import streamlit as st
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModel, Trainer, TrainingArguments, LineByLineTextDataset
import json


@st.cache()
def get_model():
    model = AutoModelForSequenceClassification.from_pretrained("siebert/sentiment-roberta-large-english", num_labels=2)
    model.load_state_dict(torch.load('model'))
    return model

@st.cache()
def get_tokenizer():
    tokenizer = AutoTokenizer.from_pretrained("siebert/sentiment-roberta-large-english")
    return tokenizer

def make_prediction():
    model = get_model()
    tokenizer = tokenizer()
    


st.header("Sentiment analysis on twitter datasets")
st.markdown("Here is a sentiment model further trained on a slice of a twitter dataset")
st.markdown("""
<img width=700px src='https://imagez.tmz.com/image/73/4by3/2020/10/05/735aaee2f6b9464ca220e62ef797dab0_md.jpg'> 
""", unsafe_allow_html=True)

text = st.text_area("Try typing something here! \n You will see how much better our model is compared to the base model! No kidding")
# ^-- показать текстовое поле. В поле text лежит строка, которая находится там в данный момент

### Loading and tokenizing data
# data = load_dataset("carblacac/twitter-sentiment-analysis")
# tokenizer = AutoTokenizer.from_pretrained("siebert/sentiment-roberta-large-english")
# dataset = data.map(lambda xs: tokenizer(xs["text"], truncation=True, padding='max_length'))
# dataset = dataset.rename_column("feeling", "labels")

with st.form(key='input_form'):
    to_analyze = st.text_input(label='Input text to be analyzed')
    button = st.form_submit_button(label='Classify')
if button:
    if to_analyze:
        make_prediction(to_analyze)
    else:
        st.markdown("Empty request. Please resubmit")

# classifier = pipeline('sentiment-analysis', model="distilbert-base-uncased-finetuned-sst-2-english")
# raw_predictions = classifier(text)
# тут уже знакомый вам код с huggingface.transformers -- его можно заменить на что угодно от fairseq до catboost

# st.markdown(f"{raw_predictions}")
# выводим результаты модели в текстовое поле, на потеху пользователю