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import streamlit as st
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Define the available models to choose from
models = {
'BERT': 'bert-base-uncased',
'RoBERTa': 'roberta-base',
'DistilBERT': 'distilbert-base-uncased'
}
# Create a drop-down menu to select the model
model_name = st.sidebar.selectbox('Select Model', list(models.keys()))
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(models[model_name])
model = AutoModelForSequenceClassification.from_pretrained(models[model_name])
# Define the classes and their corresponding labels
classes = {
0: 'Non-Toxic',
1: 'Toxic',
2: 'Severely Toxic',
3: 'Obscene',
4: 'Threat',
5: 'Insult',
6: 'Identity Hate'
}
# Create a function to generate the toxicity predictions
@st.cache(allow_output_mutation=True)
def predict_toxicity(tweet, model, tokenizer):
# Preprocess the text
inputs = tokenizer(tweet, padding=True, truncation=True, return_tensors='pt')
# Get the predictions from the model
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=1).detach().numpy()
# Get the class with the highest probability
predicted_class = int(predictions.argmax())
predicted_class_label = classes[predicted_class]
predicted_prob = predictions[0][predicted_class]
return predicted_class_label, predicted_prob
# Create a table to display the toxicity predictions
def create_table(predictions):
data = {'Tweet': [], 'Highest Toxicity Class': [], 'Probability': []}
for tweet, prediction in predictions.items():
data['Tweet'].append(tweet)
data['Highest Toxicity Class'].append(prediction[0])
data['Probability'].append(prediction[1])
df = pd.DataFrame(data)
return df
# Create the user interface
st.title('Toxicity Prediction App')
tweet_input = st.text_input('Enter a tweet:')
if st.button('Predict'):
# Generate the toxicity prediction for the tweet using the selected model
predicted_class_label, predicted_prob = predict_toxicity(tweet_input, model, tokenizer)
prediction_text = f'Prediction: {predicted_class_label} ({predicted_prob:.2f})'
st.write(prediction_text)
# Display the toxicity predictions in a table
predictions = {tweet_input: (predicted_class_label, predicted_prob)}
table = create_table(predictions)
st.table(table)
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