import os import uuid from io import BytesIO from datetime import datetime, timezone, timedelta from huggingface_hub import hf_hub_download HF_TOKEN = os.environ.get("HF_LOGGING_TOKEN") DATASET_REPO = os.environ.get("LOG_DATASET_REPO", "M3st3rJ4k3l/flux-klein-logs") MAX_LOG_DAYS = int(os.environ.get("LOG_MAX_DAYS", "7")) def _img_to_jpeg(img, quality=85): if img is None: return None try: buf = BytesIO() img.convert("RGB").save(buf, format="JPEG", quality=quality) return buf.getvalue() except Exception: return None def _build_table(pil_inputs, output_pil, prompt, seed, steps, guidance_scale, input_width, input_height, duration_seconds, success, error_message, lora_titles, lora_weights, upscale_factor, lora_prompt_text, now): import json as _json import pyarrow as pa img_struct = pa.struct([("bytes", pa.binary()), ("path", pa.string())]) hf_meta = _json.dumps({"info": {"features": { "timestamp": {"dtype": "float64", "_type": "Value"}, "prompt": {"dtype": "string", "_type": "Value"}, "seed": {"dtype": "int32", "_type": "Value"}, "steps": {"dtype": "int32", "_type": "Value"}, "guidance_scale": {"dtype": "float32", "_type": "Value"}, "input_images": {"feature": {"_type": "Image"}, "_type": "Sequence"}, "output_image": {"_type": "Image"}, "duration_seconds": {"dtype": "float32", "_type": "Value"}, "input_width": {"dtype": "int32", "_type": "Value"}, "input_height": {"dtype": "int32", "_type": "Value"}, "success": {"dtype": "bool", "_type": "Value"}, "error_message": {"dtype": "string", "_type": "Value"}, "lora_titles": {"feature": {"dtype": "string", "_type": "Value"}, "_type": "Sequence"}, "lora_weights": {"feature": {"dtype": "float32", "_type": "Value"}, "_type": "Sequence"}, "upscale_factor": {"dtype": "string", "_type": "Value"}, "lora_prompt_text": {"dtype": "string", "_type": "Value"}, }}}).encode() schema = pa.schema([ ("timestamp", pa.float64()), ("prompt", pa.string()), ("seed", pa.int32()), ("steps", pa.int32()), ("guidance_scale", pa.float32()), ("input_images", pa.list_(img_struct)), ("output_image", img_struct), ("duration_seconds", pa.float32()), ("input_width", pa.int32()), ("input_height", pa.int32()), ("success", pa.bool_()), ("error_message", pa.string()), ("lora_titles", pa.list_(pa.string())), ("lora_weights", pa.list_(pa.float32())), ("upscale_factor", pa.string()), ("lora_prompt_text", pa.string()), ], metadata={b"huggingface": hf_meta}) def _img(b): return {"bytes": b, "path": None} input_jpegs = [_img_to_jpeg(img) for img in pil_inputs] output_jpeg = _img_to_jpeg(output_pil) return pa.table({ "timestamp": pa.array([now.timestamp()], type=pa.float64()), "prompt": pa.array([prompt], type=pa.string()), "seed": pa.array([int(seed)], type=pa.int32()), "steps": pa.array([int(steps)], type=pa.int32()), "guidance_scale": pa.array([float(guidance_scale)], type=pa.float32()), "input_images": pa.array([[_img(b) for b in input_jpegs]], type=pa.list_(img_struct)), "output_image": pa.array([_img(output_jpeg) if output_jpeg else None], type=img_struct), "duration_seconds": pa.array([float(duration_seconds)], type=pa.float32()), "input_width": pa.array([int(input_width)], type=pa.int32()), "input_height": pa.array([int(input_height)], type=pa.int32()), "success": pa.array([bool(success)], type=pa.bool_()), "error_message": pa.array([str(error_message)], type=pa.string()), "lora_titles": pa.array([list(lora_titles or [])], type=pa.list_(pa.string())), "lora_weights": pa.array([[float(w) for w in (lora_weights or [])]], type=pa.list_(pa.float32())), "upscale_factor": pa.array([str(upscale_factor or "None")], type=pa.string()), "lora_prompt_text": pa.array([str(lora_prompt_text or "")], type=pa.string()), }, schema=schema) def _upload_parquet(api, repo_id, table, path_in_repo): import tempfile import pyarrow.parquet as pq tmp_path = None try: with tempfile.NamedTemporaryFile(suffix=".parquet", delete=False) as tmp: tmp_path = tmp.name pq.write_table(table, tmp_path) print(f"[log] uploading {path_in_repo} ({os.path.getsize(tmp_path)//1024}KB)") api.upload_file( path_or_fileobj=tmp_path, path_in_repo=path_in_repo, repo_id=repo_id, repo_type="dataset", ) print(f"[log] upload done — {repo_id}/{path_in_repo}") finally: if tmp_path: try: os.unlink(tmp_path) except Exception as e: print(f"[log] failed to delete temp file {tmp_path}: {e}") def _make_path(now, uid): return f"data/{now.strftime('%Y-%m-%d-%H%M%S')}-{uid}.parquet" def _file_date(path): return os.path.basename(path)[:10] def _maybe_squash_history(api, repo_id, now): marker = "metadata/last_squash.txt" today = now.strftime("%Y-%m-%d") try: try: local = hf_hub_download(repo_id=repo_id, filename=marker, repo_type="dataset", token=api.token) if open(local).read().strip() == today: return except Exception as e: print(f"[log] squash marker not found ({e}), proceeding with squash") api.super_squash_history(repo_id=repo_id, repo_type="dataset") print(f"[log] squashed history for {repo_id}") api.upload_file( path_or_fileobj=today.encode(), path_in_repo=marker, repo_id=repo_id, repo_type="dataset", ) print(f"[log] updated squash marker: {today}") except Exception as e: print(f"[log] squash warning: {e}") def _prune_old_files(api, repo_id, keep_days, now): if keep_days <= 0: return cutoff = (now - timedelta(days=keep_days)).strftime("%Y-%m-%d") try: to_delete = [ f.path for f in api.list_repo_tree(repo_id, repo_type="dataset", path_in_repo="data") if f.path.endswith(".parquet") and _file_date(f.path) < cutoff ] for path in to_delete: api.delete_file(path_in_repo=path, repo_id=repo_id, repo_type="dataset") print(f"[log] pruned: {path}") if to_delete: print(f"[log] pruned {len(to_delete)} old file(s)") except Exception as e: print(f"[log] prune warning: {e}") def log_inference(pil_inputs, output_pil, prompt, seed, steps, guidance_scale, input_width, input_height, duration_seconds, success, error_message="", *, lora_titles=None, lora_weights=None, upscale_factor="None", lora_prompt_text=""): import time as _time _t0 = _time.perf_counter() if not HF_TOKEN or not DATASET_REPO: print(f"[log] skipped — HF_LOGGING_TOKEN={'set' if HF_TOKEN else 'missing'}, " f"LOG_DATASET_REPO={'set' if DATASET_REPO else 'missing'}") return try: from huggingface_hub import HfApi now = datetime.now(timezone.utc) _t1 = _time.perf_counter() table = _build_table(pil_inputs, output_pil, prompt, seed, steps, guidance_scale, input_width, input_height, duration_seconds, success, error_message, lora_titles, lora_weights, upscale_factor, lora_prompt_text, now) print(f"[log] build_table: {_time.perf_counter() - _t1:.3f}s") uid = uuid.uuid4().hex[:8] path_in_repo = _make_path(now, uid) _t2 = _time.perf_counter() api = HfApi(token=HF_TOKEN) api.create_repo(repo_id=DATASET_REPO, repo_type="dataset", private=True, exist_ok=True) print(f"[log] create_repo: {_time.perf_counter() - _t2:.3f}s") _t3 = _time.perf_counter() _upload_parquet(api, DATASET_REPO, table, path_in_repo) print(f"[log] upload_parquet: {_time.perf_counter() - _t3:.3f}s") _t4 = _time.perf_counter() _prune_old_files(api, DATASET_REPO, MAX_LOG_DAYS, now) print(f"[log] prune_old_files: {_time.perf_counter() - _t4:.3f}s") _t5 = _time.perf_counter() _maybe_squash_history(api, DATASET_REPO, now) print(f"[log] squash_history: {_time.perf_counter() - _t5:.3f}s") except Exception as log_err: import traceback as _tb print(f"[log] WARNING: {log_err}\n{_tb.format_exc()}") finally: print(f"[log] log_inference total: {_time.perf_counter() - _t0:.3f}s")