Visión y generación de imágenes

# This example uses a public image URL. For a local image, use the Python or Node.js example.
curl https://moxus.ai/v1/chat/completions \
  -H "Authorization: Bearer sk-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.4",
    "messages": [
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "Describe this image and list the key details."},
          {
            "type": "image_url",
            "image_url": {
              "url": "https://example.com/photo.jpg"
            }
          }
        ]
      }
    ]
  }'

# base64 is a Python standard library module used to turn a local image into text for JSON.
import base64
import httpx
from openai import OpenAI

# Replace only these values: your API key and the local image filename or full path.
API_KEY = "sk-your-key"
IMAGE_PATH = "photo.jpg"
# Use image/jpeg for JPEG files. Change this to image/png for PNG files.
IMAGE_MIME_TYPE = "image/jpeg"

client = OpenAI(
    api_key=API_KEY,
    base_url="https://moxus.ai/v1",
    # Connect directly to Moxus AI without reading system or terminal proxy variables.
    http_client=httpx.Client(trust_env=False, timeout=60.0),
)

# Read the local image file and encode it as a Base64 string.
# "rb" means read in binary mode, which is required for image files.
with open(IMAGE_PATH, "rb") as image_file:
    image_base64 = base64.b64encode(image_file.read()).decode("utf-8")

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image and list the key details."},
                {
                    "type": "image_url",
                    "image_url": {
                        # This prefix tells the model that the remaining value is Base64 image data.
                        "url": f"data:{IMAGE_MIME_TYPE};base64,{image_base64}",
                    },
                },
            ],
        }
    ],
)

print(response.choices[0].message.content)

import OpenAI from "openai";

// readFileSync is a Node.js standard library method for reading the local image file.
import { readFileSync } from "node:fs";

// Put photo.png in the same project folder as the .mjs file. Change both values when the filename or format differs.
const API_KEY = "sk-your-key";
const IMAGE_PATH = "photo.png";
const IMAGE_MIME_TYPE = "image/png";

const client = new OpenAI({
  apiKey: API_KEY,
  baseURL: "https://moxus.ai/v1",
});

// Convert the local image into a Base64 string so it can be sent in JSON.
const imageBase64 = readFileSync(IMAGE_PATH).toString("base64");

const response = await client.chat.completions.create({
  model: "gpt-5.4",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Describe this image and list the key details." },
        {
          type: "image_url",
          image_url: {
            // This prefix tells the model that the remaining value is Base64 image data.
            url: `data:${IMAGE_MIME_TYPE};base64,${imageBase64}`,
          },
        },
      ],
    },
  ],
});

console.log(response.choices[0].message.content);

# prompt describes the image. size and n support depends on the selected image model.
curl https://moxus.ai/v1/images/generations \
  -H "Authorization: Bearer sk-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "grok-imagine-image",
    "prompt": "A clean product mockup of a smart desk lamp on a white table, soft studio lighting",
    "n": 1,
    "size": "1024x1024"
  }'

# base64 is a Python standard library module used to decode the returned image data.
import base64
import httpx
from openai import OpenAI

client = OpenAI(
    api_key="sk-your-key",
    base_url="https://moxus.ai/v1",
    # Connect directly to Moxus AI without reading system or terminal proxy variables.
    http_client=httpx.Client(trust_env=False, timeout=60.0),
)

response = client.images.generate(
    model="grok-imagine-image",
    prompt="A clean product mockup of a smart desk lamp on a white table, soft studio lighting",
    n=1,
    size="1024x1024",
    response_format="b64_json",
)

# With response_format="b64_json", the generated image is returned in the b64_json field.
image_base64 = response.data[0].b64_json

# Decode the Base64 string and write it as a local PNG file.
with open("generated-image.png", "wb") as image_file:
    image_file.write(base64.b64decode(image_base64))

print("Saved image to generated-image.png")

import OpenAI from "openai";

// writeFileSync is a Node.js standard library method for writing the image file.
import { writeFileSync } from "node:fs";

const client = new OpenAI({
  apiKey: "sk-your-key",
  baseURL: "https://moxus.ai/v1",
});

const response = await client.images.generate({
  model: "grok-imagine-image",
  prompt: "A clean product mockup of a smart desk lamp on a white table, soft studio lighting",
  n: 1,
  size: "1024x1024",
  response_format: "b64_json",
});

// With response_format: "b64_json", the generated image is returned in the b64_json field.
const imageBase64 = response.data[0].b64_json;

// Buffer.from(..., "base64") converts the Base64 string back to image bytes.
writeFileSync("generated-image.png", Buffer.from(imageBase64, "base64"));

console.log("Saved image to generated-image.png");