Upload 2 files

api.py

@@ -800,7 +800,12 @@ def process_video_job(job_id: str):
             }
             job["status"] = JobStatus.DONE
             
-            print(f"[{job_id}] DEBUG - job['results'] guardado: {job['results']}")
+            # Log resumido sin embeddings
+            print(f"[{job_id}] ✓ Resultados guardados:")
+            print(f"[{job_id}]   - Personatges: {len(characters)}")
+            print(f"[{job_id}]   - Segments d'àudio: {len(audio_segments)}")
+            print(f"[{job_id}]   - Face embeddings: {num_face_embeddings}")
+            print(f"[{job_id}]   - Voice embeddings: {len(voice_embeddings)}")
             
         except Exception as e_detect:
             # Si falla la detección, intentar modo fallback

face_classifier.py

@@ -35,7 +35,7 @@ def validate_and_classify_face(image_path: str) -> Optional[Dict[str, Any]]:
     try:
         from deepface import DeepFace
         
-        logger.info(f"[DeepFace] Analitzant: {image_path}")
+        print(f"[DeepFace] Analitzant: {image_path}")
         
         # Analitzar gènere amb detecció de cara
         result = DeepFace.analyze(
@@ -48,11 +48,11 @@ def validate_and_classify_face(image_path: str) -> Optional[Dict[str, Any]]:
         
         # DeepFace pot retornar llista si detecta múltiples cares
         if isinstance(result, list):
-            logger.info(f"[DeepFace] Resultado es lista con {len(result)} elementos")
+            print(f"[DeepFace] Resultado es lista con {len(result)} elementos")
             result = result[0] if result else None
         
         if not result:
-            logger.info(f"[DeepFace] No s'ha detectat cap cara")
+            print(f"[DeepFace] No s'ha detectat cap cara")
             return {
                 'is_valid_face': False,
                 'face_confidence': 0.0,
@@ -63,37 +63,37 @@ def validate_and_classify_face(image_path: str) -> Optional[Dict[str, Any]]:
             }
         
         # LOG: Ver estructura completa del resultado
-        logger.info(f"[DeepFace] Resultado completo de analyze: {result}")
+        print(f"[DeepFace] Resultado completo de analyze: {result}")
         
         # Extreure informació de gènere
         gender_info = result.get('gender', {})
-        logger.info(f"[DeepFace] gender_info type: {type(gender_info)}, value: {gender_info}")
+        print(f"[DeepFace] gender_info type: {type(gender_info)}, value: {gender_info}")
         
         if isinstance(gender_info, dict):
             # DeepFace retorna percentatges, convertir a 0-1
             man_prob = gender_info.get('Man', 0) / 100.0
             woman_prob = gender_info.get('Woman', 0) / 100.0
-            logger.info(f"[DeepFace] Extraído de dict - Man: {man_prob:.3f}, Woman: {woman_prob:.3f}")
+            print(f"[DeepFace] Extraído de dict - Man: {man_prob:.3f}, Woman: {woman_prob:.3f}")
         else:
             # Fallback si el format és diferent
-            logger.warning(f"[DeepFace] gender_info NO es dict, usando fallback 0.5/0.5")
+            print(f"[DeepFace] gender_info NO es dict, usando fallback 0.5/0.5")
             man_prob = 0.5
             woman_prob = 0.5
         
         # Determinar gènere basat en les probabilitats
         gender_diff = abs(man_prob - woman_prob)
         
-        logger.info(f"[DeepFace] Diferencia Man-Woman: {gender_diff:.3f} (threshold neutral={GENDER_NEUTRAL_THRESHOLD})")
+        print(f"[DeepFace] Diferencia Man-Woman: {gender_diff:.3f} (threshold neutral={GENDER_NEUTRAL_THRESHOLD})")
         
         # Si la diferència és petita (< threshold), considerar neutre
         if gender_diff < GENDER_NEUTRAL_THRESHOLD:
             gender = 'Neutral'
             gender_confidence = 0.5
-            logger.info(f"[DeepFace] → Asignado NEUTRAL (diferencia {gender_diff:.3f} < {GENDER_NEUTRAL_THRESHOLD})")
+            print(f"[DeepFace] → Asignado NEUTRAL (diferencia {gender_diff:.3f} < {GENDER_NEUTRAL_THRESHOLD})")
         else:
             gender = 'Man' if man_prob > woman_prob else 'Woman'
             gender_confidence = max(man_prob, woman_prob)
-            logger.info(f"[DeepFace] → Asignado {gender.upper()} (man_prob={man_prob:.3f}, woman_prob={woman_prob:.3f})")
+            print(f"[DeepFace] → Asignado {gender.upper()} (man_prob={man_prob:.3f}, woman_prob={woman_prob:.3f})")
         
         # Confiança de detecció de cara
         # DeepFace no proporciona score directamente en analyze(), pero si retornó resultado
@@ -103,14 +103,14 @@ def validate_and_classify_face(image_path: str) -> Optional[Dict[str, Any]]:
         # Si DeepFace va retornar resultat, assumir que és cara vàlida
         is_valid_face = True
         
-        logger.info(f"[DeepFace] ===== RESUMEN FINAL =====")
-        logger.info(f"[DeepFace]   is_valid_face: {is_valid_face}")
-        logger.info(f"[DeepFace]   face_confidence: {face_confidence:.3f}")
-        logger.info(f"[DeepFace]   gender: {gender}")
-        logger.info(f"[DeepFace]   gender_confidence: {gender_confidence:.3f}")
-        logger.info(f"[DeepFace]   man_prob: {man_prob:.3f}")
-        logger.info(f"[DeepFace]   woman_prob: {woman_prob:.3f}")
-        logger.info(f"[DeepFace] ==========================")
+        print(f"[DeepFace] ===== RESUMEN FINAL =====")
+        print(f"[DeepFace]   is_valid_face: {is_valid_face}")
+        print(f"[DeepFace]   face_confidence: {face_confidence:.3f}")
+        print(f"[DeepFace]   gender: {gender}")
+        print(f"[DeepFace]   gender_confidence: {gender_confidence:.3f}")
+        print(f"[DeepFace]   man_prob: {man_prob:.3f}")
+        print(f"[DeepFace]   woman_prob: {woman_prob:.3f}")
+        print(f"[DeepFace] ==========================")
         
         return {
             'is_valid_face': is_valid_face,
@@ -123,7 +123,7 @@ def validate_and_classify_face(image_path: str) -> Optional[Dict[str, Any]]:
         
     except ValueError as e:
         # ValueError significa que no es va detectar cara
-        logger.info(f"[DeepFace] No s'ha detectat cara (ValueError): {e}")
+        print(f"[DeepFace] No s'ha detectat cara (ValueError): {e}")
         return {
             'is_valid_face': False,
             'face_confidence': 0.0,
@@ -133,7 +133,7 @@ def validate_and_classify_face(image_path: str) -> Optional[Dict[str, Any]]:
             'woman_prob': 0.0
         }
     except Exception as e:
-        logger.warning(f"[DeepFace] Error validant cara: {e}")
+        print(f"[DeepFace] Error validant cara: {e}")
         return None