Update app.py

app.py

@@ -10,6 +10,10 @@ import pandas as pd
 model_name = "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+# Inicializa Modelo de Clasificación de Sentimientos
+model_name = 'pysentimiento/robertuito-sentiment-analysis'
+classifier_sent = pipeline("text-classification", model=model_name)
+
 
 def classify_text(text):
     """
@@ -19,9 +23,8 @@ def classify_text(text):
     Returns:
     str: La clasificación del texto, que puede ser "Positivo", "Negativo" o "Neutro".
     """
-    analyzer = create_analyzer(task="sentiment", lang="es")
-    result = analyzer.predict(text)[0]['label']
-    #result = classifier(text)[0]['label']
+   
+    result = classifier_sent(text)[0]['label']
     if result == "POS":
         return "Positivo"
     elif result == "NEG":
@@ -37,6 +40,8 @@ def clasificador(input1, input2):
     output0 = classifier(sequence_to_classify, candidate_labels, multi_label=False)
     output1=pd.DataFrame(output0)
     output1=output1.iloc[:,1:3]
+    #analyzer = create_analyzer(task="sentiment", lang="es")
+    #output2 = analyzer.predict(input1)
     output2=classify_text(input1)
     return output1, output2