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Updated Dockerfiles and small changes

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This commit is contained in:
Andrej Mickov
2025-11-29 00:07:05 +01:00
parent ab72c01999
commit 3ab81fad8c
13 changed files with 856 additions and 1024 deletions
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2
frontend/index.html
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@@ -4,7 +4,7 @@
<meta charset="UTF-8" /> <meta charset="UTF-8" />
<!-- <link rel="icon" type="image/svg+xml" href="/vite.svg" /> --> <!-- <link rel="icon" type="image/svg+xml" href="/vite.svg" /> -->
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>iotDashboard</title> <title>Lyncis</title>
</head> </head>
<body> <body>
<div id="root"></div> <div id="root"></div>

1046
frontend/package-lock.json generated
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File diff suppressed because it is too large Load Diff

17
frontend/package.json
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@@ -10,8 +10,23 @@
"preview": "vite preview" "preview": "vite preview"
}, },
"dependencies": { "dependencies": {
"@radix-ui/react-alert-dialog": "^1.1.15",
"@radix-ui/react-dialog": "^1.1.15",
"@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": "^19.1.1",
"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": { "devDependencies": {
"@eslint/js": "^9.36.0", "@eslint/js": "^9.36.0",

35
infrastructure/compose.yml
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@@ -8,7 +8,7 @@ services:
- redis-data:/data - redis-data:/data
restart: unless-stopped restart: unless-stopped
mqtt: mosquitto:
image: eclipse-mosquitto:2.0 image: eclipse-mosquitto:2.0
ports: ports:
- "1883:1883" - "1883:1883"
@@ -30,6 +30,39 @@ services:
- timescaledb-data:/var/lib/postgresql/data - timescaledb-data:/var/lib/postgresql/data
restart: unless-stopped 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: volumes:
redis-data: redis-data:
timescaledb-data: timescaledb-data:

248
scripts/README.md
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@@ -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 <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.

2
services/db_write/Dockerfile
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@@ -20,6 +20,8 @@ WORKDIR /app
COPY --from=builder /app/.venv /app/.venv COPY --from=builder /app/.venv /app/.venv
COPY --from=builder /app/src/ /app/src/
COPY --from=builder /app/*.py /app/ COPY --from=builder /app/*.py /app/
RUN adduser -D -u 1000 appuser && \ RUN adduser -D -u 1000 appuser && \

7
services/gpt_service/.dockerignore Normal file
View File

@@ -0,0 +1,7 @@
.env*
.md
__pycache__/
*.pyc
*.pyo
*.pyd
*.log

11
services/gpt_service/Dockerfile Normal file
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@@ -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"]

244
services/gpt_service/gpt_service.py
View File

@@ -1,5 +1,6 @@
from openai import OpenAI from openai import OpenAI
from typing import List, Dict, Any from typing import List, Dict, Any
from datetime import datetime
from config import API_KEY, MODEL_NAME, PROVIDER_NAME, HOST_URL, LOG_LEVEL from config import API_KEY, MODEL_NAME, PROVIDER_NAME, HOST_URL, LOG_LEVEL
import logging import logging
@@ -372,3 +373,246 @@ Keep all text concise: summary under 50 words, each item under 20 words.""",
except Exception as e: except Exception as e:
self.logger.error(f"Analysis failed: {str(e)}") self.logger.error(f"Analysis failed: {str(e)}")
raise 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

66
services/gpt_service/main.py
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@@ -48,6 +48,34 @@ class AnalyzeResponse(BaseModel):
data_points_analyzed: int 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") @app.get("/health")
async def health_check(): async def health_check():
return {"status": "healthy", "service": "gpt_service"} return {"status": "healthy", "service": "gpt_service"}
@@ -90,4 +118,40 @@ async def analyze_telemetry(request: AnalyzeRequest):
raise HTTPException(status_code=400, detail=str(e)) raise HTTPException(status_code=400, detail=str(e))
except Exception as e: except Exception as e:
logger.error(f"Analysis failed: {str(e)}") logger.error(f"Analysis failed: {str(e)}")
raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}") 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)}")

1
services/gpt_service/pyproject.toml
View File

@@ -8,5 +8,6 @@ dependencies = [
"fastapi>=0.121.0", "fastapi>=0.121.0",
"openai>=2.6.1", "openai>=2.6.1",
"pydantic>=2.12.3", "pydantic>=2.12.3",
"uvicorn[standard]>=0.24.0",
"python-dotenv>=1.2.1", "python-dotenv>=1.2.1",
] ]

2
services/mqtt_ingestion/Dockerfile
View File

@@ -20,6 +20,8 @@ WORKDIR /app
COPY --from=builder /app/.venv /app/.venv COPY --from=builder /app/.venv /app/.venv
COPY --from=builder /app/src/ /app/src/
COPY --from=builder /app/*.py /app/ COPY --from=builder /app/*.py /app/
RUN adduser -D -u 1000 appuser && \ RUN adduser -D -u 1000 appuser && \

199
uv.lock generated
View File

@@ -114,6 +114,18 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" }, { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" },
] ]
[[package]]
name = "click"
version = "8.3.1"
source = { registry = "https://pypi.org/simple" }
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