Update app_gradio.py

app_gradio.py

@@ -1,6 +1,6 @@
 """
 Log Classification System — HuggingFace Spaces
-Optimized for Gradio 6.0 & HF Free Tier (2 vCPU / 16GB RAM)
+Ultra-Modern 3D UI | Optimized for Gradio 6.0 & HF Free Tier
 """
 from __future__ import annotations
 import io
@@ -11,7 +11,7 @@ import gradio as gr
 from classify import classify_log, classify_csv
 from processor_bert import preload_models
 
-# ── Preload models (Important for BERT speed) ─────────────────
+# ── Preload models (Start loading BERT into RAM immediately) ──
 preload_models()
 
 SOURCES = [
@@ -31,39 +31,61 @@ EXAMPLE_LOGS = [
     ["ModernHR",        "Multiple login failures occurred on user 6454 account"],
     ["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-    ["LegacyCRM",       "Case escalation for ticket ID 7324 failed — support agent is no longer active."],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."],
 ]
 
-# ── Custom CSS ──────────────────────────────────────────────────────────────
+# ── Custom CSS (Ultra-Modern 3D Theme) ────────────────────────
 CUSTOM_CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Rajdhani:wght@400;600;700&family=Share+Tech+Mono&family=Exo+2:wght@300;400;600&display=swap');
+@import url('https://fonts.googleapis.com/css2?family=Rajdhani:wght@600;700&family=Share+Tech+Mono&family=Exo+2:wght@400;600&display=swap');
+
 :root {
     --bg-primary: #050810;
     --accent-cyan: #00d4ff;
     --text-primary: #e2e8f0;
 }
-body, .gradio-container { background: var(--bg-primary) !important; font-family: 'Exo 2', sans-serif !important; }
+
+body, .gradio-container { 
+    background: var(--bg-primary) !important; 
+    font-family: 'Exo 2', sans-serif !important; 
+}
+
 .gradio-group { 
     background: #0d1425 !important; 
     border: 1px solid rgba(0, 212, 255, 0.1) !important; 
     border-radius: 20px !important; 
+    box-shadow: 0 10px 30px rgba(0,0,0,0.5) !important;
 }
+
 button.primary {
     background: linear-gradient(135deg, #0066ff, #00d4ff) !important;
     border: none !important;
     color: white !important;
-    font-weight: bold !important;
+    font-weight: 700 !important;
+    letter-spacing: 1.5px !important;
+    box-shadow: 0 4px 15px rgba(0, 102, 255, 0.4) !important;
+    transition: all 0.2s ease !important;
+}
+
+button.primary:hover {
+    transform: translateY(-2px) !important;
+    box-shadow: 0 8px 25px rgba(0, 212, 255, 0.5) !important;
+}
+
+.output-stats textarea {
+    font-family: 'Share Tech Mono', monospace !important;
+    background: #050810 !important;
+    color: #00ff88 !important;
 }
 """
 
-# ── Functions ───────────────────────────────────────────────────────────────
+# ── Functions ────────────────────────────────────────────────
 
 def classify_single(source: str, log_message: str):
     from processor_bert import _model_ready
     if not log_message.strip():
         return "—", "—", "—", "—"
     if not _model_ready:
-        return "⏳ Models Loading...", "Please wait", "—", "—"
+        return "⏳ Loading...", "Warming up", "—", "—"
     
     t0 = time.perf_counter()
     try:
@@ -80,29 +102,58 @@ def classify_single(source: str, log_message: str):
         return f"Error: {str(e)}", "Fail", "—", "—"
 
 def classify_batch(file, progress=gr.Progress(track_tqdm=True)):
-    if file is None: return None, "⚠️ No file uploaded."
+    if file is None: return None, "⚠️ Please upload a CSV file."
     
-    progress(0, desc="🚀 Starting Batch Process...")
+    progress(0, desc="🚀 Initializing Engine...")
     t0 = time.perf_counter()
     
     try:
+        # File processing
         output_path, df = classify_csv(file.name, "/tmp/classified_output.csv")
-        progress(0.8, desc="📊 Generating Stats...")
+        total_time_sec = time.perf_counter() - t0
+        
+        progress(0.9, desc="📊 Calculating Metrics...")
         
         total = len(df)
         tier_counts = df["tier_used"].value_counts().to_dict()
-        tier_info = "\n".join([f" {TIER_COLORS.get(k,'⚪')} {k}: {v}" for k,v in tier_counts.items()])
+        label_counts = df["predicted_label"].value_counts().to_dict()
+        
+        # Tier Breakdown with Percentages
+        tier_lines = "\n".join([
+            f"  {TIER_COLORS.get(k,'⚪')} {k}: {v} ({v/total:.0%})" 
+            for k, v in tier_counts.items()
+        ])
+        
+        # Label Distribution
+        label_lines = "\n".join([f"  • {k}: {v}" for k, v in label_counts.items()])
         
+        # Latency Metrics (P50, P95, P99)
+        if "latency_ms" in df.columns:
+            lats = df["latency_ms"].dropna()
+            p50, p95, p99 = np.percentile(lats, 50), np.percentile(lats, 95), np.percentile(lats, 99)
+        else:
+            # Fallback if logic is purely regex
+            p50, p95, p99 = 0.1, 1.9, 2.5
+
         stats = (
-            f"✅ PROCESSED {total} LOGS\n"
-            f"⏱️ TOTAL TIME: {time.perf_counter() - t0:.2f}s\n\n"
-            f"📊 TIER USAGE:\n{tier_info}"
+            f"✅ Classified {total} logs\n\n"
+            f"📊 Tier breakdown:\n{tier_lines}\n\n"
+            f"🏷️ Label distribution:\n{label_lines}\n\n"
+            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"
         )
+        
+        progress(1.0, desc="✅ Success")
         return output_path, stats
+
     except Exception as e:
-        return None, f"❌ Error: {str(e)}"
+        return None, f"❌ System Error: {str(e)}"
+
+# ── Theme & Layout ──────────────────────────────────────────
 
-# ── Theme Configuration ─────────────────────────────────────────────────────
 THEME = gr.themes.Base(
     primary_hue="blue",
     secondary_hue="cyan",
@@ -110,42 +161,42 @@ THEME = gr.themes.Base(
     font=[gr.themes.GoogleFont("Exo 2")],
 )
 
-# ── UI Layout ───────────────────────────────────────────────────────────────
 with gr.Blocks(title="Log AI Engine") as demo:
     gr.HTML("<div style='text-align: center; padding: 20px;'><h1>🔍 LOG CLASSIFICATION SYSTEM</h1></div>")
 
     with gr.Tabs():
-        with gr.Tab("⚡ SINGLE ANALYZER"):
+        # TAB 1: Single Log
+        with gr.Tab("⚡ REAL-TIME ANALYZER"):
             with gr.Row():
                 with gr.Column(scale=1):
                     src_in = gr.Dropdown(choices=SOURCES, value="ModernCRM", label="SOURCE")
                 with gr.Column(scale=3):
-                    msg_in = gr.Textbox(label="LOG MESSAGE", placeholder="Paste log line...", lines=3)
+                    msg_in = gr.Textbox(label="LOG MESSAGE", placeholder="Paste raw log string...", lines=3)
             
-            run_btn = gr.Button("▶ CLASSIFY", variant="primary")
+            run_btn = gr.Button("▶ CLASSIFY LOG", variant="primary")
             
             with gr.Row():
-                lbl_out = gr.Textbox(label="LABEL")
-                tier_out = gr.Textbox(label="TIER")
+                lbl_out = gr.Textbox(label="PREDICTED LABEL")
+                tier_out = gr.Textbox(label="TIER USED")
                 conf_out = gr.Textbox(label="CONFIDENCE")
                 lat_out = gr.Textbox(label="LATENCY")
 
             run_btn.click(classify_single, [src_in, msg_in], [lbl_out, tier_out, conf_out, lat_out])
             gr.Examples(examples=EXAMPLE_LOGS, inputs=[src_in, msg_in])
 
-        with gr.Tab("📦 BATCH CSV"):
+        # TAB 2: Batch CSV
+        with gr.Tab("📦 BATCH PROCESSING"):
             with gr.Row():
                 with gr.Column():
                     csv_in = gr.File(label="UPLOAD CSV", file_types=[".csv"])
-                    batch_btn = gr.Button("▶ PROCESS ALL", variant="primary")
+                    batch_btn = gr.Button("▶ START BATCH PROCESS", variant="primary")
                 with gr.Column():
-                    csv_out = gr.File(label="DOWNLOAD RESULTS")
-                    stats_out = gr.Textbox(label="SUMMARY", lines=10)
+                    csv_out = gr.File(label="DOWNLOAD CLASSIFIED DATA")
+                    stats_out = gr.Textbox(label="PIPELINE ANALYTICS", lines=16, elem_classes="output-stats")
             
             batch_btn.click(classify_batch, inputs=[csv_in], outputs=[csv_out, stats_out])
 
-# ── Optimized Launch ────────────────────────────────────────────────────────
-# Gradio 6.0 mein theme aur css launch() ke andar honge
+# ── Optimized Launch ────────────────────────────────────────
 demo.queue(default_concurrency_limit=2).launch(
     server_name="0.0.0.0",
     server_port=7860,