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Two new pieces close the generation→validation→saturation feedback loop: 1. gemini_cli_llm_judge.py — multi-criteria validator. For each draft, judges math correctness, cell-fit (does it actually target the declared track/zone/level?), scenario realism, uniqueness vs canonical questions, and visual-asset alignment. Returns PASS/NEEDS_FIX/DROP per item. Batched (default 15 per call) for budget efficiency. 2. iterate_coverage_loop.py — drives the full loop: analyze → plan → generate → render → judge → apply → re-analyze. Self-paced: stops when (a) top priority gap drops below threshold, (b) DROP rate exceeds the saturation/hallucination threshold, (c) total API calls exceed budget, or (d) the same cell is top priority for two iterations in a row (convergence). The user no longer specifies "how many questions" — the loop generates until the corpus reaches a measurable steady state. Plus 25 round-1 visual questions generated by the new batched generator (5 batched calls × 5 cells each, zero failures). The loop is the answer to "we need balance, not just volume": every iteration's plan derives from a fresh analysis of where coverage is weakest, so generation can never over-fill an already-saturated cell.
19 lines
774 B
Python
19 lines
774 B
Python
import os
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import matplotlib.pyplot as plt
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import numpy as np
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hours = np.arange(24)
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traffic = 20 + 80 * np.exp(-((hours - 14)**2) / 4)
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active_gpus = np.where((hours >= 13) & (hours <= 17), 100, 20)
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fig, ax = plt.subplots(figsize=(8, 4))
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ax.plot(hours, traffic, label='Traffic Demand', color='#c87b2a', linewidth=2)
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ax.step(hours, active_gpus, where='post', label='Active GPUs', color='#3d9e5a', linewidth=2)
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ax.fill_between(hours, traffic, active_gpus, step='post', color='#d4edda', alpha=0.5, label='Idle Buffer')
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ax.set_xlabel('Hour of Day')
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ax.set_ylabel('GPU Count')
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ax.set_title('Duty Cycling Timeline')
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ax.legend()
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plt.grid(True, linestyle=':', alpha=0.6)
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plt.tight_layout()
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plt.savefig(os.environ.get('VISUAL_OUT_PATH', 'out.svg'), format='svg', bbox_inches='tight') |