GPT Image 2 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    "gpt-image-2",
 9    prompt="A photorealistic, cinematic shot of a cozy independent bookstore in Mumbai at golden hour. Warm afternoon sunlight streams through a tall front window onto wooden shelves packed with books; book spines are clearly visible with titles in English and Hindi Devanagari script. In the foreground, a handwritten chalkboard A-frame easel reads, in clean legible chalk lettering: first line 'मुंबई पुस्तक भंडार', second line 'Mumbai Book Store', and a smaller third line 'Open Daily  9 am – 9 pm'. Shallow depth of field, shot on a full-frame camera at 35mm f/2.0, ultra-realistic detail, natural color grading, dust motes in sunlight, film grain.",
10    size="1536x1024",
11    quality="high",
12)
13print(result["status"])                      # COMPLETED
14print(result.get("output"))                  # model output (e.g. media URL)
15print(result["metrics"]["inference_time"])   # server compute seconds
16
17# --- Or submit + poll manually (track request_id, control the cadence) ---
18from segmind import SegmindClient, InferenceFailed, InferenceTimeout
19
20client = SegmindClient()                      # reads SEGMIND_API_KEY
21payload = {
22    "prompt": "A photorealistic, cinematic shot of a cozy independent bookstore in Mumbai at golden hour. Warm afternoon sunlight streams through a tall front window onto wooden shelves packed with books; book spines are clearly visible with titles in English and Hindi Devanagari script. In the foreground, a handwritten chalkboard A-frame easel reads, in clean legible chalk lettering: first line 'मुंबई पुस्तक भंडार', second line 'Mumbai Book Store', and a smaller third line 'Open Daily  9 am – 9 pm'. Shallow depth of field, shot on a full-frame camera at 35mm f/2.0, ultra-realistic detail, natural color grading, dust motes in sunlight, film grain.",
23    "size": "1536x1024",
24    "quality": "high",
25}
26job = client.submit_async("gpt-image-2", **payload)
27print(job.request_id)                         # available immediately
28try:
29    result = job.wait(timeout=600, interval=1.0)
30except InferenceTimeout as e:
31    print("still running:", e.request_id)
32except InferenceFailed as e:
33    print("failed:", e.detail)