import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np
import torch

def main():

    st.set_page_config(  # Alternate names: setup_page, page, layout
        layout="centered",  # Can be "centered" or "wide". In the future also "dashboard", etc.
        initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
        page_title="Emoji-motion!",  # String or None. Strings get appended with "• Streamlit".
        page_icon=None,  # String, anything supported by st.image, or None.
    )

    st.title('Emoji-motion!')

    example_prompts = [
        "This space is lit!!",
        "Today is going to be awesome!",
        "I love Machine Learning",
        "Cool cool cool no doubt no doubt no doubt"]

    example = st.selectbox("Choose a pre-defined example", example_prompts)

    # Take the message which needs to be processed
    message = st.text_area('Or type a sentence to see if our AL Algorithm can detect your emotion', example)
    # st.title(message)
    st.text('')
    models_to_choose = ["AlekseyDorkin/xlm-roberta-en-ru-emoji"]

    BASE_MODEL = st.selectbox("Choose a model", models_to_choose)
    TOP_N = 5

    def preprocess(text):
        new_text = []
        for t in text.split(" "):
            t = '@user' if t.startswith('@') and len(t) > 1 else t
            t = 'http' if t.startswith('http') else t
            new_text.append(t)
        return " ".join(new_text)

    @st.cache(allow_output_mutation=True, suppress_st_warning=True, show_spinner=False)
    def load_model():
        tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
        model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL)
        return model, tokenizer

    def get_top_emojis(text, top_n=TOP_N):
        model, tokenizer = load_model()
        preprocessed = preprocess(text)
        inputs = tokenizer(preprocessed, return_tensors="pt")
        preds = model(**inputs).logits
        scores = torch.nn.functional.softmax(preds, dim=-1).detach().numpy()
        ranking = np.argsort(scores)
        ranking = ranking.squeeze()[::-1][:top_n]
        emojis = [model.config.id2label[i] for i in ranking]
        return ', '.join(map(str, emojis))

    # Define function to run when submit is clicked
    def submit(message):
        if len(message)>0:
            st.header(get_top_emojis(message))
        else:
            st.error("The text can't be empty")

    # Run algo when submit button is clicked
    if(st.button('Submit')):
        submit(message)

    st.text('')
    st.markdown('<span style="color:blue; font-size:10px">App created by [@AlekseyDorkin](https://huggingface.co/AlekseyDorkin) \
        and [@akshay7](https://huggingface.co/akshay7)</span>',unsafe_allow_html=True)


if __name__ == "__main__":
    main()