Creating Your First Integration

# Create project directory
mkdir magic-hour-tutorial
cd magic-hour-tutorial

# Create necessary directories
mkdir outputs
mkdir assets

# Create main script file
touch main.py

# Create environment file for your API key
touch .env

magic-hour-tutorial/
├── main.py          # Your main script
├── .env             # API key storage
├── outputs/         # Downloaded results go here
└── assets/          # Input files (if needed)

pip install magic-hour python-dotenv requests

MAGIC_HOUR_API_KEY=your_api_key_here

import os
import time
import requests
from pathlib import Path
from dotenv import load_dotenv
from magic_hour import Client

# Load environment variables
load_dotenv()

# Initialize the Magic Hour client
API_KEY = os.getenv("MAGIC_HOUR_API_KEY")
if not API_KEY:
    raise ValueError("MAGIC_HOUR_API_KEY not found in environment variables")

client = Client(token=API_KEY)

def main():
    """Generate an AI image using Magic Hour API"""

    print("🚀 Starting Magic Hour Integration Tutorial")
    print("-" * 50)

    # Step 1: Create the generation job
    print("\n📝 Creating AI image generation job...")
    try:
        create_response = client.v1.ai_image_generator.create(
            image_count=1,
            orientation="landscape",
            style={
                "prompt": "A serene mountain landscape at sunset with vibrant colors",
                "tool": "ai-anime-generator"
            },
            name="Tutorial Image"
        )

        job_id = create_response.id
        credits_charged = create_response.credits_charged

        print(f"✅ Job created successfully!")
        print(f"   Job ID: {job_id}")
        print(f"   Credits charged: {credits_charged}")

    except Exception as e:
        print(f"❌ Failed to create job: {e}")
        return

    # Step 2: Poll for completion
    print(f"\n⏳ Waiting for job to complete...")
    print("   This usually takes 5-15 seconds for images")

    max_attempts = 60  # Maximum 60 attempts (3 minutes)
    attempt = 0

    while attempt < max_attempts:
        try:
            # Check job status
            status_response = client.v1.image_projects.get(id=job_id)
            status = status_response.status

            print(f"   Status: {status} (attempt {attempt + 1}/{max_attempts})")

            if status == "complete":
                print("✅ Job completed successfully!")

                # Step 3: Download the result
                download_url = status_response.downloads[0].url
                download_image(download_url, job_id)
                break

            elif status == "error":
                error_info = status_response.error
                print(f"❌ Job failed with error:")
                print(f"   Code: {error_info.get('code', 'unknown')}")
                print(f"   Message: {error_info.get('message', 'No error message')}")
                return

            elif status in ["queued", "rendering"]:
                # Still processing, wait before next check
                time.sleep(3)
                attempt += 1

            else:
                print(f"⚠️  Unexpected status: {status}")
                time.sleep(3)
                attempt += 1

        except Exception as e:
            print(f"❌ Error checking status: {e}")
            return

    if attempt >= max_attempts:
        print(f"⏰ Timeout: Job did not complete within {max_attempts * 3} seconds")
        return

    print("\n✨ Tutorial completed successfully!")
    print(f"📁 Check the 'outputs/' directory for your generated image")

def download_image(url, job_id):
    """Download the generated image"""

    print(f"\n📥 Downloading result...")

    try:
        # Create outputs directory if it doesn't exist
        output_dir = Path("outputs")
        output_dir.mkdir(exist_ok=True)

        # Download the file
        response = requests.get(url, stream=True, timeout=30)
        response.raise_for_status()

        # Save with job ID in filename
        filename = f"generated_image_{job_id}.png"
        filepath = output_dir / filename

        with open(filepath, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)

        file_size = filepath.stat().st_size
        file_size_mb = file_size / (1024 * 1024)

        print(f"✅ Downloaded: {filepath}")
        print(f"   Size: {file_size_mb:.2f} MB")

    except requests.exceptions.RequestException as e:
        print(f"❌ Download failed: {e}")
    except Exception as e:
        print(f"❌ Unexpected error during download: {e}")

if __name__ == "__main__":
    main()

python main.py

🚀 Starting Magic Hour Integration Tutorial
--------------------------------------------------

📝 Creating AI image generation job...
✅ Job created successfully!
   Job ID: clx7uu86w0a5qp55yxz315r6r
   Credits charged: 5

⏳ Waiting for job to complete...
   This usually takes 5-15 seconds for images
   Status: queued (attempt 1/60)
   Status: rendering (attempt 2/60)
   Status: complete (attempt 3/60)
✅ Job completed successfully!

📥 Downloading result...
✅ Downloaded: outputs/generated_image_clx7uu86w0a5qp55yxz315r6r.png
   Size: 1.23 MB

✨ Tutorial completed successfully!
📁 Check the 'outputs/' directory for your generated image