import random
from mtranslate import translate
import streamlit as st
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline


LOGO = "https://raw.githubusercontent.com/nlp-en-es/assets/main/logo.png"

MODELS = {
    "RoBERTa Base": {
        "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-roberta-base-spanish"
    },
    "RoBERTa Base Gaussian": {
        "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-gaussian"
    },
    "RoBERTa Base Random": {
        "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-random"
    },
    "RoBERTa Base Stepwise": {
        "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-stepwise"
    },
    "RoBERTa Base Gaussian Experiment": {
        "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-gaussian-exp-512seqlen"
    },
    "RoBERTa Base Random Experiment": {
        "url": "https://api-inference.huggingface.co/models/bertin-project/bertin-base-random-exp-512seqlen"
    }
}

PROMPT_LIST = [
    "Fui a la librería a comprar un <mask>.",
    "¡Qué buen <mask> hace hoy!",
    "Hoy empiezan las vacaciones, vamos a la <mask>.",
    "Mi color favorito es el <mask>.",
    "Voy a <mask>, estoy muy cansada.",
    "Mañana vienen mis amigos de <mask>.",
    "¿Te apetece venir a <mask> conmigo?",
    "En verano hace mucho <mask>.",
    "En el bosque había <mask>."
]


@st.cache(show_spinner=False, persist=True)
def load_model(masked_text, model_name):
    model = AutoModelForMaskedLM.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    nlp = pipeline("fill-mask", model=model, tokenizer=tokenizer)
    result = nlp(masked_text)
    return result


# Page
st.set_page_config(page_title="BERTIN Demo", page_icon=LOGO)
st.title("BERTIN")

#Sidebar
st.sidebar.image(LOGO)

# Body
st.markdown(
    """
    BERTIN is a series of BERT-based models for Spanish.  
    The models are trained with Flax and using TPUs sponsored by Google since this is part of the
    [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104)
    organised by HuggingFace.
    """
)

model_name = st.selectbox("Model",(MODELS.keys()))

prompt = st.selectbox("Prompt", ["Random", "Custom"])
if prompt == "Custom":
    prompt_box = "Enter your masked text here..."
else:
    prompt_box = random.choice(PROMPT_LIST)
text = st.text_area("Enter text", prompt_box)

if st.button("Fill the mask"):
    with st.spinner(text="Getting results..."):
        st.subheader("Result")
        result = load_model(text, model_name)
        if "error" in result:
            if type(result["error"]) is str:
                st.write(f'{result["error"]}.', end=" ")
                if "estimated_time" in result:
                    st.write(
                        f'Please try again in about {result["estimated_time"]:.0f} seconds.'
                    )
            else:
                if type(result["error"]) is list:
                    for error in result["error"]:
                        st.write(f"{error}")
        else:
            result_sequence, result_token = result[0]["sequence"], result[0]["token_str"]
            st.write(result_sequence)
            st.text("English translation")
            st.write(translate(result_sequence, "en", "es"))

st.markdown(
    """
    ### Team members
    - Javier de la Rosa ([versae](https://huggingface.co/versae))
    - Eduardo González ([edugp](https://huggingface.co/edugp))
    - Paulo Villegas ([paulo](https://huggingface.co/paulo))
    - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps))
    - Manu Romero ([mrm8488](https://huggingface.co/mrm8488))
    - María Grandury ([mariagrandury](https://huggingface.co/mariagrandury))
        
    ### More information
    You can find more information about these models
    [here](https://huggingface.co/bertin-project/bertin-roberta-base-spanish).
    """
)