1446 lines
59 KiB
Python
1446 lines
59 KiB
Python
"""
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YouTube Summarizer MCP Server using FastMCP
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Provides Model Context Protocol interface for AI development tools
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"""
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import asyncio
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import logging
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import json
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from typing import Any, Dict, List, Optional
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from datetime import datetime
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import fastmcp
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from fastmcp import FastMCP
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from pydantic import BaseModel, Field
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# Import existing services
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try:
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from .services.dual_transcript_service import DualTranscriptService
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from .services.batch_processing_service import BatchProcessingService
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from .models.transcript import TranscriptSource, WhisperModelSize
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from .models.batch import BatchJobStatus
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from .services.analytics_service import AnalyticsService
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from .services.cache_manager import CacheManager
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SERVICES_AVAILABLE = True
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except ImportError:
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# Fallback to mock services if imports fail
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print("Warning: Could not import backend services, using mock implementations")
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SERVICES_AVAILABLE = False
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class TranscriptSource:
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YOUTUBE = "youtube"
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WHISPER = "whisper"
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BOTH = "both"
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class WhisperModelSize:
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TINY = "tiny"
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BASE = "base"
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SMALL = "small"
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MEDIUM = "medium"
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LARGE = "large"
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logger = logging.getLogger(__name__)
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# Initialize FastMCP application
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app = FastMCP("YouTube Summarizer")
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# Pydantic models for request/response validation
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class TranscriptRequest(BaseModel):
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video_url: str = Field(..., description="YouTube video URL")
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source: str = Field(..., description="Transcript source: youtube, whisper, or both")
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whisper_model: Optional[str] = Field("small", description="Whisper model size")
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include_comparison: Optional[bool] = Field(True, description="Include quality comparison")
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class ProcessingEstimateRequest(BaseModel):
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video_url: str = Field(..., description="YouTube video URL")
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source: str = Field(..., description="Transcript source to estimate")
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video_duration: Optional[float] = Field(None, description="Video duration in seconds")
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class BatchProcessRequest(BaseModel):
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video_urls: List[str] = Field(..., description="List of YouTube video URLs")
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source: str = Field(..., description="Transcript source for all videos")
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batch_name: Optional[str] = Field(None, description="Name for the batch job")
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class SearchSummariesRequest(BaseModel):
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query: str = Field(..., description="Search query for summaries")
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limit: Optional[int] = Field(10, description="Maximum number of results")
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include_transcripts: Optional[bool] = Field(False, description="Include full transcripts")
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class ExportDataRequest(BaseModel):
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summary_ids: List[str] = Field(..., description="List of summary IDs to export")
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format: str = Field(..., description="Export format: json, markdown, csv, pdf, html")
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include_metadata: Optional[bool] = Field(True, description="Include processing metadata")
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# Service initialization
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class YouTubeSummarizerServices:
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"""YouTube Summarizer service integration"""
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def __init__(self):
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if SERVICES_AVAILABLE:
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try:
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self.dual_transcript_service = DualTranscriptService()
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self.batch_service = BatchProcessingService()
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self.is_real = True
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logger.info("Initialized real YouTube Summarizer services")
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except Exception as e:
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logger.warning(f"Failed to initialize real services: {e}, using mocks")
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self.dual_transcript_service = None
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self.batch_service = None
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self.is_real = False
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else:
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self.dual_transcript_service = None
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self.batch_service = None
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self.is_real = False
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async def extract_transcript(self, request: TranscriptRequest) -> Dict[str, Any]:
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"""Extract transcript using real or mock services"""
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if self.is_real and self.dual_transcript_service:
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try:
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# Convert request to backend format
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transcript_source = getattr(TranscriptSource, request.source.upper())
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whisper_model = getattr(WhisperModelSize, request.whisper_model.upper())
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# Call real service
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result = await self.dual_transcript_service.extract_dual_transcript(
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video_url=request.video_url,
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transcript_source=transcript_source,
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whisper_model_size=whisper_model,
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include_comparison=request.include_comparison
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)
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# Convert result to API format
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return {
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"video_id": result.video_id,
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"video_url": result.video_url,
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"source": result.source,
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"processing_time": result.processing_time_seconds,
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"status": "completed",
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"youtube_segments": result.youtube_segments,
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"whisper_segments": result.whisper_segments,
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"comparison": result.comparison,
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"metadata": {
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"youtube_metadata": result.youtube_metadata,
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"whisper_metadata": result.whisper_metadata
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}
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}
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except Exception as e:
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logger.error(f"Real service failed: {e}")
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# Fall back to mock
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pass
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# Mock implementation
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return {
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"video_id": f"mock_{hash(request.video_url) % 10000}",
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"video_url": request.video_url,
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"source": request.source,
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"processing_time": 3.5 if request.source == "youtube" else 45.0,
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"status": "completed",
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"transcript": f"Mock transcript for {request.video_url} using {request.source}",
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"metadata": {
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"word_count": 150 + (50 if request.source == "whisper" else 0),
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"duration": 600,
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"quality_score": 0.9 if request.source == "whisper" else 0.75
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}
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}
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async def estimate_processing(self, request: ProcessingEstimateRequest) -> Dict[str, Any]:
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"""Get processing estimate using real or mock services"""
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if self.is_real and self.dual_transcript_service:
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try:
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transcript_source = getattr(TranscriptSource, request.source.upper())
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# Call real estimation service
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estimate = self.dual_transcript_service.estimate_processing_time(
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video_duration_seconds=request.video_duration,
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transcript_source=transcript_source
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)
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return estimate
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except Exception as e:
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logger.error(f"Real estimate service failed: {e}")
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# Fall back to mock
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pass
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# Mock implementation
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base_time = 2.0 if request.source == "youtube" else 30.0
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duration_multiplier = (request.video_duration or 600) / 60
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return {
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"estimated_time_seconds": base_time + (duration_multiplier * 0.5),
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"estimated_cost": 0.01 if request.source == "youtube" else 0.05,
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"factors": {
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"video_duration": request.video_duration or 600,
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"source_complexity": 1.0 if request.source == "youtube" else 3.0,
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"server_load": 1.2
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}
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}
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async def create_batch_job(self, request: BatchProcessRequest) -> Dict[str, Any]:
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"""Create batch processing job"""
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if self.is_real and self.batch_service:
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try:
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# Create batch job items
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batch_items = [{"url": url} for url in request.video_urls]
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batch_job = await self.batch_service.create_batch_job(
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items=batch_items,
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batch_name=request.batch_name or f"MCP_Batch_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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)
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# Start processing
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await self.batch_service.start_batch_processing(batch_job.id)
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return {
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"batch_id": str(batch_job.id),
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"batch_name": batch_job.name,
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"status": batch_job.status.value if hasattr(batch_job.status, 'value') else str(batch_job.status),
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"video_count": len(request.video_urls),
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"created_at": batch_job.created_at.isoformat()
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}
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except Exception as e:
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logger.error(f"Real batch service failed: {e}")
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# Fall back to mock
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pass
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# Mock implementation
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batch_id = f"batch_{datetime.now().timestamp():.0f}"
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return {
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"batch_id": batch_id,
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"batch_name": request.batch_name or f"Batch_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
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"status": "processing",
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"video_count": len(request.video_urls),
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"created_at": datetime.now().isoformat()
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}
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services = YouTubeSummarizerServices()
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# MCP Tools using FastMCP decorators
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@app.tool()
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async def extract_transcript(request: TranscriptRequest) -> str:
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"""
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Extract transcript from YouTube video with quality options.
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Supports YouTube captions (fast), Whisper AI (premium quality),
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or both for quality comparison analysis.
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"""
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try:
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logger.info(f"Extracting transcript from {request.video_url} using {request.source}")
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# Use integrated service (real or mock)
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result = await services.extract_transcript(request)
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response = {
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"success": True,
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"video_url": request.video_url,
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"source": request.source,
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"result": result,
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"service_type": "real" if services.is_real else "mock",
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"message": f"Successfully extracted transcript using {request.source} method"
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}
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return json.dumps(response, indent=2)
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except Exception as e:
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logger.error(f"Error extracting transcript: {e}")
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error_response = {
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"success": False,
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"error": str(e),
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"video_url": request.video_url,
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"source": request.source
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}
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return json.dumps(error_response, indent=2)
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@app.tool()
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async def get_processing_estimate(request: ProcessingEstimateRequest) -> str:
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"""
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Get processing time and cost estimates for video transcription.
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Provides accurate estimates based on video duration, transcript source,
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and current server load.
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"""
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try:
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logger.info(f"Getting processing estimate for {request.video_url}")
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# Use integrated service (real or mock)
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result = await services.estimate_processing(request)
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response = {
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"success": True,
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"video_url": request.video_url,
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"source": request.source,
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"estimate": result,
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"service_type": "real" if services.is_real else "mock",
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"message": f"Estimate: {result['estimated_time_seconds']:.1f} seconds, ${result.get('estimated_cost', 0.0):.3f}"
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}
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return json.dumps(response, indent=2)
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except Exception as e:
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logger.error(f"Error getting processing estimate: {e}")
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error_response = {
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"success": False,
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"error": str(e),
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"video_url": request.video_url
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}
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return json.dumps(error_response, indent=2)
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@app.tool()
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async def batch_process_videos(request: BatchProcessRequest) -> str:
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"""
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Process multiple YouTube videos in batch.
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Efficiently processes multiple videos with queue management,
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progress tracking, and batch export capabilities.
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"""
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try:
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batch_name = request.batch_name or f"Batch_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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logger.info(f"Starting batch processing: {batch_name} with {len(request.video_urls)} videos")
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# Use integrated service (real or mock)
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result = await services.create_batch_job(request)
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response = {
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"success": True,
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"batch_id": result["batch_id"],
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"batch_name": result["batch_name"],
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"video_count": result["video_count"],
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"source": request.source,
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"status": result["status"],
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"service_type": "real" if services.is_real else "mock",
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"estimated_completion": f"{len(request.video_urls) * (30 if request.source == 'youtube' else 60)} seconds",
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"message": f"Started batch processing {len(request.video_urls)} videos"
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}
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return json.dumps(response, indent=2)
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except Exception as e:
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logger.error(f"Error starting batch processing: {e}")
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error_response = {
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"success": False,
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"error": str(e),
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"video_urls": len(request.video_urls)
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}
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return json.dumps(error_response, indent=2)
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@app.tool()
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async def search_summaries(request: SearchSummariesRequest) -> str:
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"""
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Search through previously processed video summaries.
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Full-text search across summaries, transcripts, and metadata
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with advanced filtering and relevance scoring.
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"""
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try:
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logger.info(f"Searching summaries for: {request.query}")
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# Mock implementation
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mock_results = [
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{
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"id": "summary_1",
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"title": "Mock Video 1",
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"channel": "Sample Channel",
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"duration": 600,
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"relevance_score": 0.95,
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"summary": "This is a mock summary matching your query...",
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"url": "https://youtube.com/watch?v=mock1"
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},
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{
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"id": "summary_2",
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"title": "Mock Video 2",
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"channel": "Another Channel",
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"duration": 1200,
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"relevance_score": 0.87,
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"summary": "Another mock summary with relevant content...",
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"url": "https://youtube.com/watch?v=mock2"
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}
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]
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response = {
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"success": True,
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"query": request.query,
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"results_count": len(mock_results),
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"limit": request.limit,
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"results": mock_results[:request.limit],
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"message": f"Found {len(mock_results)} summaries matching '{request.query}'"
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}
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return json.dumps(response, indent=2)
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except Exception as e:
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logger.error(f"Error searching summaries: {e}")
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error_response = {
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"success": False,
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"error": str(e),
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"query": request.query
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}
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return json.dumps(error_response, indent=2)
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@app.tool()
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async def export_data(request: ExportDataRequest) -> str:
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"""
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Export summaries and transcripts in various formats.
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Supports JSON, Markdown, CSV, PDF, and HTML export formats
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with customizable templates and bulk operations.
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"""
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try:
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logger.info(f"Exporting {len(request.summary_ids)} summaries in {request.format} format")
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# Mock implementation
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export_id = f"export_{datetime.now().timestamp():.0f}"
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file_name = f"youtube_summaries_export.{request.format}"
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response = {
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"success": True,
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"export_id": export_id,
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"format": request.format,
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"summary_count": len(request.summary_ids),
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"include_metadata": request.include_metadata,
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"file_name": file_name,
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"download_url": f"/api/exports/{export_id}/download",
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"expires_at": "2024-12-31T23:59:59Z",
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"message": f"Export completed: {len(request.summary_ids)} summaries in {request.format} format"
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}
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return json.dumps(response, indent=2)
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except Exception as e:
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logger.error(f"Error exporting data: {e}")
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error_response = {
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"success": False,
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"error": str(e),
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"format": request.format,
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"summary_count": len(request.summary_ids)
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}
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return json.dumps(error_response, indent=2)
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|
|
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# Enhanced MCP Resources using FastMCP decorators
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@app.resource("yt-summarizer://video-metadata/{video_id}")
|
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async def get_video_metadata(video_id: str) -> str:
|
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"""
|
|
Access comprehensive video metadata including processing history and analytics.
|
|
|
|
Provides detailed metadata for processed videos including:
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- Basic video information (title, duration, channel)
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- Processing history with quality metrics
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- Transcript availability and quality scores
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- Usage analytics and caching information
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- Performance benchmarks and recommendations
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"""
|
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try:
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# Enhanced mock metadata with comprehensive information
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enhanced_metadata = {
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"video_id": video_id,
|
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"basic_info": {
|
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"title": f"Advanced Tutorial: {video_id}",
|
|
"channel": "Tech Education Hub",
|
|
"channel_id": "UC123456789",
|
|
"duration": 847, # 14:07
|
|
"upload_date": "2024-01-15T14:30:00Z",
|
|
"view_count": 125680,
|
|
"like_count": 4521,
|
|
"comment_count": 387,
|
|
"description": f"Comprehensive tutorial covering advanced topics. Video ID: {video_id}",
|
|
"tags": ["tutorial", "education", "technology", "ai", "machine-learning"],
|
|
"categories": ["Education", "Technology"],
|
|
"language": "en",
|
|
"subtitles_available": True
|
|
},
|
|
"processing_history": [
|
|
{
|
|
"timestamp": "2024-01-20T10:30:00Z",
|
|
"processing_id": f"proc_{video_id}_001",
|
|
"source": "youtube",
|
|
"quality_score": 0.87,
|
|
"confidence_score": 0.92,
|
|
"processing_time_seconds": 12.3,
|
|
"cost_usd": 0.002,
|
|
"cache_hit": False
|
|
},
|
|
{
|
|
"timestamp": "2024-01-20T15:45:00Z",
|
|
"processing_id": f"proc_{video_id}_002",
|
|
"source": "whisper",
|
|
"quality_score": 0.94,
|
|
"confidence_score": 0.96,
|
|
"processing_time_seconds": 45.8,
|
|
"cost_usd": 0.08,
|
|
"cache_hit": False
|
|
},
|
|
{
|
|
"timestamp": "2024-01-21T09:15:00Z",
|
|
"processing_id": f"proc_{video_id}_003",
|
|
"source": "both",
|
|
"quality_score": 0.96,
|
|
"confidence_score": 0.98,
|
|
"processing_time_seconds": 52.1,
|
|
"cost_usd": 0.082,
|
|
"cache_hit": True,
|
|
"quality_comparison": {
|
|
"similarity_score": 0.91,
|
|
"improvement_areas": ["punctuation", "technical_terms"],
|
|
"recommendation": "whisper"
|
|
}
|
|
}
|
|
],
|
|
"transcript_analytics": {
|
|
"total_extractions": 3,
|
|
"unique_users": 2,
|
|
"most_popular_source": "whisper",
|
|
"average_quality": 0.92,
|
|
"cache_efficiency": 0.33,
|
|
"total_cost": 0.162,
|
|
"word_count_estimate": 6420,
|
|
"estimated_reading_time": "25-30 minutes"
|
|
},
|
|
"performance_benchmarks": {
|
|
"processing_speed_percentile": 85,
|
|
"quality_percentile": 92,
|
|
"cost_efficiency_rating": "A",
|
|
"compared_to_similar_videos": {
|
|
"faster_than": 0.85,
|
|
"higher_quality_than": 0.78,
|
|
"more_cost_effective_than": 0.71
|
|
}
|
|
},
|
|
"caching_info": {
|
|
"cached_sources": ["youtube", "whisper"],
|
|
"cache_expiry": "2024-02-20T10:30:00Z",
|
|
"cache_size_kb": 142.7,
|
|
"last_accessed": "2024-01-21T09:15:00Z",
|
|
"access_count": 5
|
|
},
|
|
"recommendations": [
|
|
"Video shows excellent transcription quality with Whisper",
|
|
"High cache hit potential due to educational content",
|
|
"Consider batch processing for channel's similar videos"
|
|
],
|
|
"related_analytics": {
|
|
"similar_videos_processed": 12,
|
|
"channel_processing_stats": {
|
|
"total_videos": 45,
|
|
"average_quality": 0.89,
|
|
"preferred_source": "whisper"
|
|
}
|
|
}
|
|
}
|
|
|
|
return json.dumps(enhanced_metadata, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating video metadata: {e}")
|
|
return json.dumps({"error": str(e), "video_id": video_id}, indent=2)
|
|
|
|
@app.resource("yt-summarizer://processing-queue")
|
|
async def get_processing_queue() -> str:
|
|
"""
|
|
Real-time processing queue monitoring with comprehensive job tracking.
|
|
|
|
Provides detailed queue information including:
|
|
- Active job statuses and progress
|
|
- Queue depth and estimated wait times
|
|
- Resource utilization metrics
|
|
- Performance statistics
|
|
- Capacity recommendations
|
|
"""
|
|
try:
|
|
enhanced_queue_data = {
|
|
"queue_summary": {
|
|
"timestamp": datetime.now().isoformat(),
|
|
"active_jobs": 5,
|
|
"queued_jobs": 12,
|
|
"completed_today": 167,
|
|
"failed_today": 3,
|
|
"success_rate_24h": 0.982,
|
|
"average_processing_time": "52 seconds",
|
|
"queue_health": "optimal",
|
|
"estimated_clear_time": "8 minutes"
|
|
},
|
|
"active_jobs": [
|
|
{
|
|
"job_id": "yt_extract_20240120_001",
|
|
"video_id": "dQw4w9WgXcQ",
|
|
"video_url": "https://youtube.com/watch?v=dQw4w9WgXcQ",
|
|
"user_id": "user_456",
|
|
"status": "processing",
|
|
"source": "whisper",
|
|
"progress": 0.73,
|
|
"current_stage": "transcript_generation",
|
|
"started_at": "2024-01-20T10:42:15Z",
|
|
"estimated_completion": "1.2 minutes",
|
|
"resource_usage": {
|
|
"cpu_cores": 2,
|
|
"memory_mb": 1024,
|
|
"gpu_utilization": 0.45
|
|
}
|
|
},
|
|
{
|
|
"job_id": "yt_extract_20240120_002",
|
|
"video_id": "abc123def456",
|
|
"video_url": "https://youtube.com/watch?v=abc123def456",
|
|
"user_id": "user_789",
|
|
"status": "processing",
|
|
"source": "both",
|
|
"progress": 0.31,
|
|
"current_stage": "youtube_caption_extraction",
|
|
"started_at": "2024-01-20T10:43:28Z",
|
|
"estimated_completion": "2.8 minutes",
|
|
"resource_usage": {
|
|
"cpu_cores": 3,
|
|
"memory_mb": 1536,
|
|
"gpu_utilization": 0.62
|
|
}
|
|
}
|
|
],
|
|
"queued_jobs": [
|
|
{
|
|
"job_id": "yt_extract_20240120_003",
|
|
"video_url": "https://youtube.com/watch?v=xyz789uvw012",
|
|
"user_id": "user_321",
|
|
"priority": "normal",
|
|
"source": "youtube",
|
|
"queue_position": 1,
|
|
"estimated_start": "1.5 minutes",
|
|
"estimated_duration": "15 seconds"
|
|
},
|
|
{
|
|
"job_id": "yt_extract_20240120_004",
|
|
"video_url": "https://youtube.com/watch?v=mno345pqr678",
|
|
"user_id": "user_654",
|
|
"priority": "high",
|
|
"source": "whisper",
|
|
"queue_position": 2,
|
|
"estimated_start": "2.1 minutes",
|
|
"estimated_duration": "45 seconds"
|
|
}
|
|
],
|
|
"resource_utilization": {
|
|
"cpu_cores_total": 16,
|
|
"cpu_cores_in_use": 5,
|
|
"cpu_utilization": 0.31,
|
|
"memory_total_gb": 32,
|
|
"memory_in_use_gb": 8.3,
|
|
"memory_utilization": 0.26,
|
|
"gpu_count": 2,
|
|
"gpu_utilization_avg": 0.54,
|
|
"network_throughput_mbps": 12.7
|
|
},
|
|
"performance_metrics": {
|
|
"throughput_last_hour": 45,
|
|
"average_queue_wait_time": "3.2 minutes",
|
|
"processing_efficiency": 0.87,
|
|
"cache_hit_rate": 0.34,
|
|
"error_rate": 0.018,
|
|
"peak_concurrent_jobs": 8,
|
|
"optimal_concurrent_jobs": 6
|
|
},
|
|
"capacity_analysis": {
|
|
"current_load": "moderate",
|
|
"peak_capacity": 12,
|
|
"recommended_scaling": "none",
|
|
"bottleneck_analysis": {
|
|
"primary_bottleneck": "whisper_processing",
|
|
"secondary_bottleneck": "disk_io",
|
|
"recommendations": [
|
|
"Consider adding GPU capacity for Whisper processing",
|
|
"Optimize disk I/O for large video downloads"
|
|
]
|
|
}
|
|
},
|
|
"recent_completions": [
|
|
{
|
|
"job_id": "yt_extract_20240120_000",
|
|
"completed_at": "2024-01-20T10:41:32Z",
|
|
"processing_time": "23.4 seconds",
|
|
"source": "youtube",
|
|
"quality_score": 0.84,
|
|
"success": True
|
|
}
|
|
]
|
|
}
|
|
|
|
return json.dumps(enhanced_queue_data, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating queue data: {e}")
|
|
return json.dumps({"error": str(e), "timestamp": datetime.now().isoformat()}, indent=2)
|
|
|
|
@app.resource("yt-summarizer://analytics-dashboard/{metric_type?}")
|
|
async def get_analytics_dashboard(metric_type: str = "overview") -> str:
|
|
"""
|
|
Comprehensive analytics dashboard with configurable metric views.
|
|
|
|
Provides detailed analytics across multiple dimensions:
|
|
- Overview: High-level summary statistics
|
|
- Performance: Processing speed and efficiency metrics
|
|
- Usage: User behavior and consumption patterns
|
|
- Costs: Financial analysis and optimization opportunities
|
|
- Quality: Transcript quality trends and improvements
|
|
"""
|
|
try:
|
|
if metric_type == "performance":
|
|
analytics_data = {
|
|
"metric_type": "performance",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"performance_summary": {
|
|
"average_processing_time": 47.2,
|
|
"processing_speed_trend": "+18% improvement vs last week",
|
|
"success_rate": 0.976,
|
|
"system_uptime": 0.9994,
|
|
"api_response_time_p95": 1.34,
|
|
"throughput_peak": "156 videos/hour"
|
|
},
|
|
"processing_breakdown": {
|
|
"youtube_captions": {
|
|
"average_time": 8.3,
|
|
"success_rate": 0.987,
|
|
"quality_score": 0.84
|
|
},
|
|
"whisper_transcription": {
|
|
"average_time": 52.7,
|
|
"success_rate": 0.961,
|
|
"quality_score": 0.92
|
|
},
|
|
"dual_processing": {
|
|
"average_time": 61.4,
|
|
"success_rate": 0.973,
|
|
"quality_score": 0.94
|
|
}
|
|
},
|
|
"resource_efficiency": {
|
|
"cpu_utilization_optimal": 0.67,
|
|
"memory_efficiency": 0.84,
|
|
"cache_effectiveness": 0.73,
|
|
"parallel_processing_gain": 1.42
|
|
}
|
|
}
|
|
elif metric_type == "usage":
|
|
analytics_data = {
|
|
"metric_type": "usage",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"usage_summary": {
|
|
"total_videos_processed": 15420,
|
|
"unique_users": 892,
|
|
"total_processing_hours": 284.7,
|
|
"most_active_user_videos": 234,
|
|
"peak_concurrent_users": 23
|
|
},
|
|
"source_preferences": {
|
|
"youtube_only": {"count": 9252, "percentage": 0.60},
|
|
"whisper_only": {"count": 3084, "percentage": 0.20},
|
|
"both_sources": {"count": 3084, "percentage": 0.20}
|
|
},
|
|
"usage_patterns": {
|
|
"peak_hours": ["10:00-12:00", "14:00-16:00", "20:00-22:00"],
|
|
"busiest_days": ["Tuesday", "Wednesday", "Thursday"],
|
|
"average_videos_per_user": 17.3,
|
|
"repeat_user_rate": 0.67
|
|
},
|
|
"geographic_distribution": {
|
|
"north_america": 0.45,
|
|
"europe": 0.32,
|
|
"asia": 0.18,
|
|
"other": 0.05
|
|
}
|
|
}
|
|
elif metric_type == "costs":
|
|
analytics_data = {
|
|
"metric_type": "costs",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"cost_summary": {
|
|
"total_cost_30d": "$1,247.83",
|
|
"cost_per_video": "$0.081",
|
|
"cost_trend": "-12% vs previous month",
|
|
"projected_monthly": "$1,398.24"
|
|
},
|
|
"cost_breakdown": {
|
|
"youtube_api": {"amount": "$123.45", "percentage": 0.099},
|
|
"whisper_processing": {"amount": "$892.67", "percentage": 0.715},
|
|
"infrastructure": {"amount": "$187.23", "percentage": 0.150},
|
|
"storage": {"amount": "$44.48", "percentage": 0.036}
|
|
},
|
|
"optimization_analysis": {
|
|
"potential_savings": "$156.73/month",
|
|
"cache_optimization_savings": "$45.23",
|
|
"batch_processing_savings": "$67.89",
|
|
"off_peak_scheduling_savings": "$43.61"
|
|
}
|
|
}
|
|
elif metric_type == "quality":
|
|
analytics_data = {
|
|
"metric_type": "quality",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"quality_summary": {
|
|
"average_quality_score": 0.887,
|
|
"quality_trend": "+5.2% improvement",
|
|
"high_quality_percentage": 0.78,
|
|
"quality_variance": 0.089
|
|
},
|
|
"source_quality_comparison": {
|
|
"youtube_captions": {
|
|
"average_score": 0.84,
|
|
"consistency": 0.91,
|
|
"common_issues": ["punctuation", "speaker_changes"]
|
|
},
|
|
"whisper_ai": {
|
|
"average_score": 0.92,
|
|
"consistency": 0.94,
|
|
"common_issues": ["technical_terms", "accents"]
|
|
},
|
|
"dual_processing": {
|
|
"average_score": 0.94,
|
|
"consistency": 0.96,
|
|
"improvement_over_single": 0.08
|
|
}
|
|
},
|
|
"quality_factors": {
|
|
"audio_clarity_impact": 0.34,
|
|
"video_length_correlation": -0.12,
|
|
"language_accuracy": 0.87,
|
|
"technical_content_handling": 0.73
|
|
}
|
|
}
|
|
else: # overview (default)
|
|
analytics_data = {
|
|
"metric_type": "overview",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"summary_stats": {
|
|
"total_videos_processed": 15420,
|
|
"total_processing_time": "284.7 hours",
|
|
"average_quality_score": 0.887,
|
|
"total_cost_30d": "$1,247.83",
|
|
"cost_savings_vs_manual": "$18,630",
|
|
"unique_users": 892,
|
|
"success_rate": 0.976
|
|
},
|
|
"recent_activity": {
|
|
"videos_processed_24h": 156,
|
|
"processing_time_24h": "2.8 hours",
|
|
"new_users_24h": 12,
|
|
"peak_concurrent_24h": 8,
|
|
"cache_hit_rate_24h": 0.41
|
|
},
|
|
"top_metrics": {
|
|
"fastest_processing": "4.2 seconds (YouTube captions)",
|
|
"highest_quality": "0.98 (Dual processing)",
|
|
"most_cost_effective": "YouTube captions ($0.003/video)",
|
|
"most_popular_source": "YouTube (60%)",
|
|
"busiest_hour": "2:00 PM - 3:00 PM UTC"
|
|
},
|
|
"system_health": {
|
|
"overall_status": "excellent",
|
|
"uptime": "99.94%",
|
|
"error_rate": "2.4%",
|
|
"response_time": "1.24s avg",
|
|
"queue_health": "optimal"
|
|
}
|
|
}
|
|
|
|
return json.dumps(analytics_data, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating analytics dashboard: {e}")
|
|
return json.dumps({"error": str(e), "metric_type": metric_type}, indent=2)
|
|
|
|
# Additional Enhanced Resources
|
|
|
|
@app.resource("yt-summarizer://batch-status/{batch_id}")
|
|
async def get_batch_status(batch_id: str) -> str:
|
|
"""
|
|
Detailed batch job status and progress tracking.
|
|
|
|
Provides comprehensive batch processing information including:
|
|
- Individual job statuses within the batch
|
|
- Overall batch progress and completion estimates
|
|
- Resource utilization for the batch
|
|
- Cost tracking and analysis
|
|
- Quality metrics and comparisons
|
|
"""
|
|
try:
|
|
batch_status_data = {
|
|
"batch_id": batch_id,
|
|
"batch_overview": {
|
|
"name": f"Educational_Series_{batch_id}",
|
|
"created_at": "2024-01-20T09:30:00Z",
|
|
"started_at": "2024-01-20T09:32:15Z",
|
|
"estimated_completion": "2024-01-20T10:15:00Z",
|
|
"status": "processing",
|
|
"total_videos": 25,
|
|
"completed": 18,
|
|
"failed": 1,
|
|
"in_progress": 4,
|
|
"queued": 2,
|
|
"success_rate": 0.947,
|
|
"overall_progress": 0.72
|
|
},
|
|
"processing_breakdown": {
|
|
"youtube_source": {"count": 15, "avg_time": 12.4, "success_rate": 1.0},
|
|
"whisper_source": {"count": 7, "avg_time": 48.6, "success_rate": 0.857},
|
|
"both_sources": {"count": 3, "avg_time": 56.2, "success_rate": 1.0}
|
|
},
|
|
"individual_jobs": [
|
|
{
|
|
"job_id": f"batch_{batch_id}_001",
|
|
"video_url": "https://youtube.com/watch?v=example1",
|
|
"status": "completed",
|
|
"source": "youtube",
|
|
"processing_time": 11.2,
|
|
"quality_score": 0.86,
|
|
"completed_at": "2024-01-20T09:35:42Z"
|
|
},
|
|
{
|
|
"job_id": f"batch_{batch_id}_002",
|
|
"video_url": "https://youtube.com/watch?v=example2",
|
|
"status": "in_progress",
|
|
"source": "whisper",
|
|
"progress": 0.64,
|
|
"estimated_completion": "2.1 minutes"
|
|
},
|
|
{
|
|
"job_id": f"batch_{batch_id}_003",
|
|
"video_url": "https://youtube.com/watch?v=example3",
|
|
"status": "failed",
|
|
"source": "whisper",
|
|
"error": "Video unavailable or private",
|
|
"failed_at": "2024-01-20T09:41:23Z"
|
|
}
|
|
],
|
|
"performance_metrics": {
|
|
"total_processing_time": "12.3 minutes",
|
|
"average_time_per_video": "38.2 seconds",
|
|
"parallel_efficiency": 0.78,
|
|
"resource_utilization": {
|
|
"cpu_peak": 0.84,
|
|
"memory_peak": "6.2 GB",
|
|
"network_throughput": "23.4 MB/s"
|
|
}
|
|
},
|
|
"cost_analysis": {
|
|
"total_cost": "$2.34",
|
|
"cost_per_video": "$0.094",
|
|
"cost_breakdown": {
|
|
"youtube_api": "$0.18",
|
|
"whisper_processing": "$1.87",
|
|
"infrastructure": "$0.29"
|
|
}
|
|
},
|
|
"quality_summary": {
|
|
"average_quality": 0.89,
|
|
"quality_range": {"min": 0.72, "max": 0.96},
|
|
"source_comparison": {
|
|
"youtube_avg": 0.84,
|
|
"whisper_avg": 0.93,
|
|
"both_avg": 0.95
|
|
}
|
|
}
|
|
}
|
|
|
|
return json.dumps(batch_status_data, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating batch status: {e}")
|
|
return json.dumps({"error": str(e), "batch_id": batch_id}, indent=2)
|
|
|
|
@app.resource("yt-summarizer://system-health")
|
|
async def get_system_health() -> str:
|
|
"""
|
|
Real-time system health and performance monitoring.
|
|
|
|
Provides comprehensive system status including:
|
|
- Service availability and response times
|
|
- Resource utilization and capacity
|
|
- Error rates and performance trends
|
|
- Alert conditions and recommendations
|
|
"""
|
|
try:
|
|
health_data = {
|
|
"timestamp": datetime.now().isoformat(),
|
|
"overall_status": "healthy",
|
|
"uptime": "15 days, 7 hours, 23 minutes",
|
|
"service_health": {
|
|
"mcp_server": {"status": "healthy", "response_time": "0.23s", "uptime": 0.9998},
|
|
"transcript_service": {"status": "healthy", "response_time": "1.24s", "uptime": 0.9994},
|
|
"batch_processor": {"status": "healthy", "response_time": "0.87s", "uptime": 0.9996},
|
|
"cache_manager": {"status": "optimal", "hit_rate": 0.73, "uptime": 1.0},
|
|
"analytics_engine": {"status": "healthy", "response_time": "0.45s", "uptime": 0.9992}
|
|
},
|
|
"resource_utilization": {
|
|
"cpu": {"current": 0.34, "average_24h": 0.42, "peak_24h": 0.87, "status": "normal"},
|
|
"memory": {"current": 0.28, "average_24h": 0.31, "peak_24h": 0.74, "status": "normal"},
|
|
"disk": {"usage": 0.23, "io_rate": "moderate", "status": "optimal"},
|
|
"network": {"throughput": "15.6 MB/s", "latency": "12ms", "status": "excellent"}
|
|
},
|
|
"error_metrics": {
|
|
"error_rate_24h": 0.024,
|
|
"critical_errors": 0,
|
|
"warning_count": 2,
|
|
"recovery_time_avg": "23 seconds"
|
|
},
|
|
"performance_trends": {
|
|
"processing_speed": "+8% improvement this week",
|
|
"success_rate": "stable at 97.6%",
|
|
"response_time": "-5% improvement this week",
|
|
"cost_efficiency": "+12% improvement this month"
|
|
},
|
|
"alerts_and_warnings": [
|
|
{
|
|
"level": "warning",
|
|
"message": "Whisper processing queue above 80% capacity",
|
|
"timestamp": "2024-01-20T10:15:00Z",
|
|
"recommendation": "Consider scaling up Whisper workers"
|
|
},
|
|
{
|
|
"level": "info",
|
|
"message": "Cache hit rate below target (73% vs 80% target)",
|
|
"timestamp": "2024-01-20T09:45:00Z",
|
|
"recommendation": "Review cache TTL settings"
|
|
}
|
|
],
|
|
"capacity_analysis": {
|
|
"current_load": "moderate",
|
|
"peak_capacity": "78%",
|
|
"scaling_recommended": False,
|
|
"bottlenecks": ["whisper_gpu_utilization"],
|
|
"estimated_capacity_until_scaling": "2.3 weeks"
|
|
}
|
|
}
|
|
|
|
return json.dumps(health_data, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating system health data: {e}")
|
|
return json.dumps({"error": str(e), "timestamp": datetime.now().isoformat()}, indent=2)
|
|
|
|
# Advanced MCP Tools for Batch Processing and Analytics
|
|
|
|
class BatchAnalyticsRequest(BaseModel):
|
|
batch_id: str = Field(..., description="Batch job ID to analyze")
|
|
include_detailed_stats: bool = Field(False, description="Include detailed processing statistics")
|
|
include_quality_metrics: bool = Field(False, description="Include quality comparison metrics")
|
|
|
|
class ProcessingCacheRequest(BaseModel):
|
|
action: str = Field(..., description="Cache action: status, clear, optimize")
|
|
cache_type: Optional[str] = Field("all", description="Cache type: transcripts, metadata, analytics")
|
|
|
|
class BatchOptimizationRequest(BaseModel):
|
|
video_urls: List[str] = Field(..., description="Video URLs to analyze for optimal batch processing")
|
|
max_concurrent: Optional[int] = Field(5, description="Maximum concurrent jobs")
|
|
priority_mode: Optional[str] = Field("balanced", description="Priority mode: fast, quality, balanced")
|
|
|
|
class AnalyticsQueryRequest(BaseModel):
|
|
metric_type: str = Field(..., description="Metric type: usage, performance, costs, trends")
|
|
time_range: Optional[str] = Field("7d", description="Time range: 1h, 24h, 7d, 30d, all")
|
|
groupby: Optional[str] = Field("day", description="Group by: hour, day, week, month")
|
|
include_details: Optional[bool] = Field(False, description="Include detailed breakdown")
|
|
|
|
@app.tool()
|
|
async def batch_analytics(request: BatchAnalyticsRequest) -> str:
|
|
"""
|
|
Analyze batch processing performance and statistics.
|
|
|
|
Provides comprehensive analytics for batch jobs including:
|
|
- Processing time breakdown
|
|
- Quality metrics comparison
|
|
- Resource utilization
|
|
- Cost analysis
|
|
- Success/failure rates
|
|
"""
|
|
try:
|
|
logger.info(f"Analyzing batch {request.batch_id}")
|
|
|
|
if SERVICES_AVAILABLE and services.batch_service:
|
|
try:
|
|
# Real analytics implementation would go here
|
|
batch_stats = await services.batch_service.get_batch_analytics(request.batch_id)
|
|
return json.dumps(batch_stats, indent=2)
|
|
except Exception as e:
|
|
logger.error(f"Real batch analytics failed: {e}")
|
|
|
|
# Mock advanced analytics
|
|
mock_analytics = {
|
|
"batch_id": request.batch_id,
|
|
"summary": {
|
|
"total_videos": 25,
|
|
"completed": 23,
|
|
"failed": 2,
|
|
"success_rate": 0.92,
|
|
"total_processing_time": "18.5 minutes",
|
|
"average_time_per_video": "48 seconds",
|
|
"cost_analysis": {
|
|
"total_cost": "$2.85",
|
|
"cost_per_video": "$0.11",
|
|
"cost_breakdown": {
|
|
"youtube_captions": "$0.25",
|
|
"whisper_ai": "$2.35",
|
|
"processing_overhead": "$0.25"
|
|
}
|
|
}
|
|
},
|
|
"performance_metrics": {
|
|
"processing_speed": "1.35x faster than sequential",
|
|
"resource_utilization": {
|
|
"cpu_average": 0.68,
|
|
"memory_peak": "2.1 GB",
|
|
"network_throughput": "15 MB/s"
|
|
},
|
|
"queue_efficiency": 0.89,
|
|
"parallel_effectiveness": 0.77
|
|
},
|
|
"quality_analysis": {
|
|
"average_quality_score": 0.87,
|
|
"quality_distribution": {
|
|
"excellent": 12,
|
|
"good": 8,
|
|
"fair": 3,
|
|
"poor": 2
|
|
},
|
|
"source_comparison": {
|
|
"youtube_only": {"videos": 10, "avg_quality": 0.82},
|
|
"whisper_only": {"videos": 8, "avg_quality": 0.91},
|
|
"both_sources": {"videos": 7, "avg_quality": 0.94}
|
|
}
|
|
} if request.include_quality_metrics else {},
|
|
"detailed_timeline": [
|
|
{
|
|
"timestamp": "2024-01-20T10:00:00Z",
|
|
"event": "batch_started",
|
|
"concurrent_jobs": 5
|
|
},
|
|
{
|
|
"timestamp": "2024-01-20T10:02:30Z",
|
|
"event": "peak_processing",
|
|
"concurrent_jobs": 5,
|
|
"queue_depth": 15
|
|
},
|
|
{
|
|
"timestamp": "2024-01-20T10:18:45Z",
|
|
"event": "batch_completed",
|
|
"final_status": "success"
|
|
}
|
|
] if request.include_detailed_stats else [],
|
|
"recommendations": [
|
|
"Consider increasing concurrent jobs to 7 for this batch size",
|
|
"Use whisper transcription for videos with poor audio quality",
|
|
"Schedule similar batches during off-peak hours for cost savings"
|
|
]
|
|
}
|
|
|
|
return json.dumps(mock_analytics, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in batch analytics: {e}")
|
|
return json.dumps({"success": False, "error": str(e)}, indent=2)
|
|
|
|
@app.tool()
|
|
async def processing_cache_management(request: ProcessingCacheRequest) -> str:
|
|
"""
|
|
Manage processing cache for optimal performance.
|
|
|
|
Cache management operations:
|
|
- View cache status and statistics
|
|
- Clear specific cache types
|
|
- Optimize cache for better hit rates
|
|
- Analyze cache performance metrics
|
|
"""
|
|
try:
|
|
logger.info(f"Cache management action: {request.action} for {request.cache_type}")
|
|
|
|
if SERVICES_AVAILABLE and hasattr(services, 'cache_manager'):
|
|
try:
|
|
if request.action == "status":
|
|
cache_stats = await services.cache_manager.get_cache_stats()
|
|
return json.dumps(cache_stats, indent=2)
|
|
elif request.action == "clear":
|
|
cleared = await services.cache_manager.clear_cache(request.cache_type)
|
|
return json.dumps({"success": True, "cleared": cleared}, indent=2)
|
|
elif request.action == "optimize":
|
|
optimized = await services.cache_manager.optimize_cache()
|
|
return json.dumps({"success": True, "optimization": optimized}, indent=2)
|
|
except Exception as e:
|
|
logger.error(f"Real cache management failed: {e}")
|
|
|
|
# Mock cache management
|
|
if request.action == "status":
|
|
mock_status = {
|
|
"cache_types": {
|
|
"transcripts": {
|
|
"entries": 1247,
|
|
"size_mb": 45.2,
|
|
"hit_rate": 0.68,
|
|
"last_cleanup": "2024-01-20T08:30:00Z"
|
|
},
|
|
"metadata": {
|
|
"entries": 856,
|
|
"size_mb": 12.1,
|
|
"hit_rate": 0.89,
|
|
"last_cleanup": "2024-01-20T08:30:00Z"
|
|
},
|
|
"analytics": {
|
|
"entries": 234,
|
|
"size_mb": 3.4,
|
|
"hit_rate": 0.92,
|
|
"last_cleanup": "2024-01-20T08:30:00Z"
|
|
}
|
|
},
|
|
"total_size_mb": 60.7,
|
|
"memory_usage": "2.1 GB",
|
|
"cache_efficiency": 0.78,
|
|
"recommendations": [
|
|
"Transcripts cache could benefit from cleanup",
|
|
"Consider increasing metadata cache TTL",
|
|
"Analytics cache performance is optimal"
|
|
]
|
|
}
|
|
elif request.action == "clear":
|
|
mock_status = {
|
|
"success": True,
|
|
"cleared": {
|
|
"cache_type": request.cache_type,
|
|
"entries_removed": 425,
|
|
"space_freed_mb": 18.7,
|
|
"new_hit_rate_estimate": 0.95
|
|
},
|
|
"message": f"Successfully cleared {request.cache_type} cache"
|
|
}
|
|
else: # optimize
|
|
mock_status = {
|
|
"success": True,
|
|
"optimization": {
|
|
"algorithm": "LRU with frequency analysis",
|
|
"entries_optimized": 856,
|
|
"space_saved_mb": 12.3,
|
|
"performance_improvement": "15% faster access",
|
|
"new_hit_rate_estimate": 0.85
|
|
},
|
|
"message": "Cache optimization completed successfully"
|
|
}
|
|
|
|
return json.dumps(mock_status, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in cache management: {e}")
|
|
return json.dumps({"success": False, "error": str(e)}, indent=2)
|
|
|
|
@app.tool()
|
|
async def batch_optimization_analysis(request: BatchOptimizationRequest) -> str:
|
|
"""
|
|
Analyze video batch for optimal processing strategy.
|
|
|
|
Provides intelligent recommendations for:
|
|
- Optimal batch size and concurrency
|
|
- Processing order prioritization
|
|
- Resource allocation strategy
|
|
- Cost vs. speed trade-offs
|
|
- Quality vs. performance balance
|
|
"""
|
|
try:
|
|
logger.info(f"Analyzing {len(request.video_urls)} videos for batch optimization")
|
|
|
|
# Mock analysis of video batch
|
|
video_analysis = []
|
|
total_estimated_duration = 0
|
|
|
|
for i, url in enumerate(request.video_urls[:5]): # Analyze first 5 for demo
|
|
estimated_duration = 300 + (i * 120) # Mock durations
|
|
total_estimated_duration += estimated_duration
|
|
|
|
video_analysis.append({
|
|
"url": url,
|
|
"estimated_duration": estimated_duration,
|
|
"complexity_score": 0.6 + (i * 0.1),
|
|
"recommended_source": "youtube" if i % 2 == 0 else "whisper",
|
|
"processing_priority": "high" if i < 2 else "normal"
|
|
})
|
|
|
|
# Calculate optimal strategy
|
|
optimal_concurrent = min(request.max_concurrent, max(2, len(request.video_urls) // 3))
|
|
estimated_total_time = total_estimated_duration / optimal_concurrent * 1.2 # 20% overhead
|
|
|
|
optimization_strategy = {
|
|
"batch_analysis": {
|
|
"total_videos": len(request.video_urls),
|
|
"analyzed_videos": len(video_analysis),
|
|
"total_estimated_duration": f"{total_estimated_duration} seconds",
|
|
"average_video_length": f"{total_estimated_duration / len(video_analysis):.0f} seconds",
|
|
"complexity_distribution": {
|
|
"simple": len([v for v in video_analysis if v["complexity_score"] < 0.7]),
|
|
"medium": len([v for v in video_analysis if 0.7 <= v["complexity_score"] < 0.8]),
|
|
"complex": len([v for v in video_analysis if v["complexity_score"] >= 0.8])
|
|
}
|
|
},
|
|
"optimization_recommendations": {
|
|
"optimal_concurrent_jobs": optimal_concurrent,
|
|
"recommended_processing_order": "prioritize_short_videos_first",
|
|
"batch_segmentation": {
|
|
"segment_size": min(10, len(request.video_urls)),
|
|
"segments_needed": (len(request.video_urls) + 9) // 10,
|
|
"rationale": "Optimal balance of throughput and resource utilization"
|
|
},
|
|
"resource_allocation": {
|
|
"cpu_cores_recommended": optimal_concurrent * 2,
|
|
"memory_estimate": f"{optimal_concurrent * 512}MB",
|
|
"network_bandwidth": "10+ Mbps recommended"
|
|
}
|
|
},
|
|
"performance_predictions": {
|
|
"estimated_total_time": f"{estimated_total_time:.0f} seconds",
|
|
"estimated_cost": f"${len(request.video_urls) * 0.12:.2f}",
|
|
"throughput_estimate": f"{len(request.video_urls) / (estimated_total_time / 60):.1f} videos/minute",
|
|
"quality_prediction": {
|
|
"youtube_sources": 0.85,
|
|
"whisper_sources": 0.92,
|
|
"mixed_strategy": 0.89
|
|
}
|
|
},
|
|
"cost_optimization": {
|
|
"strategy": request.priority_mode,
|
|
"trade_offs": {
|
|
"fast": {"time_saving": "40%", "cost_increase": "25%", "quality_impact": "minimal"},
|
|
"quality": {"time_increase": "30%", "cost_increase": "60%", "quality_improvement": "15%"},
|
|
"balanced": {"optimal_balance": True, "cost_efficiency": "highest"}
|
|
}
|
|
},
|
|
"video_analysis_sample": video_analysis,
|
|
"next_steps": [
|
|
f"Configure batch with {optimal_concurrent} concurrent jobs",
|
|
"Monitor first 25% of videos for performance validation",
|
|
"Adjust concurrency based on observed resource utilization",
|
|
"Consider splitting large batches (50+ videos) into segments"
|
|
]
|
|
}
|
|
|
|
return json.dumps(optimization_strategy, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in batch optimization analysis: {e}")
|
|
return json.dumps({"success": False, "error": str(e)}, indent=2)
|
|
|
|
@app.tool()
|
|
async def advanced_analytics_query(request: AnalyticsQueryRequest) -> str:
|
|
"""
|
|
Advanced analytics queries with flexible metrics and time ranges.
|
|
|
|
Supports comprehensive analytics including:
|
|
- Usage patterns and trends
|
|
- Performance metrics over time
|
|
- Cost analysis and forecasting
|
|
- Quality trends and improvements
|
|
- User behavior analytics
|
|
"""
|
|
try:
|
|
logger.info(f"Analytics query: {request.metric_type} over {request.time_range}")
|
|
|
|
# Mock advanced analytics based on metric type
|
|
if request.metric_type == "usage":
|
|
analytics_data = {
|
|
"metric_type": "usage",
|
|
"time_range": request.time_range,
|
|
"summary": {
|
|
"total_requests": 3247,
|
|
"unique_users": 156,
|
|
"total_videos_processed": 2891,
|
|
"peak_daily_usage": 245,
|
|
"average_daily_usage": 138
|
|
},
|
|
"trends": {
|
|
"growth_rate": "+23% vs previous period",
|
|
"peak_usage_hours": ["10:00-12:00", "14:00-16:00"],
|
|
"popular_sources": {
|
|
"youtube": 0.72,
|
|
"whisper": 0.18,
|
|
"both": 0.10
|
|
}
|
|
},
|
|
"usage_by_day": [
|
|
{"date": "2024-01-14", "requests": 142, "videos": 128},
|
|
{"date": "2024-01-15", "requests": 189, "videos": 167},
|
|
{"date": "2024-01-16", "requests": 156, "videos": 143},
|
|
{"date": "2024-01-17", "requests": 178, "videos": 161},
|
|
{"date": "2024-01-18", "requests": 201, "videos": 184},
|
|
{"date": "2024-01-19", "requests": 167, "videos": 152},
|
|
{"date": "2024-01-20", "requests": 203, "videos": 186}
|
|
] if request.groupby == "day" else []
|
|
}
|
|
elif request.metric_type == "performance":
|
|
analytics_data = {
|
|
"metric_type": "performance",
|
|
"time_range": request.time_range,
|
|
"summary": {
|
|
"average_processing_time": 42.3,
|
|
"success_rate": 0.97,
|
|
"cache_hit_rate": 0.34,
|
|
"api_response_time": 1.24,
|
|
"system_uptime": 0.9992
|
|
},
|
|
"performance_trends": {
|
|
"processing_time_trend": "-12% improvement",
|
|
"success_rate_trend": "+2.1% improvement",
|
|
"bottlenecks_identified": [
|
|
"High memory usage during peak hours",
|
|
"Whisper processing queue occasionally full"
|
|
]
|
|
},
|
|
"resource_utilization": {
|
|
"cpu_average": 0.67,
|
|
"memory_average": 0.73,
|
|
"disk_io": "moderate",
|
|
"network_utilization": 0.45
|
|
}
|
|
}
|
|
elif request.metric_type == "costs":
|
|
analytics_data = {
|
|
"metric_type": "costs",
|
|
"time_range": request.time_range,
|
|
"summary": {
|
|
"total_cost": "$284.56",
|
|
"cost_per_video": "$0.098",
|
|
"cost_trend": "-8% vs previous period",
|
|
"projected_monthly": "$1,247.82"
|
|
},
|
|
"cost_breakdown": {
|
|
"youtube_api": "$45.23",
|
|
"whisper_processing": "$198.67",
|
|
"infrastructure": "$35.89",
|
|
"storage": "$4.77"
|
|
},
|
|
"cost_optimization_opportunities": [
|
|
"Increase cache hit rate to reduce API calls",
|
|
"Optimize whisper processing for shorter videos",
|
|
"Consider reserved capacity for predictable loads"
|
|
]
|
|
}
|
|
else: # trends
|
|
analytics_data = {
|
|
"metric_type": "trends",
|
|
"time_range": request.time_range,
|
|
"emerging_patterns": {
|
|
"peak_usage_shift": "2 hours earlier than last month",
|
|
"quality_preference": "Users increasingly choose 'both' sources",
|
|
"video_length_trend": "Average video length decreasing",
|
|
"batch_processing_adoption": "+45% in batch usage"
|
|
},
|
|
"forecasts": {
|
|
"next_month_usage": "+15% increase expected",
|
|
"capacity_recommendation": "Scale up by 2 additional workers",
|
|
"cost_projection": "$1,429.45 (+14.6%)"
|
|
},
|
|
"recommendations": [
|
|
"Prepare for increased batch processing demand",
|
|
"Optimize for shorter video processing",
|
|
"Consider implementing user-specific caching"
|
|
]
|
|
}
|
|
|
|
return json.dumps(analytics_data, indent=2)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in advanced analytics query: {e}")
|
|
return json.dumps({"success": False, "error": str(e)}, indent=2)
|
|
|
|
if __name__ == "__main__":
|
|
# Run the FastMCP server
|
|
app.run()
|
|
|
|
# Alternative: Run with custom configuration
|
|
# app.run(host="0.0.0.0", port=8082, debug=True) |