| """ |
| FaultSense — LightGBM Fault Prediction App |
| Fixes applied: |
| 1. Model loads at module level so gunicorn workers pick it up |
| 2. Select dropdown works cross-platform |
| """ |
|
|
| import os |
| import warnings |
| import numpy as np |
| import pandas as pd |
|
|
| warnings.filterwarnings("ignore") |
|
|
| from sklearn.model_selection import train_test_split |
| from sklearn.metrics import ( |
| roc_auc_score, accuracy_score, precision_score, |
| recall_score, f1_score, log_loss, confusion_matrix |
| ) |
| from sklearn.preprocessing import OneHotEncoder |
| from sklearn.compose import ColumnTransformer |
| from sklearn.pipeline import Pipeline |
| import joblib |
| from lightgbm import LGBMClassifier |
| from flask import Flask, request, jsonify, render_template_string |
|
|
| |
| |
| |
|
|
| DATA_PATH = "synthetic_nim_parallel_10000.csv" |
| MODEL_PATH = "/tmp/faultsense_model.joblib" |
|
|
| DROP_COLS = ["location"] |
| TARGET = "faulty" |
| CAT_COLS = ["equipment"] |
| NUM_COLS = ["temperature", "pressure", "vibration", "humidity"] |
|
|
| RANDOM_STATE = 42 |
| THRESHOLD = 0.5 |
|
|
| FIXED_PARAMS = dict( |
| max_depth=8, |
| num_leaves=50, |
| min_child_samples=20, |
| subsample=0.8, |
| colsample_bytree=0.8, |
| class_weight="balanced", |
| random_state=RANDOM_STATE, |
| verbose=-1, |
| ) |
|
|
| BEST_CONFIG = { |
| "learning_rate": 0.05, |
| "n_estimators": 165, |
| "train_ratio": 0.80, |
| "val_ratio": 0.10, |
| "test_ratio": 0.10, |
| } |
|
|
| EQUIPMENT_OPTIONS = ["pump", "compressor", "motor", "valve", "sensor"] |
|
|
| |
| |
| |
|
|
| def make_preprocessor(): |
| return ColumnTransformer([ |
| ("cat", OneHotEncoder(handle_unknown="ignore", sparse_output=False), CAT_COLS), |
| ("num", "passthrough", NUM_COLS), |
| ]) |
|
|
| def train_model(cfg: dict): |
| print(f"Training: lr={cfg['learning_rate']}, n_est={cfg['n_estimators']}") |
| df_raw = pd.read_csv(DATA_PATH) |
| df_raw = df_raw.drop(columns=DROP_COLS, errors="ignore") |
| X = df_raw.drop(columns=[TARGET]) |
| y = df_raw[TARGET] |
|
|
| train_r, val_r, test_r = cfg["train_ratio"], cfg["val_ratio"], cfg["test_ratio"] |
| X_trainval, X_test, y_trainval, y_test = train_test_split( |
| X, y, test_size=test_r, stratify=y, random_state=RANDOM_STATE |
| ) |
| val_relative = val_r / (train_r + val_r) |
| X_train, X_val, y_train, y_val = train_test_split( |
| X_trainval, y_trainval, test_size=val_relative, |
| stratify=y_trainval, random_state=RANDOM_STATE |
| ) |
|
|
| pipeline = Pipeline([ |
| ("pre", make_preprocessor()), |
| ("clf", LGBMClassifier( |
| n_estimators=cfg["n_estimators"], |
| learning_rate=cfg["learning_rate"], |
| **FIXED_PARAMS |
| )) |
| ]) |
| pipeline.fit(X_train, y_train) |
|
|
| y_prob = pipeline.predict_proba(X_test)[:, 1] |
| y_pred = (y_prob >= THRESHOLD).astype(int) |
|
|
| test_metrics = { |
| "test_auc": round(roc_auc_score(y_test, y_prob), 4), |
| "test_accuracy": round(accuracy_score(y_test, y_pred), 4), |
| "test_precision": round(precision_score(y_test, y_pred, zero_division=0), 4), |
| "test_recall": round(recall_score(y_test, y_pred, zero_division=0), 4), |
| "test_f1": round(f1_score(y_test, y_pred, zero_division=0), 4), |
| "test_logloss": round(log_loss(y_test, y_prob), 4), |
| } |
| cm = confusion_matrix(y_test, y_pred).tolist() |
| artifact = {"pipeline": pipeline, "config": cfg, "test_metrics": test_metrics, "cm": cm} |
| print(f"Model saved → {MODEL_PATH} AUC={test_metrics['test_auc']} F1={test_metrics['test_f1']}") |
| return artifact |
|
|
| def load_or_train(): |
| if os.path.exists(MODEL_PATH): |
| print(f"Loading saved model from {MODEL_PATH}") |
| return joblib.load(MODEL_PATH) |
| return train_model(BEST_CONFIG) |
|
|
| |
| |
| |
| ARTIFACT = load_or_train() |
|
|
| |
| |
| |
|
|
| app = Flask(__name__) |
|
|
| HTML = r"""<!DOCTYPE html> |
| <html lang="en"> |
| <head> |
| <meta charset="UTF-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> |
| <title>FaultSense — Equipment Fault Predictor</title> |
| <link href="https://fonts.googleapis.com/css2?family=Space+Mono:wght@400;700&family=DM+Sans:wght@300;500;700&display=swap" rel="stylesheet"> |
| <style> |
| *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; } |
| :root { |
| --bg: #0a0c10; --surface: #111318; --surface2: #181c24; |
| --border: #232838; --accent: #00e5a0; --accent2: #ff4d6d; |
| --accent3: #4d9fff; --text: #e8eaf0; --muted: #6b7280; |
| --mono: 'Space Mono', monospace; --sans: 'DM Sans', sans-serif; |
| } |
| html { font-size: 16px; } |
| body { background: var(--bg); color: var(--text); font-family: var(--sans); min-height: 100vh; overflow-x: hidden; } |
| body::before { |
| content: ''; position: fixed; inset: 0; |
| background-image: linear-gradient(rgba(0,229,160,.04) 1px, transparent 1px), linear-gradient(90deg, rgba(0,229,160,.04) 1px, transparent 1px); |
| background-size: 40px 40px; pointer-events: none; z-index: 0; |
| } |
| .blob { position: fixed; width: 600px; height: 600px; border-radius: 50%; filter: blur(120px); opacity: .15; pointer-events: none; animation: drift 12s ease-in-out infinite alternate; z-index: 0; } |
| .blob-1 { background: var(--accent); top: -200px; left: -200px; } |
| .blob-2 { background: var(--accent3); bottom: -200px; right: -100px; animation-delay: -6s; } |
| @keyframes drift { from { transform: translate(0,0) scale(1); } to { transform: translate(40px,30px) scale(1.05); } } |
| .wrapper { position: relative; z-index: 1; max-width: 1100px; margin: 0 auto; padding: 40px 24px 80px; } |
| header { display: flex; align-items: center; gap: 16px; margin-bottom: 48px; border-bottom: 1px solid var(--border); padding-bottom: 24px; } |
| .logo-mark { width: 44px; height: 44px; background: var(--accent); border-radius: 10px; display: grid; place-items: center; font-family: var(--mono); font-weight: 700; font-size: 18px; color: var(--bg); flex-shrink: 0; } |
| header h1 { font-family: var(--mono); font-size: 1.5rem; letter-spacing: -.5px; } |
| header p { font-size: .85rem; color: var(--muted); margin-top: 2px; } |
| .badge { margin-left: auto; font-family: var(--mono); font-size: .7rem; background: rgba(0,229,160,.12); color: var(--accent); border: 1px solid rgba(0,229,160,.3); border-radius: 6px; padding: 4px 10px; white-space: nowrap; } |
| .main-grid { display: grid; grid-template-columns: 1fr 380px; gap: 24px; align-items: start; } |
| @media (max-width: 860px) { .main-grid { grid-template-columns: 1fr; } } |
| .card { background: var(--surface); border: 1px solid var(--border); border-radius: 16px; padding: 28px; } |
| .card-title { font-family: var(--mono); font-size: .75rem; letter-spacing: 1.5px; text-transform: uppercase; color: var(--muted); margin-bottom: 20px; display: flex; align-items: center; gap: 8px; } |
| .card-title::before { content: ''; display: inline-block; width: 6px; height: 6px; background: var(--accent); border-radius: 50%; } |
| .form-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; } |
| @media (max-width: 560px) { .form-grid { grid-template-columns: 1fr; } } |
| .field { display: flex; flex-direction: column; gap: 8px; } |
| .field label { font-size: .78rem; font-family: var(--mono); color: var(--muted); letter-spacing: .5px; } |
| |
| /* CUSTOM DROPDOWN */ |
| .custom-select { position: relative; width: 100%; } |
| .cs-trigger { |
| background: var(--surface2); border: 1px solid var(--border); |
| border-radius: 10px; color: var(--text); font-family: var(--mono); |
| font-size: .9rem; padding: 11px 14px; width: 100%; cursor: pointer; |
| display: flex; justify-content: space-between; align-items: center; |
| transition: border-color .2s; |
| } |
| .cs-trigger:hover, .cs-trigger.open { border-color: var(--accent); } |
| .cs-arrow { font-size: 10px; color: var(--muted); transition: transform .2s; } |
| .cs-trigger.open .cs-arrow { transform: rotate(180deg); } |
| .cs-options { |
| position: absolute; top: calc(100% + 4px); left: 0; right: 0; |
| background: #1a1e2a; border: 1px solid var(--accent); |
| border-radius: 10px; z-index: 999; overflow: hidden; |
| display: none; flex-direction: column; |
| } |
| .cs-options.open { display: flex; } |
| .cs-option { |
| padding: 11px 14px; font-family: var(--mono); font-size: .9rem; |
| color: var(--text); cursor: pointer; transition: background .15s; |
| } |
| .cs-option:hover { background: rgba(0,229,160,.15); color: var(--accent); } |
| .cs-option.selected { color: var(--accent); background: rgba(0,229,160,.08); } |
| |
| .slider-wrap { display: flex; flex-direction: column; gap: 6px; } |
| .slider-row { display: flex; align-items: center; gap: 10px; } |
| input[type=range] { flex: 1; -webkit-appearance: none; height: 4px; border-radius: 4px; background: var(--border); outline: none; cursor: pointer; } |
| input[type=range]::-webkit-slider-thumb { -webkit-appearance: none; width: 16px; height: 16px; border-radius: 50%; background: var(--accent); transition: transform .15s; } |
| input[type=range]::-webkit-slider-thumb:hover { transform: scale(1.3); } |
| input[type=range]::-moz-range-thumb { width: 16px; height: 16px; border-radius: 50%; background: var(--accent); border: none; } |
| .slider-val { font-family: var(--mono); font-size: .85rem; color: var(--accent); min-width: 60px; text-align: right; } |
| .btn-predict { margin-top: 24px; width: 100%; padding: 14px; background: var(--accent); color: var(--bg); border: none; border-radius: 12px; font-family: var(--mono); font-size: 1rem; font-weight: 700; letter-spacing: 1px; cursor: pointer; transition: transform .15s, box-shadow .2s; } |
| .btn-predict:hover { transform: translateY(-2px); box-shadow: 0 0 32px rgba(0,229,160,.5); } |
| .btn-predict:disabled { background: var(--muted); cursor: not-allowed; transform: none; } |
| .result-card { border-radius: 16px; padding: 28px; border: 1px solid var(--border); background: var(--surface); transition: border-color .4s; } |
| .result-card.faulty { border-color: var(--accent2); background: rgba(255,77,109,.06); } |
| .result-card.healthy { border-color: var(--accent); background: rgba(0,229,160,.06); } |
| .verdict { font-family: var(--mono); font-size: 2rem; font-weight: 700; letter-spacing: -1px; margin-bottom: 6px; } |
| .verdict.faulty { color: var(--accent2); } |
| .verdict.healthy { color: var(--accent); } |
| .verdict-sub { font-size: .85rem; color: var(--muted); margin-bottom: 24px; } |
| .prob-bar-wrap { margin-bottom: 24px; } |
| .prob-label { font-family: var(--mono); font-size: .72rem; color: var(--muted); margin-bottom: 6px; display: flex; justify-content: space-between; } |
| .prob-track { height: 10px; background: var(--border); border-radius: 10px; overflow: hidden; } |
| .prob-fill { height: 100%; border-radius: 10px; transition: width .6s cubic-bezier(.4,0,.2,1); } |
| .prob-fill.faulty { background: linear-gradient(90deg, #ff4d6d, #ff8fa3); } |
| .prob-fill.healthy { background: linear-gradient(90deg, #00e5a0, #5eead4); } |
| .mini-metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 10px; } |
| .mini-metric { background: var(--surface2); border-radius: 10px; padding: 12px; border: 1px solid var(--border); } |
| .mini-metric .mm-val { font-family: var(--mono); font-size: 1.1rem; font-weight: 700; color: var(--accent3); } |
| .mini-metric .mm-key { font-size: .7rem; color: var(--muted); margin-top: 2px; font-family: var(--mono); } |
| .info-row { display: flex; justify-content: space-between; align-items: center; padding: 9px 0; border-bottom: 1px solid var(--border); font-size: .82rem; } |
| .info-row:last-child { border-bottom: none; } |
| .info-key { color: var(--muted); font-family: var(--mono); font-size: .72rem; } |
| .info-val { font-family: var(--mono); color: var(--text); font-weight: 700; } |
| .info-val.green { color: var(--accent); } |
| .history-list { max-height: 260px; overflow-y: auto; display: flex; flex-direction: column; gap: 8px; } |
| .hist-item { background: var(--surface2); border: 1px solid var(--border); border-radius: 10px; padding: 10px 14px; display: flex; justify-content: space-between; align-items: center; font-size: .78rem; } |
| .hist-equip { color: var(--muted); font-family: var(--mono); font-size: .7rem; } |
| .hist-badge { font-family: var(--mono); font-size: .68rem; padding: 3px 8px; border-radius: 6px; font-weight: 700; } |
| .hist-badge.faulty { background: rgba(255,77,109,.2); color: var(--accent2); } |
| .hist-badge.healthy { background: rgba(0,229,160,.2); color: var(--accent); } |
| .spinner { display: none; width: 20px; height: 20px; border: 2px solid rgba(10,12,16,.3); border-top-color: var(--bg); border-radius: 50%; animation: spin .6s linear infinite; margin: 0 auto; } |
| @keyframes spin { to { transform: rotate(360deg); } } |
| .btn-predict.loading .btn-text { display: none; } |
| .btn-predict.loading .spinner { display: block; } |
| .idle-state { display: flex; flex-direction: column; align-items: center; justify-content: center; gap: 12px; padding: 32px 0; color: var(--muted); text-align: center; } |
| .idle-icon { font-size: 2.5rem; opacity: .4; } |
| .toast { position: fixed; bottom: 24px; right: 24px; background: var(--surface2); border: 1px solid var(--accent2); color: var(--accent2); border-radius: 10px; padding: 12px 18px; font-family: var(--mono); font-size: .8rem; transform: translateY(80px); opacity: 0; transition: all .3s; z-index: 999; } |
| .toast.show { transform: translateY(0); opacity: 1; } |
| </style> |
| </head> |
| <body> |
| <div class="blob blob-1"></div> |
| <div class="blob blob-2"></div> |
| <div class="wrapper"> |
| <header> |
| <div class="logo-mark">FS</div> |
| <div> |
| <h1>FaultSense</h1> |
| <p>LightGBM Equipment Fault Predictor</p> |
| </div> |
| <div class="badge" id="model-badge">Model Loading…</div> |
| </header> |
| |
| <div class="main-grid"> |
| <div style="display:flex;flex-direction:column;gap:20px;"> |
| <div class="card"> |
| <div class="card-title">Sensor Readings</div> |
| <div class="form-grid"> |
| |
| <div class="field" style="grid-column:1/-1"> |
| <label>Equipment Type</label> |
| <div class="custom-select" id="equipment-wrapper"> |
| <div class="cs-trigger" id="cs-trigger" onclick="toggleDropdown()"> |
| <span id="cs-selected">Pump</span> |
| <span class="cs-arrow">▼</span> |
| </div> |
| <div class="cs-options" id="cs-options"> |
| <div class="cs-option selected" onclick="selectOption('pump','Pump')">Pump</div> |
| <div class="cs-option" onclick="selectOption('compressor','Compressor')">Compressor</div> |
| <div class="cs-option" onclick="selectOption('motor','Motor')">Motor</div> |
| <div class="cs-option" onclick="selectOption('valve','Valve')">Valve</div> |
| <div class="cs-option" onclick="selectOption('sensor','Sensor')">Sensor</div> |
| </div> |
| </div> |
| <input type="hidden" id="equipment" value="pump"> |
| </div> |
| |
| <div class="field slider-wrap"> |
| <label>Temperature (°C)</label> |
| <div class="slider-row"> |
| <input type="range" id="temperature" min="-20" max="120" step="0.5" value="40" |
| oninput="document.getElementById('temperature-val').textContent=parseFloat(this.value).toFixed(1)+'°C'"> |
| <span class="slider-val" id="temperature-val">40.0°C</span> |
| </div> |
| </div> |
| |
| <div class="field slider-wrap"> |
| <label>Pressure (bar)</label> |
| <div class="slider-row"> |
| <input type="range" id="pressure" min="0" max="20" step="0.1" value="5" |
| oninput="document.getElementById('pressure-val').textContent=parseFloat(this.value).toFixed(1)+' bar'"> |
| <span class="slider-val" id="pressure-val">5.0 bar</span> |
| </div> |
| </div> |
| |
| <div class="field slider-wrap"> |
| <label>Vibration (mm/s)</label> |
| <div class="slider-row"> |
| <input type="range" id="vibration" min="0" max="50" step="0.1" value="5" |
| oninput="document.getElementById('vibration-val').textContent=parseFloat(this.value).toFixed(1)+' mm/s'"> |
| <span class="slider-val" id="vibration-val">5.0 mm/s</span> |
| </div> |
| </div> |
| |
| <div class="field slider-wrap"> |
| <label>Humidity (%)</label> |
| <div class="slider-row"> |
| <input type="range" id="humidity" min="0" max="100" step="1" value="50" |
| oninput="document.getElementById('humidity-val').textContent=parseInt(this.value)+'%'"> |
| <span class="slider-val" id="humidity-val">50%</span> |
| </div> |
| </div> |
| |
| </div> |
| <button class="btn-predict" id="predict-btn" onclick="runPredict()"> |
| <span class="btn-text">⚡ Run Prediction</span> |
| <div class="spinner"></div> |
| </button> |
| </div> |
| |
| <div class="card"> |
| <div class="card-title">Prediction History</div> |
| <div class="history-list" id="history-list"> |
| <div style="color:var(--muted);font-size:.8rem;font-family:var(--mono);text-align:center;padding:16px 0;">No predictions yet</div> |
| </div> |
| </div> |
| </div> |
| |
| <div style="display:flex;flex-direction:column;gap:20px;"> |
| <div class="result-card" id="result-card"> |
| <div class="idle-state" id="idle-state"> |
| <div class="idle-icon">🔬</div> |
| <p>Enter sensor readings<br>and run a prediction<br>to see results here.</p> |
| </div> |
| <div id="result-content" style="display:none;"> |
| <div class="verdict" id="verdict-text"></div> |
| <div class="verdict-sub" id="verdict-sub"></div> |
| <div class="prob-bar-wrap"> |
| <div class="prob-label"><span>Fault Probability</span><span id="prob-pct"></span></div> |
| <div class="prob-track"><div class="prob-fill" id="prob-fill" style="width:0%"></div></div> |
| </div> |
| <div class="mini-metrics" id="mini-metrics"></div> |
| </div> |
| </div> |
| |
| <div class="card"> |
| <div class="card-title">Model Configuration</div> |
| <div id="model-info"> |
| <div style="color:var(--muted);font-size:.8rem;font-family:var(--mono);text-align:center;padding:12px 0;">Loading…</div> |
| </div> |
| </div> |
| </div> |
| </div> |
| </div> |
| |
| <div class="toast" id="toast"></div> |
| |
| <script> |
| let csOpen = false; |
| function toggleDropdown() { |
| csOpen = !csOpen; |
| document.getElementById('cs-trigger').classList.toggle('open', csOpen); |
| document.getElementById('cs-options').classList.toggle('open', csOpen); |
| } |
| function selectOption(value, label) { |
| document.getElementById('equipment').value = value; |
| document.getElementById('cs-selected').textContent = label; |
| document.querySelectorAll('.cs-option').forEach(o => o.classList.remove('selected')); |
| event.target.classList.add('selected'); |
| csOpen = false; |
| document.getElementById('cs-trigger').classList.remove('open'); |
| document.getElementById('cs-options').classList.remove('open'); |
| } |
| document.addEventListener('click', function(e) { |
| if (!document.getElementById('equipment-wrapper').contains(e.target)) { |
| csOpen = false; |
| document.getElementById('cs-trigger').classList.remove('open'); |
| document.getElementById('cs-options').classList.remove('open'); |
| } |
| }); |
| async function loadModelInfo() { |
| try { |
| const res = await fetch('/model_info'); |
| const data = await res.json(); |
| if (data.error) { showToast('Model not ready: ' + data.error); return; } |
| document.getElementById('model-badge').textContent = |
| 'LR=' + data.config.learning_rate + ' · N=' + data.config.n_estimators; |
| const rows = [ |
| ['Learning Rate', data.config.learning_rate], |
| ['N Estimators', data.config.n_estimators], |
| ['Split', data.config.train_ratio + '/' + data.config.val_ratio + '/' + data.config.test_ratio], |
| ['Test AUC', (data.test_metrics.test_auc * 100).toFixed(2) + '%'], |
| ['Test F1', (data.test_metrics.test_f1 * 100).toFixed(2) + '%'], |
| ['Test Accuracy', (data.test_metrics.test_accuracy * 100).toFixed(2) + '%'], |
| ['Precision', (data.test_metrics.test_precision* 100).toFixed(2) + '%'], |
| ['Recall', (data.test_metrics.test_recall * 100).toFixed(2) + '%'], |
| ]; |
| document.getElementById('model-info').innerHTML = rows.map(([k,v],i) => |
| '<div class="info-row"><span class="info-key">' + k + '</span>' + |
| '<span class="info-val' + (i >= 3 ? ' green' : '') + '">' + v + '</span></div>' |
| ).join(''); |
| } catch(e) { |
| document.getElementById('model-badge').textContent = 'Model Error'; |
| } |
| } |
| |
| async function runPredict() { |
| const btn = document.getElementById('predict-btn'); |
| btn.classList.add('loading'); |
| btn.disabled = true; |
| const payload = { |
| equipment: document.getElementById('equipment').value, |
| temperature: parseFloat(document.getElementById('temperature').value), |
| pressure: parseFloat(document.getElementById('pressure').value), |
| vibration: parseFloat(document.getElementById('vibration').value), |
| humidity: parseFloat(document.getElementById('humidity').value), |
| }; |
| try { |
| const res = await fetch('/predict', { |
| method: 'POST', |
| headers: {'Content-Type': 'application/json'}, |
| body: JSON.stringify(payload) |
| }); |
| const data = await res.json(); |
| if (data.error) { showToast('Error: ' + data.error); return; } |
| showResult(data, payload); |
| addHistory(data, payload); |
| } catch(e) { |
| showToast('Network error — please try again.'); |
| } finally { |
| btn.classList.remove('loading'); |
| btn.disabled = false; |
| } |
| } |
| |
| function showResult(data, payload) { |
| const isFaulty = data.prediction === 1; |
| const prob = (data.probability * 100).toFixed(1); |
| const cls = isFaulty ? 'faulty' : 'healthy'; |
| document.getElementById('result-card').className = 'result-card ' + cls; |
| document.getElementById('idle-state').style.display = 'none'; |
| document.getElementById('result-content').style.display = 'block'; |
| const vt = document.getElementById('verdict-text'); |
| vt.className = 'verdict ' + cls; |
| vt.textContent = isFaulty ? '⚠ FAULT DETECTED' : '✓ HEALTHY'; |
| document.getElementById('verdict-sub').textContent = isFaulty |
| ? 'High fault probability — immediate inspection recommended.' |
| : 'Equipment readings within normal operating range.'; |
| document.getElementById('prob-pct').textContent = prob + '%'; |
| const fill = document.getElementById('prob-fill'); |
| fill.className = 'prob-fill ' + cls; |
| setTimeout(() => fill.style.width = prob + '%', 50); |
| document.getElementById('mini-metrics').innerHTML = [ |
| ['Probability', prob + '%'], |
| ['Confidence', data.confidence], |
| ['Equipment', payload.equipment], |
| ['Threshold', (data.threshold * 100).toFixed(0) + '%'], |
| ].map(([k,v]) => |
| '<div class="mini-metric"><div class="mm-val">' + v + '</div><div class="mm-key">' + k + '</div></div>' |
| ).join(''); |
| } |
| |
| function addHistory(data, payload) { |
| const isFaulty = data.prediction === 1; |
| const cls = isFaulty ? 'faulty' : 'healthy'; |
| const list = document.getElementById('history-list'); |
| if (list.children.length === 1 && list.firstElementChild.style.color) list.innerHTML = ''; |
| const item = document.createElement('div'); |
| item.className = 'hist-item'; |
| item.innerHTML = |
| '<div><div style="font-family:var(--mono);font-size:.78rem;">' + payload.equipment + '</div>' + |
| '<div class="hist-equip">T=' + payload.temperature + '° P=' + payload.pressure + 'bar V=' + payload.vibration + '</div></div>' + |
| '<span class="hist-badge ' + cls + '">' + (isFaulty ? 'FAULT' : 'OK') + ' · ' + (data.probability*100).toFixed(1) + '%</span>'; |
| list.prepend(item); |
| if (list.children.length > 20) list.removeChild(list.lastChild); |
| } |
| |
| function showToast(msg) { |
| const t = document.getElementById('toast'); |
| t.textContent = msg; |
| t.classList.add('show'); |
| setTimeout(() => t.classList.remove('show'), 3500); |
| } |
| |
| loadModelInfo(); |
| </script> |
| </body> |
| </html>""" |
|
|
|
|
| |
| |
| |
|
|
| @app.route("/") |
| def index(): |
| options = "\n".join( |
| f'<option value="{e}">{e.capitalize()}</option>' |
| for e in EQUIPMENT_OPTIONS |
| ) |
| return render_template_string(HTML, equipment_options=options) |
|
|
| @app.route("/model_info") |
| def model_info(): |
| cfg = ARTIFACT["config"] |
| return jsonify({ |
| "config": cfg, |
| "test_metrics": ARTIFACT["test_metrics"], |
| "cm": ARTIFACT["cm"], |
| }) |
|
|
| @app.route("/predict", methods=["POST"]) |
| def predict(): |
| body = request.get_json(force=True) |
| try: |
| row = pd.DataFrame([{ |
| "equipment": body["equipment"], |
| "temperature": float(body["temperature"]), |
| "pressure": float(body["pressure"]), |
| "vibration": float(body["vibration"]), |
| "humidity": float(body["humidity"]), |
| }]) |
| except (KeyError, ValueError) as e: |
| return jsonify({"error": f"Bad input: {e}"}), 400 |
|
|
| prob = float(ARTIFACT["pipeline"].predict_proba(row)[0, 1]) |
| pred = int(prob >= THRESHOLD) |
| confidence = "HIGH" if prob > 0.85 or prob < 0.15 else "MEDIUM" if prob > 0.65 or prob < 0.35 else "LOW" |
|
|
| return jsonify({ |
| "prediction": pred, |
| "probability": round(prob, 4), |
| "confidence": confidence, |
| "threshold": THRESHOLD, |
| "label": "FAULTY" if pred == 1 else "HEALTHY", |
| }) |
|
|
|
|
| if __name__ == "__main__": |
| app.run(debug=False, host="0.0.0.0", port=7860) |
