License

Copyright (c) [2024] Kabeer Singh Thockchom. All rights reserved. This software and associated documentation files (the "Software") are the exclusive property of Kabeer Singh Thockchom. The Software is provided for viewing purposes only and is not open source. Unauthorized copying, modification, distribution, or use of this Software, via any medium, is strictly prohibited. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Any violation of this license may result in severe civil and criminal penalties, and will be prosecuted to the maximum extent possible under law.RACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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

Check out our AI Assistant in action:

Watch Bottega-Bot Demo Video

Demo

Check out our AI Assistant in action: Restaurant AI Assistant 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 [email protected]