API Setup Guide | GLM-Image

Integrating GLM-Image into your Python workflow unlocks powerful automation capabilities. This guide walks you through every step—from initial setup to production-ready implementation.

Prerequisites

Python 3.8 or higher
GLM-Image API key
requests library (or aiohttp for async)

Basic Implementation

import os
import requests

class GLMImageClient:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://api.glm-image.com/v1"

    def generate(self, prompt, **kwargs):
        headers = {'Authorization': f'Bearer {self.api_key}'}
        payload = {'prompt': prompt, 'width': 1024, 'height': 1024}
        response = requests.post(f"{self.base_url}/generate", json=payload, headers=headers)
        return response.json()['url']

# Usage
client = GLMImageClient("your_api_key")
url = client.generate("A sunset over mountains")

Advanced Features

Batch Processing

def generate_batch(prompts, max_workers=3):
    from concurrent.futures import ThreadPoolExecutor

    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        results = list(executor.map(client.generate, prompts))
    return results

# Process 5 prompts
prompts = ["A dragon", "A castle", "A knight", "A forest", "A sword"]
results = generate_batch(prompts)

Integration Patterns

Flask Web App

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/api/generate', methods=['POST'])
def generate_image():
    data = request.json
    prompt = data.get('prompt')
    url = client.generate(prompt)
    return jsonify({'url': url})

Production Checklist

✓

Store API keys securely (environment variables)

✓

Implement rate limiting (max 10 req/sec)

✓

Add comprehensive error handling

✓

Monitor API usage and costs