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11d5cdf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | """Gradio Dashboard for Agent Cost Optimizer.
Visualizes:
- Cost-quality frontier (scatter plot: success rate vs avg cost)
- Baseline comparison bar charts
- Per-scenario breakdown
- Module ablation impact
"""
import json
import sys
from pathlib import Path
from typing import Dict, List, Any
import gradio as gr
def load_results(path: str) -> Dict[str, Any]:
with open(path) as f:
return json.load(f)
def parse_report(report_path: str) -> str:
with open(report_path) as f:
return f.read()
def create_frontier_plot(results: Dict[str, Any]):
"""Create scatter plot data for cost-quality frontier."""
points = []
for name, data in results.items():
success = (data.get("num_success", 0) + data.get("num_partial", 0)) / max(data.get("num_tasks", 1), 1)
cost = data.get("avg_cost_success", 0)
points.append({"baseline": name, "success_rate": success, "cost_per_success": cost})
# Sort by success rate desc, cost asc for frontier
points.sort(key=lambda p: (-p["success_rate"], p["cost_per_success"]))
# Build Pareto frontier
frontier = []
min_cost = float("inf")
for p in points:
if p["cost_per_success"] <= min_cost:
frontier.append(p)
min_cost = p["cost_per_success"]
return points, frontier
def build_dashboard(results_path: str, report_path: str):
results = load_results(results_path)
report_text = parse_report(report_path)
points, frontier = create_frontier_plot(results)
with gr.Blocks(title="Agent Cost Optimizer Dashboard") as demo:
gr.Markdown("# Agent Cost Optimizer - Cost-Quality Dashboard")
gr.Markdown("Visualize cost-quality tradeoffs across routing strategies and ablations.")
with gr.Row():
with gr.Column(scale=2):
gr.Markdown("## Cost-Quality Frontier")
gr.Markdown("**X-axis**: Average cost per successful task | **Y-axis**: Success rate")
# Scatter plot using native Gradio components
scatter_data = [
[p["baseline"], f"{p['success_rate']:.1%}", f"${p['cost_per_success']:.4f}"]
for p in points
]
gr.Dataframe(
headers=["Baseline", "Success Rate", "Cost per Success"],
value=scatter_data,
label="All Baselines",
)
frontier_data = [
[p["baseline"], f"{p['success_rate']:.1%}", f"${p['cost_per_success']:.4f}"]
for p in frontier
]
gr.Dataframe(
headers=["Baseline", "Success Rate", "Cost per Success"],
value=frontier_data,
label="Pareto Frontier",
)
with gr.Column(scale=1):
gr.Markdown("## Pareto Frontier Baselines")
pareto_names = [p["baseline"] for p in frontier]
for name in pareto_names:
gr.Markdown(f"- **{name}**")
with gr.Row():
with gr.Column():
gr.Markdown("## Baseline Comparison")
comparison_data = []
for name, data in results.items():
comparison_data.append([
name,
f"{(data.get('num_success',0)+data.get('num_partial',0))/max(data.get('num_tasks',1),1):.1%}",
f"${data.get('avg_cost_success',0):.4f}",
f"${data.get('total_cost',0):.2f}",
f"{data.get('cost_reduction_vs_frontier',0):.1%}",
f"{data.get('false_done_rate',0):.1%}",
f"{data.get('unsafe_cheap_miss_rate',0):.1%}",
f"{data.get('regression_rate',0):.1%}",
])
gr.Dataframe(
headers=["Baseline", "Success", "Cost/Success", "Total Cost", "Cost Reduction", "False-DONE", "Cheap Miss", "Regression"],
value=comparison_data,
)
with gr.Row():
with gr.Column():
gr.Markdown("## Per-Scenario Breakdown (Full Optimizer)")
full_data = results.get("full_optimizer", {})
scenario_stats = full_data.get("per_scenario_stats", {})
if scenario_stats:
scenario_data = []
for scenario, stats in scenario_stats.items():
count = stats.get("count", 0)
success = stats.get("success", 0)
cost = stats.get("cost", 0)
scenario_data.append([
scenario,
str(count),
f"{success/max(count,1):.1%}",
f"${cost:.2f}",
])
gr.Dataframe(
headers=["Scenario", "Count", "Success Rate", "Total Cost"],
value=scenario_data,
)
with gr.Row():
with gr.Column():
gr.Markdown("## Ablation Impact")
gr.Markdown("Cost increase when removing each module (vs full_optimizer)")
full_cost = results.get("full_optimizer", {}).get("total_cost", 0)
ablation_data = []
for name, data in results.items():
if name.startswith("no_"):
delta = data.get("total_cost", 0) - full_cost
pct = (delta / max(full_cost, 0.001)) * 100
ablation_data.append([name, f"${delta:.2f}", f"{pct:.1f}%"])
if ablation_data:
ablation_data.sort(key=lambda x: float(x[1].replace("$", "")), reverse=True)
gr.Dataframe(
headers=["Module Removed", "Cost Increase", "% Increase"],
value=ablation_data,
)
with gr.Row():
with gr.Column():
gr.Markdown("## Full Report")
gr.Textbox(report_text, lines=40, label="Benchmark Report")
return demo
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--results", default="./eval_results_v2/baseline_results.json",
help="Path to baseline results JSON")
parser.add_argument("--report", default="./eval_results_v2/report.txt",
help="Path to report text file")
parser.add_argument("--port", type=int, default=7860)
args = parser.parse_args()
demo = build_dashboard(args.results, args.report)
demo.launch(server_name="0.0.0.0", server_port=args.port)
if __name__ == "__main__":
main()
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