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test / behavior_backend /app /services /processing /processing_service.py

Add backend application and Dockerfile

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from fastapi import BackgroundTasks
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) 

	from fastapi import BackgroundTasks
	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)