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import streamlit as st
import pandas as pd
from transformers import AutoTokenizer
from transformers import (
TFAutoModelForSequenceClassification as AutoModelForSequenceClassification,
)
from transformers import pipeline
st.title("Toxic Tweet Classifier")
demo = """Your words are like poison. They seep into my mind and make me feel worthless."""
text = ""
submit = False
model_name = ""
col1, col2, col3 = st.columns([2,1,1])
with st.container():
model_name = st.selectbox(
"Select the model you want to use below.",
("RobCaamano/toxicity",),
)
submit = st.button("Submit", type="primary")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
clf = pipeline(
"sentiment-analysis", model=model, tokenizer=tokenizer, return_all_scores=True
)
with col1:
st.subheader("Tweet")
text = st.text_area("Input text", demo, height=275)
input = tokenizer(text, return_tensors="tf")
if submit:
results = dict(d.values() for d in clf(text)[0])
classes = {k: results[k] for k in results.keys() if not k == "toxic"}
max_class = max(classes, key=classes.get)
probability = classes[max_class]
result_df = pd.DataFrame({
'Classification': [max_class],
'Probability': [probability],
'Toxic': ['Yes' if results['toxic'] >= 0.5 else 'No']
})
st.table(result_df)
expander = st.expander("Raw output")
expander.write(results)
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