Changes after Testing

Dockerfile

@@ -14,4 +14,4 @@ COPY --chown=user . /app
 USER user
 
 EXPOSE 7860
-CMD ["python", "-m", "gunicorn", "--bind", "0.0.0.0:7860", "--workers", "2", "--timeout", "120", "app:app"]
+CMD ["python", "-m", "gunicorn", "--bind", "0.0.0.0:7860", "--workers", "2", "--timeout", "120", "app:app3"]

app.py

@@ -1,6 +1,8 @@
 """
-FaultSense — LightGBM + Random Forest Fault Prediction App
-Both models trained at startup; UI lets user switch between them.
+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
@@ -18,7 +20,6 @@ from sklearn.metrics import (
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.compose import ColumnTransformer
 from sklearn.pipeline import Pipeline
-from sklearn.ensemble import RandomForestClassifier
 import joblib
 from lightgbm import LGBMClassifier
 from flask import Flask, request, jsonify, render_template_string
@@ -28,8 +29,7 @@ from flask import Flask, request, jsonify, render_template_string
 # ─────────────────────────────────────────────
 
 DATA_PATH  = "synthetic_nim_parallel_10000.csv"
-LGBM_PATH  = "/tmp/faultsense_lgbm.joblib"
-RF_PATH    = "/tmp/faultsense_rf.joblib"
+MODEL_PATH = "/tmp/faultsense_model.joblib"
 
 DROP_COLS = ["location"]
 TARGET    = "faulty"
@@ -39,7 +39,7 @@ NUM_COLS  = ["temperature", "pressure", "vibration", "humidity"]
 RANDOM_STATE = 42
 THRESHOLD    = 0.5
 
-LGBM_PARAMS = dict(
+FIXED_PARAMS = dict(
     max_depth=8,
     num_leaves=50,
     min_child_samples=20,
@@ -48,24 +48,14 @@ LGBM_PARAMS = dict(
     class_weight="balanced",
     random_state=RANDOM_STATE,
     verbose=-1,
-    learning_rate=0.05,
-    n_estimators=100,
-)
-
-RF_PARAMS = dict(
-    n_estimators=200,
-    max_depth=10,
-    min_samples_split=10,
-    min_samples_leaf=5,
-    class_weight="balanced",
-    random_state=RANDOM_STATE,
-    n_jobs=-1,
 )
 
 BEST_CONFIG = {
-    "train_ratio": 0.50,
-    "val_ratio":   0.20,
-    "test_ratio":  0.30,
+    "learning_rate": 0.05,
+    "n_estimators":  10,
+    "train_ratio":   0.50,
+    "val_ratio":     0.20,
+    "test_ratio":    0.30,
 }
 
 EQUIPMENT_OPTIONS = ["pump", "compressor", "motor", "valve", "sensor"]
@@ -80,11 +70,13 @@ def make_preprocessor():
         ("num", "passthrough", NUM_COLS),
     ])
 
-def load_data(cfg):
+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
@@ -94,70 +86,43 @@ def load_data(cfg):
         X_trainval, y_trainval, test_size=val_relative,
         stratify=y_trainval, random_state=RANDOM_STATE
     )
-    return X_train, X_val, X_test, y_train, y_val, y_test
 
-def compute_metrics(pipeline, X_test, y_test):
+    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)
-    return {
+
+    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),
-    }, confusion_matrix(y_test, y_pred).tolist()
-
-def train_lgbm(X_train, X_test, y_train, y_test):
-    print("Training LightGBM...")
-    pipeline = Pipeline([
-        ("pre", make_preprocessor()),
-        ("clf", LGBMClassifier(**LGBM_PARAMS))
-    ])
-    pipeline.fit(X_train, y_train)
-    metrics, cm = compute_metrics(pipeline, X_test, y_test)
-    print(f"LGBM  AUC={metrics['test_auc']}  F1={metrics['test_f1']}")
-    return {"pipeline": pipeline, "test_metrics": metrics, "cm": cm,
-            "config": {**BEST_CONFIG, "model": "LightGBM",
-                       "learning_rate": LGBM_PARAMS["learning_rate"],
-                       "n_estimators": LGBM_PARAMS["n_estimators"]}}
-
-def train_rf(X_train, X_test, y_train, y_test):
-    print("Training Random Forest...")
-    pipeline = Pipeline([
-        ("pre", make_preprocessor()),
-        ("clf", RandomForestClassifier(**RF_PARAMS))
-    ])
-    pipeline.fit(X_train, y_train)
-    metrics, cm = compute_metrics(pipeline, X_test, y_test)
-    print(f"RF    AUC={metrics['test_auc']}  F1={metrics['test_f1']}")
-    return {"pipeline": pipeline, "test_metrics": metrics, "cm": cm,
-            "config": {**BEST_CONFIG, "model": "Random Forest",
-                       "n_estimators": RF_PARAMS["n_estimators"],
-                       "max_depth": RF_PARAMS["max_depth"]}}
-
-def load_or_train_all():
-    X_train, X_val, X_test, y_train, y_val, y_test = load_data(BEST_CONFIG)
-    if os.path.exists(LGBM_PATH):
-        print(f"Loading LGBM from {LGBM_PATH}")
-        lgbm_artifact = joblib.load(LGBM_PATH)
-    else:
-        lgbm_artifact = train_lgbm(X_train, X_test, y_train, y_test)
-        joblib.dump(lgbm_artifact, LGBM_PATH)
-
-    if os.path.exists(RF_PATH):
-        print(f"Loading RF from {RF_PATH}")
-        rf_artifact = joblib.load(RF_PATH)
-    else:
-        rf_artifact = train_rf(X_train, X_test, y_train, y_test)
-        joblib.dump(rf_artifact, RF_PATH)
-
-    return {"lgbm": lgbm_artifact, "rf": rf_artifact}
+    }
+    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)
 
 # ─────────────────────────────────────────────
-# LOAD MODELS AT MODULE LEVEL
+# LOAD MODEL AT MODULE LEVEL (runs under gunicorn)
 # ─────────────────────────────────────────────
-ARTIFACTS = load_or_train_all()
+ARTIFACT = load_or_train()
 
 # ─────────────────────────────────────────────
 # FLASK APP
@@ -177,8 +142,7 @@ HTML = r"""<!DOCTYPE html>
 :root {
   --bg: #0a0c10; --surface: #111318; --surface2: #181c24;
   --border: #232838; --accent: #00e5a0; --accent2: #ff4d6d;
-  --accent3: #4d9fff; --accent4: #f59e0b;
-  --text: #e8eaf0; --muted: #6b7280;
+  --accent3: #4d9fff; --text: #e8eaf0; --muted: #6b7280;
   --mono: 'Space Mono', monospace; --sans: 'DM Sans', sans-serif;
 }
 html { font-size: 16px; }
@@ -192,30 +156,14 @@ body::before {
 .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: 1200px; margin: 0 auto; padding: 40px 24px 80px; }
-header { display: flex; align-items: center; gap: 16px; margin-bottom: 32px; border-bottom: 1px solid var(--border); padding-bottom: 24px; }
+.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; }
-
-/* MODEL SELECTOR TABS */
-.model-tabs { display: flex; gap: 10px; margin-bottom: 24px; }
-.model-tab {
-  flex: 1; padding: 14px 20px; border-radius: 12px; border: 1px solid var(--border);
-  background: var(--surface); cursor: pointer; font-family: var(--mono);
-  font-size: .8rem; color: var(--muted); transition: all .2s; text-align: center;
-  display: flex; flex-direction: column; gap: 4px; align-items: center;
-}
-.model-tab:hover { border-color: var(--accent); color: var(--text); }
-.model-tab.active.lgbm { border-color: var(--accent); background: rgba(0,229,160,.08); color: var(--accent); }
-.model-tab.active.rf   { border-color: var(--accent4); background: rgba(245,158,11,.08); color: var(--accent4); }
-.model-tab .tab-name { font-size: .95rem; font-weight: 700; }
-.model-tab .tab-desc { font-size: .65rem; color: inherit; opacity: .7; }
-.tab-auc { font-size: .7rem; opacity: .85; margin-top: 2px; }
-
-.main-grid { display: grid; grid-template-columns: 1fr 400px; gap: 24px; align-items: start; }
-@media (max-width: 900px) { .main-grid { grid-template-columns: 1fr; } }
+.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%; }
@@ -257,23 +205,16 @@ input[type=range]::-webkit-slider-thumb { -webkit-appearance: none; width: 16px;
 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.rf-active { background: var(--accent4); }
-.btn-predict.rf-active:hover { box-shadow: 0 0 32px rgba(245,158,11,.5); }
-.btn-predict:disabled { background: var(--muted); cursor: not-allowed; transform: none; box-shadow: none; }
-
+.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: 8px; }
-.model-used-tag { display: inline-block; font-family: var(--mono); font-size: .65rem; padding: 3px 8px; border-radius: 6px; margin-bottom: 20px; }
-.model-used-tag.lgbm { background: rgba(0,229,160,.12); color: var(--accent); border: 1px solid rgba(0,229,160,.3); }
-.model-used-tag.rf   { background: rgba(245,158,11,.12); color: var(--accent4); border: 1px solid rgba(245,158,11,.3); }
+.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; }
@@ -284,39 +225,17 @@ input[type=range]::-moz-range-thumb { width: 16px; height: 16px; border-radius:
 .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); }
-
-/* METRICS COMPARISON TABLE */
-.compare-table { width: 100%; border-collapse: collapse; }
-.compare-table th { font-family: var(--mono); font-size: .65rem; letter-spacing: 1px; text-transform: uppercase; color: var(--muted); padding: 8px 10px; text-align: left; border-bottom: 1px solid var(--border); }
-.compare-table th.lgbm-col { color: var(--accent); }
-.compare-table th.rf-col   { color: var(--accent4); }
-.compare-table td { font-family: var(--mono); font-size: .78rem; padding: 9px 10px; border-bottom: 1px solid var(--border); }
-.compare-table tr:last-child td { border-bottom: none; }
-.compare-table td.metric-name { color: var(--muted); font-size: .7rem; }
-.compare-table td.win { font-weight: 700; }
-.compare-table td.win.lgbm { color: var(--accent); }
-.compare-table td.win.rf   { color: var(--accent4); }
-.win-tag { font-size: .55rem; padding: 1px 5px; border-radius: 4px; margin-left: 5px; vertical-align: middle; }
-.win-tag.lgbm { background: rgba(0,229,160,.15); color: var(--accent); }
-.win-tag.rf   { background: rgba(245,158,11,.15); color: var(--accent4); }
-
 .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); }
-.info-val.amber { color: var(--accent4); }
-
 .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); }
-.hist-model-tag { font-family: var(--mono); font-size: .58rem; padding: 2px 6px; border-radius: 4px; margin-top: 3px; display: inline-block; }
-.hist-model-tag.lgbm { background: rgba(0,229,160,.1); color: var(--accent); }
-.hist-model-tag.rf   { background: rgba(245,158,11,.1); color: var(--accent4); }
-
 .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; }
@@ -335,25 +254,11 @@ input[type=range]::-moz-range-thumb { width: 16px; height: 16px; border-radius:
     <div class="logo-mark">FS</div>
     <div>
       <h1>FaultSense</h1>
-      <p>Multi-Model Equipment Fault Predictor</p>
+      <p>LightGBM Equipment Fault Predictor</p>
     </div>
-    <div class="badge" id="model-badge">Loading Models…</div>
+    <div class="badge" id="model-badge">Model Loading…</div>
   </header>
 
-  <!-- MODEL SELECTOR TABS -->
-  <div class="model-tabs" id="model-tabs">
-    <div class="model-tab active lgbm" id="tab-lgbm" onclick="selectModel('lgbm')">
-      <span class="tab-name">⚡ LightGBM</span>
-      <span class="tab-desc">Gradient Boosting</span>
-      <span class="tab-auc" id="lgbm-tab-auc">Loading…</span>
-    </div>
-    <div class="model-tab rf" id="tab-rf" onclick="selectModel('rf')">
-      <span class="tab-name">🌲 Random Forest</span>
-      <span class="tab-desc">Ensemble Trees</span>
-      <span class="tab-auc" id="rf-tab-auc">Loading…</span>
-    </div>
-  </div>
-
   <div class="main-grid">
     <div style="display:flex;flex-direction:column;gap:20px;">
       <div class="card">
@@ -415,20 +320,12 @@ input[type=range]::-moz-range-thumb { width: 16px; height: 16px; border-radius:
           </div>
 
         </div>
-        <button class="btn-predict lgbm-active" id="predict-btn" onclick="runPredict()">
+        <button class="btn-predict" id="predict-btn" onclick="runPredict()">
           <span class="btn-text">⚡ Run Prediction</span>
           <div class="spinner"></div>
         </button>
       </div>
 
-      <!-- METRICS COMPARISON -->
-      <div class="card">
-        <div class="card-title">Model Comparison</div>
-        <div id="compare-content">
-          <div style="color:var(--muted);font-size:.8rem;font-family:var(--mono);text-align:center;padding:12px 0;">Loading…</div>
-        </div>
-      </div>
-
       <div class="card">
         <div class="card-title">Prediction History</div>
         <div class="history-list" id="history-list">
@@ -441,12 +338,11 @@ input[type=range]::-moz-range-thumb { width: 16px; height: 16px; border-radius:
       <div class="result-card" id="result-card">
         <div class="idle-state" id="idle-state">
           <div class="idle-icon">🔬</div>
-          <p>Select a model, enter sensor<br>readings, and run a prediction<br>to see results here.</p>
+          <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 id="model-used-tag" class="model-used-tag lgbm"></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>
@@ -456,7 +352,7 @@ input[type=range]::-moz-range-thumb { width: 16px; height: 16px; border-radius:
       </div>
 
       <div class="card">
-        <div class="card-title" id="model-config-title">Active Model Config</div>
+        <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>
@@ -469,19 +365,6 @@ input[type=range]::-moz-range-thumb { width: 16px; height: 16px; border-radius:
 
 <script>
 let csOpen = false;
-let activeModel = 'lgbm';
-let modelData = {};
-
-function selectModel(model) {
-  activeModel = model;
-  document.getElementById('tab-lgbm').className = 'model-tab' + (model === 'lgbm' ? ' active lgbm' : '');
-  document.getElementById('tab-rf').className   = 'model-tab' + (model === 'rf'   ? ' active rf'   : '');
-  const btn = document.getElementById('predict-btn');
-  btn.className = model === 'lgbm' ? 'btn-predict lgbm-active' : 'btn-predict rf-active';
-  btn.querySelector('.btn-text').textContent = model === 'lgbm' ? '⚡ Run Prediction' : '🌲 Run Prediction';
-  updateModelInfo(model);
-}
-
 function toggleDropdown() {
   csOpen = !csOpen;
   document.getElementById('cs-trigger').classList.toggle('open', csOpen);
@@ -503,86 +386,30 @@ document.addEventListener('click', function(e) {
     document.getElementById('cs-options').classList.remove('open');
   }
 });
-
 async function loadModelInfo() {
   try {
     const res = await fetch('/model_info');
-    modelData = await res.json();
-    if (modelData.error) { showToast('Model error: ' + modelData.error); return; }
-
-    const lg = modelData.lgbm, rf = modelData.rf;
-    document.getElementById('lgbm-tab-auc').textContent = 'AUC ' + (lg.test_metrics.test_auc * 100).toFixed(1) + '%';
-    document.getElementById('rf-tab-auc').textContent   = 'AUC ' + (rf.test_metrics.test_auc  * 100).toFixed(1) + '%';
-    document.getElementById('model-badge').textContent  = '2 Models Ready';
-
-    // Build comparison table
-    const metrics = [
-      ['AUC',       'test_auc'],
-      ['Accuracy',  'test_accuracy'],
-      ['Precision', 'test_precision'],
-      ['Recall',    'test_recall'],
-      ['F1 Score',  'test_f1'],
-      ['Log Loss',  'test_logloss'],
+    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) + '%'],
     ];
-    const rows = metrics.map(([label, key]) => {
-      const lv = lg.test_metrics[key], rv = rf.test_metrics[key];
-      const higherBetter = key !== 'test_logloss';
-      const lgWins = higherBetter ? lv > rv : lv < rv;
-      const rfWins = higherBetter ? rv > lv : rv < lv;
-      const fmt = v => key === 'test_logloss' ? v.toFixed(4) : (v * 100).toFixed(2) + '%';
-      return `<tr>
-        <td class="metric-name">${label}</td>
-        <td class="${lgWins ? 'win lgbm' : ''}">${fmt(lv)}${lgWins ? '<span class="win-tag lgbm">▲</span>' : ''}</td>
-        <td class="${rfWins ? 'win rf' : ''}">${fmt(rv)}${rfWins ? '<span class="win-tag rf">▲</span>' : ''}</td>
-      </tr>`;
-    }).join('');
-    document.getElementById('compare-content').innerHTML = `
-      <table class="compare-table">
-        <thead><tr>
-          <th>Metric</th>
-          <th class="lgbm-col">⚡ LightGBM</th>
-          <th class="rf-col">🌲 Random Forest</th>
-        </tr></thead>
-        <tbody>${rows}</tbody>
-      </table>`;
-
-    updateModelInfo('lgbm');
-  } catch(e) {
-    document.getElementById('model-badge').textContent = 'Load Error';
-  }
-}
-
-function updateModelInfo(model) {
-  if (!modelData[model]) return;
-  const d = modelData[model];
-  const isLgbm = model === 'lgbm';
-  const color = isLgbm ? 'green' : 'amber';
-
-  const rows = isLgbm ? [
-    ['Model',         'LightGBM'],
-    ['Learning Rate', d.config.learning_rate],
-    ['N Estimators',  d.config.n_estimators],
-    ['Split',         d.config.train_ratio + '/' + d.config.val_ratio + '/' + d.config.test_ratio],
-  ] : [
-    ['Model',        'Random Forest'],
-    ['N Estimators', d.config.n_estimators],
-    ['Max Depth',    d.config.max_depth],
-    ['Split',        d.config.train_ratio + '/' + d.config.val_ratio + '/' + d.config.test_ratio],
-  ];
-
-  const metricRows = [
-    ['Test AUC',      (d.test_metrics.test_auc       * 100).toFixed(2) + '%'],
-    ['Test F1',       (d.test_metrics.test_f1        * 100).toFixed(2) + '%'],
-    ['Test Accuracy', (d.test_metrics.test_accuracy  * 100).toFixed(2) + '%'],
-    ['Precision',     (d.test_metrics.test_precision * 100).toFixed(2) + '%'],
-    ['Recall',        (d.test_metrics.test_recall    * 100).toFixed(2) + '%'],
-  ];
-
-  document.getElementById('model-info').innerHTML =
-    [...rows, ...metricRows].map(([k, v], i) =>
+    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 >= rows.length ? ' ' + color : '') + '">' + v + '</span></div>'
+      '<span class="info-val' + (i >= 3 ? ' green' : '') + '">' + v + '</span></div>'
     ).join('');
+  } catch(e) {
+    document.getElementById('model-badge').textContent = 'Model Error';
+  }
 }
 
 async function runPredict() {
@@ -590,7 +417,6 @@ async function runPredict() {
   btn.classList.add('loading');
   btn.disabled = true;
   const payload = {
-    model:       activeModel,
     equipment:   document.getElementById('equipment').value,
     temperature: parseFloat(document.getElementById('temperature').value),
     pressure:    parseFloat(document.getElementById('pressure').value),
@@ -619,7 +445,6 @@ function showResult(data, payload) {
   const isFaulty = data.prediction === 1;
   const prob = (data.probability * 100).toFixed(1);
   const cls  = isFaulty ? 'faulty' : 'healthy';
-  const isLgbm = data.model === 'lgbm';
   document.getElementById('result-card').className = 'result-card ' + cls;
   document.getElementById('idle-state').style.display = 'none';
   document.getElementById('result-content').style.display = 'block';
@@ -629,9 +454,6 @@ function showResult(data, payload) {
   document.getElementById('verdict-sub').textContent = isFaulty
     ? 'High fault probability — immediate inspection recommended.'
     : 'Equipment readings within normal operating range.';
-  const tag = document.getElementById('model-used-tag');
-  tag.className = 'model-used-tag ' + (isLgbm ? 'lgbm' : 'rf');
-  tag.textContent = isLgbm ? '⚡ LightGBM' : '🌲 Random Forest';
   document.getElementById('prob-pct').textContent = prob + '%';
   const fill = document.getElementById('prob-fill');
   fill.className = 'prob-fill ' + cls;
@@ -649,17 +471,13 @@ function showResult(data, payload) {
 function addHistory(data, payload) {
   const isFaulty = data.prediction === 1;
   const cls  = isFaulty ? 'faulty' : 'healthy';
-  const isLgbm = data.model === 'lgbm';
   const list = document.getElementById('history-list');
-  if (list.children.length === 1 && list.firstElementChild.style.color !== undefined
-      && list.firstElementChild.querySelector) list.innerHTML = '';
-  if (list.children.length === 1 && !list.firstElementChild.classList.contains('hist-item')) list.innerHTML = '';
+  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>' +
-    '<span class="hist-model-tag ' + (isLgbm ? 'lgbm' : 'rf') + '">' + (isLgbm ? '⚡ LGBM' : '🌲 RF') + '</span></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);
@@ -684,30 +502,24 @@ loadModelInfo();
 
 @app.route("/")
 def index():
-    return render_template_string(HTML)
+    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({
-        "lgbm": {
-            "config":       ARTIFACTS["lgbm"]["config"],
-            "test_metrics": ARTIFACTS["lgbm"]["test_metrics"],
-            "cm":           ARTIFACTS["lgbm"]["cm"],
-        },
-        "rf": {
-            "config":       ARTIFACTS["rf"]["config"],
-            "test_metrics": ARTIFACTS["rf"]["test_metrics"],
-            "cm":           ARTIFACTS["rf"]["cm"],
-        },
+        "config":       cfg,
+        "test_metrics": ARTIFACT["test_metrics"],
+        "cm":           ARTIFACT["cm"],
     })
 
 @app.route("/predict", methods=["POST"])
 def predict():
     body = request.get_json(force=True)
-    model_key = body.get("model", "lgbm")
-    if model_key not in ARTIFACTS:
-        return jsonify({"error": f"Unknown model '{model_key}'. Use 'lgbm' or 'rf'."}), 400
-
     try:
         row = pd.DataFrame([{
             "equipment":   body["equipment"],
@@ -719,13 +531,11 @@ def predict():
     except (KeyError, ValueError) as e:
         return jsonify({"error": f"Bad input: {e}"}), 400
 
-    artifact = ARTIFACTS[model_key]
-    prob = float(artifact["pipeline"].predict_proba(row)[0, 1])
+    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({
-        "model":       model_key,
         "prediction":  pred,
         "probability": round(prob, 4),
         "confidence":  confidence,

app3.py

@@ -0,0 +1,738 @@
+"""
+FaultSense — LightGBM + Random Forest Fault Prediction App
+Both models trained at startup; UI lets user switch between them.
+"""
+
+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
+from sklearn.ensemble import RandomForestClassifier
+import joblib
+from lightgbm import LGBMClassifier
+from flask import Flask, request, jsonify, render_template_string
+
+# ─────────────────────────────────────────────
+# CONFIG
+# ─────────────────────────────────────────────
+
+DATA_PATH  = "synthetic_nim_parallel_10000.csv"
+LGBM_PATH  = "faultsense_lgbm.joblib"
+RF_PATH    = "faultsense_rf.joblib"
+
+DROP_COLS = ["location"]
+TARGET    = "faulty"
+CAT_COLS  = ["equipment"]
+NUM_COLS  = ["temperature", "pressure", "vibration", "humidity"]
+
+RANDOM_STATE = 42
+THRESHOLD    = 0.5
+
+LGBM_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,
+    learning_rate=0.05,
+    n_estimators=100,
+)
+
+RF_PARAMS = dict(
+    n_estimators=200,
+    max_depth=10,
+    min_samples_split=10,
+    min_samples_leaf=5,
+    class_weight="balanced",
+    random_state=RANDOM_STATE,
+    n_jobs=-1,
+)
+
+BEST_CONFIG = {
+    "train_ratio": 0.50,
+    "val_ratio":   0.20,
+    "test_ratio":  0.30,
+}
+
+EQUIPMENT_OPTIONS = ["pump", "compressor", "motor", "valve", "sensor"]
+
+# ─────────────────────────────────────────────
+# MODEL TRAINING / LOADING
+# ─────────────────────────────────────────────
+
+def make_preprocessor():
+    return ColumnTransformer([
+        ("cat", OneHotEncoder(handle_unknown="ignore", sparse_output=False), CAT_COLS),
+        ("num", "passthrough", NUM_COLS),
+    ])
+
+def load_data(cfg):
+    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
+    )
+    return X_train, X_val, X_test, y_train, y_val, y_test
+
+def compute_metrics(pipeline, X_test, y_test):
+    y_prob = pipeline.predict_proba(X_test)[:, 1]
+    y_pred = (y_prob >= THRESHOLD).astype(int)
+    return {
+        "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),
+    }, confusion_matrix(y_test, y_pred).tolist()
+
+def train_lgbm(X_train, X_test, y_train, y_test):
+    print("Training LightGBM...")
+    pipeline = Pipeline([
+        ("pre", make_preprocessor()),
+        ("clf", LGBMClassifier(**LGBM_PARAMS))
+    ])
+    pipeline.fit(X_train, y_train)
+    metrics, cm = compute_metrics(pipeline, X_test, y_test)
+    print(f"LGBM  AUC={metrics['test_auc']}  F1={metrics['test_f1']}")
+    return {"pipeline": pipeline, "test_metrics": metrics, "cm": cm,
+            "config": {**BEST_CONFIG, "model": "LightGBM",
+                       "learning_rate": LGBM_PARAMS["learning_rate"],
+                       "n_estimators": LGBM_PARAMS["n_estimators"]}}
+
+def train_rf(X_train, X_test, y_train, y_test):
+    print("Training Random Forest...")
+    pipeline = Pipeline([
+        ("pre", make_preprocessor()),
+        ("clf", RandomForestClassifier(**RF_PARAMS))
+    ])
+    pipeline.fit(X_train, y_train)
+    metrics, cm = compute_metrics(pipeline, X_test, y_test)
+    print(f"RF    AUC={metrics['test_auc']}  F1={metrics['test_f1']}")
+    return {"pipeline": pipeline, "test_metrics": metrics, "cm": cm,
+            "config": {**BEST_CONFIG, "model": "Random Forest",
+                       "n_estimators": RF_PARAMS["n_estimators"],
+                       "max_depth": RF_PARAMS["max_depth"]}}
+
+def load_or_train_all():
+    X_train, X_val, X_test, y_train, y_val, y_test = load_data(BEST_CONFIG)
+    if os.path.exists(LGBM_PATH):
+        print(f"Loading LGBM from {LGBM_PATH}")
+        lgbm_artifact = joblib.load(LGBM_PATH)
+    else:
+        lgbm_artifact = train_lgbm(X_train, X_test, y_train, y_test)
+        joblib.dump(lgbm_artifact, LGBM_PATH)
+
+    if os.path.exists(RF_PATH):
+        print(f"Loading RF from {RF_PATH}")
+        rf_artifact = joblib.load(RF_PATH)
+    else:
+        rf_artifact = train_rf(X_train, X_test, y_train, y_test)
+        joblib.dump(rf_artifact, RF_PATH)
+
+    return {"lgbm": lgbm_artifact, "rf": rf_artifact}
+
+# ─────────────────────────────────────────────
+# LOAD MODELS AT MODULE LEVEL
+# ─────────────────────────────────────────────
+ARTIFACTS = load_or_train_all()
+
+# ─────────────────────────────────────────────
+# FLASK APP
+# ─────────────────────────────────────────────
+
+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; --accent4: #f59e0b;
+  --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: 1200px; margin: 0 auto; padding: 40px 24px 80px; }
+header { display: flex; align-items: center; gap: 16px; margin-bottom: 32px; 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; }
+
+/* MODEL SELECTOR TABS */
+.model-tabs { display: flex; gap: 10px; margin-bottom: 24px; }
+.model-tab {
+  flex: 1; padding: 14px 20px; border-radius: 12px; border: 1px solid var(--border);
+  background: var(--surface); cursor: pointer; font-family: var(--mono);
+  font-size: .8rem; color: var(--muted); transition: all .2s; text-align: center;
+  display: flex; flex-direction: column; gap: 4px; align-items: center;
+}
+.model-tab:hover { border-color: var(--accent); color: var(--text); }
+.model-tab.active.lgbm { border-color: var(--accent); background: rgba(0,229,160,.08); color: var(--accent); }
+.model-tab.active.rf   { border-color: var(--accent4); background: rgba(245,158,11,.08); color: var(--accent4); }
+.model-tab .tab-name { font-size: .95rem; font-weight: 700; }
+.model-tab .tab-desc { font-size: .65rem; color: inherit; opacity: .7; }
+.tab-auc { font-size: .7rem; opacity: .85; margin-top: 2px; }
+
+.main-grid { display: grid; grid-template-columns: 1fr 400px; gap: 24px; align-items: start; }
+@media (max-width: 900px) { .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.rf-active { background: var(--accent4); }
+.btn-predict.rf-active:hover { box-shadow: 0 0 32px rgba(245,158,11,.5); }
+.btn-predict:disabled { background: var(--muted); cursor: not-allowed; transform: none; box-shadow: 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: 8px; }
+.model-used-tag { display: inline-block; font-family: var(--mono); font-size: .65rem; padding: 3px 8px; border-radius: 6px; margin-bottom: 20px; }
+.model-used-tag.lgbm { background: rgba(0,229,160,.12); color: var(--accent); border: 1px solid rgba(0,229,160,.3); }
+.model-used-tag.rf   { background: rgba(245,158,11,.12); color: var(--accent4); border: 1px solid rgba(245,158,11,.3); }
+.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); }
+
+/* METRICS COMPARISON TABLE */
+.compare-table { width: 100%; border-collapse: collapse; }
+.compare-table th { font-family: var(--mono); font-size: .65rem; letter-spacing: 1px; text-transform: uppercase; color: var(--muted); padding: 8px 10px; text-align: left; border-bottom: 1px solid var(--border); }
+.compare-table th.lgbm-col { color: var(--accent); }
+.compare-table th.rf-col   { color: var(--accent4); }
+.compare-table td { font-family: var(--mono); font-size: .78rem; padding: 9px 10px; border-bottom: 1px solid var(--border); }
+.compare-table tr:last-child td { border-bottom: none; }
+.compare-table td.metric-name { color: var(--muted); font-size: .7rem; }
+.compare-table td.win { font-weight: 700; }
+.compare-table td.win.lgbm { color: var(--accent); }
+.compare-table td.win.rf   { color: var(--accent4); }
+.win-tag { font-size: .55rem; padding: 1px 5px; border-radius: 4px; margin-left: 5px; vertical-align: middle; }
+.win-tag.lgbm { background: rgba(0,229,160,.15); color: var(--accent); }
+.win-tag.rf   { background: rgba(245,158,11,.15); color: var(--accent4); }
+
+.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); }
+.info-val.amber { color: var(--accent4); }
+
+.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); }
+.hist-model-tag { font-family: var(--mono); font-size: .58rem; padding: 2px 6px; border-radius: 4px; margin-top: 3px; display: inline-block; }
+.hist-model-tag.lgbm { background: rgba(0,229,160,.1); color: var(--accent); }
+.hist-model-tag.rf   { background: rgba(245,158,11,.1); color: var(--accent4); }
+
+.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>Multi-Model Equipment Fault Predictor</p>
+    </div>
+    <div class="badge" id="model-badge">Loading Models…</div>
+  </header>
+
+  <!-- MODEL SELECTOR TABS -->
+  <div class="model-tabs" id="model-tabs">
+    <div class="model-tab active lgbm" id="tab-lgbm" onclick="selectModel('lgbm')">
+      <span class="tab-name">⚡ LightGBM</span>
+      <span class="tab-desc">Gradient Boosting</span>
+      <span class="tab-auc" id="lgbm-tab-auc">Loading…</span>
+    </div>
+    <div class="model-tab rf" id="tab-rf" onclick="selectModel('rf')">
+      <span class="tab-name">🌲 Random Forest</span>
+      <span class="tab-desc">Ensemble Trees</span>
+      <span class="tab-auc" id="rf-tab-auc">Loading…</span>
+    </div>
+  </div>
+
+  <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 lgbm-active" id="predict-btn" onclick="runPredict()">
+          <span class="btn-text">⚡ Run Prediction</span>
+          <div class="spinner"></div>
+        </button>
+      </div>
+
+      <!-- METRICS COMPARISON -->
+      <div class="card">
+        <div class="card-title">Model Comparison</div>
+        <div id="compare-content">
+          <div style="color:var(--muted);font-size:.8rem;font-family:var(--mono);text-align:center;padding:12px 0;">Loading…</div>
+        </div>
+      </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>Select a model, enter sensor<br>readings, 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 id="model-used-tag" class="model-used-tag lgbm"></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" id="model-config-title">Active Model Config</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;
+let activeModel = 'lgbm';
+let modelData = {};
+
+function selectModel(model) {
+  activeModel = model;
+  document.getElementById('tab-lgbm').className = 'model-tab' + (model === 'lgbm' ? ' active lgbm' : '');
+  document.getElementById('tab-rf').className   = 'model-tab' + (model === 'rf'   ? ' active rf'   : '');
+  const btn = document.getElementById('predict-btn');
+  btn.className = model === 'lgbm' ? 'btn-predict lgbm-active' : 'btn-predict rf-active';
+  btn.querySelector('.btn-text').textContent = model === 'lgbm' ? '⚡ Run Prediction' : '🌲 Run Prediction';
+  updateModelInfo(model);
+}
+
+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');
+    modelData = await res.json();
+    if (modelData.error) { showToast('Model error: ' + modelData.error); return; }
+
+    const lg = modelData.lgbm, rf = modelData.rf;
+    document.getElementById('lgbm-tab-auc').textContent = 'AUC ' + (lg.test_metrics.test_auc * 100).toFixed(1) + '%';
+    document.getElementById('rf-tab-auc').textContent   = 'AUC ' + (rf.test_metrics.test_auc  * 100).toFixed(1) + '%';
+    document.getElementById('model-badge').textContent  = '2 Models Ready';
+
+    // Build comparison table
+    const metrics = [
+      ['AUC',       'test_auc'],
+      ['Accuracy',  'test_accuracy'],
+      ['Precision', 'test_precision'],
+      ['Recall',    'test_recall'],
+      ['F1 Score',  'test_f1'],
+      ['Log Loss',  'test_logloss'],
+    ];
+    const rows = metrics.map(([label, key]) => {
+      const lv = lg.test_metrics[key], rv = rf.test_metrics[key];
+      const higherBetter = key !== 'test_logloss';
+      const lgWins = higherBetter ? lv > rv : lv < rv;
+      const rfWins = higherBetter ? rv > lv : rv < lv;
+      const fmt = v => key === 'test_logloss' ? v.toFixed(4) : (v * 100).toFixed(2) + '%';
+      return `<tr>
+        <td class="metric-name">${label}</td>
+        <td class="${lgWins ? 'win lgbm' : ''}">${fmt(lv)}${lgWins ? '<span class="win-tag lgbm">▲</span>' : ''}</td>
+        <td class="${rfWins ? 'win rf' : ''}">${fmt(rv)}${rfWins ? '<span class="win-tag rf">▲</span>' : ''}</td>
+      </tr>`;
+    }).join('');
+    document.getElementById('compare-content').innerHTML = `
+      <table class="compare-table">
+        <thead><tr>
+          <th>Metric</th>
+          <th class="lgbm-col">⚡ LightGBM</th>
+          <th class="rf-col">🌲 Random Forest</th>
+        </tr></thead>
+        <tbody>${rows}</tbody>
+      </table>`;
+
+    updateModelInfo('lgbm');
+  } catch(e) {
+    document.getElementById('model-badge').textContent = 'Load Error';
+  }
+}
+
+function updateModelInfo(model) {
+  if (!modelData[model]) return;
+  const d = modelData[model];
+  const isLgbm = model === 'lgbm';
+  const color = isLgbm ? 'green' : 'amber';
+
+  const rows = isLgbm ? [
+    ['Model',         'LightGBM'],
+    ['Learning Rate', d.config.learning_rate],
+    ['N Estimators',  d.config.n_estimators],
+    ['Split',         d.config.train_ratio + '/' + d.config.val_ratio + '/' + d.config.test_ratio],
+  ] : [
+    ['Model',        'Random Forest'],
+    ['N Estimators', d.config.n_estimators],
+    ['Max Depth',    d.config.max_depth],
+    ['Split',        d.config.train_ratio + '/' + d.config.val_ratio + '/' + d.config.test_ratio],
+  ];
+
+  const metricRows = [
+    ['Test AUC',      (d.test_metrics.test_auc       * 100).toFixed(2) + '%'],
+    ['Test F1',       (d.test_metrics.test_f1        * 100).toFixed(2) + '%'],
+    ['Test Accuracy', (d.test_metrics.test_accuracy  * 100).toFixed(2) + '%'],
+    ['Precision',     (d.test_metrics.test_precision * 100).toFixed(2) + '%'],
+    ['Recall',        (d.test_metrics.test_recall    * 100).toFixed(2) + '%'],
+  ];
+
+  document.getElementById('model-info').innerHTML =
+    [...rows, ...metricRows].map(([k, v], i) =>
+      '<div class="info-row"><span class="info-key">' + k + '</span>' +
+      '<span class="info-val' + (i >= rows.length ? ' ' + color : '') + '">' + v + '</span></div>'
+    ).join('');
+}
+
+async function runPredict() {
+  const btn = document.getElementById('predict-btn');
+  btn.classList.add('loading');
+  btn.disabled = true;
+  const payload = {
+    model:       activeModel,
+    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';
+  const isLgbm = data.model === 'lgbm';
+  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.';
+  const tag = document.getElementById('model-used-tag');
+  tag.className = 'model-used-tag ' + (isLgbm ? 'lgbm' : 'rf');
+  tag.textContent = isLgbm ? '⚡ LightGBM' : '🌲 Random Forest';
+  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 isLgbm = data.model === 'lgbm';
+  const list = document.getElementById('history-list');
+  if (list.children.length === 1 && list.firstElementChild.style.color !== undefined
+      && list.firstElementChild.querySelector) list.innerHTML = '';
+  if (list.children.length === 1 && !list.firstElementChild.classList.contains('hist-item')) 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>' +
+    '<span class="hist-model-tag ' + (isLgbm ? 'lgbm' : 'rf') + '">' + (isLgbm ? '⚡ LGBM' : '🌲 RF') + '</span></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>"""
+
+
+# ─────────────────────────────────────────────
+# ROUTES
+# ─────────────────────────────────────────────
+
+@app.route("/")
+def index():
+    return render_template_string(HTML)
+
+@app.route("/model_info")
+def model_info():
+    return jsonify({
+        "lgbm": {
+            "config":       ARTIFACTS["lgbm"]["config"],
+            "test_metrics": ARTIFACTS["lgbm"]["test_metrics"],
+            "cm":           ARTIFACTS["lgbm"]["cm"],
+        },
+        "rf": {
+            "config":       ARTIFACTS["rf"]["config"],
+            "test_metrics": ARTIFACTS["rf"]["test_metrics"],
+            "cm":           ARTIFACTS["rf"]["cm"],
+        },
+    })
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    body = request.get_json(force=True)
+    model_key = body.get("model", "lgbm")
+    if model_key not in ARTIFACTS:
+        return jsonify({"error": f"Unknown model '{model_key}'. Use 'lgbm' or 'rf'."}), 400
+
+    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
+
+    artifact = ARTIFACTS[model_key]
+    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({
+        "model":       model_key,
+        "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)

test.py

@@ -0,0 +1,192 @@
+"""
+FaultSense — ULTRA FAST Dataset Test Script
+Optimized with:
+✔ requests.Session (connection reuse)
+✔ ThreadPoolExecutor (parallel requests)
+✔ itertuples (fast iteration)
+✔ Same features, no removal
+"""
+
+import argparse
+import time
+import requests
+import pandas as pd
+import numpy as np
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from sklearn.metrics import (
+    accuracy_score, precision_score, recall_score,
+    f1_score, roc_auc_score, confusion_matrix
+)
+
+# ─────────────────────────────────────────────
+# CONFIG
+# ─────────────────────────────────────────────
+MODELS        = ["lgbm", "rf"]
+THRESHOLD     = 0.5
+BATCH_PRINT   = 500
+MAX_WORKERS   = 50   # 🔥 increase to 50 if strong CPU
+REQUEST_DELAY = 0.0
+
+
+# ─────────────────────────────────────────────
+# FAST REQUEST FUNCTION
+# ─────────────────────────────────────────────
+def send_predict(session, base_url, model, row):
+    payload = {
+        "model": model,
+        "equipment": row.equipment,
+        "temperature": float(row.temperature),
+        "pressure": float(row.pressure),
+        "vibration": float(row.vibration),
+        "humidity": float(row.humidity),
+    }
+    try:
+        resp = session.post(f"{base_url}/predict", json=payload, timeout=10)
+        resp.raise_for_status()
+        return resp.json()
+    except:
+        return None
+
+
+# ─────────────────────────────────────────────
+# PARALLEL PROCESSING
+# ─────────────────────────────────────────────
+def process_row(session, base_url, model, i, row):
+    result = send_predict(session, base_url, model, row)
+
+    if result is None or "error" in result:
+        return {
+            "index": i,
+            "equipment": row.equipment,
+            "true_label": int(row.faulty),
+            "pred_label": -1,
+            "probability": None,
+            "confidence": "ERROR",
+            "correct": False,
+        }
+
+    pred = result["prediction"]
+    prob = result["probability"]
+    true = int(row.faulty)
+
+    return {
+        "index": i,
+        "equipment": row.equipment,
+        "true_label": true,
+        "pred_label": pred,
+        "probability": prob,
+        "confidence": result.get("confidence", ""),
+        "correct": pred == true,
+    }
+
+
+# ─────────────────────────────────────────────
+# METRICS
+# ─────────────────────────────────────────────
+def evaluate(y_true, y_pred, y_prob, model_name):
+    acc  = accuracy_score(y_true, y_pred)
+    prec = precision_score(y_true, y_pred, zero_division=0)
+    rec  = recall_score(y_true, y_pred, zero_division=0)
+    f1   = f1_score(y_true, y_pred, zero_division=0)
+    auc  = roc_auc_score(y_true, y_prob)
+    cm   = confusion_matrix(y_true, y_pred)
+    tn, fp, fn, tp = cm.ravel()
+
+    print("\n" + "═"*50)
+    print(f"MODEL: {model_name.upper()}")
+    print("═"*50)
+    print(f"Accuracy : {acc:.4f}")
+    print(f"Precision: {prec:.4f}")
+    print(f"Recall   : {rec:.4f}")
+    print(f"F1 Score : {f1:.4f}")
+    print(f"AUC      : {auc:.4f}")
+    print(f"TP={tp} TN={tn} FP={fp} FN={fn}")
+    print("═"*50)
+
+    return {"accuracy": acc, "precision": prec, "recall": rec,
+            "f1": f1, "auc": auc, "tp": tp, "tn": tn, "fp": fp, "fn": fn}
+
+
+# ─────────────────────────────────────────────
+# MAIN
+# ─────────────────────────────────────────────
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--url", default="http://localhost:7860")
+    parser.add_argument("--csv", default="synthetic_nim_parallel_10000.csv")
+    parser.add_argument("--limit", type=int, default=None)
+    args = parser.parse_args()
+
+    print("\n🚀 FAST TEST STARTING...\n")
+
+    df = pd.read_csv(args.csv)
+    if args.limit:
+        df = df.head(args.limit)
+
+    total = len(df)
+    print(f"Rows: {total}")
+
+    # check server
+    try:
+        requests.get(f"{args.url}/model_info", timeout=5)
+        print("✅ Server reachable\n")
+    except:
+        print("❌ Server not reachable")
+        return
+
+    all_metrics = {}
+
+    for model in MODELS:
+        print(f"\n🔥 Testing {model.upper()}...\n")
+
+        session = requests.Session()
+        start = time.time()
+        records = []
+
+        # FAST ITERATION
+        rows = list(enumerate(df.itertuples(index=False)))
+
+        with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
+            futures = [
+                executor.submit(process_row, session, args.url, model, i, row)
+                for i, row in rows
+            ]
+
+            for i, future in enumerate(as_completed(futures), 1):
+                records.append(future.result())
+
+                if i % BATCH_PRINT == 0 or i == total:
+                    elapsed = time.time() - start
+                    speed = i / elapsed
+                    print(f"[{i}/{total}] Speed: {speed:.0f} req/sec")
+
+        elapsed = time.time() - start
+        print(f"\n⏱ Finished in {elapsed:.2f}s  ({total/elapsed:.0f} req/sec)\n")
+
+        df_results = pd.DataFrame(records)
+        valid = df_results[df_results["pred_label"] != -1]
+
+        y_true = valid["true_label"].values
+        y_pred = valid["pred_label"].values
+        y_prob = valid["probability"].astype(float).values
+
+        metrics = evaluate(y_true, y_pred, y_prob, model)
+        all_metrics[model] = metrics
+
+        # SAVE FILES
+        df_results.to_csv(f"test_results_{model}.csv", index=False)
+        valid[valid["correct"] == False].to_csv(f"test_mismatches_{model}.csv", index=False)
+
+    print("\n🏁 FINAL COMPARISON\n")
+
+    for metric in ["accuracy", "precision", "recall", "f1", "auc"]:
+        lv = all_metrics["lgbm"][metric]
+        rv = all_metrics["rf"][metric]
+
+        winner = "LGBM ⚡" if lv > rv else "RF 🌲"
+        print(f"{metric.upper():<10} LGBM={lv:.4f}  RF={rv:.4f}  → {winner}")
+
+
+if __name__ == "__main__":
+    main()

test_mismatches_lgbm.csv

@@ -0,0 +1,1906 @@
+index,equipment,true_label,pred_label,probability,confidence,correct
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test_mismatches_rf.csv

@@ -0,0 +1,915 @@
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testing_results.txt

@@ -0,0 +1,91 @@
+F:\Mihir\shared_env\Scripts\python.exe F:\Mihir\Trial_AI_ProjDATASET\Fault_Detection_For_Industry\test.py 
+
+🚀 FAST TEST STARTING...
+
+Rows: 10000
+✅ Server reachable
+
+
+🔥 Testing LGBM...
+
+[500/10000] Speed: 24 req/sec
+[1000/10000] Speed: 24 req/sec
+[1500/10000] Speed: 24 req/sec
+[2000/10000] Speed: 24 req/sec
+[2500/10000] Speed: 24 req/sec
+[3000/10000] Speed: 24 req/sec
+[3500/10000] Speed: 24 req/sec
+[4000/10000] Speed: 24 req/sec
+[4500/10000] Speed: 24 req/sec
+[5000/10000] Speed: 24 req/sec
+[5500/10000] Speed: 24 req/sec
+[6000/10000] Speed: 24 req/sec
+[6500/10000] Speed: 24 req/sec
+[7000/10000] Speed: 24 req/sec
+[7500/10000] Speed: 24 req/sec
+[8000/10000] Speed: 24 req/sec
+[8500/10000] Speed: 24 req/sec
+[9000/10000] Speed: 24 req/sec
+[9500/10000] Speed: 24 req/sec
+[10000/10000] Speed: 24 req/sec
+
+⏱ Finished in 413.07s  (24 req/sec)
+
+
+══════════════════════════════════════════════════
+MODEL: LGBM
+══════════════════════════════════════════════════
+Accuracy : 0.8095
+Precision: 0.2799
+Recall   : 0.5766
+F1 Score : 0.3768
+AUC      : 0.7416
+TP=576 TN=7519 FP=1482 FN=423
+══════════════════════════════════════════════════
+
+🔥 Testing RF...
+
+[500/10000] Speed: 23 req/sec
+[1000/10000] Speed: 23 req/sec
+[1500/10000] Speed: 23 req/sec
+[2000/10000] Speed: 23 req/sec
+[2500/10000] Speed: 23 req/sec
+[3000/10000] Speed: 24 req/sec
+[3500/10000] Speed: 24 req/sec
+[4000/10000] Speed: 24 req/sec
+[4500/10000] Speed: 24 req/sec
+[5000/10000] Speed: 24 req/sec
+[5500/10000] Speed: 24 req/sec
+[6000/10000] Speed: 24 req/sec
+[6500/10000] Speed: 24 req/sec
+[7000/10000] Speed: 24 req/sec
+[7500/10000] Speed: 24 req/sec
+[8000/10000] Speed: 24 req/sec
+[8500/10000] Speed: 24 req/sec
+[9000/10000] Speed: 24 req/sec
+[9500/10000] Speed: 24 req/sec
+[10000/10000] Speed: 24 req/sec
+
+⏱ Finished in 420.97s  (24 req/sec)
+
+
+══════════════════════════════════════════════════
+MODEL: RF
+══════════════════════════════════════════════════
+Accuracy : 0.9086
+Precision: 0.5556
+Recall   : 0.4254
+F1 Score : 0.4819
+AUC      : 0.7446
+TP=425 TN=8661 FP=340 FN=574
+══════════════════════════════════════════════════
+
+🏁 FINAL COMPARISON
+
+ACCURACY   LGBM=0.8095  RF=0.9086  → RF 🌲
+PRECISION  LGBM=0.2799  RF=0.5556  → RF 🌲
+RECALL     LGBM=0.5766  RF=0.4254  → LGBM ⚡
+F1         LGBM=0.3768  RF=0.4819  → RF 🌲
+AUC        LGBM=0.7416  RF=0.7446  → RF 🌲
+
+Process finished with exit code 0