space-bundle/app.py

"""

Optimization Operating System — Interactive Optimization Console

Unified platform for scheduling, routing, assignment, inventory, facility location, and packing.

"""

from __future__ import annotations

import json
import sys
from pathlib import Path

import gradio as gr
import pandas as pd
import plotly.graph_objects as go

ROOT = Path(__file__).resolve().parent
sys.path.insert(0, str(ROOT / "src"))

from optos.constants import PROBLEM_TYPES, SIZE_PRESETS, SOLVERS  # noqa: E402
from optos.pipeline import OptOSPipeline  # noqa: E402
from optos.visualization import (  # noqa: E402
    build_category_radar,
    build_gap_timeline,
    build_heatmap,
    build_progress_chart,
    build_scalability_chart,
    build_solver_comparison_chart,
)

pipeline = OptOSPipeline(ROOT / "assets")
pipeline.load()

SUMMARY = pipeline.summary
PROBLEM_CHOICES = pipeline.problem_choices()
SIZE_CHOICES = pipeline.size_choices()

CUSTOM_CSS = """

.gradio-container { max-width: 1560px !important; }

.markdown h1 { color: #4f46e5; font-weight: 700; }

.tab-nav button { font-weight: 600; }

"""

_state: dict = {"last_run": None, "last_explanation": None, "last_scenarios": None}


def _kpi_md() -> str:
    return f"""

### Optimization Operating System — Platform Overview



| Metric | Value |

|--------|-------|

| Engine version | **v{pipeline.version}** |

| Problem types | **{SUMMARY.get('problem_types', 6)}** (Scheduling, Routing, Assignment, Inventory, Facility, Packing) |

| Methods per problem | **4** (Baseline · Exact · Scalable · Robust) |

| Registered solvers | **{SUMMARY.get('solvers', 6)}** (HiGHS, CBC, CP-SAT, SCIP*, Gurobi*, Heuristic) |

| Benchmark runs | **{SUMMARY.get('total_benchmark_runs', 0)}** pre-computed |

| Instance sizes | Small · Medium · Large · Dynamic · Stochastic |



*Reference profiles — live solving uses open-source solvers on HF Spaces.*

"""


def _benchmark_df() -> pd.DataFrame:
    rows = pipeline.benchmark_table_rows()
    if not rows:
        return pd.DataFrame()
    cols = [
        "problem_label", "size", "method_label", "method_category",
        "objective_value", "optimality_gap", "elapsed_time_sec",
        "iterations", "feasible", "winner",
    ]
    return pd.DataFrame(rows)[[c for c in cols if c in rows[0]]]


def _run_optimization(problem_type, size, seed, time_limit):
    run = pipeline.run_experiment(problem_type, size, int(seed), float(time_limit))
    _state["last_run"] = run
    explanation = pipeline.explain_run(run)
    _state["last_explanation"] = explanation

    rows = []
    for r in run.results:
        rows.append({
            "Method": r.method_label,
            "Category": r.method_category,
            "Solver": r.solver_id,
            "Objective": r.metrics.objective_value,
            "Best Bound": r.metrics.best_bound,
            "Gap (%)": r.metrics.optimality_gap,
            "Time (s)": r.metrics.elapsed_time_sec,
            "Iterations": r.metrics.iterations,
            "Violations": r.metrics.constraint_violations,
            "Feasible": r.metrics.feasible,
            "Status": r.metrics.status,
            "Winner": r.method_id == run.winner,
        })
    df = pd.DataFrame(rows)
    winner_label = next((r.method_label for r in run.results if r.method_id == run.winner), run.winner)
    summary_md = f"""

**Winner:** {winner_label} · **Gap vs others:** {run.winner_gap_pct:.1f}% · **Runtime:** {run.runtime_sec:.2f}s



| Solve State | Value |

|-------------|-------|

| Best feasible objective | **{min(r.metrics.objective_value for r in run.results if r.metrics.feasible):.2f}** |

| Best bound | **{max(r.metrics.best_bound for r in run.results):.2f}** |

| Min gap | **{min(r.metrics.optimality_gap for r in run.results if r.metrics.feasible):.2f}%** |

"""
    chart_rows = [
        {
            "method_label": r.method_label,
            "method_id": r.method_id,
            "method_category": r.method_category,
            "objective_value": r.metrics.objective_value,
            "optimality_gap": r.metrics.optimality_gap,
            "elapsed_time_sec": r.metrics.elapsed_time_sec,
            "winner": r.method_id == run.winner,
        }
        for r in run.results
    ]
    fig_cmp = build_solver_comparison_chart(chart_rows)
    fig_gap = build_gap_timeline(chart_rows)
    winner_r = next((r for r in run.results if r.method_id == run.winner), run.results[0])
    fig_prog = build_progress_chart(winner_r.metrics.to_dict())
    return df, summary_md, fig_cmp, fig_gap, fig_prog


def _run_scenarios(problem_type, size, seed):
    scenarios = pipeline.run_scenarios(problem_type, size, int(seed))
    _state["last_scenarios"] = scenarios
    rows = []
    for s in scenarios:
        rows.append({
            "Scenario": s.scenario_label,
            "Type": s.scenario_type,
            "Baseline Obj": s.baseline_objective,
            "Perturbed Obj": s.perturbed_objective,
            "Delta (%)": s.delta_pct,
            "Feasible": s.feasible,
            "Binding": ", ".join(s.binding_constraints),
        })
    return pd.DataFrame(rows)


def _explain_current():
    exp = _state.get("last_run")
    expl = _state.get("last_explanation")
    if not exp or not expl:
        return "Run an optimization first.", pd.DataFrame()
    binding_df = pd.DataFrame(expl.binding_constraints)
    shadows_df = pd.DataFrame(expl.shadow_prices)
    md = f"""

### Explanation Report



**Infeasibility:** {expl.infeasibility_reason or "None — solution is feasible."}



**What-if suggestions:**

"""
    for s in expl.what_if_suggestions:
        md += f"- {s}\n"
    md += "\n**Counterfactuals:**\n"
    for c in expl.counterfactuals:
        md += f"- {c.get('action')}: expected objective ≈ {c.get('expected_objective', c.get('expected_impact', 'N/A'))}\n"
    return md, binding_df, shadows_df


def _export_json():
    run = _state.get("last_run")
    if not run:
        return "No run to export."
    return pipeline.export_json(run)


def _export_csv():
    run = _state.get("last_run")
    if not run:
        return pd.DataFrame()
    return pd.DataFrame(pipeline.export_csv_rows(run))


def _registry_md():
    lines = ["### Model Registry\n| Problem | Category | Description |"]
    lines.append("|---------|----------|-------------|")
    for k, m in PROBLEM_TYPES.items():
        lines.append(f"| {m['label']} | {m['category']} | {m['description']} |")
    lines.append("\n### Solver Registry\n| Solver | License | Available | Strengths |")
    lines.append("|--------|---------|-----------|-----------|")
    for sid, s in SOLVERS.items():
        avail = "✓" if s["available"] else "ref"
        lines.append(f"| {s['label']} | {s['license']} | {avail} | {', '.join(s['strengths'])} |")
    return "\n".join(lines)


def _methods_table(problem_type):
    info = pipeline.method_info(problem_type)
    return pd.DataFrame(info)


with gr.Blocks(title="Optimization Operating System", css=CUSTOM_CSS) as demo:
    gr.Markdown("# Optimization Operating System")
    gr.Markdown("Unified **Optimization-as-a-Service** platform — receive, solve, compare, and explain optimization problems across scheduling, routing, assignment, inventory, facility location, and packing.")

    with gr.Tab("Executive Overview"):
        gr.Markdown(_kpi_md())
        gr.Dataframe(value=_benchmark_df(), label="Pre-computed Benchmark Summary", interactive=False)
        fig_heat = build_heatmap(pipeline.comparisons)
        gr.Plot(fig_heat, label="Cross-Problem Benchmark Heatmap")
        fig_scale = build_scalability_chart(pipeline.scalability.get("rows", []))
        gr.Plot(fig_scale, label="Scalability Profile")

    with gr.Tab("Run Optimization"):
        with gr.Row():
            problem_dd = gr.Dropdown(PROBLEM_CHOICES, value="scheduling", label="Problem Type")
            size_dd = gr.Dropdown(SIZE_CHOICES, value="medium", label="Instance Size")
            seed_num = gr.Number(value=42, label="Seed", precision=0)
            time_limit = gr.Slider(5, 60, value=15, step=1, label="Time Limit (s)")
        run_btn = gr.Button("Run All Methods", variant="primary")
        run_summary = gr.Markdown()
        run_table = gr.DataFrame(label="Method Comparison", interactive=False)
        with gr.Row():
            cmp_plot = gr.Plot(label="Objective Comparison")
            gap_plot = gr.Plot(label="Gap vs Time")
        prog_plot = gr.Plot(label="Solve Progress")
        methods_preview = gr.DataFrame(label="Registered Methods", value=_methods_table("scheduling"))
        problem_dd.change(fn=_methods_table, inputs=problem_dd, outputs=methods_preview)
        run_btn.click(
            fn=_run_optimization,
            inputs=[problem_dd, size_dd, seed_num, time_limit],
            outputs=[run_table, run_summary, cmp_plot, gap_plot, prog_plot],
        )

    with gr.Tab("Scenario Engine"):
        gr.Markdown("Stress-test instances with capacity changes, demand shifts, resource removal, cost increases, and network disruptions.")
        with gr.Row():
            sc_problem = gr.Dropdown(PROBLEM_CHOICES, value="routing", label="Problem Type")
            sc_size = gr.Dropdown(SIZE_CHOICES, value="medium", label="Size")
            sc_seed = gr.Number(value=42, label="Seed", precision=0)
        sc_btn = gr.Button("Run All Scenarios", variant="primary")
        sc_table = gr.DataFrame(label="Scenario Results", interactive=False)
        sc_btn.click(fn=_run_scenarios, inputs=[sc_problem, sc_size, sc_seed], outputs=sc_table)

    with gr.Tab("Explanation Engine"):
        gr.Markdown("Binding constraints, shadow prices, infeasibility analysis, and what-if suggestions.")
        explain_btn = gr.Button("Explain Last Run", variant="primary")
        explain_md = gr.Markdown()
        with gr.Row():
            binding_df = gr.DataFrame(label="Binding Constraints", interactive=False)
            shadow_df = gr.DataFrame(label="Shadow Prices", interactive=False)
        explain_btn.click(fn=_explain_current, outputs=[explain_md, binding_df, shadow_df])

    with gr.Tab("Benchmarks"):
        gr.Dataframe(value=_benchmark_df(), label="Full Benchmark Table", interactive=False)
        fig_radar = build_category_radar(pipeline.benchmark_table_rows())
        gr.Plot(fig_radar, label="Method Category Radar")
        fig_scale2 = build_scalability_chart(pipeline.scalability.get("rows", []))
        gr.Plot(fig_scale2, label="Scalability")

    with gr.Tab("Registry"):
        gr.Markdown(_registry_md())

    with gr.Tab("Export"):
        gr.Markdown("Download results from the last optimization run as JSON or CSV.")
        export_json_btn = gr.Button("Preview JSON Export")
        export_json_out = gr.Code(language="json", label="JSON Result")
        export_csv_btn = gr.Button("Preview CSV Export")
        export_csv_out = gr.DataFrame(label="CSV Rows")
        export_json_btn.click(fn=_export_json, outputs=export_json_out)
        export_csv_btn.click(fn=_export_csv, outputs=export_csv_out)

if __name__ == "__main__":
    demo.launch()