app.py

import streamlit as st
import pandas as pd
import numpy as np
from transformers import pipeline
from PIL import Image


st.title("Toxic Tweets Sentiment Analysis")


words = "Take that, you funking cat-dragon! You smell really bad!"
text = st.text_area("Insert text for analysis below.", words)

model_list = ["distilbert-base-uncased-finetuned-sst-2-english", "bert-base-cased", "openai/clip-vit-base-patch32", "emilyalsentzer/Bio_ClinicalBERT",
              "sentence-transformers/all-mpnet-base-v2", "facebook/bart-large-cnn", "openai/clip-vit-base-patch16", "speechbrain/spkrec-ecapa-voxceleb", 
             "albert-base-v2"]
model = st.selectbox("", model_list)
sub = st.write("Pick the model to use for analyzing the text!")
button = st.button("Analyze!")
pipe = pipeline("text-classification")
if(button):
    pipe = pipeline("text-classification", model)
    results = pipe(text)
    st.write(results)