Create app.py

app.py

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+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)
+
+