egutierrez
aef8791151
- Added Appshell component with responsive navbar and main content area - Integrated ColorSchemeToggle for light/dark mode switching - Created Welcome component with styled title and introductory text - Developed ChatPage for LLM interaction with WebSocket support - Implemented Biblioteca for managing notes with rich text editor - Added LoginPage for user authentication with error handling - Introduced MessageList and MessageBubble components for chat messages - Styled components with CSS modules for consistent design
107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
import os
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import base64
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from dotenv import load_dotenv
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from sqlalchemy import Column, Integer, String
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from domains.ConexionSql.Base_conexion import ConexionBase
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from domains.base import Base
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from domains.ApiKeys.openai_apikey import OpenAICredencial
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from domains.Security.Encriptar import Encriptar_fernet
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from entrypoint import ENV_PATH
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from domains.ArquitectureLayer.Mapper import Mapper_base
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from sqlalchemy import Column, String
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from domains.ArquitectureLayer.Model import Model_base
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from domains.ArquitectureLayer.Repo import Repo_base
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# ----------------------
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# Cargar clave maestra
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# ----------------------
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load_dotenv(ENV_PATH)
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pssword = os.getenv('MASTER_PASSWORD')
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if pssword is None:
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raise ValueError("MASTER_PASSWORD no está definida en el archivo .env")
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# ----------------------
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# MODELO (SQLAlchemy)
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# ----------------------
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class OpenAICredencialModel(Base, Model_base):
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__tablename__ = 'openai_credenciales'
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id = Column(String, primary_key=True)
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titulo = Column(String, nullable=False)
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api_key = Column(String, nullable=False) # Encriptada como base64 string
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organizacion = Column(String, nullable=True)
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# ----------------------
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# MAPPER
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# ----------------------
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class OpenAICredencialMapper(Mapper_base[OpenAICredencial, OpenAICredencialModel]):
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@staticmethod
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def to_model(obj: OpenAICredencial) -> OpenAICredencialModel:
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return OpenAICredencialModel(
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id=obj.id,
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titulo=obj.titulo,
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api_key=base64.b64encode(
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Encriptar_fernet.encriptar(obj.api_key, pssword)
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).decode("utf-8"),
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organizacion=obj.organizacion
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)
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@staticmethod
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def from_model(model: OpenAICredencialModel) -> OpenAICredencial:
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return OpenAICredencial(
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id=model.id,
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titulo=model.titulo,
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api_key=Encriptar_fernet.desencriptar(
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base64.b64decode(model.api_key), pssword
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),
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organizacion=model.organizacion
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)
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@staticmethod
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def to_dict(obj: OpenAICredencial) -> dict:
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return {
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"id": obj.id,
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"titulo": obj.titulo,
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"api_key": base64.b64encode(
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Encriptar_fernet.encriptar(obj.api_key, pssword)
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).decode("utf-8"),
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"organizacion": obj.organizacion
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}
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@staticmethod
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def from_dict(data: dict) -> OpenAICredencial:
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return OpenAICredencial(
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id=data["id"],
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titulo=data["titulo"],
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api_key=Encriptar_fernet.desencriptar(
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base64.b64decode(data["api_key"]), pssword
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),
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organizacion=data.get("organizacion")
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)
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# ----------------------
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# REPO
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# ----------------------
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class OpenAICredencialRepo(Repo_base[OpenAICredencialModel, OpenAICredencial]):
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def __init__(self, conexion: ConexionBase):
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super().__init__(
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session=conexion.get_session(),
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modelo=OpenAICredencialModel,
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mapper=OpenAICredencialMapper
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)
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def get_by_titulo(self, titulo: str) -> OpenAICredencial | None:
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model = (
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self.session.query(self.Modelo)
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.filter_by(titulo=titulo, sys_deleted_at=None)
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.first()
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)
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return self.Mapper.from_model(model) if model else None |