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-3-classes"]

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-3-classes":
        path="j-hartmann/sentiment-roberta-large-english-3-classes"
        classifier = pipeline("text-classification", model=path, return_all_scores=True)
    result = classifier(user_input)[0]
    return result

if submit:
    resultf = classification(user_input, model_type)
    label = resultf['label']
    score = resultf['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})')