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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.
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@@ -188,6 +188,23 @@ async def universal_handler(update: Update, context: ContextTypes.DEFAULT_TYPE)
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await button_dispatcher(update, context)
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async def check_print_confirmation_job(context: ContextTypes.DEFAULT_TYPE) -> None:
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"""
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Job que se ejecuta para verificar la confirmación de impresión.
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"""
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job = context.job
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user_id, job_id, file_name = job.data
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logger.info(f"Running print confirmation check for job_id: {job_id}")
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confirmation_data = await asyncio.to_thread(check_for_confirmation, job_id)
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if confirmation_data:
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await context.bot.send_message(chat_id=user_id, text=f"✅ ¡Éxito! Tu archivo '{file_name}' ha sido impreso correctamente.")
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else:
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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.")
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async def handle_flow_resolution(update: Update, context: ContextTypes.DEFAULT_TYPE, result: dict):
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"""
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Maneja la acción final de un flujo completado.
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@@ -344,14 +361,13 @@ async def handle_flow_resolution(update: Update, context: ContextTypes.DEFAULT_T
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if success:
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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."
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# Esperar y verificar la confirmación
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await asyncio.sleep(60) # Espera de 60 segundos
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confirmation_data = await asyncio.to_thread(check_for_confirmation, job_id)
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if confirmation_data:
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await context.bot.send_message(chat_id=user_id, text=f"✅ ¡Éxito! Tu archivo '{file_name}' ha sido impreso correctamente.")
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else:
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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.")
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# Programar la verificación en segundo plano
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context.job_queue.run_once(
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check_print_confirmation_job,
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when=60, # segundos
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data=(user_id, job_id, file_name),
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name=f"print_job_{job_id}"
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)
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else:
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final_message = "❌ Hubo un error al enviar el archivo a la impresora."
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else:
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