Update app_gradio.py

app_gradio.py

@@ -1,6 +1,6 @@
 """
 Log Classification System — HuggingFace Spaces
-Optimized for Stability & Responsiveness
+Optimized for Gradio 6.0 & HF Free Tier (2 vCPU / 16GB RAM)
 """
 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 at startup ─────────────────
+# ── Preload models (Important for BERT speed) ─────────────────
 preload_models()
 
 SOURCES = [
@@ -34,20 +34,24 @@ EXAMPLE_LOGS = [
     ["LegacyCRM",       "Case escalation for ticket ID 7324 failed — support agent is no longer active."],
 ]
 
-# ── CSS (Cleaned up for Performance) ──
+# ── Custom CSS ──────────────────────────────────────────────────────────────
 CUSTOM_CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Rajdhani:wght@600&family=Share+Tech+Mono&family=Exo+2:wght@400;600&display=swap');
+@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');
 :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; }
-.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; 
+.gradio-group { 
+    background: #0d1425 !important; 
+    border: 1px solid rgba(0, 212, 255, 0.1) !important; 
+    border-radius: 20px !important; 
+}
+button.primary {
+    background: linear-gradient(135deg, #0066ff, #00d4ff) !important;
+    border: none !important;
+    color: white !important;
     font-weight: bold !important;
 }
 """
@@ -55,116 +59,96 @@ button.primary {
 # ── 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 or not log_message.strip():
-        return "Empty Input", "—", "—", "0 ms"
-    
+    if not log_message.strip():
+        return "—", "—", "—", "—"
     if not _model_ready:
-        return "⏳ Models Loading...", "Please wait ~30s", "0%", "—"
+        return "⏳ Models Loading...", "Please wait", "—", "—"
     
+    t0 = time.perf_counter()
     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"
+        icon = TIER_COLORS.get(result["tier"], "⚪")
+        return (
+            result["label"], 
+            f"{icon} {result['tier']}", 
+            f"{result['confidence']:.1%}" if result["confidence"] else "N/A", 
+            f"{latency:.1f} ms"
+        )
     except Exception as e:
-        return f"Error: {str(e)}", "❌ Fail", "0%", "0 ms"
+        return f"Error: {str(e)}", "Fail", "—", "—"
 
 def classify_batch(file, progress=gr.Progress(track_tqdm=True)):
-    if file is None:
-        return None, "⚠️ Please upload a CSV file."
+    if file is None: return None, "⚠️ No file uploaded."
     
-    progress(0, desc="Starting Classification...")
+    progress(0, desc="🚀 Starting Batch Process...")
     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")
+        progress(0.8, desc="📊 Generating Stats...")
         
-        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}"
+            f"⏱️ TOTAL TIME: {time.perf_counter() - t0:.2f}s\n\n"
+            f"📊 TIER USAGE:\n{tier_info}"
         )
-        
-        progress(1.0, desc="Done!")
         return output_path, stats
-
     except Exception as e:
-        return None, f"❌ Critical Error: {str(e)}"
-
-# ── UI Construction ──────────────────────────────────────────────────────────
+        return None, f"❌ Error: {str(e)}"
 
+# ── Theme Configuration ─────────────────────────────────────────────────────
 THEME = gr.themes.Base(
     primary_hue="blue",
+    secondary_hue="cyan",
     neutral_hue="slate",
     font=[gr.themes.GoogleFont("Exo 2")],
-).set(
-    block_background_fill="#0d1425",
-    block_label_text_color="#00d4ff",
-    input_background_fill="#050810",
 )
 
-with gr.Blocks(theme=THEME, css=CUSTOM_CSS, title="Log AI Engine") as demo:
-    gr.Markdown("# 🔍 LOG CLASSIFICATION ENGINE")
-    
+# ── 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():
-        # SINGLE LOG TAB
-        with gr.Tab("⚡ REAL-TIME ANALYZER"):
+        with gr.Tab("⚡ SINGLE ANALYZER"):
             with gr.Row():
                 with gr.Column(scale=1):
-                    src = gr.Dropdown(choices=SOURCES, value="ModernCRM", label="LOG SOURCE")
+                    src_in = gr.Dropdown(choices=SOURCES, value="ModernCRM", label="SOURCE")
                 with gr.Column(scale=3):
-                    msg = gr.Textbox(label="LOG MESSAGE", placeholder="Enter raw log string...", lines=3)
+                    msg_in = gr.Textbox(label="LOG MESSAGE", placeholder="Paste log line...", lines=3)
             
-            run_btn = gr.Button("ANALYZE LOG", variant="primary")
+            run_btn = gr.Button("▶ CLASSIFY", variant="primary")
             
             with gr.Row():
-                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])
+                lbl_out = gr.Textbox(label="LABEL")
+                tier_out = gr.Textbox(label="TIER")
+                conf_out = gr.Textbox(label="CONFIDENCE")
+                lat_out = gr.Textbox(label="LATENCY")
 
-        # BATCH TAB
-        with gr.Tab("📦 BATCH PROCESSING"):
+            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"):
             with gr.Row():
                 with gr.Column():
-                    file_in = gr.File(label="Upload CSV (source, log_message)")
-                    batch_btn = gr.Button("START BATCH PROCESS", variant="primary")
+                    csv_in = gr.File(label="UPLOAD CSV", file_types=[".csv"])
+                    batch_btn = gr.Button("▶ PROCESS ALL", variant="primary")
                 with gr.Column():
-                    file_out = gr.File(label="Download Classified CSV")
-                    stats_out = gr.Textbox(label="EXECUTION SUMMARY", lines=12)
+                    csv_out = gr.File(label="DOWNLOAD RESULTS")
+                    stats_out = gr.Textbox(label="SUMMARY", lines=10)
             
-            batch_btn.click(classify_batch, inputs=[file_in], outputs=[file_out, stats_out])
-
-    gr.Markdown("--- \n *System Status: Active | Tiers: Regex -> BERT -> LLM*")
+            batch_btn.click(classify_batch, inputs=[csv_in], outputs=[csv_out, stats_out])
 
-# ── Launch Logic ──
-# Concurrency limit ko 2 par rakha hai taaki 2 log se zyada ek sath process na hon (stability)
+# ── Optimized Launch ────────────────────────────────────────────────────────
+# Gradio 6.0 mein theme aur css launch() ke andar honge
 demo.queue(default_concurrency_limit=2).launch(
     server_name="0.0.0.0",
     server_port=7860,
-    show_api=False
+    theme=THEME,
+    css=CUSTOM_CSS
 )