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Delete app.py
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Jainesh212
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app.py
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import streamlit as st
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import transformers
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import pandas as pd
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
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# Load the pre-trained BERT model
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model_name = 'nlptown/bert-base-multilingual-uncased-sentiment'
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer, framework='pt', task='text-classification')
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# Define the toxicity classification function
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def classify_toxicity(text):
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result = pipeline(text)[0]
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label = result['label']
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score = result['score']
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return label, score
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# Define the Streamlit app
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def app():
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# Create a persistent DataFrame
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if 'results' not in st.session_state:
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st.session_state.results = pd.DataFrame(columns=['text', 'toxicity', 'score'])
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# Set page title and favicon
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st.set_page_config(page_title='Toxicity Classification App', page_icon=':guardsman:')
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# Set app header
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st.write('# Toxicity Classification App')
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st.write('Enter some text and the app will classify its toxicity.')
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# Create a form for users to enter their text
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with st.form(key='text_form'):
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text_input = st.text_input(label='Enter your text:')
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submit_button = st.form_submit_button(label='Classify')
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# Classify the text and display the results
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if submit_button and text_input != '':
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label, score = classify_toxicity(text_input)
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st.write('## Classification Result')
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st.write(f'**Text:** {text_input}')
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st.write(f'**Toxicity:** {label}')
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st.write(f'**Score:** {score:.2f}')
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# Add the classification result to the persistent DataFrame
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st.session_state.results = st.session_state.results.append({'text': text_input, 'toxicity': label, 'score': score}, ignore_index=True)
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# Display the persistent DataFrame
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st.write('## Classification Results')
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st.write(st.session_state.results)
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# Display a chart of the classification results
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chart_data = st.session_state.results.groupby('toxicity').size().reset_index(name='count')
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chart = st.bar_chart(chart_data.set_index('toxicity'))
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if __name__ == '__main__':
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app()
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