Update new emociones

src/expon/presentation/domain/services/sentiment_analysis_service.py

@@ -2,6 +2,7 @@ from transformers import pipeline
 import os
 from typing import Dict
 
+# Configuración de entorno para Hugging Face Spaces
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/transformers"
 os.environ["HF_HOME"] = "/tmp/huggingface"
 
@@ -12,7 +13,7 @@ class SentimentAnalysisService:
             self.pipeline = pipeline(
                 "sentiment-analysis",
                 model="finiteautomata/beto-sentiment-analysis",
-                top_k=1
+                top_k=3  # ⬅️ Mostrar las 3 emociones más probables
             )
             print("[LOG] Pipeline cargado correctamente.")
         except Exception as e:
@@ -22,10 +23,27 @@ class SentimentAnalysisService:
     def analyze(self, transcript: str) -> Dict:
         print("[LOG] Análisis de transcripción recibido.")
         try:
-            result = self.pipeline(transcript)[0]
-            print("[LOG] Resultado del modelo:", result)
-            label = result['label'].upper()
-            score = result['score']
+            results = self.pipeline(transcript)
+            print("[LOG] Resultado del modelo:", results)
+
+            # Emoción dominante = la primera (mayor score)
+            dominant = results[0]
+
+            emotion_mapping = {
+                "POS": "entusiasta",
+                "NEU": "neutro",
+                "NEG": "frustrado"
+            }
+
+            dominant_emotion = emotion_mapping.get(dominant['label'], "desconocido")
+            confidence = round(dominant['score'], 2)
+
+            # Crear diccionario de probabilidades mapeadas
+            emotion_probabilities = {
+                emotion_mapping.get(r['label'], r['label']): round(r['score'], 2)
+                for r in results
+            }
+
         except Exception as e:
             print("[ERROR] Falló la predicción:", e)
             return {
@@ -34,18 +52,8 @@ class SentimentAnalysisService:
                 "confidence": 0.0
             }
 
-        emotion_mapping = {
-            "POS": "entusiasta",
-            "NEU": "neutro",
-            "NEG": "frustrado"
-        }
-
-        mapped_emotion = emotion_mapping.get(label, "desconocido")
-
         return {
-            "dominant_emotion": mapped_emotion,
-            "emotion_probabilities": {
-                mapped_emotion: 1.0
-            },
-            "confidence": round(score, 2)
+            "dominant_emotion": dominant_emotion,
+            "emotion_probabilities": emotion_probabilities,
+            "confidence": confidence
         }