anthropic-skills/backend/app/celery/tasks.py

import tempfile
import os
import asyncio
from datetime import datetime
from typing import Optional

from app.celery import celery_app
from app.analysis.score_following import ScoreFollower
from app.analysis.metrics import PerformanceMetrics
from app.analysis.measure_analysis import MeasureAnalyzer
from app.schemas.analysis import AnalysisStatus

# This will be imported when storage_service is available
# from app.services.storage_service import storage_service

# In-memory store for analysis jobs (should be replaced with Redis/DB in production)
analysis_jobs = {}

@celery_app.task(bind=True, name="analyze_performance_task")
def analyze_performance_task(self, job_id: str, score_midi_url: str,
                           performance_midi_url: str, user_id: Optional[str] = None,
                           session_id: Optional[str] = None, analysis_type: str = "full"):
    """
    Celery task for comprehensive performance analysis
    """
    current_task = self

    current_task.update_state(
        state='PROCESSING',
        meta={'current': 0, 'total': 100, 'status': 'Downloading files...'}
    )

    try:
        # Download MIDI files
        current_task.update_state(
            state='PROCESSING',
            meta={'current': 20, 'total': 100, 'status': 'Downloading score MIDI...'}
        )

        # TODO: Implement file download when storage_service is available
        # score_content = asyncio.run(storage_service.download_file(score_midi_url))
        # For now, assume local files
        score_path = score_midi_url

        current_task.update_state(
            state='PROCESSING',
            meta={'current': 40, 'total': 100, 'status': 'Downloading performance MIDI...'}
        )

        # performance_content = asyncio.run(storage_service.download_file(performance_midi_url))
        performance_path = performance_midi_url

        # Initialize analysis components
        current_task.update_state(
            state='PROCESSING',
            meta={'current': 60, 'total': 100, 'status': 'Initializing analysis...'}
        )

        score_follower = ScoreFollower(score_midi_path=score_path)
        performance_notes = score_follower._load_score_notes(performance_path)

        # Perform alignment
        current_task.update_state(
            state='PROCESSING',
            meta={'current': 70, 'total': 100, 'status': 'Aligning performance with score...'}
        )

        alignment_result = score_follower.align_performance(performance_notes)

        if not alignment_result['success']:
            raise Exception(f"Alignment failed: {alignment_result.get('error', 'Unknown error')}")

        # Compute metrics
        current_task.update_state(
            state='PROCESSING',
            meta={'current': 80, 'total': 100, 'status': 'Computing performance metrics...'}
        )

        metrics_calculator = PerformanceMetrics(
            score_follower.score_notes,
            performance_notes
        )

        overall_assessment = metrics_calculator.compute_overall_assessment(
            alignment_result['alignment']
        )

        # Measure-level analysis
        current_task.update_state(
            state='PROCESSING',
            meta={'current': 90, 'total': 100, 'status': 'Analyzing measure performance...'}
        )

        measure_analyzer = MeasureAnalyzer(score_follower.score_notes)
        measure_results = measure_analyzer.analyze_measure_performance(
            performance_notes, alignment_result['alignment']
        )

        problem_measures = measure_analyzer.get_problem_measures(measure_results)

        # Prepare final result
        final_result = {
            'job_id': job_id,
            'performance_metrics': overall_assessment,
            'measure_metrics': measure_results,
            'problem_measures': problem_measures,
            'alignment_data': alignment_result,
            'success': True
        }

        # Update job store
        analysis_jobs[job_id] = {
            "status": AnalysisStatus.COMPLETED,
            "result": final_result,
            "created_at": datetime.utcnow()
        }

        current_task.update_state(
            state='SUCCESS',
            meta={'current': 100, 'total': 100, 'status': 'Analysis completed'}
        )

        return final_result

    except Exception as e:
        error_result = {
            'job_id': job_id,
            'success': False,
            'error': str(e)
        }

        analysis_jobs[job_id] = {
            "status": AnalysisStatus.FAILED,
            "result": error_result,
            "created_at": datetime.utcnow()
        }

        current_task.update_state(
            state='FAILURE',
            meta={'current': 0, 'total': 100, 'status': f'Failed: {str(e)}'}
        )

        return error_result


@celery_app.task(name="process_transcription_task")
def process_transcription_task(job_id: str, audio_url: str):
    """
    Celery task for audio transcription
    Placeholder for Feature 3 integration
    """
    # TODO: Implement when transcription service is available
    pass


@celery_app.task(name="generate_report_task")
def generate_report_task(session_id: str, report_type: str = "pdf"):
    """
    Celery task for report generation
    Placeholder for Feature 5 integration
    """
    # TODO: Implement when report generation is available
    pass