klingai-usage-analytics

# Kling AI Usage Analytics ## Overview Track video generation usage with structured logging, aggregate metrics, daily reports, and cost analysis. Built on JSONL event logs that can feed into any analytics platform. ## Event Logger ```python import json import time from datetime import datetime from pathlib import Path class KlingEventLogger: """Append-only JSONL event log for Kling AI operations.""" def __init__(self, log_dir: str = "logs"): self.log_dir = Path(log_dir) self.log_dir.mkdir(exist_ok=True) def _write(self, event: dict): date = datetime.utcnow().strftime("%Y-%m-%d") filepath = self.log_dir / f"kling-{date}.jsonl" event["timestamp"] = datetime.utcnow().isoformat() with open(filepath, "a") as f: f.write(json.dumps(event) + "\n") def log_submission(self, task_id, prompt, model, duration, mode): self._write({ "event": "task_submitted", "task_id": task_id, "model": model, "duration": int(duration), "mode": mode, "prompt_len": len(prompt), }) def log_completion(self, task_id, status, elapsed_sec, credits_used): self._write({ "event": "task_completed", "task_id": task_id, "status": status, "elapsed_sec": elapsed_sec, "credits_used": credits_used, }) def log_error(self, task_id, error_type, message): self._wri...