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import streamlit as st
import pandas as pd
from transformers import BertTokenizer, BertForSequenceClassification
import torch
# Load pre-trained BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=6)
model.eval()
# Function to classify text using the pre-trained BERT model
def classify_text(text):
# Tokenize input text
input_ids = tokenizer.encode(text, add_special_tokens=True)
# Convert tokenized input to tensor
input_tensor = torch.tensor([input_ids])
# Get model predictions
with torch.no_grad():
logits = model(input_tensor)[0]
# Get predicted labels
predicted_labels = torch.sigmoid(logits).numpy()
return predicted_labels
# Create a persistent DataFrame to store classification results
results_df = pd.DataFrame(columns=['Text', 'Toxic', 'Severe Toxic', 'Obscene', 'Threat', 'Insult', 'Identity Hate'])
# Streamlit app
def app():
st.title("Toxicity Classification App")
st.write("Enter text below to classify its toxicity.")
# User input
user_input = st.text_area("Enter text here:", "", key='user_input')
# Classification
if st.button("Classify"):
# Perform classification
labels = classify_text(user_input)
# Print classification results
st.write("Classification Results:")
st.write("Toxic: {:.2%}".format(labels[0][0]))
st.write("Severe Toxic: {:.2%}".format(labels[0][1]))
st.write("Obscene: {:.2%}".format(labels[0][2]))
st.write("Threat: {:.2%}".format(labels[0][3]))
st.write("Insult: {:.2%}".format(labels[0][4]))
st.write("Identity Hate: {:.2%}".format(labels[0][5]))
# Add results to persistent DataFrame
results_df.loc[len(results_df)] = [user_input, labels[0][0], labels[0][1], labels[0][2], labels[0][3], labels[0][4], labels[0][5]]
# Show results DataFrame
st.write("Classification Results DataFrame:")
st.write(results_df)
# Run the app
if __name__ == "__main__":
app()
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