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1cfd97d 755c634 1cfd97d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | #!/usr/bin/env python3
"""Minimal selection-ARM demo: given a task, screenshot, and 5 candidate
actions, ask the ARM which to execute.
The ARM is a plain chat model — serve it with anything OpenAI-compatible:
vllm serve PTeterwak/OpenWebRL-4B-SelectionARM --port 8000 \
--trust-remote-code --max-model-len 32768
(or PTeterwak/om2w-action-rm-selection-4b for the MolmoWeb-era model; that
one was served with --dtype half --max-model-len 65536.)
Usage:
python selection_infer.py --url http://127.0.0.1:8000/v1 \
--image screenshot.png --task "Find running shoes under $50" \
--candidates cands.json
where cands.json = [{"thought": "...", "action": "..."}, ...] (2-5 entries).
Output: the winning index + the model's raw reply. Parse contract: the model
replies {"selection": N} (1-indexed); on parse failure fall back to 1.
Click coordinates inside candidate actions are in NORMALIZED [0,1000] space
(both ARMs were trained that way — do not pass raw pixels).
"""
import argparse, json, re, sys
from openai import OpenAI
from selection_prompt import build_catts_vision_prompt_v2 # canonical builder
SEL = re.compile(r'\{\s*"selection"\s*:\s*(\d+)\s*\}')
def main():
p = argparse.ArgumentParser()
p.add_argument("--url", default="http://127.0.0.1:8000/v1")
p.add_argument("--model", default=None, help="served model name (default: first listed)")
p.add_argument("--image", required=True)
p.add_argument("--task", required=True)
p.add_argument("--candidates", required=True, help="json list of {thought, action}")
p.add_argument("--history", default=None, help="optional json list of prior action strings")
p.add_argument("--url-page", dest="url_page", default="", help="current page URL")
a = p.parse_args()
cands = json.load(open(a.candidates))
history = json.load(open(a.history)) if a.history else []
img_bytes = open(a.image, "rb").read()
# one singleton cluster per candidate (CLUSTER_NO_DOM semantics).
# The builder expects candidate OBJECTS with .molmo_action / .thought:
from types import SimpleNamespace
clusters = [
{"rep": SimpleNamespace(molmo_action=c["action"], thought=c.get("thought", "")),
"vote_count": 1, "cluster_key": str(i)}
for i, c in enumerate(cands)
]
messages = build_catts_vision_prompt_v2(a.task, history, a.url_page if hasattr(a, "url_page") else "",
clusters, img_bytes)
client = OpenAI(base_url=a.url, api_key="EMPTY")
model = a.model or client.models.list().data[0].id
r = client.chat.completions.create(model=model, messages=messages,
temperature=0.0, max_tokens=2048)
text = r.choices[0].message.content or ""
m = list(SEL.finditer(text))
pick = int(m[-1].group(1)) if m else 1
if not (1 <= pick <= len(cands)):
pick = 1
print(json.dumps({"selection": pick, "action": cands[pick - 1]["action"],
"raw_reply": text[-500:]}, indent=2))
if __name__ == "__main__":
sys.path.insert(0, __file__.rsplit("/", 1)[0])
main()
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