| """Lightweight runtime inference-trace logger (optional open inference log). |
| |
| Appends one JSON line per inference to IRIS_TRACE_FILE. Opt-in via IRIS_TRACE=1. |
| Privacy: no raw images or audio are stored — only metadata and the produced text, |
| truncated. The JSONL is later converted to a Hugging Face Dataset (see |
| scripts/publish_trace.py) so others can learn how the app behaves. |
| """ |
| import json |
| import os |
| import time |
| import uuid |
|
|
| ENABLED = os.environ.get("IRIS_TRACE") == "1" |
| _FILE = os.environ.get("IRIS_TRACE_FILE", "traces/iris_traces.jsonl") |
|
|
|
|
| def log(endpoint: str, lang: str, question: str, answer: str, |
| latency_s: float, model: str) -> None: |
| if not ENABLED: |
| return |
| try: |
| os.makedirs(os.path.dirname(_FILE) or ".", exist_ok=True) |
| row = { |
| "trace_id": uuid.uuid4().hex, |
| "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), |
| "endpoint": endpoint, |
| "lang": lang, |
| "question": (question or "")[:200], |
| "answer": (answer or "")[:300], |
| "latency_s": round(latency_s, 2), |
| "model": model, |
| } |
| with open(_FILE, "a", encoding="utf-8") as f: |
| f.write(json.dumps(row, ensure_ascii=False) + "\n") |
| except Exception: |
| pass |
|
|
