mrm8488/spanish-gpt2")

model = AutoModelWithLMHead.from_pretrained("mrm8488/spanish-gpt2")


def infer(input_ids, max_length, temperature, top_k, top_p, num_return_sequences):

    output_sequences = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        do_sample=True,
        num_return_sequences=num_return_sequences,
    )

    return output_sequences
default_value = "Vea cómo una red neuronal moderna completa automáticamente su texto 🤗 Este sitio, creado por el equipo de Hugging Face, le permite escribir un documento completo directamente desde su navegador, y puede activar el Transformer (Spanish GPT-2) en cualquier lugar usando la tecla Tab. Es como tener una máquina inteligente que completa tus pensamientos 😀 Comienza escribiendo un fragmento personalizado."

#prompts
st.title("Write with Spanish GPT-2 🦄")
st.write("Demo del modelo Spanish GPT-2 creado por Manuel Romero y su equipo en la Flax/Jax Commnunity Event orgranizado por Hugging Face y Google")

sent = st.text_area("Text", default_value, height = 275)
max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30)
temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)
num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1)


encoded_prompt = tokenizer.encode(sent, add_special_tokens=False, return_tensors="pt")
if encoded_prompt.size()[-1] == 0:
    input_ids = None
else:
    input_ids = encoded_prompt


output_sequences = infer(input_ids, max_length, temperature, top_k, top_p, num_return_sequences)



for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
    print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
    generated_sequences = generated_sequence.tolist()

    # Decode text
    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)

    # Remove all text after the stop token
    #text = text[: text.find(args.stop_token) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    total_sequence = (
        sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
    )

    generated_sequences.append(total_sequence)
    print(total_sequence)


st.write(generated_sequences[-1])