Smart bms gas discord #70
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This commit introduces a complete Smart Business Management System (BMS) built on Google App Script and integrated with a Discord bot. The system includes the following features: - Dynamic Reporting: Generate summaries and detailed views of tasks. - CRM/Personnel Management: Full CRUD operations for managing personnel, customers, and tenants. - Financial Calculations: Automated calculation for rent and payroll. - Document Generation: Automated creation of invoices as Google Docs from templates. The codebase is modular, with separate `.gs` files for each core feature, a central interface for Google Sheet interactions, and a main handler for Discord webhooks. A detailed `README.md` is included with full setup instructions.
Adds full support for a Telegram bot alongside the existing Discord bot. - Updates `Main.gs` to differentiate between incoming webhooks from Discord and Telegram, routing them to the appropriate handlers. - Adds `Telegram_Handler.gs` to parse commands (`/report`, `/detail`, etc.) from Telegram messages. - Adds `Telegram_Interface.gs` to send formatted responses back to the Telegram API. - Updates `README.md` with a new, optional section detailing how to create a Telegram bot with BotFather and set up the webhook. - The `Config` sheet instructions in the `README.md` are updated to include the new `TELEGRAM_BOT_TOKEN`.
Removes the Discord bot integration to streamline the project for Telegram-only use, as requested. - Deletes `Discord_Handler.gs`. - Simplifies the `doPost` function in `Main.gs` to only process Telegram webhooks. - Updates `README.md` to remove all sections related to Discord bot creation, token configuration, and webhook setup. - Renumbers and cleans up the `README.md` to provide a clear setup guide for the Telegram-only version.
Refactors the core logic to process natural language instead of relying on rigid `/command` structures.
- Introduces a new `AI_Processor.gs` module. This module contains a (currently mocked) function to interpret raw user text and convert it into a structured command object (e.g., `{command: "report", parameters: {}}`).
- Updates `Telegram_Handler.gs` to send all user text to `AI_Processor.gs` first. The handler now executes commands based on the structured data returned by the AI processor.
- Updates the `README.md` to include instructions for adding a future `AI_SERVICE_API_KEY` to the `Config` sheet.
- The core modules (`Reporting_Module`, `CRM_Module`, etc.) remain unchanged as their flexible design was already compatible with this new approach.
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This commit introduces a full-featured, open-source Python version of the Smart BMS Bot, migrating from the previous Google App Script implementation. This new version is architected for scalability and advanced functionality, featuring: - **Core Technology:** Python 3 with asynchronous handling via `python-telegram-bot`. - **Natural Language Processing (NLP):** Integrates `spaCy` with the Thai language model (`th_core_web_sm`) to understand natural user commands instead of rigid slash commands. - **Optical Character Recognition (OCR):** Utilizes `Pytesseract` and `Pillow` to extract text from images sent by the user. - **Google Sheets Integration:** Manages database operations using `gspread` and Google Service Account authentication for robust and secure access. - **Modular Structure:** The project is broken down into logical modules (`bot_logic`, `nlp_processor`, `ocr_processor`, `sheets_handler`) for maintainability. - **Configuration Management:** A `config.py` file is used to manage all secrets and settings. - **Dependency Management:** All required libraries are listed in `requirements.txt`. - **Comprehensive Documentation:** A new `README.md` provides detailed setup and usage instructions for the Python environment.
This commit introduces a major architectural update to support Telegram Mini Apps, transforming the project into a full-stack application with a clear separation between the backend and frontend. Key changes include: - **Project Restructure:** The codebase is now split into a `backend` directory for all Python server-side logic and a `frontend` directory for the HTML, CSS, and JS files of the Mini App. - **Flask API Server:** The `main.py` has been upgraded to run a Flask web server. The Telegram bot polling now runs in a dedicated background thread. The Flask server serves the static frontend files. - **API Endpoints:** Added `/api/get_tasks` and `/api/get_personnel` endpoints to the Flask app, allowing the Mini App to fetch live data from the Google Sheet database. - **Mini App Frontend:** Created a functional dashboard UI (`index.html`, `style.css`, `script.js`) that initializes the Telegram Web App SDK, displays user info, and now fetches and renders data from the backend APIs. - **New Bot Command:** A `/dashboard` command has been added. When used, the bot replies with a `ReplyKeyboardMarkup` button that directly opens the Mini App inside the user's Telegram client. - **Documentation & Quality:** The `README.md` has been extensively updated to reflect the new project structure and provides detailed instructions for running the Flask server and using `ngrok` for local development. A `.gitignore` has also been added.
…t the full booking conversation handler. This commit finalizes the migration of the Hotel OS Bot from a Google Colab notebook to a structured and professional Python application. Key changes include: - **Architectural Refactor:** The entire codebase is now modular, with logic separated into handlers, database, processors, and sheets modules. - **Complete Booking Flow:** The previously incomplete multi-step conversation handler for booking reservations (`reserve`) has been fully implemented, allowing users to create new bookings from start to finish. - **Bug Fixes & Robustness:** Corrected several issues, including undefined state variables and missing imports, and improved the overall flow of conversations. - **Documentation:** The `README.md` provides a comprehensive guide to setting up and running the project. The application is now a complete and functional version of the system designed in the notebook, ready for deployment.
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