File size: 6,460 Bytes
c4c440d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | import json
import os
import sys
import tempfile
import time
import wave
from pathlib import Path
from typing import Any, Callable
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from dream_customs.models import HostedASRClient, HostedMiniCPMTextClient, HostedMiniCPMVisionClient
from dream_customs.prompts import negotiation_prompt, today_tip_prompt
from dream_customs.pipeline import build_intake, build_qa_state
from dream_customs.schema import TodayTipCard
class TextFallback:
def generate_negotiation(self, _prompt: str) -> dict[str, Any]:
return {"visitor_name": "fallback", "questions": ["fallback"], "tone_note": "fallback"}
def generate_today_tip(self, _prompt: str) -> TodayTipCard:
return TodayTipCard(
dream_summary="fallback",
main_question="fallback",
dream_anchors=["fallback"],
followup_questions=[],
user_answers=[],
interpretation="fallback",
today_tip="fallback",
tiny_action="fallback",
caring_note="fallback",
safety_note="",
)
class VisionFallback:
def extract_clues(self, _image_path: str) -> list[str]:
return ["fallback"]
def extract_witness(self, _image_path: str):
raise RuntimeError("fallback witness should not be used in this benchmark")
class ASRFallback:
def transcribe(self, _audio_path: str) -> str:
return "fallback"
def _measure(name: str, fn: Callable[[], Any]) -> dict[str, Any]:
start = time.perf_counter()
try:
value = fn()
ok = True
error = ""
except Exception as exc: # pragma: no cover - benchmark safety net
value = None
ok = False
error = exc.__class__.__name__
elapsed = time.perf_counter() - start
return {
"name": name,
"ok": ok,
"elapsed_seconds": round(elapsed, 3),
"fallback": _looks_like_fallback(value),
"error": error,
}
def _looks_like_fallback(value: Any) -> bool:
if isinstance(value, dict):
return value.get("visitor_name") == "fallback"
if isinstance(value, TodayTipCard):
return value.dream_summary == "fallback"
if isinstance(value, list):
return value == ["fallback"]
return value == "fallback"
def _write_probe_wav() -> str:
temp = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
temp.close()
with wave.open(temp.name, "wb") as wav:
wav.setnchannels(1)
wav.setsampwidth(2)
wav.setframerate(16000)
wav.writeframes(b"\x00\x00" * 1600)
return temp.name
def main() -> int:
token = os.getenv("DREAM_CUSTOMS_HOSTED_TOKEN", "")
text_endpoint = os.getenv("DREAM_CUSTOMS_TEXT_ENDPOINT", "").strip()
vision_endpoint = os.getenv("DREAM_CUSTOMS_VISION_ENDPOINT", "").strip()
asr_endpoint = os.getenv("DREAM_CUSTOMS_ASR_ENDPOINT", "").strip()
image_path = os.getenv("DREAM_CUSTOMS_SMOKE_IMAGE", "").strip()
audio_path = os.getenv("DREAM_CUSTOMS_SMOKE_AUDIO", "").strip()
text_timeout = float(os.getenv("DREAM_CUSTOMS_BENCH_TEXT_TIMEOUT", "9"))
vision_timeout = float(os.getenv("DREAM_CUSTOMS_BENCH_VISION_TIMEOUT", "9"))
asr_timeout = float(os.getenv("DREAM_CUSTOMS_BENCH_ASR_TIMEOUT", "9"))
text_budget = int(float(os.getenv("DREAM_CUSTOMS_BENCH_TEXT_BUDGET_MS", "8000")))
vision_budget = int(float(os.getenv("DREAM_CUSTOMS_BENCH_VISION_BUDGET_MS", "9000")))
asr_budget = int(float(os.getenv("DREAM_CUSTOMS_BENCH_ASR_BUDGET_MS", "8000")))
intake = build_intake(
dream_text="I dreamed my phone died while I waited for an elevator.",
mood="Anxious",
)
answers = "The dead phone felt closest to being behind before I even start."
state = build_qa_state(
intake,
questions=["Which detail feels closest to your waking life right now?"],
answers=[answers],
language="en",
)
report: dict[str, Any] = {
"configured": {
"text_endpoint": bool(text_endpoint),
"vision_endpoint": bool(vision_endpoint),
"asr_endpoint": bool(asr_endpoint),
"token": bool(token),
"image_path": bool(image_path),
"audio_path": bool(audio_path),
},
"budgets_ms": {"text": text_budget, "vision": vision_budget, "asr": asr_budget},
"results": [],
}
if text_endpoint:
text_client = HostedMiniCPMTextClient(
endpoint=text_endpoint,
token=token,
timeout=text_timeout,
max_tokens=560,
latency_budget_ms=text_budget,
fallback=TextFallback(),
)
report["results"].append(
_measure(
"text_negotiation",
lambda: text_client.generate_negotiation(negotiation_prompt(intake, "en")),
)
)
report["results"].append(
_measure(
"text_today_tip",
lambda: text_client.generate_today_tip(today_tip_prompt(state, "en")),
)
)
if vision_endpoint and image_path and Path(image_path).exists():
vision_client = HostedMiniCPMVisionClient(
endpoint=vision_endpoint,
token=token,
timeout=vision_timeout,
max_tokens=220,
latency_budget_ms=vision_budget,
fallback=VisionFallback(),
)
report["results"].append(_measure("vision_clues", lambda: vision_client.extract_clues(image_path)))
if asr_endpoint:
probe_audio = ""
try:
probe_audio = audio_path if audio_path and Path(audio_path).exists() else _write_probe_wav()
asr_client = HostedASRClient(
endpoint=asr_endpoint,
token=token,
timeout=asr_timeout,
latency_budget_ms=asr_budget,
fallback=ASRFallback(),
)
report["results"].append(_measure("asr_transcribe", lambda: asr_client.transcribe(probe_audio)))
finally:
if probe_audio and probe_audio != audio_path:
try:
os.unlink(probe_audio)
except OSError:
pass
print(json.dumps(report, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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