app.py

import streamlit as st
from transformers import pipeline


def run_model(text_in, model_in):
    classifier = pipeline(task="sentiment-analysis",
                          model=model_in)
    analysis = classifier(text_in)
    to_output = ""
    for output in analysis:
        to_output += "Sentiment: ", output["label"], " Confidence Score: ", "{0:.2f}".format(
            output["score"] * 100), "\n"
    global markdown_text
    markdown_text = to_output


models_available = {"Roberta Large English": "siebert/sentiment-roberta-large-english",
                    "Generic": "Seethal/sentiment_analysis_generic_dataset",
                    "Twitter Roberta": "cardiffnlp/twitter-roberta-base-sentiment"}

st.title("Sentiment Analysis Web Application")
text_input = st.text_area(
    label="Enter the text to analyze", value="I Love Pizza")
model_picked = st.selectbox(
    "Choose a model to run on", options=models_available.keys())

st.button("Submit", on_click=run_model, args=(
    text_input, models_available[model_picked]))

if len(markdown_text) > 0:
    st.markdown(body=markdown_text)