Update app_gradio.py

app_gradio.py

@@ -1,17 +1,17 @@
 """
 Log Classification System — HuggingFace Spaces
-Ultra-modern 3D UI with custom CSS
+Optimized for Stability & Responsiveness
 """
 from __future__ import annotations
 import io
 import time
 import pandas as pd
-import numpy as np  # <-- Added numpy for percentiles
+import numpy as np
 import gradio as gr
 from classify import classify_log, classify_csv
 from processor_bert import preload_models
 
-# ── Preload models in background at startup ─────────────────
+# ── Preload models at startup ─────────────────
 preload_models()
 
 SOURCES = [
@@ -32,547 +32,139 @@ EXAMPLE_LOGS = [
     ["BillingSystem",   "GET /v2/servers/detail HTTP/1.1 status: 200 len: 1583 time: 0.19"],
     ["AnalyticsEngine", "System crashed due to disk I/O failure on node-3"],
     ["LegacyCRM",       "Case escalation for ticket ID 7324 failed — support agent is no longer active."],
-    ["LegacyCRM",       "The 'BulkEmailSender' feature will be deprecated in v5.0. Use 'EmailCampaignManager'."],
 ]
 
-# ── Custom CSS — 3D Modern Dark Theme ──────────────────────────────────────
+# ── CSS (Cleaned up for Performance) ──
 CUSTOM_CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Rajdhani:wght@400;500;600;700&family=Share+Tech+Mono&family=Exo+2:wght@300;400;600;700&display=swap');
-
+@import url('https://fonts.googleapis.com/css2?family=Rajdhani:wght@600&family=Share+Tech+Mono&family=Exo+2:wght@400;600&display=swap');
 :root {
     --bg-primary: #050810;
-    --bg-secondary: #0a0f1e;
-    --bg-card: #0d1425;
-    --bg-card-hover: #111a30;
     --accent-cyan: #00d4ff;
-    --accent-blue: #0066ff;
-    --accent-purple: #7c3aed;
-    --accent-green: #00ff88;
-    --accent-orange: #ff6b00;
     --text-primary: #e2e8f0;
-    --text-secondary: #94a3b8;
-    --text-muted: #475569;
-    --border-glow: rgba(0, 212, 255, 0.3);
-    --shadow-3d: 0 20px 60px rgba(0, 0, 0, 0.8), 0 0 40px rgba(0, 102, 255, 0.15);
-    --glow-cyan: 0 0 20px rgba(0, 212, 255, 0.4), 0 0 40px rgba(0, 212, 255, 0.2);
-    --glow-blue: 0 0 20px rgba(0, 102, 255, 0.4);
-}
-
-/* ── Base ── */
-body, .gradio-container {
-    background: var(--bg-primary) !important;
-    font-family: 'Exo 2', sans-serif !important;
-    color: var(--text-primary) !important;
-}
-
-.gradio-container {
-    background:
-        radial-gradient(ellipse at 20% 20%, rgba(0, 102, 255, 0.08) 0%, transparent 50%),
-        radial-gradient(ellipse at 80% 80%, rgba(124, 58, 237, 0.08) 0%, transparent 50%),
-        radial-gradient(ellipse at 50% 50%, rgba(0, 212, 255, 0.03) 0%, transparent 70%),
-        var(--bg-primary) !important;
-    min-height: 100vh;
-}
-
-/* ── Header ── */
-.main-header {
-    text-align: center;
-    padding: 48px 24px 32px;
-    position: relative;
-}
-
-.main-header::before {
-    content: '';
-    position: absolute;
-    top: 0; left: 50%;
-    transform: translateX(-50%);
-    width: 600px; height: 2px;
-    background: linear-gradient(90deg, transparent, var(--accent-cyan), var(--accent-blue), transparent);
-    box-shadow: var(--glow-cyan);
-}
-
-/* ── Tab Navigation ── */
-.tab-nav {
-    background: rgba(13, 20, 37, 0.8) !important;
-    border: 1px solid rgba(0, 212, 255, 0.15) !important;
-    border-radius: 16px !important;
-    padding: 6px !important;
-    backdrop-filter: blur(20px) !important;
-    box-shadow: var(--shadow-3d) !important;
-}
-
-.tab-nav button {
-    font-family: 'Rajdhani', sans-serif !important;
-    font-weight: 600 !important;
-    font-size: 14px !important;
-    letter-spacing: 1.5px !important;
-    text-transform: uppercase !important;
-    color: var(--text-secondary) !important;
-    background: transparent !important;
-    border: none !important;
-    border-radius: 10px !important;
-    padding: 12px 24px !important;
-    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
-}
-
-.tab-nav button.selected {
-    color: var(--accent-cyan) !important;
-    background: linear-gradient(135deg, rgba(0, 212, 255, 0.1), rgba(0, 102, 255, 0.1)) !important;
-    box-shadow: 0 0 20px rgba(0, 212, 255, 0.2), inset 0 1px 0 rgba(0, 212, 255, 0.3) !important;
-    border: 1px solid rgba(0, 212, 255, 0.3) !important;
-}
-
-/* ── Cards / Blocks ── */
-.gradio-group, .gr-group {
-    background: var(--bg-card) !important;
-    border: 1px solid rgba(0, 212, 255, 0.1) !important;
-    border-radius: 20px !important;
-    box-shadow: var(--shadow-3d), inset 0 1px 0 rgba(255,255,255,0.03) !important;
-    transition: all 0.4s ease !important;
-    transform: perspective(1000px) rotateX(0deg);
-    position: relative;
-    overflow: hidden;
-}
-
-.gradio-group::before {
-    content: '';
-    position: absolute;
-    top: 0; left: 0; right: 0;
-    height: 1px;
-    background: linear-gradient(90deg, transparent, rgba(0, 212, 255, 0.5), transparent);
-}
-
-.gradio-group:hover {
-    border-color: rgba(0, 212, 255, 0.25) !important;
-    box-shadow: var(--shadow-3d), var(--glow-cyan) !important;
-    transform: perspective(1000px) translateY(-4px) !important;
-}
-
-/* ── Labels ── */
-label span, .gr-label {
-    font-family: 'Rajdhani', sans-serif !important;
-    font-weight: 600 !important;
-    letter-spacing: 1.5px !important;
-    text-transform: uppercase !important;
-    font-size: 11px !important;
-    color: var(--accent-cyan) !important;
-    opacity: 0.85;
-}
-
-/* ── Inputs ── */
-input, textarea, select, .gr-input {
-    background: rgba(5, 8, 16, 0.8) !important;
-    border: 1px solid rgba(0, 212, 255, 0.15) !important;
-    border-radius: 12px !important;
-    color: var(--text-primary) !important;
-    font-family: 'Share Tech Mono', monospace !important;
-    font-size: 13px !important;
-    transition: all 0.3s ease !important;
-    padding: 12px 16px !important;
-}
-
-input:focus, textarea:focus {
-    border-color: var(--accent-cyan) !important;
-    box-shadow: 0 0 0 3px rgba(0, 212, 255, 0.1), var(--glow-cyan) !important;
-    outline: none !important;
-    background: rgba(0, 212, 255, 0.03) !important;
-}
-
-/* ── Dropdown ── */
-.gr-dropdown select, .gradio-dropdown {
-    background: rgba(5, 8, 16, 0.9) !important;
-    border: 1px solid rgba(0, 212, 255, 0.2) !important;
-    border-radius: 12px !important;
-    color: var(--accent-cyan) !important;
-    font-family: 'Rajdhani', sans-serif !important;
-    font-weight: 600 !important;
 }
-
-/* ── Primary Button ── */
-button.primary, .gr-button-primary, button[variant="primary"] {
-    font-family: 'Rajdhani', sans-serif !important;
-    font-weight: 700 !important;
-    font-size: 15px !important;
-    letter-spacing: 2px !important;
-    text-transform: uppercase !important;
-    background: linear-gradient(135deg, #0066ff 0%, #00d4ff 50%, #0066ff 100%) !important;
-    background-size: 200% 200% !important;
-    border: none !important;
-    border-radius: 12px !important;
-    padding: 14px 32px !important;
-    color: #fff !important;
-    box-shadow:
-        0 8px 32px rgba(0, 102, 255, 0.4),
-        0 2px 8px rgba(0, 0, 0, 0.5),
-        inset 0 1px 0 rgba(255,255,255,0.2) !important;
-    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
-    animation: gradientShift 3s ease infinite !important;
-    position: relative !important;
-    overflow: hidden !important;
-}
-
-button.primary::before {
-    content: '';
-    position: absolute;
-    top: -50%; left: -60%;
-    width: 40%; height: 200%;
-    background: rgba(255,255,255,0.1);
-    transform: skewX(-20deg);
-    transition: left 0.6s ease;
-}
-
-button.primary:hover::before {
-    left: 120%;
-}
-
-button.primary:hover {
-    transform: translateY(-3px) scale(1.02) !important;
-    box-shadow:
-        0 16px 48px rgba(0, 102, 255, 0.5),
-        0 0 30px rgba(0, 212, 255, 0.3),
-        inset 0 1px 0 rgba(255,255,255,0.3) !important;
-}
-
-button.primary:active {
-    transform: translateY(0px) scale(0.98) !important;
-}
-
-@keyframes gradientShift {
-    0%, 100% { background-position: 0% 50%; }
-    50% { background-position: 100% 50%; }
-}
-
-/* ── Output Textboxes — 3D Result Cards ── */
-.output-card input, .output-card textarea {
-    background: linear-gradient(135deg, rgba(0, 212, 255, 0.05), rgba(0, 102, 255, 0.05)) !important;
-    border: 1px solid rgba(0, 212, 255, 0.2) !important;
-    border-radius: 14px !important;
-    font-family: 'Share Tech Mono', monospace !important;
-    font-size: 16px !important;
+body, .gradio-container { background: var(--bg-primary) !important; font-family: 'Exo 2', sans-serif !important; }
+.gradio-group { border: 1px solid rgba(0, 212, 255, 0.2) !important; border-radius: 15px !important; transition: 0.3s; }
+.gradio-group:hover { border-color: var(--accent-cyan) !important; box-shadow: 0 0 15px rgba(0, 212, 255, 0.2); }
+button.primary { 
+    background: linear-gradient(135deg, #0066ff, #00d4ff) !important; 
+    border: none !important; 
     font-weight: bold !important;
-    color: var(--accent-cyan) !important;
-    text-align: center !important;
-    box-shadow: inset 0 2px 8px rgba(0,0,0,0.3), 0 0 20px rgba(0, 212, 255, 0.1) !important;
-}
-
-/* ── Table / DataFrame ── */
-table {
-    border-collapse: separate !important;
-    border-spacing: 0 4px !important;
-    font-family: 'Share Tech Mono', monospace !important;
-    font-size: 12px !important;
-}
-
-th {
-    background: rgba(0, 102, 255, 0.2) !important;
-    color: var(--accent-cyan) !important;
-    font-family: 'Rajdhani', sans-serif !important;
-    letter-spacing: 1.5px !important;
-    text-transform: uppercase !important;
-    font-size: 11px !important;
-    padding: 10px 16px !important;
-    border: none !important;
-}
-
-td {
-    background: rgba(13, 20, 37, 0.6) !important;
-    color: var(--text-secondary) !important;
-    padding: 8px 16px !important;
-    border: none !important;
-    border-top: 1px solid rgba(0, 212, 255, 0.05) !important;
-    transition: background 0.2s ease !important;
-}
-
-tr:hover td {
-    background: rgba(0, 212, 255, 0.05) !important;
-    color: var(--text-primary) !important;
-}
-
-/* ── Markdown ── */
-.prose, .markdown {
-    color: var(--text-secondary) !important;
-    font-family: 'Exo 2', sans-serif !important;
-}
-
-.prose h1, .markdown h1 {
-    font-family: 'Rajdhani', sans-serif !important;
-    font-size: 3rem !important;
-    font-weight: 700 !important;
-    letter-spacing: 3px !important;
-    text-transform: uppercase !important;
-    background: linear-gradient(135deg, #ffffff 0%, var(--accent-cyan) 40%, var(--accent-blue) 100%) !important;
-    -webkit-background-clip: text !important;
-    -webkit-text-fill-color: transparent !important;
-    background-clip: text !important;
-    filter: drop-shadow(0 0 30px rgba(0, 212, 255, 0.3)) !important;
-    margin-bottom: 8px !important;
-}
-
-.prose h2, .markdown h2 {
-    font-family: 'Rajdhani', sans-serif !important;
-    font-size: 1.4rem !important;
-    font-weight: 600 !important;
-    letter-spacing: 2px !important;
-    color: var(--accent-cyan) !important;
-    text-transform: uppercase !important;
-    border-bottom: 1px solid rgba(0, 212, 255, 0.2) !important;
-    padding-bottom: 8px !important;
-}
-
-.prose p, .markdown p {
-    color: var(--text-secondary) !important;
-    line-height: 1.7 !important;
-    font-size: 14px !important;
 }
-
-.prose strong, .markdown strong {
-    color: var(--accent-cyan) !important;
-}
-
-/* ── Code blocks ── */
-code, pre {
-    font-family: 'Share Tech Mono', monospace !important;
-    background: rgba(0, 212, 255, 0.05) !important;
-    border: 1px solid rgba(0, 212, 255, 0.15) !important;
-    border-radius: 8px !important;
-    color: var(--accent-cyan) !important;
-    font-size: 12px !important;
-}
-
-/* ── Examples Table ── */
-.examples {
-    background: var(--bg-card) !important;
-    border: 1px solid rgba(0, 212, 255, 0.1) !important;
-    border-radius: 14px !important;
-    overflow: hidden !important;
-}
-
-.examples table th {
-    background: rgba(0, 102, 255, 0.15) !important;
-}
-
-/* ── File Upload ── */
-.gr-file {
-    background: rgba(5, 8, 16, 0.8) !important;
-    border: 2px dashed rgba(0, 212, 255, 0.25) !important;
-    border-radius: 16px !important;
-    transition: all 0.3s ease !important;
-}
-
-.gr-file:hover {
-    border-color: var(--accent-cyan) !important;
-    background: rgba(0, 212, 255, 0.03) !important;
-    box-shadow: var(--glow-cyan) !important;
-}
-
-/* ── Scrollbar ── */
-::-webkit-scrollbar { width: 6px; height: 6px; }
-::-webkit-scrollbar-track { background: var(--bg-secondary); }
-::-webkit-scrollbar-thumb {
-    background: linear-gradient(var(--accent-blue), var(--accent-cyan));
-    border-radius: 3px;
-}
-
-/* ── Pulsing accent line ── */
-@keyframes pulse-glow {
-    0%, 100% { opacity: 0.4; box-shadow: 0 0 10px rgba(0,212,255,0.3); }
-    50% { opacity: 1; box-shadow: 0 0 30px rgba(0,212,255,0.8); }
-}
-
-/* ── Tier badge colors ── */
-.tier-regex  { color: #00ff88 !important; }
-.tier-bert   { color: #00d4ff !important; }
-.tier-llm    { color: #ffd700 !important; }
 """
 
 # ── Functions ───────────────────────────────────────────────────────────────
+
 def classify_single(source: str, log_message: str):
+    # Dynamic import to check model status
     from processor_bert import _model_ready
-    if not log_message.strip():
-        return "—", "—", "—", "—"
+    
+    if not log_message or not log_message.strip():
+        return "Empty Input", "—", "—", "0 ms"
+    
     if not _model_ready:
-        return "⏳ Model loading...", "Please wait ~60s", "—", "—"
-    t0 = time.perf_counter()
-    result = classify_log(source, log_message)
-    latency_ms = (time.perf_counter() - t0) * 1000
-    label      = result["label"]
-    tier       = result["tier"]
-    confidence = f"{result['confidence']:.1%}" if result["confidence"] is not None else "N/A"
-    icon       = TIER_COLORS.get(tier, "⚪")
-    return label, f"{icon} {tier}", confidence, f"{latency_ms:.1f} ms"
-
+        return "⏳ Models Loading...", "Please wait ~30s", "0%", "—"
+    
+    try:
+        t0 = time.perf_counter()
+        result = classify_log(source, log_message)
+        latency = (time.perf_counter() - t0) * 1000
+        
+        label = result.get("label", "Unknown")
+        tier = result.get("tier", "N/A")
+        conf = f"{result.get('confidence', 0):.1%}"
+        icon = TIER_COLORS.get(tier, "⚪")
+        
+        return label, f"{icon} {tier}", conf, f"{latency:.1f} ms"
+    except Exception as e:
+        return f"Error: {str(e)}", "❌ Fail", "0%", "0 ms"
 
-def classify_batch(file):
+def classify_batch(file, progress=gr.Progress(track_tqdm=True)):
     if file is None:
         return None, "⚠️ Please upload a CSV file."
-        
-    t0 = time.perf_counter() # Start Total Timer
+    
+    progress(0, desc="Starting Classification...")
+    t0 = time.perf_counter()
     
     try:
+        # Note: If classify_csv is slow, we wrap it in progress feedback
         output_path, df = classify_csv(file.name, "/tmp/classified_output.csv")
-    except ValueError as e:
-        return None, f"⚠️ {e}"
-    except Exception as e:
-        return None, f"❌ Error: {e}"
         
-    total_time_sec = time.perf_counter() - t0 # End Total Timer
-    total = len(df)
-    
-    tier_counts  = df["tier_used"].value_counts().to_dict()
-    label_counts = df["predicted_label"].value_counts().to_dict()
-    
-    tier_lines  = "\n".join(f"  {TIER_COLORS.get(k,'⚪')} {k}: {v} ({v/total:.0%})" for k, v in tier_counts.items())
-    label_lines = "\n".join(f"  • {k}: {v}" for k, v in label_counts.items())
-    
-    # Calculate Latencies
-    if "latency_ms" in df.columns and not df["latency_ms"].empty:
-        latencies = df["latency_ms"].dropna()
-        p50 = np.percentile(latencies, 50)
-        p95 = np.percentile(latencies, 95)
-        p99 = np.percentile(latencies, 99)
-        latency_stats = (
-            f"⏱️ Performance Metrics:\n"
-            f"  • Total Time: {total_time_sec:.2f} s\n"
-            f"  • P50 Latency: {p50:.1f} ms\n"
-            f"  • P95 Latency: {p95:.1f} ms\n"
-            f"  • P99 Latency: {p99:.1f} ms"
-        )
-    else:
-        latency_stats = (
-            f"⏱️ Performance Metrics:\n"
-            f"  • Total Time: {total_time_sec:.2f} s\n"
-            f"  • (Latency stats unavailable: 'latency_ms' not found in output)"
+        progress(0.8, desc="Generating Statistics...")
+        total = len(df)
+        tier_counts = df["tier_used"].value_counts().to_dict()
+        label_counts = df["predicted_label"].value_counts().to_dict()
+        
+        # Stats Formatting
+        tier_info = "\n".join([f" {TIER_COLORS.get(k,'⚪')} {k}: {v}" for k,v in tier_counts.items()])
+        label_info = "\n".join([f" • {k}: {v}" for k,v in label_counts.items()])
+        
+        total_time = time.perf_counter() - t0
+        stats = (
+            f"✅ PROCESSED {total} LOGS\n"
+            f"⏱️ TOTAL TIME: {total_time:.2f}s\n\n"
+            f"📊 TIER BREAKDOWN:\n{tier_info}\n\n"
+            f"🏷️ LABEL DISTRIBUTION:\n{label_info}"
         )
-    
-    stats = (
-        f"✅ Classified {total} logs\n\n"
-        f"📊 Tier breakdown:\n{tier_lines}\n\n"
-        f"🏷️ Label distribution:\n{label_lines}\n\n"
-        f"{latency_stats}"
-    )
-    return output_path, stats
+        
+        progress(1.0, desc="Done!")
+        return output_path, stats
 
+    except Exception as e:
+        return None, f"❌ Critical Error: {str(e)}"
+
+# ── UI Construction ──────────────────────────────────────────────────────────
 
-# ── UI ───────────────────────────────────────────────────────────────────────
 THEME = gr.themes.Base(
     primary_hue="blue",
-    secondary_hue="cyan",
     neutral_hue="slate",
-    font=[gr.themes.GoogleFont("Exo 2"), "sans-serif"],
-    font_mono=[gr.themes.GoogleFont("Share Tech Mono"), "monospace"],
+    font=[gr.themes.GoogleFont("Exo 2")],
 ).set(
-    body_background_fill="#050810",
-    body_text_color="#e2e8f0",
     block_background_fill="#0d1425",
-    block_border_color="rgba(0,212,255,0.15)",
     block_label_text_color="#00d4ff",
     input_background_fill="#050810",
-    input_border_color="rgba(0,212,255,0.2)",
-    button_primary_background_fill="linear-gradient(135deg, #0066ff, #00d4ff)",
-    button_primary_text_color="#ffffff",
-    border_color_accent="#00d4ff",
-    color_accent_soft="rgba(0,212,255,0.1)",
 )
 
-with gr.Blocks(title="LOG CLASSIFICATION SYSTEM") as demo:
-
-    gr.Markdown("""
-# 🔍 LOG CLASSIFICATION SYSTEM
-**3-tier hybrid pipeline** — 🟢 Regex · 🔵 BERT + ML · 🟡 LLM  
-*Enterprise-grade log monitoring at production scale*
-""")
-
+with gr.Blocks(theme=THEME, css=CUSTOM_CSS, title="Log AI Engine") as demo:
+    gr.Markdown("# 🔍 LOG CLASSIFICATION ENGINE")
+    
     with gr.Tabs():
-
-        # ── Tab 1: Single Log ─────────────────────────────────────────────
-        with gr.Tab("⚡ SINGLE LOG"):
+        # SINGLE LOG TAB
+        with gr.Tab("⚡ REAL-TIME ANALYZER"):
             with gr.Row():
                 with gr.Column(scale=1):
-                    source_input = gr.Dropdown(
-                        choices=SOURCES,
-                        value="ModernCRM",
-                        label="SOURCE SYSTEM",
-                    )
+                    src = gr.Dropdown(choices=SOURCES, value="ModernCRM", label="LOG SOURCE")
                 with gr.Column(scale=3):
-                    log_input = gr.Textbox(
-                        label="LOG MESSAGE",
-                        placeholder="Paste a log message here...",
-                        lines=3,
-                    )
-
-            classify_btn = gr.Button("▶  CLASSIFY LOG", variant="primary", size="lg")
-
+                    msg = gr.Textbox(label="LOG MESSAGE", placeholder="Enter raw log string...", lines=3)
+            
+            run_btn = gr.Button("ANALYZE LOG", variant="primary")
+            
             with gr.Row():
-                label_out      = gr.Textbox(label="🏷️ PREDICTED LABEL",  interactive=False)
-                tier_out       = gr.Textbox(label="⚙️  TIER USED",        interactive=False)
-                confidence_out = gr.Textbox(label="📈 CONFIDENCE",        interactive=False)
-                latency_out    = gr.Textbox(label="⏱️  LATENCY",          interactive=False)
-
-            classify_btn.click(
-                fn=classify_single,
-                inputs=[source_input, log_input],
-                outputs=[label_out, tier_out, confidence_out, latency_out],
-            )
-
-            gr.Examples(
-                examples=EXAMPLE_LOGS,
-                inputs=[source_input, log_input],
-                label="📋 EXAMPLE LOGS — click to try",
-            )
-
-        # ── Tab 2: Batch CSV ──────────────────────────────────────────────
-        with gr.Tab("📦 BATCH CSV"):
-            gr.Markdown("""
-### Bulk Classification
-Upload a CSV with columns: **`source`**, **`log_message`** Output includes: `predicted_label`, `tier_used`, `confidence`, `latency_ms`
-""")
+                out_lbl = gr.Textbox(label="PREDICTED CATEGORY")
+                out_tier = gr.Textbox(label="TIER")
+                out_conf = gr.Textbox(label="CONFIDENCE")
+                out_lat = gr.Textbox(label="LATENCY")
+            
+            run_btn.click(classify_single, [src, msg], [out_lbl, out_tier, out_conf, out_lat])
+            gr.Examples(examples=EXAMPLE_LOGS, inputs=[src, msg])
+
+        # BATCH TAB
+        with gr.Tab("📦 BATCH PROCESSING"):
             with gr.Row():
                 with gr.Column():
-                    csv_input  = gr.File(label="📂 UPLOAD CSV", file_types=[".csv"])
-                    batch_btn  = gr.Button("▶  CLASSIFY ALL", variant="primary")
+                    file_in = gr.File(label="Upload CSV (source, log_message)")
+                    batch_btn = gr.Button("START BATCH PROCESS", variant="primary")
                 with gr.Column():
-                    csv_output = gr.File(label="📥 DOWNLOAD RESULTS")
-                    # Increased lines to 16 to properly fit the latency metrics
-                    stats_out  = gr.Textbox(label="📊 STATISTICS", lines=16, interactive=False)
-
-            batch_btn.click(
-                fn=classify_batch,
-                inputs=[csv_input],
-                outputs=[csv_output, stats_out],
-            )
-
-            gr.Markdown("""
-**Sample CSV format:**
-            """)
-
-            # ── Tab 3: Architecture ───────────────────────────────────────────
-        with gr.Tab("🏗️ ARCHITECTURE"):
-            gr.Markdown("""
-## 3-Tier Hybrid Pipeline
-
-| Tier | Method | Coverage | Latency | Trigger |
-|------|--------|----------|---------|---------|
-| 🟢 **Regex** | Python `re` patterns | ~21% | < 1ms | Fixed patterns |
-| 🔵 **BERT** | `all-MiniLM-L6-v2` + LogReg | ~79% | 20–80ms | High-volume categories |
-| 🟡 **LLM** | HuggingFace Inference API | ~0.3% | 500–2000ms | LegacyCRM + rare patterns |
-
-## Model Performance
-- **Training data**: 2,410 synthetic enterprise logs
-- **Confidence threshold**: 0.5 (below → escalate to LLM)
-- **Source-aware routing**: `LegacyCRM` → LLM directly
-
-## Environment Variables
-| Secret | Purpose |
-|--------|---------|
-| `HF_TOKEN` | LLM inference for LegacyCRM logs |
-""")
-
-# Seedha launch karein, bina if __name__ == "__main__": ke
-#demo.launch(server_name="0.0.0.0", server_port=7860, theme=THEME, css=CUSTOM_CSS)
-# .queue() is mandatory for production level responsiveness
-demo.queue(default_concurrency_limit=5).launch(
-    server_name="0.0.0.0", 
-    server_port=7860, 
-    theme=THEME, 
-    css=CUSTOM_CSS
+                    file_out = gr.File(label="Download Classified CSV")
+                    stats_out = gr.Textbox(label="EXECUTION SUMMARY", lines=12)
+            
+            batch_btn.click(classify_batch, inputs=[file_in], outputs=[file_out, stats_out])
+
+    gr.Markdown("--- \n *System Status: Active | Tiers: Regex -> BERT -> LLM*")
+
+# ── Launch Logic ──
+# Concurrency limit ko 2 par rakha hai taaki 2 log se zyada ek sath process na hon (stability)
+demo.queue(default_concurrency_limit=2).launch(
+    server_name="0.0.0.0",
+    server_port=7860,
+    show_api=False
 )