Files
talia_bot/talia_bot/main.py
google-labs-jules[bot] 8cd1fd2782 feat: Implement JSON-driven conversational flow engine
This commit completely refactors the bot's architecture to use a generic, JSON-driven flow engine, replacing the previous hardcoded `ConversationHandler` logic.

Key changes include:
- **New Flow Engine:** Introduces `talia_bot/modules/flow_engine.py` to manage conversational state based on definitions in `talia_bot/data/flows.json`.
- **Centralized Flow Definitions:** All conversational flows for Admin, Crew, and Client roles are now defined in `talia_bot/data/flows.json`.
- **Persistent Conversations:** Adds a `conversations` table to the database (`talia_bot/db.py`) to persist user state, making flows robust across bot restarts.
- **Universal Handler:** Refactors `main.py` to use a `universal_handler` that processes all user input (text, audio, documents, callbacks) and routes it through the flow engine.
- **Asynchronous Refactoring:** Converts key modules (`vikunja.py`, `llm_engine.py`) to be fully asynchronous using `httpx` and `openai` async clients for better performance.
- **Non-Blocking Print Jobs:** Replaces the blocking `asyncio.sleep` in the print confirmation flow with a non-blocking `JobQueue` background task, ensuring the bot remains responsive.
- **New Modules:** Adds `mailer.py`, `imap_listener.py`, and `transcription.py` to support the print and audio transcription flows.
- **Updated Documentation:** The `README.md` and `.env.example` have been comprehensively updated to reflect the new architecture, configuration, and setup instructions.
2025-12-21 02:58:30 +00:00

493 lines
21 KiB
Python

# talia_bot/main.py
# Este es el archivo principal del bot. Aquí se inicia todo y se configuran los comandos.
import logging
import asyncio
from telegram import Update
from telegram.ext import (
Application,
CommandHandler,
CallbackQueryHandler,
ConversationHandler,
MessageHandler,
ContextTypes,
filters,
)
# Importamos las configuraciones y herramientas que creamos en otros archivos
from talia_bot.config import TELEGRAM_BOT_TOKEN
from talia_bot.modules.identity import get_user_role
from talia_bot.modules.onboarding import handle_start as onboarding_handle_start
from talia_bot.modules.onboarding import get_admin_secondary_menu
from talia_bot.modules.agenda import get_agenda
from talia_bot.modules.citas import request_appointment
from talia_bot.modules.equipo import (
propose_activity_start,
get_description,
get_duration,
cancel_proposal,
view_requests_status,
DESCRIPTION,
DURATION,
)
from talia_bot.modules.aprobaciones import view_pending, handle_approval_action
from talia_bot.modules.servicios import get_service_info
from talia_bot.modules.admin import get_system_status
from talia_bot.modules.debug import print_handler
import json
from telegram import InlineKeyboardButton, InlineKeyboardMarkup
import io
from talia_bot.modules.vikunja import get_projects, add_comment_to_task, update_task_status, get_project_tasks, create_task
from talia_bot.db import setup_database
from talia_bot.modules.flow_engine import FlowEngine
from talia_bot.modules.transcription import transcribe_audio
import uuid
from talia_bot.modules.llm_engine import analyze_client_pitch
from talia_bot.modules.calendar import create_event
from talia_bot.modules.mailer import send_email_with_attachment
from talia_bot.modules.imap_listener import check_for_confirmation
from talia_bot.config import ADMIN_ID, VIKUNJA_INBOX_PROJECT_ID
from talia_bot.scheduler import schedule_daily_summary
# Configuramos el sistema de logs para ver mensajes de estado en la consola
logging.basicConfig(
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO
)
logger = logging.getLogger(__name__)
# Instanciamos el motor de flujos
flow_engine = FlowEngine()
async def send_step_message(update: Update, context: ContextTypes.DEFAULT_TYPE, step: dict, collected_data: dict = None):
"""
Envía el mensaje de un paso del flujo, construyendo el teclado dinámicamente.
"""
keyboard = []
input_type = step.get("input_type")
collected_data = collected_data or {}
if input_type == "keyboard" and "options" in step:
for option in step["options"]:
keyboard.append([InlineKeyboardButton(option, callback_data=option)])
elif input_type == "dynamic_keyboard_vikunja":
projects = await get_projects()
if projects:
for project in projects:
keyboard.append([InlineKeyboardButton(project['title'], callback_data=f"project_{project['id']}")])
else:
await update.effective_message.reply_text("No se pudieron cargar los proyectos de Vikunja.")
return
elif input_type == "dynamic_keyboard_vikunja_tasks":
project_id_str = collected_data.get('PROJECT_SELECT', '').split('_')[-1]
if project_id_str.isdigit():
project_id = int(project_id_str)
tasks = await get_project_tasks(project_id)
if tasks:
for task in tasks:
keyboard.append([InlineKeyboardButton(task['title'], callback_data=f"task_{task['id']}")])
else:
await update.effective_message.reply_text("Este proyecto no tiene tareas. Puedes añadir una o seleccionar otro proyecto.")
# Aquí podríamos opcionalmente terminar el flujo o devolver al paso anterior.
return
else:
await update.effective_message.reply_text("Error: No se pudo identificar el proyecto para buscar tareas.")
return
reply_markup = InlineKeyboardMarkup(keyboard) if keyboard else None
# Si la actualización es de un botón, edita el mensaje. Si no, envía uno nuevo.
if update.callback_query:
await update.callback_query.edit_message_text(
text=step["question"], reply_markup=reply_markup, parse_mode='Markdown'
)
else:
await update.message.reply_text(
text=step["question"], reply_markup=reply_markup, parse_mode='Markdown'
)
async def universal_handler(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""
Handler universal que gestiona todos los flujos de conversación.
"""
user_id = update.effective_user.id
user_role = get_user_role(user_id)
state = flow_engine.get_conversation_state(user_id)
if state:
response_data = None
if update.callback_query:
response_data = update.callback_query.data
await update.callback_query.answer()
elif update.message and update.message.text:
response_data = update.message.text
elif update.message and update.message.voice:
voice_file = await update.message.voice.get_file()
file_buffer = io.BytesIO()
await voice_file.download_to_memory(file_buffer)
file_buffer.seek(0)
file_buffer.name = "voice_message.oga"
await update.message.reply_text("Transcribiendo audio... ⏳")
response_data = await transcribe_audio(file_buffer)
if response_data is None:
await update.message.reply_text("Lo siento, no pude entender el audio. ¿Podrías intentarlo de nuevo?")
return
elif update.message and update.message.document:
# Guardamos la información del archivo para el paso de resolución
response_data = {
"file_id": update.message.document.file_id,
"file_name": update.message.document.file_name,
}
if response_data:
result = flow_engine.handle_response(user_id, response_data)
if result.get("status") == "in_progress":
# Pasamos los datos recolectados para que el siguiente paso los pueda usar si es necesario
current_state = flow_engine.get_conversation_state(user_id)
await send_step_message(update, context, result["step"], current_state.get("collected_data"))
elif result.get("status") == "complete":
await handle_flow_resolution(update, context, result)
elif result.get("status") == "error":
await update.effective_message.reply_text(f"Error: {result.get('message', 'Ocurrió un error.')}")
return
trigger = None
is_callback = False
if update.callback_query:
trigger = update.callback_query.data
is_callback = True
await update.callback_query.answer()
elif update.message and update.message.text:
trigger = update.message.text
# Flujo automático para clientes
if not trigger and user_role == 'client' and not state:
flow_to_start = next((f for f in flow_engine.flows if f.get("trigger_automatic")), None)
if flow_to_start:
logger.info(f"Starting automatic flow '{flow_to_start['id']}' for client {user_id}")
initial_step = flow_engine.start_flow(user_id, flow_to_start['id'])
if initial_step:
await send_step_message(update, context, initial_step)
return
if trigger:
for flow in flow_engine.flows:
if trigger == flow.get('trigger_button') or trigger == flow.get('trigger_command'):
logger.info(f"Starting flow '{flow['id']}' for user {user_id} via trigger '{trigger}'")
initial_step = flow_engine.start_flow(user_id, flow['id'])
if initial_step:
await send_step_message(update, context, initial_step)
return
# Si ninguna acción de flujo se disparó y es un callback, podría ser una acción del menú principal
if is_callback:
logger.info(f"Callback '{trigger}' no fue manejado por el motor de flujos. Pasando al dispatcher legado.")
await button_dispatcher(update, context)
async def check_print_confirmation_job(context: ContextTypes.DEFAULT_TYPE) -> None:
"""
Job que se ejecuta para verificar la confirmación de impresión.
"""
job = context.job
user_id, job_id, file_name = job.data
logger.info(f"Running print confirmation check for job_id: {job_id}")
confirmation_data = await asyncio.to_thread(check_for_confirmation, job_id)
if confirmation_data:
await context.bot.send_message(chat_id=user_id, text=f"✅ ¡Éxito! Tu archivo '{file_name}' ha sido impreso correctamente.")
else:
await context.bot.send_message(chat_id=user_id, text=f"⚠️ El trabajo de impresión para '{file_name}' fue enviado, pero no he recibido una confirmación de la impresora. Por favor, verifica la bandeja de la impresora.")
async def handle_flow_resolution(update: Update, context: ContextTypes.DEFAULT_TYPE, result: dict):
"""
Maneja la acción final de un flujo completado.
"""
resolution_step = result.get("resolution")
collected_data = result.get("data", {})
if not resolution_step:
logger.info(f"Flujo completado sin paso de resolución. Datos: {collected_data}")
final_message = "Proceso completado. ✅"
if update.callback_query:
await update.callback_query.edit_message_text(final_message)
else:
await update.effective_message.reply_text(final_message)
return
resolution_type = resolution_step.get("input_type")
final_message = resolution_step.get("question", "Hecho. ✅")
logger.info(f"Resolviendo flujo con tipo '{resolution_type}' y datos: {collected_data}")
# Lógica de resolución
if resolution_type == "resolution_api_success":
action = collected_data.get("ACTION_TYPE")
task_id_str = collected_data.get("TASK_SELECT", "").split('_')[-1]
update_content = collected_data.get("UPDATE_CONTENT")
if task_id_str.isdigit():
task_id = int(task_id_str)
if action == "💬 Agregar Comentario":
await add_comment_to_task(task_id=task_id, comment=update_content)
elif action == "🔄 Actualizar Estatus":
await update_task_status(task_id=task_id, status_text=update_content)
elif action == "✅ Marcar Completado":
await update_task_status(task_id=task_id, is_done=True)
elif resolution_type == "resolution_notify_admin":
admin_id = context.bot_data.get("ADMIN_ID", ADMIN_ID) # Obtener ADMIN_ID de config
if admin_id:
user_info = (
f"✨ **Nueva Solicitud de Onboarding** ✨\n\n"
f"Un nuevo candidato ha completado el formulario:\n\n"
f"👤 **Nombre:** {collected_data.get('ONBOARD_START', 'N/A')}\n"
f"🏢 **Base:** {collected_data.get('ONBOARD_ORIGIN', 'N/A')}\n"
f"📧 **Email:** {collected_data.get('ONBOARD_EMAIL', 'N/A')}\n"
f"📱 **Teléfono:** {collected_data.get('ONBOARD_PHONE', 'N/A')}\n\n"
f"Por favor, revisa y añade al usuario al sistema si es aprobado."
)
await context.bot.send_message(chat_id=admin_id, text=user_info, parse_mode='Markdown')
elif resolution_type == "rag_analysis_resolution":
pitch = collected_data.get("IDEA_PITCH")
display_name = update.effective_user.full_name
final_message = await analyze_client_pitch(pitch, display_name)
elif resolution_type == "resolution_event_created":
from dateutil.parser import parse
from datetime import datetime, timedelta
date_str = collected_data.get("BLOCK_DATE", "Hoy")
time_str = collected_data.get("BLOCK_TIME", "")
title = collected_data.get("BLOCK_TITLE", "Bloqueado por Talia")
try:
# Interpretar la fecha
if date_str.lower() == 'hoy':
start_date = datetime.now()
elif date_str.lower() == 'mañana':
start_date = datetime.now() + timedelta(days=1)
else:
start_date = parse(date_str)
# Interpretar el rango de tiempo
time_parts = [part.strip() for part in time_str.replace('a', '-').split('-')]
start_time_obj = parse(time_parts[0])
end_time_obj = parse(time_parts[1])
start_time = start_date.replace(hour=start_time_obj.hour, minute=start_time_obj.minute, second=0, microsecond=0)
end_time = start_date.replace(hour=end_time_obj.hour, minute=end_time_obj.minute, second=0, microsecond=0)
except (ValueError, IndexError):
final_message = "❌ Formato de fecha u hora no reconocido. Por favor, usa algo como 'Hoy', 'Mañana', o '10am - 11am'."
if update.callback_query:
await update.callback_query.edit_message_text(final_message)
else:
await update.effective_message.reply_text(final_message)
return
event = await asyncio.to_thread(
create_event,
summary=title,
start_time=start_time,
end_time=end_time,
attendees=[] # Añadir asistentes si fuera necesario
)
if not event:
final_message = "❌ Hubo un error al crear el evento en el calendario."
elif resolution_type == "resolution_saved":
idea_action = collected_data.get("IDEA_ACTION")
idea_content = collected_data.get('IDEA_CONTENT', 'N/A')
if idea_action == "✅ Crear Tarea":
if VIKUNJA_INBOX_PROJECT_ID:
new_task = await create_task(
project_id=int(VIKUNJA_INBOX_PROJECT_ID),
title=idea_content
)
if new_task:
final_message = "Tarea creada exitosamente en tu bandeja de entrada de Vikunja."
else:
final_message = "❌ Hubo un error al crear la tarea en Vikunja."
else:
final_message = "❌ Error: El ID del proyecto de bandeja de entrada de Vikunja no está configurado."
elif idea_action == "📓 Guardar Nota":
admin_id = ADMIN_ID
idea_category = collected_data.get('IDEA_CATEGORY', 'N/A')
message = (
f"🧠 **Nueva Idea Capturada (Guardada como Nota)** 🧠\n\n"
f"**Categoría:** {idea_category}\n\n"
f"**Contenido:**\n{idea_content}"
)
await context.bot.send_message(chat_id=admin_id, text=message, parse_mode='Markdown')
elif resolution_type == "resolution_email_sent":
file_info = collected_data.get("UPLOAD_FILE")
user_id = update.effective_user.id
if isinstance(file_info, dict):
file_id = file_info.get("file_id")
file_name = file_info.get("file_name")
if file_id and file_name:
job_id = str(uuid.uuid4())
subject_data = {
"job_id": job_id,
"telegram_id": user_id,
"filename": file_name
}
subject = f"DATA:{json.dumps(subject_data)}"
file_obj = await context.bot.get_file(file_id)
file_buffer = io.BytesIO()
await file_obj.download_to_memory(file_buffer)
file_buffer.seek(0)
success = await send_email_with_attachment(
file_content=file_buffer.getvalue(),
filename=file_name,
subject=subject
)
if success:
final_message = f"Recibido. 📨\n\nTu trabajo de impresión ha sido enviado (Job ID: {job_id}). Te notificaré cuando la impresora confirme que ha sido impreso."
# Programar la verificación en segundo plano
context.job_queue.run_once(
check_print_confirmation_job,
when=60, # segundos
data=(user_id, job_id, file_name),
name=f"print_job_{job_id}"
)
else:
final_message = "❌ Hubo un error al enviar el archivo a la impresora."
else:
final_message = "❌ No se encontró la información del archivo."
else:
final_message = "❌ Error en el formato de los datos del archivo."
elif resolution_type == "system_output_nfc":
# Lógica para devolver un JSON con los datos para el tag NFC
nfc_data = {
"name": collected_data.get("WIZARD_START"),
"employee_id": collected_data.get("NUM_EMP"),
"branch": collected_data.get("SUCURSAL"),
"telegram_id": collected_data.get("TELEGRAM_ID"),
}
final_message = f"```json\n{json.dumps(nfc_data, indent=2)}\n```"
# Enviar el mensaje de confirmación final
if update.callback_query:
await update.callback_query.edit_message_text(final_message)
else:
await update.effective_message.reply_text(final_message)
async def start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""
Se ejecuta cuando el usuario escribe /start.
Muestra un mensaje de bienvenida y un menú según el rol del usuario.
"""
chat_id = update.effective_chat.id
user_role = get_user_role(chat_id)
logger.info(f"Usuario {chat_id} inició conversación con el rol: {user_role}")
response_text, reply_markup = onboarding_handle_start(user_role)
await update.message.reply_text(response_text, reply_markup=reply_markup, parse_mode='Markdown')
async def button_dispatcher(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""
Dispatcher legado para manejar botones que no inician flujos.
"""
query = update.callback_query
# No se necesita await query.answer() aquí porque ya se llamó en universal_handler
logger.info(f"Dispatcher legado manejando consulta: {query.data}")
response_text = "Acción no reconocida."
reply_markup = None
simple_handlers = {
'view_agenda': get_agenda,
'view_requests_status': view_requests_status,
'schedule_appointment': request_appointment,
'get_service_info': get_service_info,
'view_system_status': get_system_status,
'manage_users': lambda: "Función de gestión de usuarios no implementada.",
}
complex_handlers = {
'admin_menu': get_admin_secondary_menu,
'view_pending': view_pending,
}
try:
if query.data in simple_handlers:
handler = simple_handlers[query.data]
if asyncio.iscoroutinefunction(handler):
response_text = await handler()
else:
response_text = handler()
elif query.data in complex_handlers:
handler = complex_handlers[query.data]
if asyncio.iscoroutinefunction(handler):
response_text, reply_markup = await handler()
else:
response_text, reply_markup = handler()
elif query.data.startswith(('approve:', 'reject:')):
response_text = handle_approval_action(query.data)
elif query.data == 'start_create_tag':
response_text = "Para crear un tag, por favor usa el comando /create_tag."
else:
# Si llega aquí, es una acción que ni el motor ni el dispatcher conocen.
await query.edit_message_text(text=f"Lo siento, la acción '{query.data}' no se reconoce.")
return
except Exception as exc:
logger.exception(f"Error al procesar la acción {query.data} en el dispatcher legado: {exc}")
response_text = "❌ Ocurrió un error al procesar tu solicitud."
reply_markup = None
await query.edit_message_text(text=response_text, reply_markup=reply_markup, parse_mode='Markdown')
def main() -> None:
"""Función principal que arranca el bot."""
if not TELEGRAM_BOT_TOKEN:
logger.error("TELEGRAM_BOT_TOKEN no está configurado en las variables de entorno.")
return
setup_database()
application = Application.builder().token(TELEGRAM_BOT_TOKEN).build()
schedule_daily_summary(application)
# Handlers principales
application.add_handler(CommandHandler("start", start))
application.add_handler(CommandHandler("print", print_handler))
# El handler universal para flujos (prioridad 0)
application.add_handler(CallbackQueryHandler(universal_handler), group=0)
# El dispatcher legado se mantiene para callbacks no manejados por el motor de flujos (prioridad 1)
# Nota: La lógica de paso ahora está dentro del universal_handler
application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, universal_handler), group=0)
application.add_handler(MessageHandler(filters.VOICE, universal_handler), group=0)
application.add_handler(MessageHandler(filters.Document.ALL, universal_handler), group=0)
logger.info("Iniciando Talía Bot...")
application.run_polling()
if __name__ == "__main__":
main()