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bda28c2 a61bbef bda28c2 74f6ea1 1229aa8 bda28c2 74f6ea1 bda28c2 74f6ea1 bda28c2 74f6ea1 bda28c2 74f6ea1 bda28c2 | 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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import os
import json
from openai import OpenAI
# Mock classes to match BrowserGym official sample structure
class BrowserGymAction:
def __init__(self, action_str):
self.action_str = action_str
def parse_model_action(text):
# Dummmy parser for official sample
return text.strip()
# Constants for official sample
SYSTEM_PROMPT = "You are a web browsing agent."
MODEL_NAME = os.getenv("MODEL_NAME", "step-fun-3.5-flash") # Use env var if available
TEMPERATURE = 0.0
MAX_TOKENS = 512
FALLBACK_ACTION = "stop"
MAX_STEPS = 10
def main():
# Prefer injected API_KEY and API_BASE_URL
api_key = os.getenv("API_KEY") or os.getenv("OPENAI_API_KEY", "EMPTY")
base_url = os.getenv("API_BASE_URL") or "https://api.openai.com/v1"
client = OpenAI(
api_key=api_key,
base_url=base_url,
default_headers={
"HTTP-Referer": "https://huggingface.co/spaces/armaan020/AegisOpenEnv",
"X-Title": "AegisOpenEnv Official Sample check"
} if "openrouter" in base_url.lower() else None
)
print(f"Official Sample Inference Logic Initialized (Model: {MODEL_NAME})...")
# --- START OF USER PROVIDED SNIPPET ---
history = []
task_name = "official_sample_task"
print(f"[START] task={task_name}", flush=True)
step_count = 0
score = 0.5
try:
# Dummy loop to represent the user snippet's context
for step in range(MAX_STEPS):
# user_content would normally be defined here with AXTree/Accessibility logs
user_content = [{"type": "text", "text": "Task: Navigate to example.com"}]
messages = [
{
"role": "system",
"content": [{"type": "text", "text": SYSTEM_PROMPT}],
},
{
"role": "user",
"content": user_content,
},
]
try:
completion = client.chat.completions.create(
model=MODEL_NAME,
messages=messages,
temperature=TEMPERATURE,
max_tokens=MAX_TOKENS,
stream=False,
)
response_text = completion.choices[0].message.content or ""
except Exception as exc:
failure_msg = f"Model request failed ({exc}). Using fallback action."
print(failure_msg, flush=True)
response_text = FALLBACK_ACTION
action_str = parse_model_action(response_text)
print(f"Step {step}: model suggested -> {action_str}", flush=True)
step_count += 1
reward = 0.5 # dummy reward
score += reward
print(f"[STEP] step={step_count} reward={reward}", flush=True)
break # Break here for safety in dummy script
else:
print(f"Reached max steps ({MAX_STEPS}).", flush=True)
finally:
print(f"[END] task={task_name} score={score} steps={step_count}", flush=True)
# --- END OF USER PROVIDED SNIPPET ---
if __name__ == "__main__":
main()
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