LLAVA 1.6 7B Serverless API

 1# pip install "segmind>=1.1.0"
 2# export SEGMIND_API_KEY="YOUR_API_KEY"
 3import segmind
 4
 5# Async (v2): submit to the queue and block until COMPLETED.
 6# run() returns the final result dict (600s deadline, 1.0s poll by default).
 7result = segmind.run(
 8    "llava-v1.6",
 9    images="https://segmind-sd-models.s3.amazonaws.com/display_images/llava-input.jpg",
10    prompt="Describe meta data of the image in this format, keep them short and factually correct:\n1. Category,\n2. Primary Colors,\n3. Additional Colors,\n4. Primary Material,\n5. Secondary Materials,\n6. Style and a couple others if you can find any according to the product.\n give it in json",
11)
12print(result["status"])                      # COMPLETED
13print(result.get("output"))                  # model output (e.g. media URL)
14print(result["metrics"]["inference_time"])   # server compute seconds
15
16# --- Or submit + poll manually (track request_id, control the cadence) ---
17from segmind import SegmindClient, InferenceFailed, InferenceTimeout
18
19client = SegmindClient()                      # reads SEGMIND_API_KEY
20payload = {
21    "images": "https://segmind-sd-models.s3.amazonaws.com/display_images/llava-input.jpg",
22    "prompt": "Describe meta data of the image in this format, keep them short and factually correct:\n1. Category,\n2. Primary Colors,\n3. Additional Colors,\n4. Primary Material,\n5. Secondary Materials,\n6. Style and a couple others if you can find any according to the product.\n give it in json",
23}
24job = client.submit_async("llava-v1.6", **payload)
25print(job.request_id)                         # available immediately
26try:
27    result = job.wait(timeout=600, interval=1.0)
28except InferenceTimeout as e:
29    print("still running:", e.request_id)
30except InferenceFailed as e:
31    print("failed:", e.detail)