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| import streamlit as st | |
| from transformers import pipeline | |
| # Sentiment Analysis Pipeline | |
| pipe = pipeline('sentiment-analysis') | |
| # Toxicity Classifier | |
| model_path = "citizenlab/distilbert-base-multilingual-cased-toxicity" | |
| toxicity_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path) | |
| st.title("Plataforma de Diálogos Participativos") | |
| # Text area for input | |
| text = st.text_area("Añade el texto a evaluar") | |
| # Create columns for buttons | |
| col1, col2 = st.columns(2) | |
| # Place each button in a separate column to make them appear on the same row | |
| run_sentiment_analysis = col1.button("Evaluar Sentimiento") | |
| run_toxicity_analysis = col2.button("Evaluar Toxicidad") | |
| # Container for output | |
| output_container = st.container() | |
| # Check if the sentiment analysis button has been pressed and if there's text in the text area | |
| if run_sentiment_analysis and text: | |
| with output_container: | |
| sentiment_output = pipe(text) | |
| st.write("Resultado del análisis de sentimiento:") | |
| st.json(sentiment_output) | |
| elif run_sentiment_analysis and not text: | |
| st.warning("Por favor, añade un texto para evaluar el sentimiento.") | |
| # Check if the toxicity analysis button has been pressed and if there's text in the text area | |
| if run_toxicity_analysis and text: | |
| with output_container: | |
| toxicity_output = toxicity_classifier(text) | |
| st.write("Resultado del análisis de toxicidad:") | |
| st.json(toxicity_output) | |
| elif run_toxicity_analysis and not text: | |
| st.warning("Por favor, añade un texto para evaluar la toxicidad.") | |