ByteMyMood

Where your feelings feed the recipe.

Overview

ByteMyMood is an intelligent recipe recommendation system that uses a multi-agent architecture to understand your mood and preferences, plan meals, and execute cooking instructions.

Features

• Mood-based recipe recommendations - AI understands your emotional state
• Weather-aware suggestions - Considers current weather conditions
• Personalized cooking plans - Adapts to your preferences and skills
• Visual cooking guidance - AI-generated images for each step
• Smart shopping lists - Paper-like format organized by store sections
• Equipment verification - Ensures you have the right tools
• Robust error handling - Retry logic for API failures
• User profile management - Persistent preferences and mood tracking
• Recipe verification - Ensures all suggestions are real and accessible

Architecture

The system is built on a multi-agent architecture with three specialized sub-agents:

1. Inspiration Agent

• Analyzes user's mood and preferences
• Generates creative recipe ideas
• Considers dietary restrictions and preferences
• Integrates weather data for contextual suggestions
• Verifies recipes through web search
• Located in bytemymood/sub_agents/inspiration/

Tools & Agent-tools:

• Weather Check Agent: Uses weather_api_tool to get current weather conditions
• Recipe Grounding Agent: Uses google_search_grounding to verify recipe authenticity
• Memory Tools: memorize for storing user preferences and mood data

2. Planning Agent

• Breaks down recipes into manageable steps
• Creates detailed cooking plans
• Generates shopping lists
• Analyzes available ingredients from fridge photos
• Verifies kitchen equipment requirements
• Located in bytemymood/sub_agents/planning/

Tools & Agent-tools:

• Fridge Agent: Analyzes available ingredients using memorize and memorize_list
• Shopping List Agent: Generates organized shopping lists using memorize and memorize_list
• Memory Tools: memorize and memorize_list for tracking ingredients and lists

3. Execution Agent

• Provides step-by-step cooking instructions
• Generates visual aids for each cooking step
• Handles real-time adjustments
• Manages cooking process
• Located in bytemymood/sub_agents/execution/

Tools:

• Image Generation: gemini_image_generation_tool for creating cooking step visuals
• Prompt Enhancement: prompt_enhance_tool for optimizing image generation prompts
• Memory Tools: memorize and memorize_list for tracking cooking progress

Shared Tools & Infrastructure

• Memory System (memory.py): User profile management and conversation state
• Weather API (weather.py): Real-time weather data with retry logic
• Search API (search.py): Recipe verification and web search capabilities
• Image Generation (image_generation/): Google Gemini-powered visual cooking aids

Installation Flow

Step-by-Step Setup

1. Clone and Navigate

git clone <repository-url>
cd ByteMyMood

2. Install uv Package Manager

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or using pip
pip install uv

3. Set Up Virtual Environment and Dependencies

# Create virtual environment and install all dependencies
uv sync

# Install project in editable mode
uv pip install -e .

4. Configure API Keys

Create a .env file in the project root:

# Create .env file
touch .env

Add your API keys to .env:

GOOGLE_API_KEY=your_google_api_key
GOOGLE_GEOCODING_API_KEY=your_geocoding_key
GOOGLE_WEATHER_API_KEY=your_weather_key

5. Authenticate Google Cloud

# Install Google Cloud CLI if not already installed
# https://cloud.google.com/sdk/docs/install

# Authenticate your account
gcloud auth application-default login

Running ByteMyMood

Command Line Interface

# Start ByteMyMood CLI
uv run adk run bytemymood

Web Interface

# Launch web interface
uv run adk web

☁️ Cloud Run Deployment

Deploy ByteMyMood to Google Cloud Run for scalable, serverless hosting with automatic scaling and global distribution.

🔧 Prerequisites

Before deploying, ensure you have the following set up:

1. Google Cloud CLI installed and configured
2. Google Cloud Project with billing enabled
3. ADK CLI installed (pip install google-adk)

🚀 Step 1: Prepare Your Google Cloud Environment

Authenticate and Configure gcloud CLI

# Log in to your GCP account
gcloud auth login

# Set your default project (replace with your actual project ID)
gcloud config set project YOUR_PROJECT_ID

# Set your deployment region
gcloud config set run/region us-central1

# Enable required APIs
gcloud services enable run.googleapis.com
gcloud services enable aiplatform.googleapis.com
gcloud services enable cloudbuild.googleapis.com

🚀 Step 2: Create Cloud Run Service via Google Cloud Console

We'll create the Cloud Run service directly through the Google Cloud Console.

2.1 Navigate to Cloud Run Console

1. Go to Google Cloud Console
2. Select your project from the project dropdown
3. Navigate to Cloud Run in the left sidebar
4. Click "CREATE SERVICE"

2.2 Configure Service Settings

Basic Settings:

• Service name: bytemymood-service
• Region: Choose your preferred region (e.g., us-central1)
• CPU allocation: CPU is only allocated during request processing
• Memory: 512 MiB (recommended for ADK applications)

Container Settings:

• Container image URL: Leave blank (will be set by ADK deployment)
• Port: 8080 (default for Cloud Run)
• Request timeout: 300 seconds (5 minutes for AI processing)

2.3 Configure Environment Variables

You have two options for configuring environment variables:

Option A: Using Google Cloud Console (Recommended) Click "VARIABLES & SECRETS" and add the following environment variables:

Variable Name	Value	Description
GOOGLE_GENAI_USE_VERTEXAI	TRUE	Enable Vertex AI for Gemini models
GOOGLE_CLOUD_PROJECT	YOUR_PROJECT_ID	Your Google Cloud project ID
GOOGLE_CLOUD_LOCATION	global	Vertex AI location (keep as global)
GOOGLE_CLOUD_REGION	us-central1	Specific region for Vertex AI

Option B: Using .env File Create a .env file in your project root:

# Create .env file
touch .env

Add the following content to .env:

# Cloud Run deployment configuration
GOOGLE_GENAI_USE_VERTEXAI=TRUE
GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_ID
GOOGLE_CLOUD_LOCATION=global
GOOGLE_CLOUD_REGION=us-central1

Important Notes:

• Replace YOUR_PROJECT_ID with your actual GCP project ID
• GOOGLE_CLOUD_LOCATION must remain global
• These settings enable your Cloud Run service to access Vertex AI automatically
• If using Option B (.env file), the ADK deployment will automatically read these variables

2.4 Configure Security & Networking

Authentication:

• Authentication: Allow unauthenticated invocations (for public access)
• Service account: Use default compute service account

Networking:

• VPC connector: None (default)
• Ingress: Allow all traffic

2.5 Advanced Settings (Optional)

Scaling:

• Minimum number of instances: 0 (for cost optimization)
• Maximum number of instances: 100 (adjust based on expected traffic)
• Concurrency: 80 (requests per instance)

Resource Limits:

• CPU: 1 (vCPU)
• Memory: 512 MiB

2.6 Create the Service

1. Review all settings
2. Click "CREATE"
3. Wait for the service to be created (this may take a few minutes)

Note: The service will be created but won't have a container image yet. We'll deploy the actual application in the next step.

🚀 Step 3: Deploy to Cloud Run

Navigate to your ByteMyMood project root directory and run the deployment command:

# Deploy ByteMyMood to Cloud Run
adk deploy cloud_run \
  --project=YOUR_CLOUD_PROJECT_ID \
  --region=us-central1 \
  --service_name=your-cloud-run-service \
  --with_ui \
  ./bytemymood

Command Parameters:

• --project: Your Google Cloud project ID
• --region: Your preferred Cloud Run region
• --service_name: Name for your Cloud Run service
• --with_ui: Enables the web interface
• ./bytemymood: Path to your ADK project

Project Structure

bytemymood/
├── agent.py                    
├── prompt.py                   
├── sub_agents/                
│   ├── inspiration/         
│   │   ├── agent.py         
│   │   └── prompt.py         
│   ├── planning/            
│   │   ├── agent.py        
│   │   └── prompt.py         
│   └── execution/            
│       ├── agent.py         
│       └── prompt.py         
├── tools/                    
│   ├── memory.py             
│   ├── weather.py            
│   ├── search.py             
│   └── image_generation/     
│       ├── image_generation.py      
│       └── image_generation_prompt.py 
├── profiles/                  
│   ├── user_profile_default.json           
│   ├── user_profile_example.json           
│   └── user_profile_example_with_recipe.json 
├── shared_libraries/         
│   ├── constants.py         
│   └── types.py              
└── tests/                    
    └── unit/                 
        ├── test_agents.py    
        ├── test_tools.py     
        ├── test_profile.py   
        ├── test_image_generation.py 
        └── test_weather_api.py 

License

See LICENSE for details.