Update app.py

app.py

@@ -1,32 +1,39 @@
 import streamlit as st
 from transformers import pipeline
 
-# Define the sentiment analysis pipeline
+# Sentiment Analysis Pipeline
 pipe = pipeline('sentiment-analysis')
 
-st.title("Plataforma de Diálogos Participativos")
-
-# Split the page into two columns. The first column will occupy 1/4 of the page width and the second column 3/4.
-col1, col2 = st.beta_columns([1,3])
-
-with col1:
-    # Text area for input
-    text = st.text_area("Añade el texto a evaluar")
-
-    # Buttons for different analysis (for this example, I'm assuming just sentiment analysis, you can add more)
-    run_analysis = st.button("Evaluar Sentimiento")
-
-with col2:
-    st.write("Resultados")
-    
-    # Create a container for output to occupy the lower half of the layout
-    output_container = st.beta_container()
-
-    if run_analysis and text:
-        with output_container:
-            out = pipe(text)
-            st.json(out)
-    elif run_analysis and not text:
-        st.warning("Por favor, añade un texto para evaluar.")
+# 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 buttons for different analyses
+run_sentiment_analysis = st.button("Evaluar Sentimiento")
+run_toxicity_analysis = st.button("Evaluar Toxicidad")
+
+# Container for output
+output_container = st.beta_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.")