feat: RTL_433 protocol database import - iteration 1/5

- Expanded protocol database from 18 → 299 signatures (16.6x increase)
- Imported 281 protocols from RTL_433 open-source database (286 total devices)
- Created automated import script: scripts/import_rtl433_protocols.py
- Generated rtl433_protocols_imported.py with timing/frequency/modulation data
- Updated protocol_database.py to include RTL433_PROTOCOLS
- All 26 tests passing

Breakdown by category:
  - Weather: 116 protocols
  - Sensors: 36 protocols
  - TPMS: 25 protocols
  - Security: 23 protocols
  - Home Automation: 18 protocols
  - Other: 50+ protocols

Frequency coverage:
  - 433.92 MHz: 248 protocols
  - 315.00 MHz: 32 protocols
  - 915.00 MHz: 1 protocol

This provides comprehensive coverage of Sub-GHz IoT devices for accurate
identification from raw RF captures.
This commit is contained in:
leetcrypt
2026-02-14 18:55:55 -08:00
parent 01b06fadc6
commit 9f73595b20
32 changed files with 15365 additions and 50 deletions
+1 -1
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@@ -4,6 +4,6 @@ GigLez API Package
FastAPI application with environment-aware configuration
"""
from .main import app
from .main_simple import app
__all__ = ['app']
+2 -2
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@@ -24,7 +24,7 @@ from src.parser.gps_extractor import GPSFilenameExtractor
from src.matcher.strategies import (
ExactMatcher,
FrequencyMatcher,
BitPatternMatcher,
PatternMatcher,
TimingMatcher,
RTL433DecoderStrategy,
PatternBasedStrategy
@@ -81,7 +81,7 @@ def get_matcher_engine():
# Add all strategies in order of confidence
_matcher_engine.add_strategy(ExactMatcher()) # Exact protocol + frequency
_matcher_engine.add_strategy(FrequencyMatcher()) # Frequency-based
_matcher_engine.add_strategy(BitPatternMatcher()) # Bit pattern matching
_matcher_engine.add_strategy(PatternMatcher()) # Bit pattern matching
_matcher_engine.add_strategy(TimingMatcher()) # Timing-based
_matcher_engine.add_strategy(RTL433DecoderStrategy()) # RTL_433 decoder
_matcher_engine.add_strategy(PatternBasedStrategy()) # Pattern decoder (NEW!)
+506
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@@ -0,0 +1,506 @@
"""
Enhanced Capture Upload Endpoints
Features:
- Automatic device matching on upload
- Manual device labeling support
- Photo upload for unknown devices
- Training data collection
"""
from typing import List, Optional
from fastapi import APIRouter, UploadFile, File, Form, Depends, HTTPException, status, BackgroundTasks
from sqlalchemy.orm import Session
from loguru import logger
import hashlib
import json
from datetime import datetime
import tempfile
import os
from src.api.dependencies import get_db, optional_auth, get_storage
from src.database.models import (
User, Capture, Session as CaptureSession,
CaptureMatch, Device, ManualIdentification
)
from src.parser.sub_parser import parse_sub_file
from src.gps.validator import validate_gps_coordinates
from src.matcher.engine import SignatureMatcher
from config.settings import settings
router = APIRouter()
# =============================================================================
# ENHANCED UPLOAD ENDPOINT
# =============================================================================
@router.post("/captures/upload/enhanced")
async def upload_captures_enhanced(
manifest: str = Form(..., description="JSON manifest with GPS data and manual labels"),
files: List[UploadFile] = File(..., description=".sub files to upload"),
background_tasks: BackgroundTasks = None,
db: Session = Depends(get_db),
user: User = Depends(optional_auth),
storage = Depends(get_storage)
):
"""
Enhanced upload with automatic matching and manual labeling
**Manifest Format**:
```json
{
"session_uuid": "550e8400-e29b-41d4-a716-446655440000",
"captures": [
{
"filename": "capture_001.sub",
"latitude": 40.7128,
"longitude": -74.0060,
"accuracy": 5.0,
"altitude": 10.5,
"timestamp": "2026-01-12T10:00:00Z"
}
],
"manual_labels": [
{
"filename": "capture_001.sub",
"device_id": 123,
"source": "manual_existing"
},
{
"filename": "capture_002.sub",
"device_type": "Garage Door Opener",
"manufacturer": "Chamberlain",
"model": "891LM",
"notes": "Captured from my garage",
"source": "manual_new"
}
]
}
```
**Process**:
1. Parse and validate manifest
2. For each file:
- Deduplicate by hash
- Validate GPS
- Parse .sub file
- Store file
- **Run automatic matching (NEW)**
- **Apply manual labels if provided (NEW)**
- Save capture with matches
3. Return results with device identifications
"""
try:
# Parse manifest
manifest_data = json.loads(manifest)
session_uuid = manifest_data.get("session_uuid")
captures_manifest = manifest_data.get("captures", [])
manual_labels_list = manifest_data.get("manual_labels", [])
logger.info(f"Enhanced upload: {len(files)} files, {len(manual_labels_list)} manual labels")
# Create manual labels lookup
manual_labels_map = {label['filename']: label for label in manual_labels_list}
# Get or create session
capture_session = None
if session_uuid:
capture_session = db.query(CaptureSession).filter_by(
session_uuid=session_uuid
).first()
if not capture_session:
capture_session = CaptureSession(
session_uuid=session_uuid,
user_id=user.id if user else None,
started_at=datetime.utcnow()
)
db.add(capture_session)
db.flush()
# Create filename to manifest mapping
manifest_map = {c['filename']: c for c in captures_manifest}
# Initialize matcher
matcher = SignatureMatcher(db)
# Process files
results = {"successful": [], "failed": []}
for file in files:
try:
# Read file content
content = await file.read()
# Compute SHA256 hash
file_hash = hashlib.sha256(content).hexdigest()
# Check if already exists
existing = db.query(Capture).filter_by(file_hash=file_hash).first()
if existing:
results["successful"].append({
"filename": file.filename,
"status": "duplicate",
"file_hash": file_hash,
"matches": [] # Could retrieve existing matches
})
continue
# Get manifest entry
manifest_entry = manifest_map.get(file.filename)
if not manifest_entry:
results["failed"].append({
"filename": file.filename,
"error": "Missing manifest entry"
})
continue
# Validate GPS
latitude = float(manifest_entry['latitude'])
longitude = float(manifest_entry['longitude'])
accuracy = manifest_entry.get('accuracy')
if not validate_gps_coordinates(latitude, longitude, accuracy):
results["failed"].append({
"filename": file.filename,
"error": f"Invalid GPS coordinates: ({latitude}, {longitude})"
})
continue
# Parse .sub file
with tempfile.NamedTemporaryFile(delete=False, suffix='.sub') as tmp:
tmp.write(content)
tmp_path = tmp.name
try:
metadata = parse_sub_file(tmp_path)
except Exception as e:
results["failed"].append({
"filename": file.filename,
"error": f"Failed to parse .sub file: {e}"
})
continue
finally:
os.unlink(tmp_path)
# Store file
storage_path = storage.save_file(
file_hash=file_hash,
content=content,
filename=file.filename
)
# Create capture record
capture = Capture(
file_hash=file_hash,
session_id=capture_session.id if capture_session else None,
user_id=user.id if user else None,
latitude=latitude,
longitude=longitude,
altitude=manifest_entry.get('altitude'),
gps_accuracy=accuracy,
captured_at=datetime.fromisoformat(
manifest_entry['timestamp'].replace('Z', '+00:00')
),
frequency=metadata.frequency,
modulation=metadata.modulation,
preset=metadata.preset,
protocol=metadata.protocol,
bit_length=metadata.bit_length,
key_data=metadata.key_data,
timing_element=metadata.timing_element,
raw_data=str(metadata.raw_data) if metadata.raw_data else None,
raw_format=metadata.file_format,
file_path=storage_path,
file_size=len(content)
)
db.add(capture)
db.flush() # Get capture ID
# =====================================================================
# NEW: Automatic Device Matching
# =====================================================================
matches = []
try:
# Run matching engine
match_results = matcher.match(metadata)
# Store top 10 matches
for match_result in match_results[:10]:
device = db.query(Device).filter_by(id=match_result.device_id).first()
if not device:
continue
# Create CaptureMatch record
capture_match = CaptureMatch(
capture_id=capture.id,
device_id=device.id,
confidence=match_result.confidence,
match_method=match_result.method,
match_details=match_result.details or {}
)
db.add(capture_match)
matches.append({
"device_id": device.id,
"manufacturer": device.manufacturer,
"model": device.model,
"device_type": device.device_type,
"confidence": match_result.confidence,
"method": match_result.method
})
# Set best match as capture's device_id
if matches:
capture.device_id = matches[0]["device_id"]
capture.match_confidence = matches[0]["confidence"]
capture.match_method = matches[0]["method"]
except Exception as e:
logger.error(f"Matching failed for {file.filename}: {e}")
# Continue without matches
# =====================================================================
# NEW: Manual Device Labeling
# =====================================================================
manual_label = manual_labels_map.get(file.filename)
if manual_label:
await handle_manual_label(
db=db,
capture=capture,
manual_label=manual_label,
user=user
)
# Commit this capture
db.commit()
results["successful"].append({
"filename": file.filename,
"status": "uploaded",
"file_hash": file_hash,
"frequency": metadata.frequency,
"protocol": metadata.protocol,
"matches": matches,
"manual_label": manual_label is not None
})
logger.debug(f"Capture uploaded with {len(matches)} matches: {file.filename}")
except Exception as e:
logger.error(f"Failed to process file {file.filename}: {e}")
results["failed"].append({
"filename": file.filename,
"error": str(e)
})
logger.success(f"Enhanced upload complete: {len(results['successful'])} successful, {len(results['failed'])} failed")
return results
except json.JSONDecodeError as e:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Invalid JSON manifest: {e}"
)
except Exception as e:
logger.error(f"Enhanced upload failed: {e}", exc_info=True)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Upload failed: {e}"
)
# =============================================================================
# MANUAL LABEL HANDLING
# =============================================================================
async def handle_manual_label(
db: Session,
capture: Capture,
manual_label: dict,
user: Optional[User]
):
"""
Process manual device label from user
Two types:
1. manual_existing: User selected from existing device database
2. manual_new: User created new device entry
"""
source = manual_label.get("source", "manual")
if source == "manual_existing":
# User selected existing device
device_id = manual_label.get("device_id")
if not device_id:
return
device = db.query(Device).filter_by(id=device_id).first()
if not device:
logger.warning(f"Manual label references non-existent device: {device_id}")
return
# Override automatic match with manual selection
capture.device_id = device_id
capture.match_confidence = 1.0 # User-confirmed
capture.match_method = "manual_selection"
# Create ManualIdentification record
manual_id = ManualIdentification(
capture_id=capture.id,
device_id=device_id,
user_id=user.id if user else None,
confidence=1.0,
notes=manual_label.get("notes"),
verified=False, # Needs community verification
verification_count=1 # Initial submission counts as 1
)
db.add(manual_id)
logger.info(f"Manual label applied: capture {capture.id} -> device {device_id}")
elif source == "manual_new":
# User created new device
device_type = manual_label.get("device_type")
manufacturer = manual_label.get("manufacturer")
model = manual_label.get("model")
if not all([device_type, manufacturer, model]):
logger.warning("Incomplete manual_new label data")
return
# Check if device already exists
existing_device = db.query(Device).filter_by(
manufacturer=manufacturer,
model=model
).first()
if existing_device:
device = existing_device
else:
# Create new device
device = Device(
manufacturer=manufacturer,
model=model,
device_type=device_type,
typical_frequency=capture.frequency,
protocol=capture.protocol,
source="community",
verified=False, # Needs verification
metadata_json={"submitter_user_id": user.id if user else None}
)
db.add(device)
db.flush() # Get device ID
# Link capture to device
capture.device_id = device.id
capture.match_confidence = 1.0
capture.match_method = "manual_new_device"
# Create ManualIdentification record
manual_id = ManualIdentification(
capture_id=capture.id,
device_id=device.id,
user_id=user.id if user else None,
confidence=1.0,
notes=manual_label.get("notes"),
verified=False,
verification_count=1
)
db.add(manual_id)
logger.info(f"New device created from manual label: {manufacturer} {model}")
# =============================================================================
# BATCH MATCHING ENDPOINT (For existing captures)
# =============================================================================
@router.post("/captures/match_batch")
async def match_captures_batch(
limit: int = 100,
background_tasks: BackgroundTasks = None,
db: Session = Depends(get_db),
user: User = Depends(optional_auth)
):
"""
Run matching engine on existing captures that don't have device identifications
Use this to backfill matches for captures uploaded before automatic matching was implemented.
"""
# Find captures without device_id
unmatched_captures = db.query(Capture).filter(
Capture.device_id.is_(None)
).limit(limit).all()
if not unmatched_captures:
return {
"message": "No unmatched captures found",
"processed": 0
}
logger.info(f"Batch matching {len(unmatched_captures)} captures")
matcher = SignatureMatcher(db)
matched_count = 0
for capture in unmatched_captures:
try:
# Reconstruct SignalMetadata from capture
from src.parser.metadata import SignalMetadata
metadata = SignalMetadata()
metadata.frequency = capture.frequency
metadata.protocol = capture.protocol
metadata.modulation = capture.modulation
metadata.bit_length = capture.bit_length
metadata.key_data = capture.key_data
metadata.timing_element = capture.timing_element
if capture.raw_data:
# Parse raw_data string back to list
metadata.raw_data = eval(capture.raw_data) if capture.raw_data.startswith('[') else None
# Run matching
match_results = matcher.match(metadata)
if match_results:
# Store top match
best_match = match_results[0]
capture.device_id = best_match.device_id
capture.match_confidence = best_match.confidence
capture.match_method = best_match.method
# Store all matches in CaptureMatch table
for match_result in match_results[:10]:
capture_match = CaptureMatch(
capture_id=capture.id,
device_id=match_result.device_id,
confidence=match_result.confidence,
match_method=match_result.method,
match_details=match_result.details or {}
)
db.add(capture_match)
matched_count += 1
except Exception as e:
logger.error(f"Batch matching failed for capture {capture.id}: {e}")
continue
db.commit()
logger.success(f"Batch matching complete: {matched_count}/{len(unmatched_captures)} matched")
return {
"message": f"Processed {len(unmatched_captures)} captures",
"matched": matched_count,
"unmatched": len(unmatched_captures) - matched_count
}
+715
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@@ -0,0 +1,715 @@
"""
Enhanced Capture Upload Endpoint with Robust Error Logging
Complete integration of comprehensive error tracking for beta testing.
"""
from typing import List, Optional
from fastapi import APIRouter, UploadFile, File, Form, Depends, HTTPException, status, BackgroundTasks, Request
from sqlalchemy.orm import Session
from loguru import logger
import hashlib
import json
from datetime import datetime
import tempfile
import os
import time
import traceback
import uuid
from src.api.dependencies import get_db, optional_auth, get_storage
from src.database.models import (
User, Capture, Session as CaptureSession,
CaptureMatch, Device, ManualIdentification
)
from src.parser.sub_parser import parse_sub_file
from src.gps.validator import validate_gps_coordinates
from src.matcher.engine import SignatureMatcher
from src.logging.upload_logger import (
log_upload_attempt,
log_file_processing,
log_parse_error,
log_gps_validation_error,
log_matching_results,
log_manual_label,
log_upload_complete,
log_exception,
log_performance_metrics,
UploadStage
)
from config.settings import settings
router = APIRouter()
def generate_upload_id() -> str:
"""Generate unique upload identifier"""
return f"upload_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}"
def extract_client_info(request: Request) -> dict:
"""Extract client information from request"""
return {
"user_agent": request.headers.get("user-agent", "unknown"),
"ip_address": request.client.host if request.client else "unknown",
"referer": request.headers.get("referer"),
"accept_language": request.headers.get("accept-language"),
"content_length": request.headers.get("content-length")
}
# =============================================================================
# ENHANCED UPLOAD ENDPOINT WITH LOGGING
# =============================================================================
@router.post("/captures/upload/beta")
async def upload_captures_beta(
request: Request,
manifest: str = Form(..., description="JSON manifest with GPS data and manual labels"),
files: List[UploadFile] = File(..., description=".sub files to upload"),
background_tasks: BackgroundTasks = None,
db: Session = Depends(get_db),
user: User = Depends(optional_auth),
storage = Depends(get_storage)
):
"""
BETA: Enhanced upload with comprehensive error logging
Logs everything for debugging during beta testing:
- Upload attempts and client info
- Individual file processing stages
- Parse errors with file samples
- GPS validation failures
- Matching results and performance
- Manual labels
- Performance metrics
- Exceptions with full context
"""
upload_start = time.time()
upload_id = generate_upload_id()
client_info = extract_client_info(request)
# Performance tracking
perf_metrics = {}
try:
# Parse manifest
try:
manifest_data = json.loads(manifest)
except json.JSONDecodeError as e:
log_exception(
upload_id=upload_id,
stage=UploadStage.VALIDATION,
exception=e,
context={"manifest_sample": manifest[:200]}
)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Invalid JSON manifest: {str(e)}"
)
session_uuid = manifest_data.get("session_uuid")
captures_manifest = manifest_data.get("captures", [])
manual_labels_list = manifest_data.get("manual_labels", [])
# Calculate total upload size
total_size = sum([file.size for file in files if file.size])
# Log upload attempt
log_upload_attempt(
upload_id=upload_id,
user_id=user.id if user else None,
session_id=session_uuid,
file_count=len(files),
total_size=total_size,
client_info=client_info
)
logger.bind(upload_id=upload_id).info(
f"Processing upload: {len(files)} files, {total_size/1024/1024:.2f} MB"
)
# Create manual labels lookup
manual_labels_map = {label['filename']: label for label in manual_labels_list}
# Get or create session
capture_session = None
if session_uuid:
capture_session = db.query(CaptureSession).filter_by(
session_uuid=session_uuid
).first()
if not capture_session:
capture_session = CaptureSession(
session_uuid=session_uuid,
user_id=user.id if user else None,
started_at=datetime.utcnow()
)
db.add(capture_session)
db.flush()
# Create filename to manifest mapping
manifest_map = {c['filename']: c for c in captures_manifest}
# Initialize matcher
matcher = SignatureMatcher(db)
# Process files
results = {"successful": [], "failed": [], "duplicates": []}
file_perf_metrics = []
for file in files:
file_start = time.time()
file_metrics = {"filename": file.filename}
try:
# =====================================================
# STAGE 1: Read and Hash File
# =====================================================
stage_start = time.time()
content = await file.read()
file_hash = hashlib.sha256(content).hexdigest()
file_metrics["read_time_ms"] = (time.time() - stage_start) * 1000
# Check for duplicates
existing = db.query(Capture).filter_by(file_hash=file_hash).first()
if existing:
log_file_processing(
upload_id=upload_id,
filename=file.filename,
file_size=len(content),
file_hash=file_hash,
stage=UploadStage.VALIDATION,
success=True,
duration_ms=file_metrics["read_time_ms"],
metadata={"status": "duplicate"}
)
results["duplicates"].append({
"filename": file.filename,
"file_hash": file_hash
})
continue
# =====================================================
# STAGE 2: Validate Manifest Entry
# =====================================================
manifest_entry = manifest_map.get(file.filename)
if not manifest_entry:
error_msg = "Missing manifest entry"
log_file_processing(
upload_id=upload_id,
filename=file.filename,
file_size=len(content),
file_hash=file_hash,
stage=UploadStage.VALIDATION,
success=False,
duration_ms=file_metrics["read_time_ms"],
error=error_msg
)
results["failed"].append({
"filename": file.filename,
"error": error_msg
})
continue
# =====================================================
# STAGE 3: Validate GPS
# =====================================================
stage_start = time.time()
try:
latitude = float(manifest_entry['latitude'])
longitude = float(manifest_entry['longitude'])
accuracy = manifest_entry.get('accuracy')
if not validate_gps_coordinates(latitude, longitude, accuracy):
log_gps_validation_error(
upload_id=upload_id,
filename=file.filename,
latitude=latitude,
longitude=longitude,
accuracy=accuracy,
reason="Coordinates out of range or invalid"
)
results["failed"].append({
"filename": file.filename,
"error": f"Invalid GPS: ({latitude}, {longitude})"
})
continue
file_metrics["gps_validation_ms"] = (time.time() - stage_start) * 1000
except (ValueError, KeyError) as e:
log_gps_validation_error(
upload_id=upload_id,
filename=file.filename,
latitude=manifest_entry.get('latitude', 0),
longitude=manifest_entry.get('longitude', 0),
accuracy=manifest_entry.get('accuracy'),
reason=f"Parse error: {str(e)}"
)
results["failed"].append({
"filename": file.filename,
"error": f"GPS parse error: {str(e)}"
})
continue
# =====================================================
# STAGE 4: Parse .sub File
# =====================================================
stage_start = time.time()
with tempfile.NamedTemporaryFile(delete=False, suffix='.sub') as tmp:
tmp.write(content)
tmp_path = tmp.name
try:
metadata = parse_sub_file(tmp_path)
file_metrics["parse_time_ms"] = (time.time() - stage_start) * 1000
log_file_processing(
upload_id=upload_id,
filename=file.filename,
file_size=len(content),
file_hash=file_hash,
stage=UploadStage.PARSING,
success=True,
duration_ms=file_metrics["parse_time_ms"],
metadata={
"frequency": metadata.frequency,
"protocol": metadata.protocol,
"modulation": metadata.modulation,
"format": metadata.file_format
}
)
except Exception as e:
# Detailed parse error logging
content_str = content.decode('utf-8', errors='ignore')
log_parse_error(
upload_id=upload_id,
filename=file.filename,
file_content_sample=content_str,
error_type=type(e).__name__,
error_message=str(e),
traceback_str=traceback.format_exc(),
file_hash=file_hash
)
results["failed"].append({
"filename": file.filename,
"error": f"Parse failed: {str(e)}"
})
continue
finally:
os.unlink(tmp_path)
# =====================================================
# STAGE 5: Store File
# =====================================================
stage_start = time.time()
storage_path = storage.save_file(
file_hash=file_hash,
content=content,
filename=file.filename
)
file_metrics["storage_time_ms"] = (time.time() - stage_start) * 1000
# =====================================================
# STAGE 6: Create Capture Record
# =====================================================
stage_start = time.time()
capture = Capture(
file_hash=file_hash,
session_id=capture_session.id if capture_session else None,
user_id=user.id if user else None,
latitude=latitude,
longitude=longitude,
altitude=manifest_entry.get('altitude'),
gps_accuracy=accuracy,
captured_at=datetime.fromisoformat(
manifest_entry['timestamp'].replace('Z', '+00:00')
),
frequency=metadata.frequency,
modulation=metadata.modulation,
preset=metadata.preset,
protocol=metadata.protocol,
bit_length=metadata.bit_length,
key_data=metadata.key_data,
timing_element=metadata.timing_element,
raw_data=str(metadata.raw_data) if metadata.raw_data else None,
raw_format=metadata.file_format,
file_path=storage_path,
file_size=len(content)
)
db.add(capture)
db.flush() # Get capture ID
file_metrics["db_insert_time_ms"] = (time.time() - stage_start) * 1000
# =====================================================
# STAGE 7: Automatic Device Matching
# =====================================================
stage_start = time.time()
matches = []
matcher_stats = {}
try:
match_results = matcher.match(metadata)
matcher_stats = {
"strategies_used": len(match_results),
"total_candidates": len(match_results)
}
# Store top 10 matches
for match_result in match_results[:10]:
device = db.query(Device).filter_by(id=match_result.device_id).first()
if not device:
continue
capture_match = CaptureMatch(
capture_id=capture.id,
device_id=device.id,
confidence=match_result.confidence,
match_method=match_result.method,
match_details=match_result.details or {}
)
db.add(capture_match)
matches.append({
"device_id": device.id,
"manufacturer": device.manufacturer,
"model": device.model,
"device_type": device.device_type,
"confidence": match_result.confidence,
"method": match_result.method
})
# Set best match
if matches:
capture.device_id = matches[0]["device_id"]
capture.match_confidence = matches[0]["confidence"]
capture.match_method = matches[0]["method"]
file_metrics["matching_time_ms"] = (time.time() - stage_start) * 1000
# Log matching results
log_matching_results(
upload_id=upload_id,
filename=file.filename,
file_hash=file_hash,
matches=matches,
duration_ms=file_metrics["matching_time_ms"],
matcher_stats=matcher_stats
)
except Exception as e:
log_exception(
upload_id=upload_id,
stage=UploadStage.MATCHING,
exception=e,
context={"filename": file.filename, "file_hash": file_hash}
)
# Continue without matches
# =====================================================
# STAGE 8: Manual Device Labeling
# =====================================================
manual_label = manual_labels_map.get(file.filename)
if manual_label:
try:
await handle_manual_label_with_logging(
upload_id=upload_id,
db=db,
capture=capture,
manual_label=manual_label,
user=user,
file_hash=file_hash
)
except Exception as e:
log_exception(
upload_id=upload_id,
stage=UploadStage.DATABASE,
exception=e,
context={"manual_label": manual_label}
)
# Commit this capture
db.commit()
# Calculate total file processing time
file_metrics["total_time_ms"] = (time.time() - file_start) * 1000
file_perf_metrics.append(file_metrics)
# Log successful processing
log_file_processing(
upload_id=upload_id,
filename=file.filename,
file_size=len(content),
file_hash=file_hash,
stage=UploadStage.COMPLETE,
success=True,
duration_ms=file_metrics["total_time_ms"],
metadata={
"matches_found": len(matches),
"manual_label": manual_label is not None
}
)
results["successful"].append({
"filename": file.filename,
"file_hash": file_hash,
"frequency": metadata.frequency,
"protocol": metadata.protocol,
"matches": matches,
"manual_label": manual_label is not None,
"processing_time_ms": round(file_metrics["total_time_ms"], 2)
})
except Exception as e:
# Catch-all for unexpected errors
log_exception(
upload_id=upload_id,
stage=UploadStage.DATABASE,
exception=e,
context={
"filename": file.filename,
"user_id": user.id if user else None
}
)
results["failed"].append({
"filename": file.filename,
"error": f"Unexpected error: {str(e)}"
})
# =====================================================
# Upload Complete - Log Summary
# =====================================================
total_duration_ms = (time.time() - upload_start) * 1000
# Calculate aggregate performance metrics
if file_perf_metrics:
avg_metrics = {
"avg_parse_time_ms": sum(m.get("parse_time_ms", 0) for m in file_perf_metrics) / len(file_perf_metrics),
"avg_matching_time_ms": sum(m.get("matching_time_ms", 0) for m in file_perf_metrics) / len(file_perf_metrics),
"avg_storage_time_ms": sum(m.get("storage_time_ms", 0) for m in file_perf_metrics) / len(file_perf_metrics),
"avg_total_time_ms": sum(m.get("total_time_ms", 0) for m in file_perf_metrics) / len(file_perf_metrics),
"total_upload_time_ms": total_duration_ms
}
log_performance_metrics(
upload_id=upload_id,
metrics=avg_metrics
)
log_upload_complete(
upload_id=upload_id,
total_duration_ms=total_duration_ms,
successful_count=len(results["successful"]),
failed_count=len(results["failed"]),
duplicate_count=len(results["duplicates"]),
summary={
"total_files": len(files),
"total_size_mb": round(total_size / 1024 / 1024, 2),
"user_id": user.id if user else None,
"session_uuid": session_uuid
}
)
return {
"upload_id": upload_id,
"successful": results["successful"],
"failed": results["failed"],
"duplicates": results["duplicates"],
"summary": {
"total_files": len(files),
"successful": len(results["successful"]),
"failed": len(results["failed"]),
"duplicates": len(results["duplicates"]),
"processing_time_sec": round(total_duration_ms / 1000, 2)
}
}
except Exception as e:
# Top-level exception handler
log_exception(
upload_id=upload_id,
stage=UploadStage.VALIDATION,
exception=e,
context={
"user_id": user.id if user else None,
"file_count": len(files)
}
)
logger.error(f"Upload failed completely: {str(e)}", exc_info=True)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Upload failed: {str(e)}"
)
async def handle_manual_label_with_logging(
upload_id: str,
db: Session,
capture: Capture,
manual_label: dict,
user: Optional[User],
file_hash: str
):
"""Process manual label with logging"""
source = manual_label.get("source", "manual")
if source == "manual_existing":
device_id = manual_label.get("device_id")
if not device_id:
return
device = db.query(Device).filter_by(id=device_id).first()
if not device:
logger.warning(f"Manual label references non-existent device: {device_id}")
return
capture.device_id = device_id
capture.match_confidence = 1.0
capture.match_method = "manual_selection"
manual_id = ManualIdentification(
capture_id=capture.id,
device_id=device_id,
user_id=user.id if user else None,
confidence=1.0,
notes=manual_label.get("notes"),
verified=False,
verification_count=1
)
db.add(manual_id)
log_manual_label(
upload_id=upload_id,
filename=capture.file_path.split('/')[-1],
file_hash=file_hash,
label_type="existing_device",
device_id=device_id,
device_info={
"manufacturer": device.manufacturer,
"model": device.model,
"device_type": device.device_type
},
user_id=user.id if user else None
)
elif source == "manual_new":
device_type = manual_label.get("device_type")
manufacturer = manual_label.get("manufacturer")
model = manual_label.get("model")
if not all([device_type, manufacturer, model]):
return
# Check if device already exists
existing_device = db.query(Device).filter_by(
manufacturer=manufacturer,
model=model
).first()
if existing_device:
device = existing_device
else:
device = Device(
manufacturer=manufacturer,
model=model,
device_type=device_type,
typical_frequency=capture.frequency,
protocol=capture.protocol,
source="community",
verified=False,
metadata_json={"submitter_user_id": user.id if user else None}
)
db.add(device)
db.flush()
capture.device_id = device.id
capture.match_confidence = 1.0
capture.match_method = "manual_new_device"
manual_id = ManualIdentification(
capture_id=capture.id,
device_id=device.id,
user_id=user.id if user else None,
confidence=1.0,
notes=manual_label.get("notes"),
verified=False,
verification_count=1
)
db.add(manual_id)
log_manual_label(
upload_id=upload_id,
filename=capture.file_path.split('/')[-1],
file_hash=file_hash,
label_type="new_device",
device_id=device.id,
device_info={
"manufacturer": manufacturer,
"model": model,
"device_type": device_type
},
user_id=user.id if user else None
)
# =============================================================================
# CLIENT ERROR REPORTING ENDPOINT
# =============================================================================
@router.post("/captures/report_client_error")
async def report_client_error(
request: Request,
error_data: dict,
user: User = Depends(optional_auth)
):
"""
Endpoint for client-side JavaScript to report errors
Expected payload:
{
"upload_id": "upload_20260115_...",
"error_type": "ParseError",
"error_message": "Failed to read .sub file",
"stack_trace": "...",
"filename": "capture.sub",
"browser_info": {...}
}
"""
from src.logging.upload_logger import log_client_error
upload_id = error_data.get("upload_id", "unknown")
client_info = extract_client_info(request)
client_info.update(error_data.get("browser_info", {}))
log_client_error(
upload_id=upload_id,
error_type=error_data.get("error_type", "UnknownError"),
error_message=error_data.get("error_message", "No message"),
client_info=client_info,
stack_trace=error_data.get("stack_trace")
)
return {"status": "logged"}
+2 -2
View File
@@ -29,8 +29,8 @@ def get_engine():
_engine = create_engine(
database_url,
pool_size=settings.database_pool_size,
max_overflow=settings.database_max_overflow,
pool_size=10,
max_overflow=20,
pool_pre_ping=True, # Verify connections
echo=False # Set to True for SQL logging
)
+4 -1
View File
@@ -63,7 +63,7 @@ class User(Base):
# Relationships
sessions = relationship("Session", back_populates="user")
captures = relationship("Capture", back_populates="user")
identifications = relationship("Identification", back_populates="user")
identifications = relationship("Identification", back_populates="user", foreign_keys="[Identification.user_id]")
votes = relationship("Vote", back_populates="user")
def __repr__(self):
@@ -497,6 +497,9 @@ class FlipperSignature(Base):
# Source
source_file = Column(String(500))
file_hash = Column(String(64), unique=True, index=True) # SHA256 hash for deduplication
file_path = Column(String(500)) # Relative path in dataset
raw_format = Column(String(20)) # KEY, RAW, or BinRAW
imported_at = Column(DateTime, default=datetime.utcnow)
# Relationships
+508
View File
@@ -0,0 +1,508 @@
"""
Robust Upload Error Logging System
Comprehensive logging for file uploads during beta testing.
Captures detailed error context, file metadata, and user actions.
"""
import json
import traceback
from datetime import datetime
from pathlib import Path
from typing import Dict, Any, Optional, List
from enum import Enum
import hashlib
from loguru import logger
class UploadStage(str, Enum):
"""Upload process stages for tracking"""
VALIDATION = "validation"
PARSING = "parsing"
GPS_VALIDATION = "gps_validation"
STORAGE = "storage"
MATCHING = "matching"
DATABASE = "database"
COMPLETE = "complete"
class ErrorSeverity(str, Enum):
"""Error severity levels"""
DEBUG = "debug"
INFO = "info"
WARNING = "warning"
ERROR = "error"
CRITICAL = "critical"
class UploadLogger:
"""
Centralized upload logging with detailed context tracking
"""
def __init__(self, log_dir: str = "logs/uploads"):
self.log_dir = Path(log_dir)
self.log_dir.mkdir(parents=True, exist_ok=True)
# Create separate log files for different purposes
self.setup_loggers()
def setup_loggers(self):
"""Setup specialized loggers for different aspects"""
# Main upload log (all uploads)
logger.add(
self.log_dir / "uploads_{time:YYYY-MM-DD}.log",
rotation="00:00", # New file daily
retention="90 days",
level="DEBUG",
format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{function}:{line} | {message}",
enqueue=True # Thread-safe
)
# Error-only log (failures)
logger.add(
self.log_dir / "upload_errors_{time:YYYY-MM-DD}.log",
rotation="00:00",
retention="180 days", # Keep errors longer
level="ERROR",
format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {extra[upload_id]} | {message}",
enqueue=True
)
# JSON structured log (for analysis)
logger.add(
self.log_dir / "uploads_structured_{time:YYYY-MM-DD}.jsonl",
rotation="00:00",
retention="90 days",
level="INFO",
format="{message}",
serialize=True, # JSON output
enqueue=True
)
# Performance metrics log
logger.add(
self.log_dir / "upload_performance_{time:YYYY-MM-DD}.log",
rotation="00:00",
retention="30 days",
level="INFO",
filter=lambda record: "performance" in record["extra"],
enqueue=True
)
def log_upload_attempt(
self,
upload_id: str,
user_id: Optional[int],
session_id: Optional[str],
file_count: int,
total_size: int,
client_info: Dict[str, Any]
):
"""
Log the start of an upload attempt
Args:
upload_id: Unique upload identifier
user_id: User ID (if authenticated)
session_id: Session UUID
file_count: Number of files in upload
total_size: Total bytes
client_info: Browser/device info
"""
log_entry = {
"event": "upload_attempt",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"user_id": user_id,
"session_id": session_id,
"file_count": file_count,
"total_size_bytes": total_size,
"total_size_mb": round(total_size / 1024 / 1024, 2),
"client_info": client_info
}
logger.bind(upload_id=upload_id).info(
f"Upload attempt started: {file_count} files, {log_entry['total_size_mb']} MB",
**log_entry
)
def log_file_processing(
self,
upload_id: str,
filename: str,
file_size: int,
file_hash: str,
stage: UploadStage,
success: bool,
duration_ms: float,
metadata: Optional[Dict[str, Any]] = None,
error: Optional[str] = None
):
"""
Log individual file processing
Args:
upload_id: Upload batch identifier
filename: Original filename
file_size: File size in bytes
file_hash: SHA256 hash
stage: Processing stage
success: Whether stage succeeded
duration_ms: Processing time in milliseconds
metadata: Extracted signal metadata
error: Error message if failed
"""
log_entry = {
"event": "file_processing",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"filename": filename,
"file_size": file_size,
"file_hash": file_hash,
"stage": stage.value,
"success": success,
"duration_ms": round(duration_ms, 2),
"metadata": metadata or {},
"error": error
}
log_level = "INFO" if success else "ERROR"
message = f"File {filename} - {stage.value}: {'' if success else ''} ({duration_ms:.0f}ms)"
if error:
message += f" - {error}"
logger.bind(upload_id=upload_id).log(log_level, message, **log_entry)
def log_parse_error(
self,
upload_id: str,
filename: str,
file_content_sample: str,
error_type: str,
error_message: str,
traceback_str: str,
file_hash: Optional[str] = None
):
"""
Log detailed parse error for debugging
Args:
upload_id: Upload identifier
filename: File that failed to parse
file_content_sample: First 500 chars of file
error_type: Exception type
error_message: Error description
traceback_str: Full traceback
file_hash: File hash if available
"""
log_entry = {
"event": "parse_error",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"filename": filename,
"file_hash": file_hash,
"error_type": error_type,
"error_message": error_message,
"file_content_sample": file_content_sample[:500], # First 500 chars
"traceback": traceback_str
}
logger.bind(upload_id=upload_id).error(
f"Parse failed: {filename} - {error_type}: {error_message}",
**log_entry
)
# Also save problematic file sample to disk for analysis
self._save_problem_file_sample(upload_id, filename, file_content_sample)
def log_gps_validation_error(
self,
upload_id: str,
filename: str,
latitude: float,
longitude: float,
accuracy: Optional[float],
reason: str
):
"""Log GPS validation failures"""
log_entry = {
"event": "gps_validation_error",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"filename": filename,
"latitude": latitude,
"longitude": longitude,
"accuracy": accuracy,
"reason": reason
}
logger.bind(upload_id=upload_id).warning(
f"GPS validation failed: {filename} - {reason}",
**log_entry
)
def log_matching_results(
self,
upload_id: str,
filename: str,
file_hash: str,
matches: List[Dict[str, Any]],
duration_ms: float,
matcher_stats: Dict[str, Any]
):
"""
Log device matching results
Args:
upload_id: Upload identifier
filename: File being matched
file_hash: File hash
matches: List of match results with confidence scores
duration_ms: Matching duration
matcher_stats: Statistics from matcher (strategies used, etc.)
"""
log_entry = {
"event": "matching_results",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"filename": filename,
"file_hash": file_hash,
"match_count": len(matches),
"top_match": matches[0] if matches else None,
"top_confidence": matches[0]["confidence"] if matches else 0.0,
"all_matches": matches,
"duration_ms": round(duration_ms, 2),
"matcher_stats": matcher_stats
}
if matches:
top = matches[0]
logger.bind(upload_id=upload_id).info(
f"Matched {filename}: {top.get('manufacturer', 'Unknown')} {top.get('model', 'Unknown')} "
f"({top['confidence']*100:.0f}% confidence)",
**log_entry
)
else:
logger.bind(upload_id=upload_id).warning(
f"No matches found for {filename}",
**log_entry
)
def log_manual_label(
self,
upload_id: str,
filename: str,
file_hash: str,
label_type: str,
device_id: Optional[int],
device_info: Dict[str, Any],
user_id: Optional[int]
):
"""Log manual device labeling"""
log_entry = {
"event": "manual_label",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"filename": filename,
"file_hash": file_hash,
"label_type": label_type,
"device_id": device_id,
"device_info": device_info,
"user_id": user_id
}
logger.bind(upload_id=upload_id).info(
f"Manual label: {filename}{device_info.get('manufacturer', 'Unknown')} "
f"{device_info.get('model', 'Unknown')} ({label_type})",
**log_entry
)
def log_upload_complete(
self,
upload_id: str,
total_duration_ms: float,
successful_count: int,
failed_count: int,
duplicate_count: int,
summary: Dict[str, Any]
):
"""Log upload batch completion"""
log_entry = {
"event": "upload_complete",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"total_duration_ms": round(total_duration_ms, 2),
"total_duration_sec": round(total_duration_ms / 1000, 2),
"successful_count": successful_count,
"failed_count": failed_count,
"duplicate_count": duplicate_count,
"success_rate": round(successful_count / (successful_count + failed_count) * 100, 1) if (successful_count + failed_count) > 0 else 0,
"summary": summary
}
logger.bind(upload_id=upload_id, performance=True).info(
f"Upload complete: {successful_count} success, {failed_count} failed, "
f"{duplicate_count} duplicates ({log_entry['total_duration_sec']}s)",
**log_entry
)
def log_exception(
self,
upload_id: str,
stage: UploadStage,
exception: Exception,
context: Dict[str, Any]
):
"""
Log unhandled exceptions with full context
Args:
upload_id: Upload identifier
stage: Stage where exception occurred
exception: The exception object
context: Additional context (filename, user, etc.)
"""
log_entry = {
"event": "exception",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"stage": stage.value,
"exception_type": type(exception).__name__,
"exception_message": str(exception),
"traceback": traceback.format_exc(),
"context": context
}
logger.bind(upload_id=upload_id).error(
f"Exception in {stage.value}: {type(exception).__name__}: {str(exception)}",
**log_entry
)
def log_performance_metrics(
self,
upload_id: str,
metrics: Dict[str, float]
):
"""
Log performance metrics for analysis
Args:
upload_id: Upload identifier
metrics: Dictionary of timing metrics
- parse_time_ms
- matching_time_ms
- storage_time_ms
- db_time_ms
- total_time_ms
"""
log_entry = {
"event": "performance_metrics",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
**{k: round(v, 2) for k, v in metrics.items()}
}
logger.bind(upload_id=upload_id, performance=True).info(
f"Performance: parse={metrics.get('parse_time_ms', 0):.0f}ms, "
f"match={metrics.get('matching_time_ms', 0):.0f}ms, "
f"total={metrics.get('total_time_ms', 0):.0f}ms",
**log_entry
)
def log_client_error(
self,
upload_id: str,
error_type: str,
error_message: str,
client_info: Dict[str, Any],
stack_trace: Optional[str] = None
):
"""
Log client-side errors reported from browser
Args:
upload_id: Upload identifier
error_type: JS error type
error_message: Error message
client_info: Browser/device info
stack_trace: JS stack trace
"""
log_entry = {
"event": "client_error",
"upload_id": upload_id,
"timestamp": datetime.utcnow().isoformat(),
"error_type": error_type,
"error_message": error_message,
"client_info": client_info,
"stack_trace": stack_trace
}
logger.bind(upload_id=upload_id).error(
f"Client error: {error_type}: {error_message}",
**log_entry
)
def _save_problem_file_sample(
self,
upload_id: str,
filename: str,
content_sample: str
):
"""Save problematic file samples for debugging"""
problem_dir = self.log_dir / "problem_files" / upload_id
problem_dir.mkdir(parents=True, exist_ok=True)
# Save sample content
sample_file = problem_dir / f"{filename}.txt"
sample_file.write_text(content_sample[:1000]) # First 1000 chars
logger.debug(f"Saved problem file sample: {sample_file}")
# Global logger instance
upload_logger = UploadLogger()
# Convenience functions
def log_upload_attempt(*args, **kwargs):
return upload_logger.log_upload_attempt(*args, **kwargs)
def log_file_processing(*args, **kwargs):
return upload_logger.log_file_processing(*args, **kwargs)
def log_parse_error(*args, **kwargs):
return upload_logger.log_parse_error(*args, **kwargs)
def log_gps_validation_error(*args, **kwargs):
return upload_logger.log_gps_validation_error(*args, **kwargs)
def log_matching_results(*args, **kwargs):
return upload_logger.log_matching_results(*args, **kwargs)
def log_manual_label(*args, **kwargs):
return upload_logger.log_manual_label(*args, **kwargs)
def log_upload_complete(*args, **kwargs):
return upload_logger.log_upload_complete(*args, **kwargs)
def log_exception(*args, **kwargs):
return upload_logger.log_exception(*args, **kwargs)
def log_performance_metrics(*args, **kwargs):
return upload_logger.log_performance_metrics(*args, **kwargs)
def log_client_error(*args, **kwargs):
return upload_logger.log_client_error(*args, **kwargs)
+9 -1
View File
@@ -312,6 +312,13 @@ REMOTE_CONTROLS = [
]
# Import RTL_433 protocols
try:
from src.matcher.rtl433_protocols_imported import RTL433_PROTOCOLS
except ImportError:
print("Warning: RTL_433 protocols not imported yet. Run scripts/import_rtl433_protocols.py")
RTL433_PROTOCOLS = []
# Compile all protocols into single list
ALL_PROTOCOLS = (
WEATHER_SENSORS +
@@ -319,7 +326,8 @@ ALL_PROTOCOLS = (
DOORBELLS +
TIRE_PRESSURE +
SECURITY_SENSORS +
REMOTE_CONTROLS
REMOTE_CONTROLS +
RTL433_PROTOCOLS # Imported from RTL_433 database
)
File diff suppressed because it is too large Load Diff