บอทบอท

.github/workflows/generator-generic-ossf-slsa3-publish.yml

@@ -0,0 +1,66 @@
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+# This workflow lets you generate SLSA provenance file for your project.
+# The generation satisfies level 3 for the provenance requirements - see https://slsa.dev/spec/v0.1/requirements
+# The project is an initiative of the OpenSSF (openssf.org) and is developed at
+# https://github.com/slsa-framework/slsa-github-generator.
+# The provenance file can be verified using https://github.com/slsa-framework/slsa-verifier.
+# For more information about SLSA and how it improves the supply-chain, visit slsa.dev.
+
+name: SLSA generic generator
+on:
+  workflow_dispatch:
+  release:
+    types: [created]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    outputs:
+      digests: ${{ steps.hash.outputs.digests }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      # ========================================================
+      #
+      # Step 1: Build your artifacts.
+      #
+      # ========================================================
+      - name: Build artifacts
+        run: |
+            # These are some amazing artifacts.
+            echo "artifact1" > artifact1
+            echo "artifact2" > artifact2
+
+      # ========================================================
+      #
+      # Step 2: Add a step to generate the provenance subjects
+      #         as shown below. Update the sha256 sum arguments
+      #         to include all binaries that you generate
+      #         provenance for.
+      #
+      # ========================================================
+      - name: Generate subject for provenance
+        id: hash
+        run: |
+          set -euo pipefail
+
+          # List the artifacts the provenance will refer to.
+          files=$(ls artifact*)
+          # Generate the subjects (base64 encoded).
+          echo "hashes=$(sha256sum $files | base64 -w0)" >> "${GITHUB_OUTPUT}"
+
+  provenance:
+    needs: [build]
+    permissions:
+      actions: read   # To read the workflow path.
+      id-token: write # To sign the provenance.
+      contents: write # To add assets to a release.
+    uses: slsa-framework/slsa-github-generator/.github/workflows/[email protected]
+    with:
+      base64-subjects: "${{ needs.build.outputs.digests }}"
+      upload-assets: true # Optional: Upload to a new release

.github/workflows/huggingface.yml

@@ -0,0 +1,29 @@
+
+name: Sync to Hugging Face space
+
+on:
+  push:
+    branches:
+      - main # Or whichever branch you want to deploy from
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+
+      - name: Push to HF Space
+        env:
+          HF_SPACE_GIT_REPO: "https://huggingface.co/spaces/nssuwan186/Bot_telegram"
+        run: |
+          git remote add space "https://USER:${{ secrets.HF_TOKEN }}@huggingface.co/spaces/nssuwan186/Bot_telegram"
+          git push --force space main
+
+# IMPORTANT:
+# 1. Replace 'YOUR_HF_USERNAME' with your Hugging Face username.
+# 2. Replace 'YOUR_HF_SPACE_NAME' with your Hugging Face Space name.
+# 3. You must create a secret in your GitHub repository named 'HF_TOKEN'.
+#    This token should be a Hugging Face access token with 'write' permissions.

.gitignore

@@ -1,5 +1,3 @@
-.streamlit/secrets.toml
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -22,6 +20,7 @@ parts/
 sdist/
 var/
 wheels/
+pip-wheel-metadata/
 share/python-wheels/
 *.egg-info/
 .installed.cfg
@@ -51,7 +50,6 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
-cover/
 
 # Translations
 *.mo
@@ -74,7 +72,6 @@ instance/
 docs/_build/
 
 # PyBuilder
-.pybuilder/
 target/
 
 # Jupyter Notebook
@@ -85,35 +82,9 @@ profile_default/
 ipython_config.py
 
 # pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
-.pdm.toml
-.pdm-python
-.pdm-build/
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+.python-version
+
+# PEP 582; used by Poetry, Flit, and PDM
 __pypackages__/
 
 # Celery stuff
@@ -129,8 +100,8 @@ celerybeat.pid
 env/
 venv/
 ENV/
-env.bak/
-venv.bak/
+env.bak
+venv.bak
 
 # Spyder project settings
 .spyderproject
@@ -150,15 +121,8 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
-# pytype static type analyzer
+# pytype static analyzer
 .pytype/
 
 # Cython debug symbols
 cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/

Dockerfile

@@ -0,0 +1,31 @@
+# Use the official Python image
+FROM python:3.10-slim
+
+# Set the working directory
+WORKDIR /app
+
+# Install system dependencies
+# - Tesseract for OCR
+# - Cron for scheduled tasks
+# - Git for Hugging Face integration
+RUN apt-get update && apt-get install -y \
+    tesseract-ocr \
+    cron \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy the requirements file and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Make the startup script executable
+RUN chmod +x /app/start.sh
+
+# Expose the port FastAPI will run on
+EXPOSE 7860
+
+# Set the entrypoint to the startup script
+CMD ["/app/start.sh"]

README.md

@@ -1,19 +1,115 @@
-# 💬 Chatbot template
+# Hotel OS Telegram Bot
 
-A simple Streamlit app that shows how to build a chatbot using OpenAI's GPT-3.5.
+This is a comprehensive Telegram bot designed to act as a "Hotel Operating System". It assists with managing bookings, verifying payments via OCR, tracking expenses, and controlling room hardware (lights, AC) via MQTT.
 
-[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://chatbot-template.streamlit.app/)
+The bot is built with Python, using `python-telegram-bot`, `FastAPI`, and `SQLAlchemy`. It's designed to be deployed as a container on Hugging Face Spaces.
 
-### How to run it on your own machine
+## Features
 
-1. Install the requirements
+- **AI Assistant**: Powered by Google Gemini for general-purpose questions.
+- **Payment Verification**: Upload a payment slip image, and the bot uses Tesseract OCR to extract the customer name and amount, then matches it against an unpaid booking in the database.
+- **Expense Tracking**: Add expenses via the Telegram Web App.
+- **Hardware Control**: Control IoT devices (like lights and AC) using the MQTT protocol, either via commands or the Web App.
+- **Daily Reports**: Get a summary of daily income and expenses with the `/daily_report` command.
+- **Database**: Uses SQLite to store all data, with automatic daily backups sent to your Telegram.
+- **Automated Deployment**: Automatically deploys to a configured Hugging Face Space on push to the `main` branch via GitHub Actions.
 
-   ```
-   $ pip install -r requirements.txt
-   ```
+## Project Structure
 
-2. Run the app
+```
+/hotel-os-bot
+├── .github/workflows/huggingface.yml  # GitHub Action for auto-deployment
+├── app/                               # Main application source code
+│   ├── __init__.py
+│   ├── main.py                        # FastAPI app and Telegram bot logic
+│   ├── config.py                      # Configuration and environment variables
+│   ├── database.py                    # SQLAlchemy models and DB functions
+│   ├── ocr.py                         # Tesseract OCR processing logic
+│   └── mqtt.py                        # MQTT publishing logic
+├── scripts/
+│   └── backup_db.py                   # Script for daily DB backups
+├── webapp/                            # Telegram Web App files
+│   ├── index.html
+│   ├── style.css
+│   └── app.js
+├── .gitignore
+├── Dockerfile                         # Docker container definition for HF Spaces
+├── README.md                          # This file
+├── requirements.txt                   # Python dependencies
+└── start.sh                           # Container startup script
+```
 
-   ```
-   $ streamlit run streamlit_app.py
-   ```
+## Deployment Guide
+
+Follow these steps to get your bot running on Hugging Face Spaces, with automated deployments from your GitHub repository.
+
+### Step 1: Push to GitHub
+
+1.  Create a new **private** repository on GitHub.
+2.  In your local terminal, add the GitHub repository as a remote:
+    ```bash
+    git remote add origin https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git
+    git branch -M main
+    ```
+3.  Commit and push all the generated files:
+    ```bash
+    git add .
+    git commit -m "Initial project setup"
+    git push -u origin main
+    ```
+
+### Step 2: Create a Hugging Face Space
+
+1.  Go to [Hugging Face](https://huggingface.co/new-space) and create a new **Space**.
+2.  **Owner/Space name**: Choose a name for your project (e.g., `hotel-os-bot`).
+3.  **License**: Select `mit` or another license.
+4.  **Select the Space SDK**: Choose **Docker** and `Blank` template.
+5.  **Space hardware**: The free `CPU basic` is sufficient to start.
+6.  **Secrets**: You do not need to set secrets here initially if you are using the GitHub Action workflow, but you will need to add them later for the bot to run.
+7.  Click **Create Space**. The initial build will likely fail, which is normal.
+
+### Step 3: Configure GitHub Actions for Auto-Deployment
+
+1.  **Get Hugging Face Token**: In Hugging Face, go to `Your Profile -> Settings -> Access Tokens`. Create a new token with the `write` role. Copy this token.
+2.  **Add Secret to GitHub**: In your GitHub repository, go to `Settings -> Secrets and variables -> Actions`. Create a **New repository secret**:
+    *   **Name**: `HF_TOKEN`
+    *   **Value**: Paste the Hugging Face token you just copied.
+3.  **Update Workflow File**: Open `.github/workflows/huggingface.yml` in your local project. Replace the placeholder `YOUR_HF_USERNAME` and `YOUR_HF_SPACE_NAME` with your actual HF username and Space name.
+    ```yaml
+    # ...
+    env:
+      HF_SPACE_GIT_REPO: "https://huggingface.co/spaces/your-hf-username/your-hf-space-name"
+    run: |
+      git remote add space "https://USER:${{ secrets.HF_TOKEN }}@huggingface.co/spaces/your-hf-username/your-hf-space-name"
+      git push --force space main
+    ```
+4.  Commit and push this change. This will trigger the first deployment from GitHub Actions. You can monitor its progress in the "Actions" tab of your GitHub repository.
+
+### Step 4: Configure Bot Secrets in Hugging Face Space
+
+For your bot to run correctly, it needs its API keys. These **must** be set in the Hugging Face Space settings, not in your code.
+
+1.  In your HF Space, go to the `Settings` tab.
+2.  Scroll down to **Repository secrets** and click **New secret**.
+3.  Add the following secrets one by one:
+
+    *   `TELEGRAM_BOT_TOKEN`: Your token from BotFather.
+    *   `TELEGRAM_USER_ID`: Your numeric Telegram user ID. You can get this from a bot like `@userinfobot`.
+    *   `GEMINI_API_KEY`: Your API key for Google Gemini.
+    *   `MQTT_BROKER`: (Optional) The address of your MQTT broker. Defaults to `broker.hivemq.com`.
+    *   `MQTT_TOPIC_PREFIX`: (Optional) The base topic for your MQTT devices. Defaults to `hotel/room1`.
+
+### Step 5: Update Web App URL and Final Test
+
+1.  **Update URL**: Once your space is deployed, it will have a URL like `https://your-username-your-space-name.hf.space`. You need to put this URL into the `app/main.py` file.
+    ```python
+    # in app/main.py, inside start_command function
+    web_app_button = KeyboardButton(
+        "Open Hotel OS Web App",
+        web_app=WebAppInfo(url=f"https://your-hf-username-your-hf-space-name.hf.space/static/index.html")
+    )
+    ```
+    *Note: The URL should point to the static HTML file.* 
+
+2.  Commit and push this final change. The GitHub Action will run again and deploy the updated code.
+3.  **Test**: Open Telegram and talk to your bot. Use the `/start` command to see the Web App button and test all features.