GPT-Bayes Webapp

A cloud-based Marketing Mix Modeling (MMM) solution deployed on Google Cloud Platform.

Quick Deployment

./deploy.sh  # Deploy the latest version to production

System Architecture

GPT-Bayes consists of two main components:

1. MMM Agent Alpha - A specialized GPT model with API integration

   • Interface: MMM Agent Alpha
   • Authentication: [email protected]
   • Function: Provides user interface for MMM analysis

2. Backend Service

   • Production URL: https://nextgen-mmm.pymc-labs.com
   • Function: Handles model fitting and parameter management via API endpoints
   • Infrastructure: Hosted on Google Cloud Engine (GCE) under the gpt-bayes project

Project Structure

Core Services

• app.py - Main Flask application
• test_mmm_async.py - Local API testing utility

Infrastructure Configuration

• nginx/ - NGINX reverse proxy settings
• dockerfile - Container specifications
• start.sh - Container initialization
• deploy.sh - Deployment automation
• environment.yml - Development environment specifications

AI Agent Settings

• gpt-agent/gpt_prompt.md - System instructions
• gpt-agent/api_spec.json - API specifications
• gpt-agent/knowledge/ - Reference documentation
• gpt-agent/privacy_policy.md - Data handling guidelines

Testing Resources

• test-data/ - Example datasets

Deployment Guide

The application runs on Google Compute Engine (GCE) under the gpt-bayes project, accessible at https://nextgen-mmm.pymc-labs.com.

Standard Deployment

Use deploy.sh to update the application. This script handles:

• Updating the container in Google Artifact Registry (GAR)
• Deploying to the production environment

./deploy.sh

Server Management

Access the production server:

gcloud compute ssh gpt-bayes --zone us-central1-a

Container management commands:

# List containers
docker ps -a

# Monitor container logs
docker attach CONTAINER_ID

# Access container shell
docker exec -it CONTAINER_ID /bin/bash

Container Registry Management

Build and publish to Google Artifact Registry:

gcloud builds submit

Note: This updates the container image but doesn't affect the production deployment.

Server Instance Management

View available Container-Optimized OS images:

gcloud compute images list --project cos-cloud --no-standard-images

Update production container:

# Clear existing containers
gcloud compute ssh gpt-bayes --zone us-central1-a --command 'docker system prune -f -a'

# Deploy new container
gcloud compute instances update-container gpt-bayes \
  --zone=us-central1-a \
  --container-image=us-central1-docker.pkg.dev/bayes-gpt/gpt-bayes/gpt-bayes:latest

Create new server instance:

gcloud compute instances create gpt-bayes \
 --machine-type e2-standard-4 \
 --boot-disk-size 20GB \
 --image image-name \
 --image-project cos-cloud \
 --zone us-central1 \
 --metadata container-image=your-container-image-name \
 --tags http-server \
 --firewall-create allow-http

NGINX Configuration (Advanced)

Deploy NGINX reverse proxy updates:

cd nginx
gcloud builds submit

Update backend IP address:

1. Navigate to nginx/nginx.conf
2. Modify the proxy_pass directive with the new IP
3. Example: proxy_pass http://35.208.203.115:5000;

Local Development

1. Environment Setup

Create development environment:

# Using conda
conda env create -f environment.yml

# Using mamba (faster)
mamba env create -f environment.yml

# Activate environment
conda activate base

2. Development Server

Launch the development stack:

1. Start Redis:

redis-server

2. Start Celery worker (new terminal):

celery -A app.celery worker --loglevel=info

3. Start Flask (new terminal):

python app.py --port 5001

4. Run tests:

# Test local instance
python test_mmm_async.py local

# Test production instance
python test_mmm_async.py deployed

The test suite:

• Generates sample MMM data
• Submits to specified API endpoint
• Monitors result generation
• Displays model analytics