From 32c1f51c6d9beb447ecd6b4ca740ab4f0a07d6eb Mon Sep 17 00:00:00 2001 From: ADAMSmugwe Date: Thu, 12 Mar 2026 21:34:45 +0300 Subject: [PATCH] refactor enterprise_integration.py: switch to logging , add error handling , CLI keyword parameter and also improved demo robustnees --- examples/enterprise_integration.py | 115 +++++++++++++++-------------- 1 file changed, 59 insertions(+), 56 deletions(-) diff --git a/examples/enterprise_integration.py b/examples/enterprise_integration.py index 4696f88..19ced79 100644 --- a/examples/enterprise_integration.py +++ b/examples/enterprise_integration.py @@ -19,15 +19,17 @@ python examples/enterprise_integration.py llm-prompt """ -from __future__ import annotations import argparse import asyncio import json import sys import time - import numpy as np +import logging + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger("ai-ear.enterprise") # --------------------------------------------------------------------------- @@ -69,36 +71,32 @@ async def analyse(self, chunk: AudioChunk) -> SpeechResult: confidence=0.95 if detected else 0.0, ) - print("\nšŸ”Œ Enterprise Integration Demo: Custom Analyser (BYOM)\n") - - keyword_spotter = KeywordSpotter(keywords=["hey ai", "alert", "emergency"]) - memory = AuralMemory() - pipeline = AudioPipeline(analyzers=[keyword_spotter], memory=memory) - - keywords_detected: list[str] = [] - - async def on_result(result): - if result.speech and result.speech.text: - keywords_detected.append(result.speech.text) - print(f" šŸ”‘ Keyword detected: '{result.speech.text}'") - - pipeline.on_result(on_result) - await pipeline.start() - - SR = 16_000 - for i in range(6): - chunk = AudioChunk( - samples=np.zeros(SR * 2, dtype=np.float32), - sample_rate=SR, - source_id="enterprise_mic", - ) - await pipeline.process(chunk) - await asyncio.sleep(0.01) - - await pipeline.stop() - - print(f"\n Total keywords detected: {len(keywords_detected)}") - print(f" Keywords: {keywords_detected}\n") + try: + logger.info("šŸ”Œ Enterprise Integration Demo: Custom Analyser (BYOM)") + keyword_spotter = KeywordSpotter(keywords=["hey ai", "alert", "emergency"]) + memory = AuralMemory() + pipeline = AudioPipeline(analyzers=[keyword_spotter], memory=memory) + keywords_detected: list[str] = [] + async def on_result(result): + if result.speech and result.speech.text: + keywords_detected.append(result.speech.text) + logger.info(f" šŸ”‘ Keyword detected: '{result.speech.text}'") + pipeline.on_result(on_result) + await pipeline.start() + SR = 16_000 + for i in range(6): + chunk = AudioChunk( + samples=np.zeros(SR * 2, dtype=np.float32), + sample_rate=SR, + source_id="enterprise_mic", + ) + await pipeline.process(chunk) + await asyncio.sleep(0.01) + await pipeline.stop() + logger.info(f" Total keywords detected: {len(keywords_detected)}") + logger.info(f" Keywords: {keywords_detected}") + except Exception as e: + logger.error(f"Custom analyser demo failed: {e}") # --------------------------------------------------------------------------- @@ -115,7 +113,8 @@ async def demo_alerting() -> None: from ai_ear.core.pipeline import AudioPipeline from ai_ear.utils.audio import generate_tone - print("\nšŸšØ Enterprise Integration Demo: Real-Time Alerting\n") + try: + logger.info("šŸšØ Enterprise Integration Demo: Real-Time Alerting") class AlertSystem: """Simulates routing events to PagerDuty / Slack / SIEM etc.""" @@ -133,9 +132,9 @@ async def handle_event(self, event) -> None: "routing": "P1_PAGERDUTY" if event.severity >= 0.8 else "SLACK_CHANNEL", } self.alerts.append(alert) - print(f" šŸšØ [{alert['routing']}] {alert['description']} (severity={event.severity:.1f})") + logger.warning(f" šŸšØ [{alert['routing']}] {alert['description']} (severity={event.severity:.1f})") else: - print(f" ā„¹ļø [{event.event_type.value}] {event.description}") + logger.info(f" ā„¹ļø [{event.event_type.value}] {event.description}") alerts = AlertSystem() memory = AuralMemory() @@ -159,8 +158,10 @@ async def handle_event(self, event) -> None: await pipeline._process_and_dispatch(chunk) await asyncio.sleep(0.05) - await pipeline.stop() - print(f"\n Total high-severity alerts routed: {len(alerts.alerts)}\n") + await pipeline.stop() + logger.info(f" Total high-severity alerts routed: {len(alerts.alerts)}") + except Exception as e: + logger.error(f"Alerting demo failed: {e}") # --------------------------------------------------------------------------- @@ -176,7 +177,8 @@ async def demo_llm_prompt() -> None: from ai_ear.core.models import AudioChunk, EmotionLabel, EmotionProfile, SpeechSegment from ai_ear.core.pipeline import AudioPipeline - print("\nšŸ¤– Enterprise Integration Demo: LLM Context Injection\n") + try: + logger.info("šŸ¤– Enterprise Integration Demo: LLM Context Injection") memory = AuralMemory(context_window_s=120) pipeline = AudioPipeline( @@ -206,12 +208,13 @@ async def demo_llm_prompt() -> None: summary = memory.context_summary() system_prompt = _build_system_prompt(summary) - print(" Generated LLM System Prompt:") - print(" " + "ā”€" * 60) - for line in system_prompt.split("\n"): - print(f" {line}") - print(" " + "ā”€" * 60) - print() + logger.info(" Generated LLM System Prompt:") + logger.info(" " + "ā”€" * 60) + for line in system_prompt.split("\n"): + logger.info(f" {line}") + logger.info(" " + "ā”€" * 60) + except Exception as e: + logger.error(f"LLM prompt demo failed: {e}") def _build_system_prompt(summary: dict) -> str: @@ -259,17 +262,16 @@ def demo_serve() -> None: from ai_ear.api.server import create_app from ai_ear.core.config import Settings - print("\nšŸŒ Starting AI Ear API Server on http://0.0.0.0:8080\n") - print(" Endpoints:") - print(" GET /health ā€” liveness probe") - print(" GET /info ā€” configuration summary") - print(" POST /analyse ā€” analyse an uploaded audio file") - print(" GET /memory/context ā€” aural context summary") - print(" GET /memory/transcript ā€” recent speech transcript") - print(" GET /memory/events ā€” recent aural events") - print(" WS /stream ā€” real-time WebSocket audio stream") - print(" GET /pipeline/stats ā€” pipeline throughput stats") - print() + logger.info("šŸŒ Starting AI Ear API Server on http://0.0.0.0:8080") + logger.info(" Endpoints:") + logger.info(" GET /health ā€” liveness probe") + logger.info(" GET /info ā€” configuration summary") + logger.info(" POST /analyse ā€” analyse an uploaded audio file") + logger.info(" GET /memory/context ā€” aural context summary") + logger.info(" GET /memory/transcript ā€” recent speech transcript") + logger.info(" GET /memory/events ā€” recent aural events") + logger.info(" WS /stream ā€” real-time WebSocket audio stream") + logger.info(" GET /pipeline/stats ā€” pipeline throughput stats") settings = Settings() app = create_app(settings) @@ -283,7 +285,8 @@ def demo_serve() -> None: def main() -> int: parser = argparse.ArgumentParser(description="AI Ear enterprise integration examples") sub = parser.add_subparsers(dest="command") - sub.add_parser("custom-analyser", help="BYOM custom analyser demo") + custom = sub.add_parser("custom-analyser", help="BYOM custom analyser demo") + custom.add_argument("--keywords", nargs="+", default=["hey ai", "alert", "emergency"], help="Keywords for spotter") sub.add_parser("alerting", help="Real-time alerting demo") sub.add_parser("llm-prompt", help="LLM context injection demo") sub.add_parser("serve", help="Start the API server")