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refactor enterprise_integration.py: switch to logging , add error han… #2

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32c1f51
refactor enterprise_integration.py: switch to logging , add error han…
ADAMSmugwe Mar 12, 2026
61b6f9b
feat: implement emotion_shift event generation
ADAMSmugwe Mar 27, 2026
5aeca1d
feat: resolve documentation and settings inconsistencies
ADAMSmugwe Mar 27, 2026
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1 change: 1 addition & 0 deletions ai_ear/__main__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@ def main(argv: list[str] | None = None) -> int:
app,
host=args.host,
port=args.port,
workers=settings.api_workers,
reload=args.reload,
log_level="info",
)
Expand Down
1 change: 1 addition & 0 deletions ai_ear/core/config.py
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Expand Up @@ -26,6 +26,7 @@ class Settings(BaseSettings):
# ------------------------------------------------------------------ API
api_host: str = Field(default="0.0.0.0", description="API server bind host")
api_port: int = Field(default=8080, ge=1, le=65535, description="API server bind port")
api_workers: int = Field(default=1, ge=1, description="Number of uvicorn worker processes")
api_cors_origins: list[str] = Field(
default=["*"], description="Allowed CORS origins"
)
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17 changes: 17 additions & 0 deletions ai_ear/core/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@
AudioChunk,
AuralEvent,
AuralEventType,
EmotionLabel,
EnvironmentLabel,
)

Expand Down Expand Up @@ -82,6 +83,7 @@ def __init__(
self._prev_env_by_source: dict[str, EnvironmentLabel | None] = {}
self._prev_music_active_by_source: dict[str, bool] = {}
self._prev_speech_active_by_source: dict[str, bool] = {}
self._prev_emotion_by_source: dict[str, EmotionLabel | None] = {}

# ------------------------------------------------------------------
# Analyser registration
Expand Down Expand Up @@ -330,6 +332,21 @@ def _derive_events(self, result: AnalysisResult) -> list[AuralEvent]:
)
)

# Emotion shift (tracked per source)
emotion_now = result.emotion.dominant if result.emotion else None
prev_emotion = self._prev_emotion_by_source.get(src)
if emotion_now is not None and emotion_now != prev_emotion:
events.append(
AuralEvent(
event_type=AuralEventType.EMOTION_SHIFT,
source_id=src,
description=f"Emotion shifted to '{emotion_now.value}'",
payload={"previous": prev_emotion, "current": emotion_now},
severity=result.emotion.arousal if result.emotion else 0.0,
)
)
self._prev_emotion_by_source[src] = emotion_now

return events

@property
Expand Down
4 changes: 2 additions & 2 deletions docs/configuration.md
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Expand Up @@ -18,8 +18,8 @@ All settings can be overridden via environment variables prefixed with `AIEAR_`.
| `AIEAR_ENVIRONMENT_ENABLED` | bool | `true` | Enable environment classification |
| `AIEAR_ENVIRONMENT_NOISE_GATE_DB` | float | `-50.0` | Frames below this dBFS level are classified as silence |
| `AIEAR_MEMORY_MAX_RESULTS` | int | `500` | Rolling buffer size for analysis results |
| `AIEAR_MEMORY_MAX_EVENTS` | int | `200` | Rolling buffer size for aural events |
| `AIEAR_MEMORY_CONTEXT_WINDOW_S` | float | `120.0` | Default context window for `context_summary()` |
| `AIEAR_MEMORY_MAX_EVENTS` | int | `1000` | Rolling buffer size for aural events |
| `AIEAR_MEMORY_CONTEXT_WINDOW_S` | float | `60.0` | Default context window for `context_summary()` |
| `AIEAR_API_HOST` | string | `"0.0.0.0"` | API server listen host |
| `AIEAR_API_PORT` | int | `8080` | API server listen port |
| `AIEAR_API_WORKERS` | int | `1` | Number of uvicorn worker processes |
Expand Down
115 changes: 59 additions & 56 deletions examples/enterprise_integration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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")


# ---------------------------------------------------------------------------
Expand Down Expand Up @@ -69,36 +71,32 @@ async def analyse(self, chunk: AudioChunk) -> SpeechResult:
confidence=0.95 if detected else 0.0,
)

print("\n🔌 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" 🔑 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("🔌 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" 🔑 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}")


# ---------------------------------------------------------------------------
Expand All @@ -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("\n🚨 Enterprise Integration Demo: Real-Time Alerting\n")
try:
logger.info("🚨 Enterprise Integration Demo: Real-Time Alerting")

class AlertSystem:
"""Simulates routing events to PagerDuty / Slack / SIEM etc."""
Expand All @@ -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" 🚨 [{alert['routing']}] {alert['description']} (severity={event.severity:.1f})")
logger.warning(f" 🚨 [{alert['routing']}] {alert['description']} (severity={event.severity:.1f})")
else:
print(f" ℹ️ [{event.event_type.value}] {event.description}")
logger.info(f" ℹ️ [{event.event_type.value}] {event.description}")

alerts = AlertSystem()
memory = AuralMemory()
Expand All @@ -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}")


# ---------------------------------------------------------------------------
Expand All @@ -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("\n🤖 Enterprise Integration Demo: LLM Context Injection\n")
try:
logger.info("🤖 Enterprise Integration Demo: LLM Context Injection")

memory = AuralMemory(context_window_s=120)
pipeline = AudioPipeline(
Expand Down Expand Up @@ -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(" " + "─" * 60)
for line in system_prompt.split("\n"):
print(f" {line}")
print(" " + "─" * 60)
print()
logger.info(" Generated LLM System Prompt:")
logger.info(" " + "─" * 60)
for line in system_prompt.split("\n"):
logger.info(f" {line}")
logger.info(" " + "─" * 60)
except Exception as e:
logger.error(f"LLM prompt demo failed: {e}")


def _build_system_prompt(summary: dict) -> str:
Expand Down Expand Up @@ -259,17 +262,16 @@ def demo_serve() -> None:
from ai_ear.api.server import create_app
from ai_ear.core.config import Settings

print("\n🌐 Starting AI Ear API Server on http://0.0.0.0:8080\n")
print(" Endpoints:")
print(" GET /health — liveness probe")
print(" GET /info — configuration summary")
print(" POST /analyse — analyse an uploaded audio file")
print(" GET /memory/context — aural context summary")
print(" GET /memory/transcript — recent speech transcript")
print(" GET /memory/events — recent aural events")
print(" WS /stream — real-time WebSocket audio stream")
print(" GET /pipeline/stats — pipeline throughput stats")
print()
logger.info("🌐 Starting AI Ear API Server on http://0.0.0.0:8080")
logger.info(" Endpoints:")
logger.info(" GET /health — liveness probe")
logger.info(" GET /info — configuration summary")
logger.info(" POST /analyse — analyse an uploaded audio file")
logger.info(" GET /memory/context — aural context summary")
logger.info(" GET /memory/transcript — recent speech transcript")
logger.info(" GET /memory/events — recent aural events")
logger.info(" WS /stream — real-time WebSocket audio stream")
logger.info(" GET /pipeline/stats — pipeline throughput stats")

settings = Settings()
app = create_app(settings)
Expand All @@ -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")
Expand Down