import streamlit as st
from transformers import pipeline
from transformers import AutoTokenizer as AT, AutoModelForSequenceClassification as AFSC

modName = "madhurjindal/autonlp-Gibberish-Detector-492513457" # Gibberish Detection Model from HuggingFace

mod = AFSC.from_pretrained(modName)
TKR = AT.from_pretrained(modName)

st.title("Gibberish Detector")

user_input = st.text_input("Enter some words", "[Pre-populted Text]: pasghetti")
st.markdown("Input was: ", user_input)

classifier = pipeline("sentiment-analysis", model=mod, tokenizer=TKR)
# result = classifier(["This is a sample text made by Sean Ramirez.", "This is another sample text."]) # leftover from initial testing

if user_input is not None:
    col = st.columns(1)
    predicts = pipeline("sentiment-analysis", model=mod, tokenizer=TKR)

    col.header("Probabilities")
    for p in predicts:
        col.subheader(f"{ p['label']}: { round(p['score'] * 100, 1)}%")