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fb78c46 e27dbae fb78c46 3e0a4f1 e27dbae 3e0a4f1 fb78c46 3e0a4f1 fb78c46 | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | import os
import requests
import json
from openai import OpenAI
# =============================
# ENV VARIABLES (STRICT)
# =============================
API_BASE_URL = os.environ.get("API_BASE_URL")
# Prefer OPENAI_API_KEY, but keep API_KEY as a backward-compatible fallback.
API_KEY = os.environ.get("OPENAI_API_KEY") or os.environ.get("API_KEY")
MODEL_NAME = os.environ.get("MODEL_NAME", "gpt-4o-mini")
ENV_URL = "http://localhost:7860"
# =============================
# 🔥 GLOBAL LLM CLIENT
# =============================
try:
if not API_KEY:
raise ValueError("Missing OPENAI_API_KEY/API_KEY environment variable")
if API_BASE_URL:
client = OpenAI(api_key=API_KEY, base_url=API_BASE_URL)
else:
client = OpenAI(api_key=API_KEY)
print("✅ LLM CLIENT INITIALIZED", flush=True)
except Exception as e:
print(f"❌ CLIENT INIT FAILED: {e}", flush=True)
client = None
# =============================
# LLM ACTION (RESPONSES API)
# =============================
def get_llm_action(issue):
try:
if client is None:
raise Exception("Client not initialized")
print("🚀 CALLING LLM...", flush=True)
response = client.responses.create(
model=MODEL_NAME,
input=issue
)
print("✅ LLM RESPONSE RECEIVED", flush=True)
# Extract text safely
output_text = (getattr(response, "output_text", None) or "").strip()
if not output_text:
output_text = "I'll help you with this."
return {
"category": "tech",
"response": output_text[:120],
"escalate": False,
"resolve": True
}
except Exception as e:
print(f"❌ LLM ERROR: {e}", flush=True)
return {
"category": "tech",
"response": "Temporary issue. Please try again.",
"escalate": False,
"resolve": True
}
# =============================
# MAIN INFERENCE
# =============================
def run_inference(level="easy"):
print(f"[START] task=supportdesk_{level} env=SupportDeskEnv model={MODEL_NAME}", flush=True)
total_reward = 0.0
steps_taken = 0
done = False
try:
res = requests.post(f"{ENV_URL}/reset", json={"level": level}, timeout=5)
obs = res.json().get("observation", {})
while not done and steps_taken < 5:
steps_taken += 1
issue = obs.get("user_message", "Help needed")
# 🔥 ALWAYS CALL LLM
action = get_llm_action(issue)
step_res = requests.post(
f"{ENV_URL}/step",
json=action,
timeout=5
).json()
reward = step_res.get("reward", {}).get("score", 0.0)
done = step_res.get("done", False)
obs = step_res.get("observation", {})
total_reward += reward
print(
f"[STEP] step={steps_taken} action={json.dumps(action)} reward={reward} done={done}",
flush=True
)
score = total_reward / max(steps_taken, 1)
score = min(max(score, 0.0), 1.0)
success = score >= 0.6
print(
f"[END] success={success} steps={steps_taken} score={score}",
flush=True
)
except Exception as e:
print(
f"[END] success=False steps={steps_taken} score=0.0 error={e}",
flush=True
)
# =============================
# ENTRY POINT
# =============================
if __name__ == "__main__":
run_inference("easy")
run_inference("medium")
run_inference("hard") |