Restaurant AI Assistant

Welcome to the Restaurant AI Assistant, an intelligent chatbot designed to enhance the dining experience for customers of Bottega Restaurant.

Table of Contents

• Features
• Demo
• Installation
• Usage
• Technologies Used
• Contributing
• License

Features

• Menu Information: Fetch details about menu items, including descriptions and prices.
• Order Management: Place orders, modify cart contents, and check order status.
• Personalization: See your previous order, get recommendations from AI.

Demo

Set Up

Enter your API keys in the env file

ANTHROPIC_API_KEY (https://console.anthropic.com/)

TWILIO_ACCOUNT_SID (https://console.twilio.com/)

TWILIO_AUTH_TOKEN (https://console.twilio.com/)

twilio_phone_number

restaurant_phone_number

(Recommended) Setup LangChain API for tracing and monitoring (https://smith.langchain.com)

LANGCHAIN_API_KEY

LANGCHAIN_TRACING_V2 = True

LANGCHAIN_PROJECT = "Project Name of Choice"

STRIPE_SECRET_KEY

Usage

1. Start the Flask server:

   python app.py
   

2. Open your web browser and navigate to http://localhost:5000

3. Start interacting with the AI Assistant to explore menu items, place orders, or get assistance with your dining experience.

Technologies Used

• Python
• Flask
• SQLite
• LangChain + LangGraph
• Anthropic Claude Sonnet 3.5
• React (for the frontend)
• Twilio API for text messaging feature
• Stripe API for payments

Contributing

We welcome contributions to improve the Restaurant AI Assistant. Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Commit your changes (git commit -m 'Add some amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

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Deploying Aramex Chatbot to Google Cloud Platform

Prerequisites

• Mac M1 computer
• Docker Desktop installed and running
• Google Cloud SDK installed
• A Google Cloud Platform account with a project set up
• Git repository with the Aramex chatbot code

Step 1: Prepare the Local Environment

1. Open Terminal and navigate to your project directory.

2. Ensure you have a .env file in your project root with all necessary environment variables:

   ANTHROPIC_API_KEY=your_anthropic_api_key
   TWILIO_ACCOUNT_SID=your_twilio_account_sid
   TWILIO_AUTH_TOKEN=your_twilio_auth_token
   LANGCHAIN_API_KEY=your_langchain_api_key
   LANGCHAIN_TRACING_V2=true
   LANGCHAIN_PROJECT=ARABOT_DEMO_1
   twilio_phone_number=your_twilio_phone_number
   restaurant_phone_number=your_restaurant_phone_number
   

3. Update the app.py to use the correct port:

   if __name__ == '__main__':
       app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))

Step 2: Create and Configure Dockerfile

Create a Dockerfile in your project root with the following content:

FROM --platform=linux/amd64 python:3.11-slim

WORKDIR /app

COPY build ./build
COPY app.py .
COPY requirements.txt .
COPY customer_chatbot_new.db .
COPY customer_chatbot_new_memory.db .
COPY .env .

RUN apt-get update && apt-get install -y --no-install-recommends \
    gcc \
    && rm -rf /var/lib/apt/lists/*

RUN pip install --no-cache-dir -r requirements.txt

EXPOSE 8080

CMD ["python", "app.py"]

Step 3: Build and Push Docker Image

1. Enable Docker BuildKit:

   export DOCKER_BUILDKIT=1

2. Create a new builder instance:

   docker buildx create --name mybuilder --use

3. Build and push the Docker image:

   docker buildx build --platform linux/amd64 -t gcr.io/[PROJECT-ID]/aramex-chatbot:latest . --push

   Replace [PROJECT-ID] with your Google Cloud Project ID.

Step 4: Deploy to Google Cloud Platform

1. Authenticate with Google Cloud:

   gcloud auth login
   gcloud config set project [PROJECT-ID]

2. Enable required GCP services:

   gcloud services enable run.googleapis.com containerregistry.googleapis.com

3. Deploy to Cloud Run:

   gcloud run deploy aramex-chatbot \
     --image gcr.io/[PROJECT-ID]/aramex-chatbot:latest \
     --platform managed \
     --region [REGION] \
     --allow-unauthenticated \
     --port 8080

   Replace [REGION] with your desired region (e.g., us-central1).

Step 4: Verify Deployment

1. After successful deployment, Cloud Run will provide a URL where your application is hosted.
2. Open the provided URL in a web browser to verify that your Aramex chatbot is running correctly.
3. Test the chatbot functionality to ensure it's working as expected in the cloud environment.

Troubleshooting

• If you encounter issues with the build process, try removing the existing builder and creating a new one:

  docker buildx rm mybuilder
  docker buildx create --name mybuilder --use

• If environment variables are not being recognized, double-check that they are correctly set in the Cloud Run console or through the gcloud command.

• Monitor the application logs in the Google Cloud Console for any runtime errors or issues.

Maintenance

• To update your application, make changes to your local code, rebuild the Docker image with a new tag, and redeploy using the same gcloud run deploy command with the new image tag.

• Regularly update your dependencies and base Docker image to ensure you have the latest security patches.

For any questions or support, please contact sonnythomas0618@gmail.com