Delete app.py

app.py

@@ -1,59 +0,0 @@
-import streamlit as st
-import transformers
-import pandas as pd
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
-
-# Load the pre-trained BERT model
-model_name = 'nlptown/bert-base-multilingual-uncased-sentiment'
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer, framework='pt', task='text-classification')
-
-# Define the toxicity classification function
-def classify_toxicity(text):
-    result = pipeline(text)[0]
-    label = result['label']
-    score = result['score']
-    return label, score
-
-
-# Define the Streamlit app
-def app():
-    # Create a persistent DataFrame
-    if 'results' not in st.session_state:
-        st.session_state.results = pd.DataFrame(columns=['text', 'toxicity', 'score'])
-    
-    # Set page title and favicon
-    st.set_page_config(page_title='Toxicity Classification App', page_icon=':guardsman:')
-
-    # Set app header
-    st.write('# Toxicity Classification App')
-    st.write('Enter some text and the app will classify its toxicity.')
-
-    # Create a form for users to enter their text
-    with st.form(key='text_form'):
-        text_input = st.text_input(label='Enter your text:')
-        submit_button = st.form_submit_button(label='Classify')
-
-    # Classify the text and display the results
-    if submit_button and text_input != '':
-        label, score = classify_toxicity(text_input)
-        st.write('## Classification Result')
-        st.write(f'**Text:** {text_input}')
-        st.write(f'**Toxicity:** {label}')
-        st.write(f'**Score:** {score:.2f}')
-
-        # Add the classification result to the persistent DataFrame
-        st.session_state.results = st.session_state.results.append({'text': text_input, 'toxicity': label, 'score': score}, ignore_index=True)
-
-    # Display the persistent DataFrame
-    st.write('## Classification Results')
-    st.write(st.session_state.results)
-
-    # Display a chart of the classification results
-    chart_data = st.session_state.results.groupby('toxicity').size().reset_index(name='count')
-    chart = st.bar_chart(chart_data.set_index('toxicity'))
-
-if __name__ == '__main__':
-    app()