Delete app_gradio.py

app_gradio.py

@@ -1,187 +0,0 @@
-"""
-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-    ["LegacyCRM",       "Case escalation for ticket ID 7324 failed — support agent is no longer active."],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)