Corregir visualización de miniaturas y modelos en la tabla de caras registradas

streamlit_app.py

@@ -1751,10 +1751,11 @@ def main():
                 for name, info in st.session_state.face_database.items():
                     # Extract face image if available
                     face_img = None
-                    if 'image' in info and info['image'] is not None:
+                    # Primero verificar si existe 'face_image' (formato nuevo)
+                    if 'face_image' in info and info['face_image'] is not None:
                         try:
-                            if is_valid_image(info['image']):
-                                face_img = info['image']
+                            if is_valid_image(info['face_image']):
+                                face_img = info['face_image']
                             else:
                                 print(f"Invalid face image for {name}")
                         except Exception as e:
@@ -1764,18 +1765,29 @@ def main():
                     num_embeddings = 0
                     models_used = []
                     
-                    if 'embeddings' in info and info['embeddings']:
+                    # Verificar si la estructura usa el formato nuevo con listas
+                    if 'embeddings' in info and isinstance(info['embeddings'], list):
                         num_embeddings = len(info['embeddings'])
-                        # Extract models from embeddings
-                        for emb in info['embeddings']:
-                            if 'model' in emb and emb['model'] not in models_used:
-                                models_used.append(emb['model'])
+                        
+                        # Si hay una lista de modelos, usarla
+                        if 'models' in info and isinstance(info['models'], list):
+                            models_used = info['models']
+                        else:
+                            # Si no, intentar extraer modelos de los embeddings si tienen formato de diccionario
+                            for i, emb in enumerate(info['embeddings']):
+                                if isinstance(emb, dict) and 'model' in emb:
+                                    if emb['model'] not in models_used:
+                                        models_used.append(emb['model'])
+                                else:
+                                    # Si no tiene formato de diccionario, usar un nombre genérico
+                                    if f"Model {i+1}" not in models_used:
+                                        models_used.append(f"Model {i+1}")
                     
                     # Add row to data
                     data.append({
                         "Name": name,
                         "Face": face_img,
-                        "Images": 1 if face_img is not None else 0,
+                        "Images": info.get('count', 1) if face_img is not None else 0,
                         "Embeddings": num_embeddings,
                         "Models": ", ".join(models_used) if models_used else "None"
                     })