CHANGE: Enable debug

src/.env

@@ -0,0 +1,8 @@
+FLASK_DEBUG=true
+FLASK_PORT=5000
+FLASK_HOST=0.0.0.0
+DB_HOST=10.10.5.32
+DB_PORT=5432
+DB_NAME=postgres
+DB_USER=postgres
+DB_PASSWORD=tfmuocdfcarvajal

src/app.py

@@ -1,6 +1,8 @@
 from flask import Flask, jsonify, request
 import threading
+import os
 from datetime import datetime
+from dotenv import load_dotenv
 
 from core.model_loader import ModelManager
 from core.predictor import PredictionHandler
@@ -9,38 +11,66 @@ from config.models_config import ModelConfig
 
 app = Flask(__name__)
 
+# Configuración desde variables de entorno
+load_dotenv()
+DEBUG_MODE = os.getenv('FLASK_DEBUG', 'false').lower() in ('true', '1', 't')
+PORT = int(os.getenv('FLASK_PORT', '5000'))
+HOST = os.getenv('FLASK_HOST', '0.0.0.0')
+
 # Inicializar componentes
 model_config = ModelConfig()
-model_manager = ModelManager(model_config)
+model_manager = ModelManager(model_config, debug_mode=DEBUG_MODE)
 prediction_handler = PredictionHandler(model_manager, model_config)
 model_trainer = ModelTrainer(model_manager, model_config)
 
-# Comprobar que las funciones estan disponibles en el código
-@app.before_first_request
-def check_functions():
-    """Verificar que todas las funciones existen"""
-    from utils.dynamic_loader import execute_function
-    
-    for model_type in model_config.get_all_model_types():
-        config = model_config.get_model_config(model_type)
-        print(f"Verificando modelo: {model_type}")
-        print(f"  Módulo: {config.get('module')}")
-        print(f"  Función entrenamiento: {config.get('train_function')}")
-        print(f"  Función datos: {config.get('data_function')}")
+# Función de debug
+def debug_check_functions():
+    """Verificar que todas las funciones existen (solo en modo debug)"""
+    if DEBUG_MODE:
+        import importlib
+        from utils.dynamic_loader import execute_function
         
-        # Verificar que la función de entrenamiento existe
-        try:
-            module = importlib.import_module(config.get('module'))
-            if hasattr(module, config.get('train_function')):
-                print(f"  ✓ Función de entrenamiento encontrada")
-            else:
-                print(f"  ✗ Función de entrenamiento NO encontrada")
-        except Exception as e:
-            print(f"  ✗ Error: {str(e)}")
+        print("=" * 50)
+        print("DEBUG: Verificando funciones de modelos")
+        print("=" * 50)
+        
+        for model_type in model_config.get_all_model_types():
+            config = model_config.get_model_config(model_type)
+            print(f"\nModelo: {model_type}")
+            print(f"  Descripción: {config.get('description', 'Sin descripción')}")
+            print(f"  Módulo: {config.get('module')}")
+            print(f"  Función entrenamiento: {config.get('train_function')}")
+            print(f"  Función datos: {config.get('data_function')}")
+            print(f"  Parámetros requeridos: {config.get('required_params', [])}")
+            
+            # Verificar que la función de entrenamiento existe
+            try:
+                module = importlib.import_module(config.get('module'))
+                if hasattr(module, config.get('train_function')):
+                    print(f"  ✓ Función de entrenamiento encontrada")
+                else:
+                    print(f"  ✗ Función de entrenamiento NO encontrada")
+                    
+                    # Listar funciones disponibles en el módulo
+                    available_funcs = [f for f in dir(module) if not f.startswith('_')]
+                    print(f"    Funciones disponibles: {', '.join(available_funcs[:10])}")
+                    if len(available_funcs) > 10:
+                        print(f"    ... y {len(available_funcs) - 10} más")
+            except ImportError as e:
+                print(f"  ✗ Error importando módulo: {str(e)}")
+            except Exception as e:
+                print(f"  ✗ Error: {str(e)}")
+        
+        print("\n" + "=" * 50)
+        print(f"DEBUG: Inicializando {len(model_config.get_all_model_types())} modelos")
+        print("=" * 50)
 
 # Inicializar modelos al arrancar
 model_manager.init_models()
 
+# Ejecutar chequeo de debug si está activado
+debug_check_functions()
+
 @app.route("/train", methods=["POST"])
 def train():
     """Entrenar modelos"""
@@ -78,6 +108,11 @@ def register_new_model():
     
     try:
         new_config = model_config.register_model_type(data)
+        
+        if DEBUG_MODE:
+            print(f"DEBUG: Nuevo modelo registrado - {data['type']}")
+            print(f"       Configuración: {new_config}")
+        
         return jsonify({
             "status": "model type registered",
             "model_type": data["type"],
@@ -103,7 +138,8 @@ def list_models():
             "loaded": model is not None,
             "required_params": model_config_info.get("required_params", []),
             "module": model_config_info.get("module"),
-            "train_function": model_config_info.get("train_function")
+            "train_function": model_config_info.get("train_function"),
+            "output_type": model_config_info.get("output_type", "unknown")
         }
         
         # Añadir información del archivo si existe
@@ -125,11 +161,74 @@ def health():
     loaded_models = model_manager.get_loaded_models_status()
     all_models = model_config.get_all_model_types()
     
-    return jsonify({
+    response = {
         "status": "healthy" if all(loaded_models.values()) else "partial",
         "models_loaded": loaded_models,
         "total_configured": len(all_models),
-        "loaded_count": sum(1 for v in loaded_models.values() if v)
+        "loaded_count": sum(1 for v in loaded_models.values() if v),
+        "debug_mode": DEBUG_MODE
+    }
+    
+    if DEBUG_MODE:
+        response["environment"] = {
+            "host": HOST,
+            "port": PORT,
+            "python_version": os.getenv("PYTHON_VERSION", "unknown")
+        }
+    
+    return jsonify(response)
+
+@app.route("/debug", methods=["GET"])
+def debug_info():
+    """Endpoint de información de debug (solo disponible en modo debug)"""
+    if not DEBUG_MODE:
+        return jsonify({"error": "Debug mode is disabled"}), 403
+    
+    import sys
+    import importlib
+    
+    # Información del sistema
+    system_info = {
+        "python_version": sys.version,
+        "platform": sys.platform,
+        "working_directory": os.getcwd(),
+        "environment_variables": {
+            k: v for k, v in os.environ.items() 
+            if k.startswith(('FLASK_', 'DB_', 'PYTHON'))
+        }
+    }
+    
+    # Información de módulos cargados
+    loaded_modules = {}
+    for model_type in model_config.get_all_model_types():
+        config = model_config.get_model_config(model_type)
+        module_name = config.get('module')
+        
+        if module_name in sys.modules:
+            module = sys.modules[module_name]
+            loaded_modules[module_name] = {
+                "file": getattr(module, '__file__', 'unknown'),
+                "functions": [f for f in dir(module) if not f.startswith('_')]
+            }
+    
+    # Información de memoria de modelos
+    models_memory = {}
+    for model_type, model in model_manager.models_cache.items():
+        if model is not None:
+            try:
+                models_memory[model_type] = {
+                    "type": type(model).__name__,
+                    "attributes": [attr for attr in dir(model) if not attr.startswith('_')]
+                }
+            except:
+                models_memory[model_type] = {"type": "unknown"}
+    
+    return jsonify({
+        "system": system_info,
+        "loaded_modules": loaded_modules,
+        "models_in_memory": models_memory,
+        "debug_mode": DEBUG_MODE,
+        "timestamp": datetime.now().isoformat()
     })
 
 @app.route("/", methods=["GET"])
@@ -171,12 +270,31 @@ def index():
         }
     ]
     
+    if DEBUG_MODE:
+        endpoints.append({
+            "path": "/debug",
+            "method": "GET",
+            "description": "Información de debug",
+            "note": "Solo disponible en modo debug"
+        })
+    
     return jsonify({
         "service": "Model Prediction API",
         "version": "1.0.0",
+        "debug_mode": DEBUG_MODE,
         "endpoints": endpoints,
         "available_models": model_config.get_all_model_types()
     })
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=5000, debug=True)
+    print(f"🚀 Iniciando servidor en {HOST}:{PORT}")
+    print(f"🔧 Modo debug: {'ACTIVADO' if DEBUG_MODE else 'DESACTIVADO'}")
+    print(f"📊 Modelos configurados: {len(model_config.get_all_model_types())}")
+    
+    if DEBUG_MODE:
+        print("\n📋 Variables de entorno relevantes:")
+        for key, value in os.environ.items():
+            if key.startswith(('FLASK_', 'DB_')):
+                print(f"  {key}: {value}")
+    
+    app.run(host=HOST, port=PORT, debug=DEBUG_MODE)

src/core/model_loader.py

@@ -2,6 +2,7 @@ import joblib
 import os
 from datetime import date
 from typing import Dict, Any, Optional
+import sys
 
 from config.models_config import ModelConfig
 from utils.dynamic_loader import execute_function
@@ -10,42 +11,70 @@ from model_registry import register, load_meta
 class ModelManager:
     """Gestión de carga y cache de modelos"""
     
-    def __init__(self, model_config: ModelConfig):
+    def __init__(self, model_config: ModelConfig, debug_mode: bool = False):
         self.model_config = model_config
+        self.debug_mode = debug_mode
         self.models_cache: Dict[str, Any] = {}
         self.model_dir = "models"
     
     def init_models(self):
         """Inicializar todos los modelos configurados"""
+        print(f"\n📦 Inicializando {len(self.model_config.get_all_model_types())} modelos...")
+        
         for model_type in self.model_config.get_all_model_types():
             try:
                 self.models_cache[model_type] = self._load_or_train_model(model_type)
-                print(f"✓ Modelo '{model_type}' cargado correctamente")
+                print(f"  ✓ Modelo '{model_type}' cargado correctamente")
             except Exception as e:
-                print(f"✗ Error cargando modelo '{model_type}': {str(e)}")
+                print(f"  ✗ Error cargando modelo '{model_type}': {str(e)}")
+                if self.debug_mode:
+                    import traceback
+                    traceback.print_exc()
                 self.models_cache[model_type] = None
+        
+        loaded_count = sum(1 for v in self.models_cache.values() if v is not None)
+        print(f"\n✅ {loaded_count}/{len(self.models_cache)} modelos inicializados")
     
     def _load_or_train_model(self, model_type: str):
         """Cargar o entrenar un modelo dinámicamente"""
         config = self.model_config.get_model_config(model_type)
         
+        if self.debug_mode:
+            print(f"  [DEBUG] Procesando modelo: {model_type}")
+            print(f"  [DEBUG] Configuración: {config}")
+        
         # Intentar cargar el modelo existente
         model = self._load_model_from_disk(model_type)
         if model is not None:
+            if self.debug_mode:
+                print(f"  [DEBUG] Modelo cargado desde disco: {model_type}")
             return model
         
+        if self.debug_mode:
+            print(f"  [DEBUG] Modelo no encontrado en disco, entrenando nuevo: {model_type}")
+        
         # Si no existe, entrenar nuevo modelo
-        print(f"Modelo {model_type} no se encontro, creación del modelo...")
+        print(f"  🔄 Modelo {model_type} no encontrado, entrenando nuevo...")
         
         # Obtener datos
         df = self._load_data_for_model(model_type, config)
         
+        if df.empty:
+            raise ValueError(f"No se pudieron cargar datos para el modelo {model_type}")
+        
+        if self.debug_mode:
+            print(f"  [DEBUG] Datos cargados: {len(df)} filas, {len(df.columns)} columnas")
+            print(f"  [DEBUG] Columnas: {list(df.columns)}")
+        
         # Entrenar modelo
         model = self._train_model(model_type, config, df)
         
         # Guardar modelo
         filename = self._save_model(model_type, model, len(df))
         
+        if self.debug_mode:
+            print(f"  [DEBUG] Modelo guardado como: {filename}")
+        
         return model
     
     def _load_model_from_disk(self, model_type: str):
@@ -54,34 +83,81 @@ class ModelManager:
         file = meta.get("current", {}).get(model_type)
         
         if not file:
+            if self.debug_mode:
+                print(f"  [DEBUG] No hay metadatos para el modelo: {model_type}")
             return None
         
         model_path = os.path.join(self.model_dir, file)
         if not os.path.exists(model_path):
+            if self.debug_mode:
+                print(f"  [DEBUG] Archivo no encontrado: {model_path}")
             return None
         
+        if self.debug_mode:
+            print(f"  [DEBUG] Cargando modelo desde: {model_path}")
+            file_size = os.path.getsize(model_path)
+            print(f"  [DEBUG] Tamaño del archivo: {file_size / 1024:.2f} KB")
+        
         return joblib.load(model_path)
     
     def _load_data_for_model(self, model_type: str, config: Dict[str, Any]):
         """Cargar datos para un modelo específico"""
-        from db import fetch_data, fetch_data_legacy
-        
         data_function = config.get("data_function")
         
-        if data_function == "fetch_data":
-            return fetch_data()
-        elif data_function == "fetch_data_legacy":
-            return fetch_data_legacy()
-        else:
-            # Ejecutar función personalizada
-            module_name = config.get("module", "models_train")
-            return execute_function(module_name, data_function)
+        if self.debug_mode:
+            print(f"  [DEBUG] Función de datos: {data_function}")
+        
+        try:
+            if data_function == "fetch_data":
+                from db import fetch_data
+                df = fetch_data()
+            elif data_function == "fetch_data_legacy":
+                from db import fetch_data_legacy
+                df = fetch_data_legacy()
+            else:
+                # Ejecutar función personalizada
+                module_name = config.get("module", "models_train")
+                if self.debug_mode:
+                    print(f"  [DEBUG] Ejecutando función personalizada: {module_name}.{data_function}")
+                df = execute_function(module_name, data_function)
+            
+            if self.debug_mode and not df.empty:
+                print(f"  [DEBUG] Datos cargados exitosamente")
+                print(f"  [DEBUG] Forma de los datos: {df.shape}")
+                print(f"  [DEBUG] Primeras filas:\n{df.head()}")
+            
+            return df
+            
+        except Exception as e:
+            if self.debug_mode:
+                import traceback
+                print(f"  [DEBUG] Error cargando datos:")
+                traceback.print_exc()
+            raise ValueError(f"Error cargando datos para {model_type}: {str(e)}")
     
     def _train_model(self, model_type: str, config: Dict[str, Any], df):
         """Entrenar un modelo"""
         module_name = config.get("module", "models_train")
         train_function = config.get("train_function")
-        return execute_function(module_name, train_function, df)
+        
+        if self.debug_mode:
+            print(f"  [DEBUG] Entrenando con: {module_name}.{train_function}")
+        
+        try:
+            model = execute_function(module_name, train_function, df)
+            
+            if self.debug_mode:
+                print(f"  [DEBUG] Modelo entrenado exitosamente")
+                print(f"  [DEBUG] Tipo de modelo: {type(model).__name__}")
+            
+            return model
+            
+        except Exception as e:
+            if self.debug_mode:
+                import traceback
+                print(f"  [DEBUG] Error entrenando modelo:")
+                traceback.print_exc()
+            raise ValueError(f"Error entrenando modelo {model_type}: {str(e)}")
     
     def _save_model(self, model_type: str, model, rows: int) -> str:
         """Guardar modelo en disco"""
@@ -89,11 +165,20 @@ class ModelManager:
         filename = f"{model_type}_xgb_{today}.joblib"
         
         os.makedirs(self.model_dir, exist_ok=True)
-        joblib.dump(model, os.path.join(self.model_dir, filename))
+        model_path = os.path.join(self.model_dir, filename)
+        
+        if self.debug_mode:
+            print(f"  [DEBUG] Guardando modelo en: {model_path}")
+        
+        joblib.dump(model, model_path)
         
         # Registrar en metadata
         register(model_type, filename, rows)
         
+        if self.debug_mode:
+            file_size = os.path.getsize(model_path)
+            print(f"  [DEBUG] Modelo guardado: {file_size / 1024:.2f} KB")
+        
         return filename
     
     def get_model(self, model_type: str):
@@ -103,6 +188,9 @@ class ModelManager:
     def reload_model(self, model_type: str):
         """Recargar un modelo específico"""
         try:
+            if self.debug_mode:
+                print(f"  [DEBUG] Recargando modelo: {model_type}")
+            
             self.models_cache[model_type] = self._load_or_train_model(model_type)
             return True
         except Exception as e:

src/pyproject.toml

@@ -20,7 +20,8 @@ dependencies = [
    'pandas',
    'xgboost',
    'scikit-learn',
-   'joblib'
+   'joblib',
+   'python-dotenv'
 ]
 
 [tool.setuptools]

src/requirements.txt

@@ -4,4 +4,5 @@ xgboost
 scikit-learn
 joblib
 SQLAlchemy
-psycopg2-binary
+psycopg2-binary
+python-dotenv