update def upload_and_analyze

src/expon/presentation/application/internal/commandservices/audio_upload_service.py

@@ -1,7 +1,7 @@
 from uuid import UUID, uuid4
 from datetime import datetime, timezone
 from fastapi import UploadFile
-from pydub.utils import mediainfo
+import os
 
 from src.expon.presentation.domain.model.aggregates.presentation import Presentation
 from src.expon.presentation.domain.model.valueobjects.audio_metadata import AudioMetadata
@@ -10,8 +10,6 @@ from src.expon.presentation.domain.services.sentiment_analysis_service import Se
 from src.expon.presentation.infrastructure.persistence.jpa.repositories.presentation_repository import PresentationRepository
 from src.expon.presentation.infrastructure.services.storage.local_storage_service import LocalStorageService
 
-import os
-
 class AudioUploadService:
     def __init__(
         self,
@@ -27,30 +25,31 @@ class AudioUploadService:
 
     def upload_and_analyze(self, file: UploadFile, user_id: UUID = UUID("00000000-0000-0000-0000-000000000000")):
         # 1. Guardar archivo original
-        file_path = self.storage_service.save(file)
-
+        filename = self.storage_service.save(file)
+        full_path = os.path.join(self.storage_service.base_path, filename)
+        
         # 2. Transcribir directamente con AssemblyAI
-        result = self.transcription_service.transcribe(file_path)
-
+        result = self.transcription_service.transcribe(full_path)
+        
         transcript = result["text"]
         confidence = result.get("confidence", 1.0)
-
-        # 3. Simular metadata básica (AssemblyAI no devuelve duración ni sample_rate)
+        
+        # 3. Simular metadata básica
         metadata = AudioMetadata(
-            duration=0.0,           # Placeholder, se puede estimar si se requiere
-            sample_rate=16000,      # Valor asumido estándar
+            duration=0.0,
+            sample_rate=16000,
             language="es"
         )
-
+        
         # 4. Analizar emoción
         emotion_data = self.sentiment_service.analyze(transcript)
         print("[DEBUG] Transcripción exitosa. Texto:", transcript[:50])
-
+        
         # 5. Crear entidad Presentation
         presentation = Presentation(
             id=uuid4(),
             user_id=user_id,
-            filename=file.filename,
+            filename=filename,
             transcript=transcript,
             dominant_emotion=emotion_data["dominant_emotion"],
             emotion_probabilities=emotion_data["emotion_probabilities"],
@@ -58,14 +57,14 @@ class AudioUploadService:
             metadata=metadata,
             created_at=datetime.now(timezone.utc)
         )
-
+        
         # 6. Guardar en base de datos
         self.repository.save(presentation)
-
+        
         # 7. Eliminar archivo temporal
         try:
-            os.remove(file_path)
-        except Exception:
-            pass
-
+            os.remove(full_path)
+        except Exception as e:
+            print(f"[WARNING] No se pudo eliminar el archivo temporal: {e}")
+            
         return presentation