Autoglm Image Recognition

Use the AutoGLM Image Recognition API to analyze and describe image content. Use this skill when the user needs image analysis, object or scene recognition, OCR-like text extraction, or a general image description. The token is fetched automatically from the local service at http://127.0.0.1:18432/get_token, so no manual environment variable setup is required. If the user provides a local image file, you must first run upload-mix.py to upload it and obtain a public URL before using this skill.

AutoGLM Image Recognition Skill

Use the AutoGLM Image Recognition API to analyze and describe an image.

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Prerequisite: Get a Public Image URL

This skill requires image_url to be a publicly accessible URL. Choose the correct path based on the source of the image:

Image source	What to do
Existing public URL (http:// or https://)	Use it directly with no extra processing
Local file (user upload or local path)	You must run upload-mix.py first, then pass the returned public URL

Important: If the user provides a local image, such as an uploaded file or a local disk path, do not pass the file path directly. Run upload-mix.py first to upload the file, obtain a public URL, and only then perform image recognition.

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Step 1 for a Local Image: Upload with upload-mix.py

If the image is a local file, upload it first:

bash

python upload-mix.py "<local image path>"

Example:

bash

python upload-mix.py "/home/user/photo.jpg"

Response structure:

json

{
  "code": 0,
  "msg": "SUCCESS",
  "time": 1773199477734,
  "trace": "78dd001f3ec04c37b6a1d58b5db70fce",
  "data": {
    "message": "",
    "oss_info": [
      {
        "filename": "photo.jpg",
        "oss_name": "auto_fly/xxx/photo.jpg",
        "oss_url": "https://autoglm-agent.aminer.cn/auto_fly/xxx/photo.jpg"
      }
    ]
  }
}

Extract data.oss_info[0].oss_url from the response. That value is the image_url needed for the recognition step.

---

Step 2: Image Recognition API

Item	Value
URL	https://autoglm-api.autoglm.ai/agentdr/v1/assistant/skills/image-recognition
Method	POST
Request body	See below

Request body:

json

{
  "prompt": "Describe the image",
  "image_url": "https://example.com/image.jpg"
}

Field	Description	Required
image_url	A publicly accessible URL for the image. For local images, upload first with upload-mix.py and use data.oss_info[0].oss_url	Yes
prompt	An instruction such as "Describe the image" or "Extract the text shown in the image"	Optional, default is "Describe the image"

Signed headers (generated dynamically for each request):

• X-Auth-Appid: 100003
• X-Auth-TimeStamp: current Unix timestamp in seconds
• X-Auth-Sign: MD5(100003 + "&" + timestamp + "&" + 38d2391985e2369a5fb8227d8e6cd5e5)

---

Run the Script

Use image-recognition.py in the same directory:

bash

# Pass only the image URL and use the default prompt
python image-recognition.py "https://example.com/image.jpg"

# Pass the image URL with a custom prompt
python image-recognition.py "https://example.com/image.jpg" "Extract the text shown in the image"

Note: Image recognition may take longer than other calls. Wait for the response. If you need a timeout, change the request call in image-recognition.py to:

python

with urllib.request.urlopen(req, timeout=300) as resp:

A timeout of 300 seconds is recommended.

---

Full Workflow

text

User provides a local image
       ↓
Run upload-mix.py to upload the image
  python upload-mix.py "<local image path>"
       ↓
Extract data.oss_info[0].oss_url as image_url
       ↓
Run image-recognition.py
  python image-recognition.py "<image_url>" ["<prompt>"]
       ↓
Present data.text to the user

If the user already provides a public URL, skip the upload step:

text

User provides a public image URL
       ↓
Run image-recognition.py
  python image-recognition.py "<image_url>" ["<prompt>"]
       ↓
Present data.text to the user

---

Response Handling

Response Structure

json

{
  "code": 0,
  "msg": "SUCCESS",
  "time": 1773137796961,
  "trace": "298d5fe1efdd4da58ca46d1700d8054b",
  "data": {
    "text": "Detailed image recognition result...",
    "tokens": 5588
  }
}

Output Requirements

1. Present the recognition result directly Return the contents of data.text directly to the user and preserve the original formatting, including any Markdown emphasis.