import streamlit as st
from transformers import pipeline, AutoTokenizer

st.title('Sentiment Analyser App')
st.write('Welcome to my sentiment analysis app!')
model_options=["sentiment-analysis", "twitter-xlm-roberta-base-sentiment", "sentiment-roberta-large-english"]

form = st.form(key='sentiment-form')
model_type = form.selectbox(label="Select a model", options=model_options)
user_input = form.text_area(label='Enter your text to analyse', value="Hey how are you?")
submit = form.form_submit_button('Submit')

def classification(user_input, type):
    if type=="sentiment-analysis":
        classifier = pipeline("sentiment-analysis")   
    elif type=="twitter-xlm-roberta-base-sentiment":
        path="cardiffnlp/twitter-xlm-roberta-base-sentiment"
        classifier = pipeline("sentiment-analysis", model=path, tokenizer=path)
    elif type=="sentiment-roberta-large-english":
        path="siebert/sentiment-roberta-large-english"
        classifier = pipeline("sentiment-analysis", model=path)
    result = classifier(user_input)
    return result

if submit:
    resultf = classification(user_input, model_type)
    # if model_type=="sentiment-roberta-large-english":
    #     st.write(str(resultf[0]['label']) + ": " + str(resultf[0]['score']))
    #     st.write(str(resultf[1]['label']) + ": " + str(resultf[1]['score']))
    #     st.write(str(resultf[2]['label']) + ": " + str(resultf[2]['score']))
    # else:
    label = resultf[0]['label']
    score = resultf[0]['score']
    if (label == 'POSITIVE') or (label =='Positive') or (label =='positive'):
            st.success(f'{label} sentiment (score: {score})')
    else:
        st.error(f'{label} sentiment (score: {score})')