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from sqlalchemy.orm import Session
import json
import logging
import asyncio
from app.db.repositories.video import VideoRepository
from app.db.repositories.results import ResultsRepository
from app.models.processing import ProcessingRequest, ProcessingStatus
from app.services.processing.video_processor import process_video
from app.core.exceptions import VideoNotFoundError, ResultNotFoundError, VideoProcessingError
from app.utils.logging_utils import setup_logger
from app.db.base import SessionLocal
# Configure logging
logger = setup_logger(__name__)
class ProcessingService:
"""Service for video processing operations."""
def __init__(self, db: Session):
self.db = db
self.video_repo = VideoRepository(db)
self.results_repo = ResultsRepository(db)
async def process_video(self, request: ProcessingRequest, background_tasks: BackgroundTasks) -> ProcessingStatus:
"""
Process a video.
Args:
request: Processing request parameters
background_tasks: FastAPI background tasks
Returns:
ProcessingStatus object
Raises:
VideoNotFoundError: If the video is not found
VideoProcessingError: If there is an error processing the video
"""
video_id = request.video_id
# Check if video exists
db_video = self.video_repo.get_by_id(video_id)
if not db_video:
raise VideoNotFoundError(video_id)
try:
# Update status
self.video_repo.update_status(video_id, "processing")
# Get model name from request or use default
model_name = getattr(request, 'model_name', "gpt-4o")
# Start processing in background
background_tasks.add_task(
self._process_video_task,
video_id=video_id,
video_path=db_video.file_path,
frame_rate=request.frame_rate,
backend=request.backend,
language=request.language,
generate_annotated_video=request.generate_annotated_video,
model_name=model_name
)
return ProcessingStatus(
video_id=video_id,
status="processing"
)
except Exception as e:
logger.error(f"Error processing video {video_id}: {str(e)}")
self.video_repo.update_status(video_id, "failed")
raise VideoProcessingError(f"Error processing video: {str(e)}")
def get_processing_status(self, video_id: str) -> ProcessingStatus:
"""
Get the processing status of a video.
Args:
video_id: ID of the video
Returns:
ProcessingStatus object
Raises:
VideoNotFoundError: If the video is not found
"""
db_video = self.video_repo.get_by_id(video_id)
if not db_video:
raise VideoNotFoundError(video_id)
return ProcessingStatus(
video_id=video_id,
status=db_video.status
)
def get_processing_results(self, video_id: str) -> dict:
"""
Get the processing results of a video.
Args:
video_id: ID of the video
Returns:
Dictionary with processing results
Raises:
VideoNotFoundError: If the video is not found
ResultNotFoundError: If the processing result is not found
"""
# Get the video
video = self.video_repo.get_by_id(video_id)
if not video:
raise VideoNotFoundError(video_id)
# Get the processing result
result = self.results_repo.get_by_video_id(video_id)
if not result:
return {
"status": video.status,
"message": "No processing results available yet"
}
# Prepare response
response = {
"status": video.status,
"processing_date": result.processing_date.isoformat(),
"transcript": result.transcript,
"emotion_analysis": result.emotion_analysis,
"overall_summary": result.overall_summary,
"transcript_analysis": result.transcript_analysis,
"recommendations": result.recommendations,
"body_language_analysis": result.body_language_analysis,
"body_language_data": result.body_language_data,
"eye_contact_analysis": result.eye_contact_analysis,
"eye_contact_data": result.eye_contact_data,
"face_analysis_data": result.face_analysis_data
}
return response
async def _process_video_task(
self,
video_id: str,
video_path: str,
frame_rate: int,
backend: str,
language: str,
generate_annotated_video: bool,
model_name: str = "gpt-4o"
):
"""
Background task to process a video.
Args:
video_id: ID of the video
video_path: Path to the video file
frame_rate: Frame rate for processing
backend: Backend for face detection
language: Language of the video
generate_annotated_video: Whether to generate an annotated video
model_name: The name of the model to use for AI analysis (default: gpt-4o)
"""
try:
# Force mediapipe backend for best GPU performance on Mac M3
if backend == 'opencv' or not backend or backend == "retinaface":
logger.info(f"Backend '{backend}' doesn't support GPU acceleration or is not recommended.")
logger.info(f"Switching to 'mediapipe' for GPU-accelerated frame analysis.")
backend = "mediapipe"
# Ensure we're using a GPU-compatible backend
if backend not in ['mediapipe', 'ssd', 'mtcnn']:
logger.info(f"Backend '{backend}' may not support GPU acceleration.")
logger.info(f"Consider using 'mediapipe' for best GPU performance on Mac M3.")
logger.info(f"Starting video processing task for {video_id}")
logger.info(f"Video path: {video_path}")
logger.info(f"Frame rate: {frame_rate}")
logger.info(f"Backend: {backend}")
logger.info(f"Language: {language}")
logger.info(f"Generate annotated video: {generate_annotated_video}")
logger.info(f"Model name for analysis: {model_name}")
logger.info(f"Offloading video processing for {video_id} to a separate thread.")
# Process the video in a separate thread to avoid blocking the event loop
transcript, analysis = await asyncio.to_thread(
process_video, # The synchronous, CPU-bound function
video_path=video_path,
frame_rate=frame_rate,
backend=backend,
language=language,
generate_annotated_video=generate_annotated_video,
video_id=video_id,
status_callback=lambda progress: self._update_progress(video_id, progress),
model_name=model_name
)
logger.info(f"Threaded video processing for {video_id} completed.")
# Parse the comprehensive analysis
try:
analysis_data = json.loads(analysis) if analysis else {}
logger.info(f"Received analysis data: {analysis_data}")
# Log the keys for debugging
logger.info(f"Keys in analysis_data: {list(analysis_data.keys())}")
# Extract data from the comprehensive analysis
emotion_analysis = analysis_data.get("Emotion Analysis", {})
overall_summary = analysis_data.get("Overall Summary", "")
transcript_analysis = analysis_data.get("Transcript Analysis", {})
recommendations = analysis_data.get("Recommendations", {})
body_language_analysis = analysis_data.get("Body Language Analysis", {})
eye_contact_analysis = analysis_data.get("Eye Contact Analysis", {})
# Try both capitalized and non-capitalized versions since the format may vary
eye_contact_data = analysis_data.get("eye_contact_analysis", {})
body_language_data = analysis_data.get("body_language_analysis", {})
face_analysis_data = analysis_data.get("face_analysis", {})
# Check if data exists under any key - key names might be inconsistent
if not eye_contact_data and "eye_contact_analysis" in str(analysis_data).lower():
logger.info(f"Searching for eye_contact_data in analysis_data string representation")
for key in analysis_data.keys():
if "eye" in key.lower() and "contact" in key.lower():
logger.info(f"Found potential eye contact key: {key}")
eye_contact_data = analysis_data.get(key, {})
break
if not body_language_data and "body_language_analysis" in str(analysis_data).lower():
logger.info(f"Searching for body_language_data in analysis_data string representation")
for key in analysis_data.keys():
if "body" in key.lower() and "language" in key.lower():
logger.info(f"Found potential body language key: {key}")
body_language_data = analysis_data.get(key, {})
break
if not face_analysis_data and "face_analysis" in str(analysis_data).lower():
logger.info(f"Searching for face_analysis_data in analysis_data string representation")
for key in analysis_data.keys():
if "face" in key.lower() and "analysis" in key.lower():
logger.info(f"Found potential face analysis key: {key}")
face_analysis_data = analysis_data.get(key, {})
break
logger.info(f"Parsed analysis data: {emotion_analysis, overall_summary, transcript_analysis, recommendations, body_language_analysis}")
logger.info(f"Parsed eye contact data: {eye_contact_data}")
logger.info(f"Parsed body language data: {body_language_data}")
logger.info(f"Parsed face analysis data: {face_analysis_data}")
except Exception as e:
logger.error(f"Error parsing analysis JSON: {e}")
emotion_analysis = {}
overall_summary = ""
transcript_analysis = {}
recommendations = {}
body_language_data = {}
body_language_analysis = {}
eye_contact_data = {}
eye_contact_analysis = {}
face_analysis_data = {}
# Save results to database
self.results_repo.create(
video_id=video_id,
transcript=transcript or "",
emotion_analysis=emotion_analysis,
overall_summary=overall_summary,
transcript_analysis=transcript_analysis,
recommendations=recommendations,
body_language_analysis=body_language_analysis,
body_language_data=body_language_data,
eye_contact_analysis=eye_contact_analysis,
eye_contact_data=eye_contact_data,
face_analysis_data=face_analysis_data
)
# Update video status
self.video_repo.update_status(video_id, "completed")
logger.info(f"Video {video_id} processing completed successfully")
except Exception as e:
# Update status on error
logger.error(f"Error processing video {video_id}: {str(e)}")
self.video_repo.update_status(video_id, "failed")
def _update_progress(self, video_id: str, progress: float):
"""
Update the processing progress of a video.
Args:
video_id: ID of the video
progress: Processing progress (0-100)
"""
self.video_repo.update_progress(video_id, progress) |