Veo 3.1 Fast Serverless API

 1# pip install "segmind>=1.1.0"
 2# export SEGMIND_API_KEY="YOUR_API_KEY"
 3from segmind import SegmindClient, InferenceFailed, InferenceTimeout
 4
 5# Async (v2) — recommended for long-running / video models.
 6# run() blocks up to 600s; submit_async + job.wait(timeout=...) sets a longer
 7# deadline and keeps the request_id so you can re-poll later.
 8client = SegmindClient()                      # reads SEGMIND_API_KEY
 9payload = {
10    "seed": 42,
11    "image": "https://segmind-resources.s3.amazonaws.com/input/b130a60c-8ee2-4962-b5be-b8b87ece92b7-691b7010-c304-4c07-a9b8-5e03f5e5435a.jpeg",
12    "prompt": "A hands-on workshop where participants are learning about Veo 3.1 Fast, presenter guiding them on laptops and tablets, modern classroom setup with projectors displaying reference images transforming into videos",
13    "duration": 8,
14    "last_frame": "https://segmind-resources.s3.amazonaws.com/input/0c6e38bc-ac47-4573-ace4-cf51c427962a-5af85fc2-bbbf-43ea-83b6-88cfd1e09374.jpeg",
15    "resolution": "1080p",
16    "aspect_ratio": "16:9",
17    "generate_audio": True,
18}
19job = client.submit_async("veo-3.1-fast", **payload)
20print(job.request_id)                         # available immediately
21try:
22    result = job.wait(timeout=900, interval=2.0)
23    print(result["status"])                  # COMPLETED
24    print(result.get("output"))              # model output (e.g. video URL)
25except InferenceTimeout as e:
26    print("still running:", e.request_id)    # re-poll later with this id
27except InferenceFailed as e:
28    print("failed:", e.detail)
29
30# Fast models (<=600s) can use the one-liner instead:
31# result = segmind.run("veo-3.1-fast", **payload)