| |
| """ |
| hf_pro/app.py |
| ============= |
| Interactive Web UI & Real-Time Orchestrator for Hugging Face Pro High-Budget Benchmark. |
| Runs on port 7860 (Hugging Face Spaces default). |
| """ |
|
|
| import os |
| import sys |
| import time |
| import json |
| import threading |
| import subprocess |
| from fastapi import FastAPI |
| from fastapi.responses import HTMLResponse, JSONResponse |
| import uvicorn |
|
|
| app = FastAPI(title="Hyperliquid Microstructure 2x3 Benchmark") |
|
|
| |
| state = { |
| "status": "INITIALIZING", |
| "active_model": None, |
| "completed_models": 0, |
| "total_models": 6, |
| "current_trial": 0, |
| "total_trials": 100, |
| "results": [], |
| "logs": [] |
| } |
|
|
| def log_msg(msg: str): |
| timestamp = time.strftime("%H:%M:%S") |
| entry = f"[{timestamp}] {msg}" |
| print(entry) |
| state["logs"].append(entry) |
| if len(state["logs"]) > 200: |
| state["logs"].pop(0) |
|
|
| def run_high_budget_benchmark(): |
| log_msg("🚀 Hugging Face Pro Benchmark worker initialized.") |
| state["status"] = "RUNNING" |
| |
| |
| try: |
| import torch |
| has_cuda = torch.cuda.is_available() |
| log_msg(f"Hardware check: CUDA Available = {has_cuda}") |
| if has_cuda: |
| log_msg(f"Active GPU: {torch.cuda.get_device_name(0)}") |
| else: |
| log_msg(f"High-vCPU fallback: {os.cpu_count()} threads available") |
| |
| import run_hf_pro_high_budget as runner |
| |
| |
| log_msg("Streaming multi-asset partitions from Hugging Face ProbioticFarmer/hyperliquid-augmented-copytrading...") |
| train_dates = [f"2026-08-{d:02d}" for d in range(15, 31, 2)] + [f"2026-09-{d:02d}" for d in range(1, 14, 2)] |
| test_dates = [f"2026-09-{d:02d}" for d in range(15, 30, 2)] + [f"2026-10-{d:02d}" for d in range(1, 14, 2)] |
| |
| df_tr_raw, df_te_raw = runner.stream_hf_pro_dataset(train_dates, test_dates, max_rows_day=3000) |
| log_msg(f"Streamed {len(df_tr_raw):,} train fills and {len(df_te_raw):,} test fills.") |
| |
| |
| log_msg("Building non-stationary-proof microstructure features (Who, What, Conditions)...") |
| df_train, feat_cols, cat_cols, num_cols = runner.build_features(df_tr_raw) |
| df_test, _, _, _ = runner.build_features(df_te_raw) |
| |
| val_idx = int(len(df_train) * 0.8) |
| X_tr = df_train.iloc[:val_idx][feat_cols] |
| y_tr = df_train.iloc[:val_idx]['target'] |
| X_val = df_train.iloc[val_idx:][feat_cols] |
| y_val = df_train.iloc[val_idx:]['target'] |
| X_te = df_test[feat_cols] |
| y_te = df_test['target'] |
| |
| |
| state["active_model"] = "CatBoost By-Hand" |
| log_msg("Training CatBoost By-Hand...") |
| |
| runner.main() |
| |
| if os.path.exists("results_hf_pro_high_budget.json"): |
| with open("results_hf_pro_high_budget.json") as f: |
| state["results"] = json.load(f) |
| |
| state["status"] = "COMPLETED" |
| log_msg("🏆 All 6 matrix configurations successfully evaluated!") |
| except Exception as e: |
| log_msg(f"❌ Execution error: {e}") |
| state["status"] = "ERROR" |
|
|
| @app.on_event("startup") |
| def startup_event(): |
| thread = threading.Thread(target=run_high_budget_benchmark, daemon=True) |
| thread.start() |
|
|
| @app.get("/api/state") |
| def get_state(): |
| return JSONResponse(state) |
|
|
| @app.get("/", response_class=HTMLResponse) |
| def index(): |
| return """ |
| <!DOCTYPE html> |
| <html lang="en"> |
| <head> |
| <meta charset="UTF-8"> |
| <title>Hyperliquid Microstructure Benchmark (2x3 Matrix)</title> |
| <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> |
| <style> |
| :root { |
| --bg: #090d16; |
| --card: #131b2e; |
| --border: #1f2d4a; |
| --accent: #38bdf8; |
| --text: #f1f5f9; |
| --text-muted: #94a3b8; |
| --green: #10b981; |
| --amber: #f59e0b; |
| } |
| body { |
| margin: 0; |
| background: var(--bg); |
| color: var(--text); |
| font-family: 'Inter', sans-serif; |
| padding: 30px; |
| } |
| .header { |
| display: flex; |
| justify-content: space-between; |
| align-items: center; |
| border-bottom: 1px solid var(--border); |
| padding-bottom: 20px; |
| margin-bottom: 30px; |
| } |
| .badge { |
| background: rgba(56, 189, 248, 0.15); |
| color: var(--accent); |
| padding: 6px 14px; |
| border-radius: 20px; |
| font-weight: 600; |
| font-size: 0.85rem; |
| border: 1px solid rgba(56, 189, 248, 0.3); |
| } |
| .grid { |
| display: grid; |
| grid-template-columns: 2fr 1fr; |
| gap: 25px; |
| } |
| .card { |
| background: var(--card); |
| border: 1px solid var(--border); |
| border-radius: 12px; |
| padding: 24px; |
| } |
| table { |
| width: 100%; |
| border-collapse: collapse; |
| margin-top: 15px; |
| } |
| th, td { |
| padding: 12px 14px; |
| text-align: left; |
| border-bottom: 1px solid var(--border); |
| font-size: 0.9rem; |
| } |
| th { |
| color: var(--text-muted); |
| font-weight: 600; |
| text-transform: uppercase; |
| font-size: 0.75rem; |
| letter-spacing: 0.05em; |
| } |
| .terminal { |
| background: #050811; |
| border: 1px solid var(--border); |
| border-radius: 8px; |
| padding: 15px; |
| font-family: 'JetBrains Mono', monospace; |
| font-size: 0.8rem; |
| color: #38bdf8; |
| height: 380px; |
| overflow-y: auto; |
| line-height: 1.5; |
| } |
| </style> |
| </head> |
| <body> |
| <div class="header"> |
| <div> |
| <h1 style="margin: 0 0 6px 0; font-size: 1.6rem;">⚡ Hyperliquid Microstructure Benchmark</h1> |
| <p style="margin: 0; color: var(--text-muted); font-size: 0.95rem;">High-Budget Tier (Hugging Face Pro Dedicated Compute) • 2x3 Matrix Evaluation</p> |
| </div> |
| <div id="status-badge" class="badge">INITIALIZING WORKLOAD</div> |
| </div> |
| |
| <div class="grid"> |
| <div class="card"> |
| <h3 style="margin-top: 0;">🏆 2x3 Factorial Experimental Matrix</h3> |
| <table id="matrix-table"> |
| <thead> |
| <tr> |
| <th>Model</th> |
| <th>Tuning</th> |
| <th>PR-AUC (Primary)</th> |
| <th>ROC-AUC</th> |
| <th>Brier Score</th> |
| <th>Latency (ms/1k)</th> |
| </tr> |
| </thead> |
| <tbody id="matrix-body"> |
| <tr><td colspan="6" style="text-align:center; color: var(--text-muted);">Waiting for first benchmark iteration to complete...</td></tr> |
| </tbody> |
| </table> |
| </div> |
| |
| <div class="card"> |
| <h3 style="margin-top: 0;">📟 Live High-Budget Log Stream</h3> |
| <div id="log-box" class="terminal">Starting runner daemon...</div> |
| </div> |
| </div> |
| |
| <script> |
| async function pollState() { |
| try { |
| const res = await fetch('/api/state'); |
| const data = await res.json(); |
| |
| document.getElementById('status-badge').innerText = data.status + (data.active_model ? ' (' + data.active_model + ')' : ''); |
| |
| const logBox = document.getElementById('log-box'); |
| logBox.innerHTML = data.logs.join('<br>'); |
| logBox.scrollTop = logBox.scrollHeight; |
| |
| if (data.results && data.results.length > 0) { |
| const tbody = document.getElementById('matrix-body'); |
| tbody.innerHTML = data.results.map(r => ` |
| <tr> |
| <td><strong>${r.model}</strong></td> |
| <td>${r.tuning}</td> |
| <td style="color: #38bdf8; font-weight: 600;">${r.pr_auc}</td> |
| <td>${r.roc_auc}</td> |
| <td>${r.brier_score}</td> |
| <td>${r.inference_ms_per_1k} ms</td> |
| </tr> |
| `).join(''); |
| } |
| } catch (e) { |
| console.error(e); |
| } |
| } |
| setInterval(pollState, 2500); |
| pollState(); |
| </script> |
| </body> |
| </html> |
| """ |
|
|
| if __name__ == "__main__": |
| uvicorn.run(app, host="0.0.0.0", port=7860) |
|
|
