🥩 Autonomous Dining Hall Macro Optimizer

A serverless Python pipeline that scrapes university dining hall menus, bypasses enterprise web application firewalls (WAF), and utilizes an LLM to generate an optimized, high-protein daily meal plan delivered directly to Discord.

🚀 The Architecture

Traditional scraping methods against the university's FoodPro software trigger an HTTP 403 Forbidden block when executed from cloud data centers (like Azure/GitHub) due to aggressive rate-limiting on the hidden label.aspx macro pages.

This project circumvents the firewall by:

1. Executing a single, stealthy GET request to extract the raw, unstructured HTML of the daily menu.
2. Passing the unstructured text to Google's Gemini 2.5 Flash API.
3. Utilizing prompt engineering to heuristically parse the menu, estimate the macronutrients, and mathematically optimize a 1500-calorie, high-protein payload.
4. Dispatching the formatted results via a Discord Webhook.

This entire process runs autonomously every morning at 6:30 AM via GitHub Actions.

📸 Output Demonstration

(Insert a screenshot of the Discord output here)

🛠️ Tech Stack

• Language: Python 3.x
• Parsing: BeautifulSoup4, Requests
• AI Integration: Google Generative AI SDK (google-genai)
• Automation: GitHub Actions (cron scheduling)

⚙️ Deployment Instructions

To deploy your own instance of this optimizer:

1. Fork this repository.
2. Obtain a free Google AI Studio API Key.
3. Create a Discord Webhook URL in your private server.
4. Navigate to your repository's Settings > Secrets and variables > Actions.
5. Add the following Repository Secrets:
   • GEMINI_API_KEY: Your Google API key.
   • DISCORD_WEBHOOK: Your Discord webhook URL.
6. The GitHub Action will automatically trigger daily at 06:30 AM server time, or you can trigger it manually via the Actions tab.

⚠️ Security Notice

Never hardcode your API keys or Webhook URLs directly into main.py. This repository relies exclusively on environment variables injected at runtime to maintain cryptographic hygiene.