app.py

import streamlit as st
from transformers import pipeline

if "sentiment" not in st.session_state:
    st.session_state.sentiment = ""

if "score" not in st.session_state:
    st.session_state.score = ""


def run_sentiment_model(text_in, model_in):
    classifier = pipeline(task="sentiment-analysis",
                          model=model_in)
    analysis = classifier(text_in)
    st.session_state.sentiment = analysis[0]["label"]
    st.session_state.score = "{:.2f}".format(analysis[0]["score"] * 100)


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 Section (Milestone-2)")
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_sentiment_model, args=(
    text_input, models_available[model_picked]))

st.markdown(body="Sentiment: {}, Confidence Score: {} %".format(
    st.session_state.sentiment, st.session_state.score))

st.title("Patentability Score Section (Milestone-3)")