update app.py

app.py

@@ -1,14 +1,77 @@
 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.")
 
-# Using object notation
-add_selectbox = st.sidebar.selectbox(
-    "How would you like to be contacted?",
-    ("Email", "Home phone", "Mobile phone")
-)
-
-# Using "with" notation
-with st.sidebar:
-    add_radio = st.radio(
-        "Choose a shipping method",
-        ("Standard (5-15 days)", "Express (2-5 days)")
-    )