Upload app_gradio.py

app_gradio.py

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+"""
+Log Classification System — HuggingFace Spaces
+Gradio UI for the 3-tier hybrid log classification pipeline.
+"""
+from __future__ import annotations
+import io
+import time
+import pandas as pd
+import gradio as gr
+from classify import classify_log, classify_csv
+
+# ── Source options ──────────────────────────────────────────────────────────
+SOURCES = [
+    "ModernCRM",
+    "ModernHR",
+    "BillingSystem",
+    "AnalyticsEngine",
+    "ThirdPartyAPI",
+    "LegacyCRM",
+]
+
+TIER_COLORS = {
+    "Regex":        "🟢",
+    "BERT":         "🔵",
+    "LLM":          "🟡",
+    "LLM (fallback)": "🟠",
+}
+
+EXAMPLE_LOGS = [
+    ["ModernCRM",       "User User12345 logged in."],
+    ["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+    ["LegacyCRM",       "Case escalation for ticket ID 7324 failed — support agent is no longer active."],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. Use 'EmailCampaignManager'."],
+]
+
+
+# ── Single log tab ──────────────────────────────────────────────────────────
+def classify_single(source: str, log_message: str):
+    if not log_message.strip():
+        return "—", "—", "—", "—"
+
+    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",
+    )
+
+
+# ── Batch CSV tab ───────────────────────────────────────────────────────────
+def classify_batch(file):
+    if file is None:
+        return None, "⚠️ Please upload a CSV file."
+
+    try:
+        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 = 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())
+
+    stats = (
+        f"✅ Classified {total} logs\n\n"
+        f"📊 Tier breakdown:\n{tier_lines}\n\n"
+        f"🏷️ Label distribution:\n{label_lines}"
+    )
+
+    return output_path, stats
+
+
+# ── UI ──────────────────────────────────────────────────────────────────────
+with gr.Blocks(title="Log Classification System", theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("""
+# 🔍 Log Classification System
+**3-tier hybrid pipeline** → 🟢 Regex · 🔵 BERT + LogReg · 🟡 LLM
+Built to mimic production enterprise log monitoring architecture.
+""")
+
+    with gr.Tabs():
+
+        # ── Tab 1: Single Log ────────────────────────────────────────────
+        with gr.Tab("Single Log"):
+            with gr.Row():
+                source_input = gr.Dropdown(
+                    choices=SOURCES,
+                    value="ModernCRM",
+                    label="Source System",
+                )
+                log_input = gr.Textbox(
+                    label="Log Message",
+                    placeholder="Paste a log message here...",
+                    lines=3,
+                )
+
+            classify_btn = gr.Button("Classify", 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 Upload"):
+            gr.Markdown("""
+Upload a CSV with columns: **`source`**, **`log_message`**  
+Download the classified CSV with added columns: `predicted_label`, `tier_used`, `confidence`.
+""")
+            with gr.Row():
+                with gr.Column():
+                    csv_input  = gr.File(label="📂 Upload CSV", file_types=[".csv"])
+                    batch_btn  = gr.Button("Classify All", variant="primary")
+                with gr.Column():
+                    csv_output = gr.File(label="📥 Download Classified CSV")
+                    stats_out  = gr.Textbox(label="📊 Stats", lines=12, interactive=False)
+
+            batch_btn.click(
+                fn=classify_batch,
+                inputs=[csv_input],
+                outputs=[csv_output, stats_out],
+            )
+
+            gr.Markdown("""
+**Sample CSV format:**
+```
+source,log_message
+ModernCRM,User User123 logged in.
+LegacyCRM,Case escalation for ticket ID 7324 failed.
+BillingSystem,GET /api/v2/invoice HTTP/1.1 status: 500
+```
+""")
+
+        # ── Tab 3: Architecture ──────────────────────────────────────────
+        with gr.Tab("Architecture"):
+            gr.Markdown("""
+## 🏗️ 3-Tier Hybrid Pipeline
+
+| Tier | Method | Coverage | Latency | When Used |
+|------|--------|----------|---------|-----------|
+| 🟢 Regex | Python `re` patterns | ~21% | < 1ms | Fixed patterns (login, backup, etc.) |
+| 🔵 BERT | `all-MiniLM-L6-v2` + LogReg | ~79% | 20–80ms | High-volume categories with 150+ samples |
+| 🟡 LLM | HuggingFace Inference API | ~0.3% | 500–2000ms | LegacyCRM logs, rare patterns |
+
+## 📊 Model Performance (from training)
+- **BERT + LogReg** trained on 2,410 synthetic enterprise logs
+- **Confidence threshold**: 0.5 (below → escalate to LLM)
+- **Source-aware routing**: `LegacyCRM` bypasses ML entirely (only 7 training samples)
+
+## 🔑 Environment Variables
+| Secret | Required For |
+|--------|-------------|
+| `HF_TOKEN` | LLM inference (LegacyCRM logs) |
+""")
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)