Pruna P Image Upscale 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    "p-image-upscale",
 9    image="https://segmind-resources.s3.amazonaws.com/input/p-image-upscale-input.png",
10    target=4,
11    output_format="jpg",
12    output_quality=80,
13    enhance_details=False,
14    enhance_realism=False,
15    disable_safety_checker=False,
16)
17print(result["status"])                      # COMPLETED
18print(result.get("output"))                  # model output (e.g. media URL)
19print(result["metrics"]["inference_time"])   # server compute seconds
20
21# --- Or submit + poll manually (track request_id, control the cadence) ---
22from segmind import SegmindClient, InferenceFailed, InferenceTimeout
23
24client = SegmindClient()                      # reads SEGMIND_API_KEY
25payload = {
26    "image": "https://segmind-resources.s3.amazonaws.com/input/p-image-upscale-input.png",
27    "target": 4,
28    "output_format": "jpg",
29    "output_quality": 80,
30    "enhance_details": False,
31    "enhance_realism": False,
32    "disable_safety_checker": False,
33}
34job = client.submit_async("p-image-upscale", **payload)
35print(job.request_id)                         # available immediately
36try:
37    result = job.wait(timeout=600, interval=1.0)
38except InferenceTimeout as e:
39    print("still running:", e.request_id)
40except InferenceFailed as e:
41    print("failed:", e.detail)