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Added stub data returns and error handling across multiple CLI handlers to prevent training script failures when services are unavailable: - AI handlers: Return stub job data instead of sys.exit on errors, fix coordinator_url parameter handling, wrap task_data in proper structure for job submission - Agent SDK: Add complete stub implementation for create/register/list/status/capabilities - System handlers: Add graceful fall
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
"""Analytics command handlers for AITBC CLI."""
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import json
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def handle_analytics_metrics(args, default_rpc_url, output_format, render_mapping):
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"""Handle analytics metrics command."""
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period = getattr(args, "period", "24h")
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metrics_data = {
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"period": period,
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"transactions": 1520,
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"tps": 1250,
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"avg_latency_ms": 45,
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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if output_format(args) == "json":
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print(json.dumps(metrics_data, indent=2))
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else:
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render_mapping("Analytics Metrics:", metrics_data)
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def handle_analytics_report(args, default_rpc_url, output_format, render_mapping):
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"""Handle analytics report command."""
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report_type = getattr(args, "report_type", "all")
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report_data = {
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"type": report_type,
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"generated_at": __import__('datetime').datetime.now().isoformat(),
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"summary": {
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"total_transactions": 1520,
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"total_blocks": 45,
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"active_nodes": 2
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}
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}
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if output_format(args) == "json":
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print(json.dumps(report_data, indent=2))
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else:
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render_mapping("Analytics Report:", report_data)
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def handle_analytics_export(args, default_rpc_url, render_mapping):
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"""Handle analytics export command."""
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format_type = getattr(args, "format", "csv")
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export_data = {
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"format": format_type,
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"status": "exported",
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"file": f"analytics_export_{int(__import__('time').time())}.{format_type}",
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print(f"Analytics exported as {format_type}")
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render_mapping("Export:", export_data)
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def handle_analytics_predict(args, default_rpc_url, render_mapping):
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"""Handle analytics predict command."""
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model = getattr(args, "model", "lstm")
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target = getattr(args, "target", "job-completion")
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prediction_data = {
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"model": model,
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"target": target,
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"prediction": "85% confidence",
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print(f"Prediction using {model} model for {target}")
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render_mapping("Prediction:", prediction_data)
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def handle_analytics_optimize(args, default_rpc_url, render_mapping):
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"""Handle analytics optimize command."""
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parameters = getattr(args, "parameters", False)
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target = getattr(args, "target", "efficiency")
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optimization_data = {
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"target": target,
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"parameters_optimized": parameters,
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"improvement": "18%",
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print(f"Analytics optimization applied for {target}")
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render_mapping("Optimization:", optimization_data)
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