diff --git a/frontend/index.html b/frontend/index.html index 294bea9..fd9df0b 100644 --- a/frontend/index.html +++ b/frontend/index.html @@ -4,7 +4,7 @@ - iotDashboard + Lyncis
diff --git a/frontend/package-lock.json b/frontend/package-lock.json index bd2ad04..6051b75 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -10,40 +10,32 @@ "dependencies": { "@radix-ui/react-alert-dialog": "^1.1.15", "@radix-ui/react-dialog": "^1.1.15", - "@radix-ui/react-dropdown-menu": "^2.1.16", - "@radix-ui/react-toast": "^1.2.15", - "@tanstack/react-query": "^5.62.8", - "axios": "^1.7.9", - "clsx": "^2.1.1", - "date-fns": "^4.1.0", - "lucide-react": "^0.462.0", + "@tailwindcss/vite": "^4.1.17", + "@tanstack/react-query": "^5.90.10", + "@types/react-grid-layout": "^1.3.6", + "axios": "^1.13.2", + "daisyui": "^5.5.5", + "gridstack": "^12.3.3", + "ical.js": "^2.2.1", "qrcode.react": "^4.2.0", "react": "^19.1.1", "react-dom": "^19.1.1", "react-grid-layout": "^1.5.2", - "react-hook-form": "^7.54.2", + "react-hook-form": "^7.66.1", "react-hot-toast": "^2.6.0", - "react-router": "^7.1.0", - "react-router-dom": "^7.9.5", - "recharts": "^2.15.0", - "tailwind-merge": "^2.5.5", - 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"react-dom": "^19.1.1" + "react-dom": "^19.1.1", + "react-grid-layout": "^1.5.2", + "react-hook-form": "^7.66.1", + "react-hot-toast": "^2.6.0", + "react-router-dom": "^7.9.6", + "recharts": "^3.4.1" }, "devDependencies": { "@eslint/js": "^9.36.0", diff --git a/infrastructure/compose.yml b/infrastructure/compose.yml index a93f4be..80a35a7 100644 --- a/infrastructure/compose.yml +++ b/infrastructure/compose.yml @@ -8,7 +8,7 @@ services: - redis-data:/data restart: unless-stopped - mqtt: + mosquitto: image: eclipse-mosquitto:2.0 ports: - "1883:1883" @@ -30,6 +30,39 @@ services: - timescaledb-data:/var/lib/postgresql/data restart: unless-stopped + db-write: + build: + context: ../services/db_write + env_file: + - .env.dbw + depends_on: + - timescaledb + - redis + - mqtt-ingestion + restart: + unless-stopped + + mqtt-ingestion: + build: + context: ../services/mqtt_ingestion + env_file: + - .env.mqi + depends_on: + - mosquitto + - redis + restart: + unless-stopped + + # gpt-service: + # build: + # context: ./services/gpt_service + # ports: + # - "8001:8001" + # restart: unless-stopped + + + + volumes: redis-data: timescaledb-data: diff --git a/scripts/README.md b/scripts/README.md deleted file mode 100644 index c83850d..0000000 --- a/scripts/README.md +++ /dev/null @@ -1,248 +0,0 @@ -# MQTT Data Generator - -A Python script that simulates realistic environmental sensor data and publishes it to your MQTT broker for testing the IoT Dashboard. - -## Features - -✅ **8 Environmental Metrics**: Temperature, Humidity, CO2, Pressure, Light, Noise, PM2.5, VOC -✅ **Realistic Patterns**: Daily cycles (office hours, night time) -✅ **Random Walk**: Natural sensor drift and variations -✅ **Anomaly Injection**: 5% chance of anomalies for testing alerts -✅ **Self-Correcting**: Values drift back toward optimal ranges (simulates HVAC) -✅ **TLS/mTLS Support**: Secure connections with certificates - -## Installation - -```bash -# Install dependencies -pip install paho-mqtt - -# Or using uv -uv pip install paho-mqtt -``` - -## Quick Start - -### 1. Basic Usage (Localhost, No TLS) - -```bash -python scripts/mqtt_data_generator.py --device-id office-sensor-01 --interval 5 -``` - -### 2. With Specific Metrics - -```bash -python scripts/mqtt_data_generator.py \ - --device-id lab-sensor \ - --metrics temperature humidity co2 \ - --interval 10 -``` - -### 3. With TLS (Port 8883) - -```bash -python scripts/mqtt_data_generator.py \ - --device-id secure-sensor \ - --broker localhost \ - --port 8883 \ - --tls \ - --ca-cert infrastructure/mosquitto/certs/ca.crt \ - --client-cert path/to/device.crt \ - --client-key path/to/device.key \ - --interval 5 -``` - -### 4. Limited Duration (Testing) - -```bash -# Run for 5 minutes -python scripts/mqtt_data_generator.py \ - --device-id test-sensor \ - --duration 300 \ - --interval 2 -``` - -## Command Line Arguments - -| Argument | Required | Default | Description | -|----------|----------|---------|-------------| -| `--broker` | No | localhost | MQTT broker hostname | -| `--port` | No | 1883 | MQTT broker port (8883 for TLS) | -| `--device-id` | **Yes** | - | Device ID for MQTT topics | -| `--metrics` | No | all | Specific metrics to publish | -| `--interval` | No | 5 | Publish interval in seconds | -| `--duration` | No | 0 | Run duration in seconds (0 = infinite) | -| `--tls` | No | False | Enable TLS/SSL encryption | -| `--ca-cert` | No | - | Path to CA certificate | -| `--client-cert` | No | - | Path to client certificate (mTLS) | -| `--client-key` | No | - | Path to client private key (mTLS) | - -## Available Metrics - -All metrics follow the standards in `services/gpt_service/METRICS_REFERENCE.md`: - -- **temperature** - Indoor temperature (15-28°C) -- **humidity** - Relative humidity (20-70%) -- **co2** - Carbon dioxide concentration (400-1500 ppm) -- **pressure** - Atmospheric pressure (1000-1030 hPa) -- **light** - Illuminance (200-1000 lux) -- **noise** - Sound level (30-70 dB) -- **pm25** - Fine particulate matter (0-50 µg/m³) -- **voc** - Volatile organic compounds (0-500 ppb) - -## MQTT Topic Format - -Published to standard format: `devices/{device_id}/{metric}` - -Examples: -- `devices/office-sensor-01/temperature` -- `devices/office-sensor-01/humidity` -- `devices/lab-sensor/co2` - -## Data Patterns - -### Daily Cycles -- **Business hours (9-17)**: Higher CO2, temperature, noise -- **Night time (22-6)**: Lower light, CO2, noise -- **All day**: Natural variations within ranges - -### Realistic Behavior -- **Random walk**: Small incremental changes -- **Self-correcting**: HVAC-like drift toward optimal ranges -- **Anomalies**: 5% chance of spikes/drops for alert testing - -### Example Values - -``` -[14:23:45] Iteration 1 - 📊 temperature: 21.34 °C -> devices/office-sensor-01/temperature - 📊 humidity: 45.67 % -> devices/office-sensor-01/humidity - 📊 co2: 678.0 ppm -> devices/office-sensor-01/co2 - 📊 pressure: 1015.23 hPa -> devices/office-sensor-01/pressure - 📊 light: 456.0 lux -> devices/office-sensor-01/light - 📊 noise: 42.5 dB -> devices/office-sensor-01/noise - 📊 pm25: 8.3 µg/m³ -> devices/office-sensor-01/pm25 - 📊 voc: 120.0 ppb -> devices/office-sensor-01/voc -``` - -## Multi-Device Simulation - -Run multiple instances with different device IDs: - -```bash -# Terminal 1 - Office sensor -python scripts/mqtt_data_generator.py --device-id office-01 & - -# Terminal 2 - Lab sensor -python scripts/mqtt_data_generator.py --device-id lab-01 & - -# Terminal 3 - Warehouse sensor -python scripts/mqtt_data_generator.py --device-id warehouse-01 & -``` - -## Testing Scenarios - -### Normal Operations -```bash -python scripts/mqtt_data_generator.py \ - --device-id normal-sensor \ - --interval 5 -``` - -### High-Frequency Monitoring -```bash -python scripts/mqtt_data_generator.py \ - --device-id fast-sensor \ - --interval 1 -``` - -### Limited Metrics (CO2 monitoring) -```bash -python scripts/mqtt_data_generator.py \ - --device-id co2-monitor \ - --metrics co2 temperature humidity \ - --interval 10 -``` - -## Integration with IoT Dashboard - -1. **Register device** (if using device_manager): -```bash -curl -X POST http://localhost:8000/devices/register \ - -H "Content-Type: application/json" \ - -d '{"name":"Office Sensor","location":"Building A - Floor 2"}' -``` - -2. **Start data generator** with the device ID: -```bash -python scripts/mqtt_data_generator.py --device-id -``` - -3. **View in dashboard**: Data will appear in the frontend automatically - -4. **Test AI analysis**: Use the AI Insights widget to analyze patterns - -## Troubleshooting - -### Connection Refused -- Check MQTT broker is running: `docker compose -f infrastructure/compose.yml ps` -- Verify port: 1883 (plain) or 8883 (TLS) - -### TLS Certificate Errors -- Ensure CA certificate path is correct -- For mTLS, verify client cert/key match device registration -- Check certificate hasn't expired - -### No Data in Dashboard -- Verify mqtt_ingestion service is running -- Check Redis stream: `redis-cli XLEN mqtt:ingestion` -- Verify db_write service is running -- Check device_id matches registered device - -### High CPU Usage -- Increase `--interval` value -- Reduce number of metrics -- Run fewer instances - -## Performance - -- **CPU**: ~1-2% per instance at 5s interval -- **Memory**: ~20MB per instance -- **Network**: ~100 bytes per metric per publish -- **Recommended**: Max 10 instances on a single machine - -## Example Output - -``` -============================================================ -🌡️ MQTT Environmental Sensor Data Generator -============================================================ -Device ID: office-sensor-01 -Metrics: temperature, humidity, co2, pressure, light, noise, pm25, voc -Interval: 5s -Duration: Infinite -============================================================ -✓ Connected to MQTT broker at localhost:1883 - -[14:23:45] Iteration 1 - 📊 temperature: 21.34 °C -> devices/office-sensor-01/temperature - 📊 humidity: 45.67 % -> devices/office-sensor-01/humidity - 📊 co2: 678.0 ppm -> devices/office-sensor-01/co2 - 📊 pressure: 1015.23 hPa -> devices/office-sensor-01/pressure - 📊 light: 456.0 lux -> devices/office-sensor-01/light - 📊 noise: 42.5 dB -> devices/office-sensor-01/noise - 📊 pm25: 8.3 µg/m³ -> devices/office-sensor-01/pm25 - 📊 voc: 120.0 ppb -> devices/office-sensor-01/voc - -[14:23:50] Iteration 2 - ... -``` - -## Stopping the Generator - -- **Ctrl+C**: Graceful shutdown -- **Automatic**: If `--duration` specified - -## License - -Part of the IoT Dashboard project. diff --git a/services/db_write/Dockerfile b/services/db_write/Dockerfile index 9942af5..ea7b8e1 100644 --- a/services/db_write/Dockerfile +++ b/services/db_write/Dockerfile @@ -20,6 +20,8 @@ WORKDIR /app COPY --from=builder /app/.venv /app/.venv +COPY --from=builder /app/src/ /app/src/ + COPY --from=builder /app/*.py /app/ RUN adduser -D -u 1000 appuser && \ diff --git a/services/gpt_service/.dockerignore b/services/gpt_service/.dockerignore new file mode 100644 index 0000000..c273d88 --- /dev/null +++ b/services/gpt_service/.dockerignore @@ -0,0 +1,7 @@ +.env* +.md +__pycache__/ +*.pyc +*.pyo +*.pyd +*.log \ No newline at end of file diff --git a/services/gpt_service/Dockerfile b/services/gpt_service/Dockerfile new file mode 100644 index 0000000..77f708f --- /dev/null +++ b/services/gpt_service/Dockerfile @@ -0,0 +1,11 @@ +FROM ghcr.io/astral-sh/uv:python3.13-alpine + +WORKDIR /app + +COPY pyproject.toml ./ + +RUN uv sync + +COPY . . + +CMD ["uv", "run", "uvicorn","main:app", "--host", "0.0.0.0" ,"--port", "8001"] diff --git a/services/gpt_service/gpt_service.py b/services/gpt_service/gpt_service.py index 9d04ab1..272f5a8 100644 --- a/services/gpt_service/gpt_service.py +++ b/services/gpt_service/gpt_service.py @@ -1,5 +1,6 @@ from openai import OpenAI from typing import List, Dict, Any +from datetime import datetime from config import API_KEY, MODEL_NAME, PROVIDER_NAME, HOST_URL, LOG_LEVEL import logging @@ -372,3 +373,246 @@ Keep all text concise: summary under 50 words, each item under 20 words.""", except Exception as e: self.logger.error(f"Analysis failed: {str(e)}") raise + + async def generate_daily_briefing( + self, + briefing_type: str, + current_time: str, + indoor_data: Dict[str, Any] | None = None, + outdoor_data: Dict[str, Any] | None = None, + health_data: Dict[str, Any] | None = None, + calendar_events: List[Dict[str, Any]] | None = None, + ) -> Dict[str, Any]: + """ + Generate a daily briefing for office workers. + + Args: + briefing_type: 'schedule', 'environment', or 'full' + current_time: Current time in ISO format + indoor_data: Indoor environment readings (temp, humidity, co2, etc.) + outdoor_data: Weather and air quality data + health_data: Health/fitness metrics (steps, heart rate, etc.) + calendar_events: List of upcoming calendar events + + Returns: + Dict with status_line, status_emoji, insights, recommendations, context + """ + try: + # Build context sections + context_sections = [] + + # Current time context + try: + dt = datetime.fromisoformat(current_time.replace('Z', '+00:00')) + time_str = dt.strftime("%A, %B %d at %I:%M %p") + except: + time_str = current_time + context_sections.append(f"Current Time: {time_str}") + + # Indoor environment + if indoor_data: + indoor_lines = ["Indoor Environment:"] + for key, value in indoor_data.items(): + if value is not None: + indoor_lines.append(f" - {key}: {value}") + context_sections.append("\n".join(indoor_lines)) + + # Outdoor conditions + if outdoor_data: + outdoor_lines = ["Outdoor Conditions:"] + for key, value in outdoor_data.items(): + if value is not None: + outdoor_lines.append(f" - {key}: {value}") + context_sections.append("\n".join(outdoor_lines)) + + # Health metrics + if health_data: + health_lines = ["Health & Activity:"] + for key, value in health_data.items(): + if value is not None: + health_lines.append(f" - {key}: {value}") + context_sections.append("\n".join(health_lines)) + + # Calendar events + if calendar_events: + cal_lines = ["Upcoming Schedule:"] + for event in calendar_events[:10]: # Limit to 10 events + start = event.get('start', 'TBD') + summary = event.get('summary', 'Untitled') + location = event.get('location', '') + loc_str = f" at {location}" if location else "" + cal_lines.append(f" - {start}: {summary}{loc_str}") + context_sections.append("\n".join(cal_lines)) + + context = "\n\n".join(context_sections) + + # Build briefing-specific prompts + prompts = { + "schedule": f"""You are a smart wellness coach for office workers (software engineers, tech/finance professionals). +Generate a Schedule Briefing focused on calendar and activity optimization. + +{context} + +Environmental thresholds for reference: +- CO2 > 1000ppm: cognitive performance drops, ventilate before focused work +- PM2.5 > 35: poor outdoor air, avoid outdoor activities +- Temperature 20-24°C: optimal for focus work +- Steps goal: 10,000/day, take breaks every 2 hours + +Respond in this EXACT JSON format (no markdown, just valid JSON): +{{ + "status_emoji": "🟢|🟡|🔴", + "status_line": "One sentence summary of schedule/activity outlook", + "insights": [ + "Actionable insight tied to specific time or event", + "Another insight about activity or scheduling", + "Insight about outdoor activity timing based on conditions" + ], + "recommendations": [ + "Primary recommendation with specific timing", + "Secondary recommendation" + ] +}} + +Guidelines: +- Tie insights to SPECIFIC upcoming events or time slots +- Suggest activity breaks during schedule gaps +- Recommend outdoor activity timing based on air quality +- Keep each insight under 25 words +- Be specific about times (e.g., "before your 2pm meeting" not "later today") +- Focus on productivity, movement, and work-life balance""", + + "environment": f"""You are a smart wellness coach for office workers (software engineers, tech/finance professionals). +Generate an Environment Briefing focused on indoor workspace conditions. + +{context} + +Environmental thresholds and their impacts: +- CO2 400-800ppm: optimal | 800-1000ppm: acceptable | >1000ppm: ventilate (affects cognition) +- Temperature 20-22°C: optimal | 18-24°C: acceptable | outside: adjust +- Humidity 40-60%: optimal | <30%: too dry | >70%: too humid +- PM2.5 0-12: good | 12-35: moderate | >35: unhealthy +- Light 400-500 lux: optimal for desk work + +Respond in this EXACT JSON format (no markdown, just valid JSON): +{{ + "status_emoji": "🟢|🟡|🔴", + "status_line": "One sentence summary of workspace conditions", + "insights": [ + "Insight about most important environmental factor with action", + "Insight about another condition affecting work", + "Insight about ventilation or air quality" + ], + "recommendations": [ + "Primary action to improve workspace", + "Secondary recommendation" + ] +}} + +Guidelines: +- If CO2 is high, mention opening windows BEFORE the next meeting/focused work +- Tie environmental actions to upcoming activities when possible +- Be specific about what to do (e.g., "open window" not "improve ventilation") +- Mention how conditions affect cognitive performance +- Keep each insight under 25 words""", + + "full": f"""You are a smart wellness coach for office workers (software engineers, tech/finance professionals). +Generate a comprehensive Daily Briefing combining schedule, environment, and health. + +{context} + +Environmental thresholds: +- CO2 > 1000ppm: cognitive performance drops +- PM2.5 > 35: poor outdoor air quality +- Temperature 20-24°C: optimal for focus + +Health targets: +- 10,000 steps/day +- Movement break every 2 hours +- Heart rate zones for activity planning + +Respond in this EXACT JSON format (no markdown, just valid JSON): +{{ + "status_emoji": "🟢|🟡|🔴", + "status_line": "One sentence overall summary", + "insights": [ + "Most important cross-domain insight (environment + schedule)", + "Health/activity insight tied to schedule", + "Environmental condition affecting upcoming work", + "Outdoor activity timing recommendation" + ], + "recommendations": [ + "Primary recommendation combining multiple factors", + "Secondary recommendation" + ] +}} + +Guidelines: +- Connect environment to schedule (e.g., "ventilate before your debugging session") +- Suggest activity timing based on air quality AND schedule gaps +- Prioritize actionable insights over status reports +- Be specific about times and actions +- Keep each insight under 25 words""" + } + + prompt = prompts.get(briefing_type, prompts["full"]) + + system_prompt = """You are an expert wellness coach for knowledge workers. You help office workers (especially software engineers and tech/finance professionals) maintain optimal productivity and health by providing actionable, timing-specific advice. + +Your insights should: +1. Be tied to specific times or upcoming events +2. Explain WHY something matters (e.g., "CO2 affects focus") +3. Give specific actions (e.g., "open the window now" not "improve air") +4. Consider work patterns (deep work, meetings, breaks) + +Always respond with valid JSON only, no markdown formatting.""" + + self.logger.info(f"Generating {briefing_type} briefing") + + response = self.client.chat.completions.create( + model=self.model_name, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt} + ], + temperature=0.7, + max_tokens=1000 + ) + + result_text = response.choices[0].message.content + self.logger.info(f"Briefing generated. Tokens used: {response.usage.total_tokens}") + + # Parse JSON response + import json + # Clean potential markdown wrapping + if result_text.startswith("```"): + result_text = result_text.split("```")[1] + if result_text.startswith("json"): + result_text = result_text[4:] + result_text = result_text.strip() + + result = json.loads(result_text) + + return { + "status_emoji": result.get("status_emoji", "🟢"), + "status_line": result.get("status_line", "Briefing generated"), + "insights": result.get("insights", []), + "recommendations": result.get("recommendations", []), + "briefing_type": briefing_type, + "generated_at": current_time, + } + + except json.JSONDecodeError as e: + self.logger.error(f"Failed to parse briefing JSON: {e}") + self.logger.error(f"Raw response: {result_text}") + return { + "status_emoji": "⚠️", + "status_line": "Briefing generated with formatting issues", + "insights": [result_text[:200] if result_text else "Unable to generate insights"], + "recommendations": [], + "briefing_type": briefing_type, + "generated_at": current_time, + } + except Exception as e: + self.logger.error(f"Briefing generation failed: {str(e)}") + raise diff --git a/services/gpt_service/main.py b/services/gpt_service/main.py index 15fbeb4..320fe56 100644 --- a/services/gpt_service/main.py +++ b/services/gpt_service/main.py @@ -48,6 +48,34 @@ class AnalyzeResponse(BaseModel): data_points_analyzed: int +class CalendarEvent(BaseModel): + summary: str + start: str + end: str | None = None + location: str | None = None + + +class DailyBriefingRequest(BaseModel): + briefing_type: Literal["schedule", "environment", "full"] = Field( + default="full", + description="Type of briefing to generate" + ) + current_time: str = Field(..., description="Current time in ISO format") + indoor_data: Dict[str, Any] | None = Field(None, description="Indoor environment readings") + outdoor_data: Dict[str, Any] | None = Field(None, description="Weather and air quality data") + health_data: Dict[str, Any] | None = Field(None, description="Health/fitness metrics") + calendar_events: List[CalendarEvent] | None = Field(None, description="Upcoming calendar events") + + +class DailyBriefingResponse(BaseModel): + status_emoji: str + status_line: str + insights: List[str] + recommendations: List[str] + briefing_type: str + generated_at: str + + @app.get("/health") async def health_check(): return {"status": "healthy", "service": "gpt_service"} @@ -90,4 +118,40 @@ async def analyze_telemetry(request: AnalyzeRequest): raise HTTPException(status_code=400, detail=str(e)) except Exception as e: logger.error(f"Analysis failed: {str(e)}") - raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}") \ No newline at end of file + raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}") + + +@app.post("/daily-briefing", response_model=DailyBriefingResponse) +async def generate_daily_briefing(request: DailyBriefingRequest): + """ + Generate a daily briefing for office workers. + Combines environment, schedule, and health data into actionable insights. + """ + try: + if not gpt_service: + raise HTTPException(status_code=503, detail="GPT service not initialized") + + logger.info(f"Generating {request.briefing_type} briefing") + + # Convert calendar events to dicts + calendar_events = None + if request.calendar_events: + calendar_events = [event.model_dump() for event in request.calendar_events] + + result = await gpt_service.generate_daily_briefing( + briefing_type=request.briefing_type, + current_time=request.current_time, + indoor_data=request.indoor_data, + outdoor_data=request.outdoor_data, + health_data=request.health_data, + calendar_events=calendar_events, + ) + + return DailyBriefingResponse(**result) + + except ValueError as e: + logger.error(f"Validation error: {str(e)}") + raise HTTPException(status_code=400, detail=str(e)) + except Exception as e: + logger.error(f"Briefing generation failed: {str(e)}") + raise HTTPException(status_code=500, detail=f"Briefing generation failed: {str(e)}") \ No newline at end of file diff --git a/services/gpt_service/pyproject.toml b/services/gpt_service/pyproject.toml index 57e50de..c336dbd 100644 --- a/services/gpt_service/pyproject.toml +++ b/services/gpt_service/pyproject.toml @@ -8,5 +8,6 @@ dependencies = [ "fastapi>=0.121.0", "openai>=2.6.1", "pydantic>=2.12.3", + "uvicorn[standard]>=0.24.0", "python-dotenv>=1.2.1", ] diff --git a/services/mqtt_ingestion/Dockerfile b/services/mqtt_ingestion/Dockerfile index bd1fcb2..14625d5 100644 --- a/services/mqtt_ingestion/Dockerfile +++ b/services/mqtt_ingestion/Dockerfile @@ -20,6 +20,8 @@ WORKDIR /app COPY --from=builder /app/.venv /app/.venv +COPY --from=builder /app/src/ /app/src/ + COPY --from=builder /app/*.py /app/ RUN adduser -D -u 1000 appuser && \ diff --git a/uv.lock b/uv.lock index e3dac6c..24d1635 100644 --- a/uv.lock +++ b/uv.lock @@ -114,6 +114,18 @@ wheels = [ { url = 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