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| import streamlit as st | |
| from transformers import pipeline, RobertaTokenizerFast, TFRobertaForSequenceClassification, AutoTokenizer, AutoModelForSequenceClassification | |
| # Sentiment Analysis Pipeline | |
| sentiment_pipe = pipeline('sentiment-analysis') | |
| # Toxicity Classifier | |
| model_path_toxic = "citizenlab/distilbert-base-multilingual-cased-toxicity" | |
| toxicity_classifier = pipeline("text-classification", model=model_path_toxic, tokenizer=model_path_toxic) | |
| # Emotion Analysis | |
| tokenizer_emotion = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa") | |
| model_emotion = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa") | |
| emotion = pipeline('sentiment-analysis', model=model_emotion, tokenizer=tokenizer_emotion) | |
| # User Needs Analysis | |
| tokenizer_needs = AutoTokenizer.from_pretrained("thusken/nb-bert-base-user-needs") | |
| model_needs = AutoModelForSequenceClassification.from_pretrained("thusken/nb-bert-base-user-needs") | |
| user_needs = pipeline('text-classification', model=model_needs, tokenizer=tokenizer_needs) | |
| st.title("Plataforma de Diálogos Participativos") | |
| # Text area for input in sidebar | |
| text = st.sidebar.text_area("Añade el texto a evaluar") | |
| # Create columns for buttons in sidebar | |
| col1, col2, col3, col4 = st.sidebar.columns(4) | |
| # Place each button in a separate column | |
| run_sentiment_analysis = col1.button("Evaluar Sentimiento") | |
| run_toxicity_analysis = col2.button("Evaluar Toxicidad") | |
| run_emotion_analysis = col3.button("Evaluar Emoción") | |
| run_user_needs_analysis = col4.button("Evaluar Necesidades del Usuario") | |
| # Container for output in main layout | |
| output_container = st.container() | |
| # Sentiment analysis | |
| if run_sentiment_analysis and text: | |
| with output_container: | |
| sentiment_output = sentiment_pipe(text) | |
| label = sentiment_output[0]['label'] | |
| score = round(sentiment_output[0]['score'] * 100, 2) | |
| st.markdown(f"**Resultado del análisis de sentimiento:**\n\n- **Etiqueta:** {label}\n- **Confianza:** {score}%") | |
| elif run_sentiment_analysis and not text: | |
| st.sidebar.warning("Por favor, añade un texto para evaluar el sentimiento.") | |
| # Toxicity analysis | |
| if run_toxicity_analysis and text: | |
| with output_container: | |
| toxicity_output = toxicity_classifier(text) | |
| label = toxicity_output[0]['label'] | |
| score = round(toxicity_output[0]['score'] * 100, 2) | |
| st.markdown(f"**Resultado del análisis de toxicidad:**\n\n- **Etiqueta:** {label}\n- **Confianza:** {score}%") | |
| elif run_toxicity_analysis and not text: | |
| st.sidebar.warning("Por favor, añade un texto para evaluar la toxicidad.") | |
| # Emotion analysis | |
| if run_emotion_analysis and text: | |
| with output_container: | |
| emotion_output = emotion(text) | |
| label = emotion_output[0]['label'] | |
| score = round(emotion_output[0]['score'] * 100, 2) | |
| st.markdown(f"**Resultado del análisis de emoción:**\n\n- **Etiqueta:** {label}\n- **Confianza:** {score}%") | |
| elif run_emotion_analysis and not text: | |
| st.sidebar.warning("Por favor, añade un texto para evaluar la emoción.") | |
| # User needs analysis | |
| if run_user_needs_analysis and text: | |
| with output_container: | |
| needs_output = user_needs(text) | |
| label = needs_output[0]['label'] | |
| score = round(needs_output[0]['score'] * 100, 2) | |
| st.markdown(f"**Resultado del análisis de necesidades del usuario:**\n\n- **Etiqueta:** {label}\n- **Confianza:** {score}%") | |
| elif run_user_needs_analysis and not text: | |
| st.sidebar.warning("Por favor, añade un texto para evaluar las necesidades del usuario.") | |