Added video studio router and endpoints. Added research router and endpoints. Added youtube router and endpoints. Added onboarding utils router and endpoints. Added onboarding utils service. Added onboarding utils models. Added onboarding utils routes. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils.
This commit is contained in:
ajaysi
@@ -40,26 +40,43 @@ class Step3ResearchService:
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async def discover_competitors_for_onboarding(
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self,
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user_url: str,
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session_id: str,
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user_id: str,
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industry_context: Optional[str] = None,
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num_results: int = 25,
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website_analysis_data: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""
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Discover competitors for onboarding Step 3.
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Args:
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user_url: The user's website URL
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session_id: Onboarding session ID
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user_id: Clerk user ID for finding the correct session
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industry_context: Industry context for better discovery
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num_results: Number of competitors to discover
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Returns:
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Dictionary containing competitor discovery results
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"""
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try:
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logger.info(f"Starting research analysis for session {session_id}, URL: {user_url}")
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logger.info(f"Starting research analysis for user {user_id}, URL: {user_url}")
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# Find the correct onboarding session for this user
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with get_db_session() as db:
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from models.onboarding import OnboardingSession
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session = db.query(OnboardingSession).filter(
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OnboardingSession.user_id == user_id
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).first()
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if not session:
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logger.error(f"No onboarding session found for user {user_id}")
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return {
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"success": False,
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"error": f"No onboarding session found for user {user_id}"
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}
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actual_session_id = str(session.id) # Convert to string for consistency
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logger.info(f"Found onboarding session {actual_session_id} for user {user_id}")
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# Step 1: Discover social media accounts
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logger.info("Step 1: Discovering social media accounts...")
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social_media_results = await self.exa_service.discover_social_media_accounts(user_url)
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@@ -92,7 +109,7 @@ class Step3ResearchService:
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# Store research data in database
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await self._store_research_data(
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session_id=session_id,
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session_id=actual_session_id,
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user_url=user_url,
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competitors=enhanced_competitors,
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industry_context=industry_context,
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@@ -108,11 +125,11 @@ class Step3ResearchService:
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industry_context
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)
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logger.info(f"Successfully discovered {len(enhanced_competitors)} competitors for session {session_id}")
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logger.info(f"Successfully discovered {len(enhanced_competitors)} competitors for user {user_id}")
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return {
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"success": True,
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"session_id": session_id,
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"session_id": actual_session_id,
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"user_url": user_url,
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"competitors": enhanced_competitors,
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"social_media_accounts": social_media_results.get("social_media_accounts", {}),
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@@ -129,7 +146,7 @@ class Step3ResearchService:
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return {
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"success": False,
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"error": str(e),
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"session_id": session_id,
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"session_id": actual_session_id if 'actual_session_id' in locals() else session_id,
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"user_url": user_url
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}
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@@ -398,38 +415,62 @@ class Step3ResearchService:
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"""
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try:
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with get_db_session() as db:
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# Get or create onboarding session
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# Get onboarding session
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session = db.query(OnboardingSession).filter(
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OnboardingSession.id == session_id
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OnboardingSession.id == int(session_id)
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).first()
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if not session:
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logger.error(f"Onboarding session {session_id} not found")
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return False
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# Update session with research data
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research_data = {
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"step3_research_data": {
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"user_url": user_url,
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"competitors": competitors,
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"industry_context": industry_context,
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"analysis_metadata": analysis_metadata,
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"completed_at": datetime.utcnow().isoformat()
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}
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# Store each competitor in CompetitorAnalysis table
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from models.onboarding import CompetitorAnalysis
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for competitor in competitors:
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# Create competitor analysis record
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competitor_record = CompetitorAnalysis(
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session_id=session.id,
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competitor_url=competitor.get("url", ""),
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competitor_domain=competitor.get("domain", ""),
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analysis_data={
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"title": competitor.get("title", ""),
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"summary": competitor.get("summary", ""),
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"relevance_score": competitor.get("relevance_score", 0.5),
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"highlights": competitor.get("highlights", []),
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"favicon": competitor.get("favicon"),
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"image": competitor.get("image"),
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"published_date": competitor.get("published_date"),
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"author": competitor.get("author"),
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"competitive_analysis": competitor.get("competitive_insights", {}),
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"content_insights": competitor.get("content_insights", {}),
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"industry_context": industry_context,
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"analysis_metadata": analysis_metadata,
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"completed_at": datetime.utcnow().isoformat()
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}
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)
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db.add(competitor_record)
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# Store summary in session for quick access (backward compatibility)
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research_summary = {
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"user_url": user_url,
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"total_competitors": len(competitors),
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"industry_context": industry_context,
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"completed_at": datetime.utcnow().isoformat(),
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"analysis_metadata": analysis_metadata
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}
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# Merge with existing data
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if session.step_data:
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session.step_data.update(research_data)
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else:
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session.step_data = research_data
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# Store summary in session (this requires step_data field to exist)
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# For now, we'll skip this since the model doesn't have step_data
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# TODO: Add step_data JSON column to OnboardingSession model if needed
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db.commit()
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logger.info(f"Research data stored for session {session_id}")
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logger.info(f"Stored {len(competitors)} competitors in CompetitorAnalysis table for session {session_id}")
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return True
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except Exception as e:
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logger.error(f"Error storing research data: {str(e)}")
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logger.error(f"Error storing research data: {str(e)}", exc_info=True)
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return False
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async def get_research_data(self, session_id: str) -> Dict[str, Any]:
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@@ -117,7 +117,7 @@ async def discover_competitors(
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# Perform competitor discovery with Clerk user ID
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result = await step3_research_service.discover_competitors_for_onboarding(
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user_url=request.user_url,
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session_id=clerk_user_id, # Use Clerk user ID for isolation
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user_id=clerk_user_id, # Use Clerk user ID to find correct session
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industry_context=request.industry_context,
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num_results=request.num_results,
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website_analysis_data=request.website_analysis_data
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14
backend/api/research/__init__.py
Normal file
14
backend/api/research/__init__.py
Normal file
@@ -0,0 +1,14 @@
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"""
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Research API Module
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Standalone API endpoints for the Research Engine.
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Can be used by any tool or directly via API.
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Author: ALwrity Team
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Version: 2.0
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"""
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from .router import router
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__all__ = ["router"]
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739
backend/api/research/router.py
Normal file
739
backend/api/research/router.py
Normal file
@@ -0,0 +1,739 @@
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"""
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Research API Router
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Standalone API endpoints for the Research Engine.
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These endpoints can be used by:
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- Frontend Research UI
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- Blog Writer (via adapter)
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- Podcast Maker
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- YouTube Creator
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- Any other content tool
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Author: ALwrity Team
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Version: 2.0
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"""
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from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
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from pydantic import BaseModel, Field
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from typing import Optional, List, Dict, Any
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from loguru import logger
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import uuid
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import asyncio
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from services.database import get_db
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from services.research.core import (
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ResearchEngine,
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ResearchContext,
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ResearchPersonalizationContext,
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ContentType,
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ResearchGoal,
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ResearchDepth,
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ProviderPreference,
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)
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from services.research.core.research_context import ResearchResult
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from middleware.auth_middleware import get_current_user
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# Intent-driven research imports
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from models.research_intent_models import (
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ResearchIntent,
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IntentInferenceRequest,
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IntentInferenceResponse,
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IntentDrivenResearchResult,
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ResearchQuery,
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ExpectedDeliverable,
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ResearchPurpose,
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ContentOutput,
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ResearchDepthLevel,
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)
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from services.research.intent import (
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ResearchIntentInference,
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IntentQueryGenerator,
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IntentAwareAnalyzer,
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)
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router = APIRouter(prefix="/api/research", tags=["Research Engine"])
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# Request/Response models
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class ResearchRequest(BaseModel):
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"""API request for research."""
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query: str = Field(..., description="Main research query or topic")
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keywords: List[str] = Field(default_factory=list, description="Additional keywords")
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# Research configuration
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goal: Optional[str] = Field(default="factual", description="Research goal: factual, trending, competitive, etc.")
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depth: Optional[str] = Field(default="standard", description="Research depth: quick, standard, comprehensive, expert")
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provider: Optional[str] = Field(default="auto", description="Provider preference: auto, exa, tavily, google")
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# Personalization
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content_type: Optional[str] = Field(default="general", description="Content type: blog, podcast, video, etc.")
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industry: Optional[str] = None
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target_audience: Optional[str] = None
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tone: Optional[str] = None
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# Constraints
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max_sources: int = Field(default=10, ge=1, le=25)
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recency: Optional[str] = None # day, week, month, year
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# Domain filtering
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include_domains: List[str] = Field(default_factory=list)
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exclude_domains: List[str] = Field(default_factory=list)
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# Advanced mode
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advanced_mode: bool = False
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# Raw provider parameters (only if advanced_mode=True)
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exa_category: Optional[str] = None
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exa_search_type: Optional[str] = None
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tavily_topic: Optional[str] = None
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tavily_search_depth: Optional[str] = None
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tavily_include_answer: bool = False
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tavily_time_range: Optional[str] = None
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class ResearchResponse(BaseModel):
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"""API response for research."""
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success: bool
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task_id: Optional[str] = None # For async requests
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# Results (if synchronous)
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sources: List[Dict[str, Any]] = Field(default_factory=list)
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keyword_analysis: Dict[str, Any] = Field(default_factory=dict)
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competitor_analysis: Dict[str, Any] = Field(default_factory=dict)
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suggested_angles: List[str] = Field(default_factory=list)
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# Metadata
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provider_used: Optional[str] = None
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search_queries: List[str] = Field(default_factory=list)
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# Error handling
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error_message: Optional[str] = None
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error_code: Optional[str] = None
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class ProviderStatusResponse(BaseModel):
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"""API response for provider status."""
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exa: Dict[str, Any]
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tavily: Dict[str, Any]
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google: Dict[str, Any]
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# In-memory task storage for async research
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_research_tasks: Dict[str, Dict[str, Any]] = {}
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def _convert_to_research_context(request: ResearchRequest, user_id: str) -> ResearchContext:
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"""Convert API request to ResearchContext."""
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# Map string enums
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goal_map = {
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"factual": ResearchGoal.FACTUAL,
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"trending": ResearchGoal.TRENDING,
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"competitive": ResearchGoal.COMPETITIVE,
|
||||
"educational": ResearchGoal.EDUCATIONAL,
|
||||
"technical": ResearchGoal.TECHNICAL,
|
||||
"inspirational": ResearchGoal.INSPIRATIONAL,
|
||||
}
|
||||
|
||||
depth_map = {
|
||||
"quick": ResearchDepth.QUICK,
|
||||
"standard": ResearchDepth.STANDARD,
|
||||
"comprehensive": ResearchDepth.COMPREHENSIVE,
|
||||
"expert": ResearchDepth.EXPERT,
|
||||
}
|
||||
|
||||
provider_map = {
|
||||
"auto": ProviderPreference.AUTO,
|
||||
"exa": ProviderPreference.EXA,
|
||||
"tavily": ProviderPreference.TAVILY,
|
||||
"google": ProviderPreference.GOOGLE,
|
||||
"hybrid": ProviderPreference.HYBRID,
|
||||
}
|
||||
|
||||
content_type_map = {
|
||||
"blog": ContentType.BLOG,
|
||||
"podcast": ContentType.PODCAST,
|
||||
"video": ContentType.VIDEO,
|
||||
"social": ContentType.SOCIAL,
|
||||
"email": ContentType.EMAIL,
|
||||
"newsletter": ContentType.NEWSLETTER,
|
||||
"whitepaper": ContentType.WHITEPAPER,
|
||||
"general": ContentType.GENERAL,
|
||||
}
|
||||
|
||||
# Build personalization context
|
||||
personalization = ResearchPersonalizationContext(
|
||||
creator_id=user_id,
|
||||
content_type=content_type_map.get(request.content_type or "general", ContentType.GENERAL),
|
||||
industry=request.industry,
|
||||
target_audience=request.target_audience,
|
||||
tone=request.tone,
|
||||
)
|
||||
|
||||
return ResearchContext(
|
||||
query=request.query,
|
||||
keywords=request.keywords,
|
||||
goal=goal_map.get(request.goal or "factual", ResearchGoal.FACTUAL),
|
||||
depth=depth_map.get(request.depth or "standard", ResearchDepth.STANDARD),
|
||||
provider_preference=provider_map.get(request.provider or "auto", ProviderPreference.AUTO),
|
||||
personalization=personalization,
|
||||
max_sources=request.max_sources,
|
||||
recency=request.recency,
|
||||
include_domains=request.include_domains,
|
||||
exclude_domains=request.exclude_domains,
|
||||
advanced_mode=request.advanced_mode,
|
||||
exa_category=request.exa_category,
|
||||
exa_search_type=request.exa_search_type,
|
||||
tavily_topic=request.tavily_topic,
|
||||
tavily_search_depth=request.tavily_search_depth,
|
||||
tavily_include_answer=request.tavily_include_answer,
|
||||
tavily_time_range=request.tavily_time_range,
|
||||
)
|
||||
|
||||
|
||||
@router.get("/providers/status", response_model=ProviderStatusResponse)
|
||||
async def get_provider_status():
|
||||
"""
|
||||
Get status of available research providers.
|
||||
|
||||
Returns availability and priority of Exa, Tavily, and Google providers.
|
||||
"""
|
||||
engine = ResearchEngine()
|
||||
return engine.get_provider_status()
|
||||
|
||||
|
||||
@router.post("/execute", response_model=ResearchResponse)
|
||||
async def execute_research(
|
||||
request: ResearchRequest,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Execute research synchronously.
|
||||
|
||||
For quick research needs. For longer research, use /start endpoint.
|
||||
"""
|
||||
try:
|
||||
if not current_user:
|
||||
raise HTTPException(status_code=401, detail="Authentication required")
|
||||
|
||||
user_id = str(current_user.get('id', ''))
|
||||
if not user_id:
|
||||
raise HTTPException(status_code=401, detail="Invalid user ID in authentication token")
|
||||
|
||||
logger.info(f"[Research API] Execute request: {request.query[:50]}...")
|
||||
|
||||
engine = ResearchEngine()
|
||||
context = _convert_to_research_context(request, user_id)
|
||||
|
||||
result = await engine.research(context)
|
||||
|
||||
return ResearchResponse(
|
||||
success=result.success,
|
||||
sources=result.sources,
|
||||
keyword_analysis=result.keyword_analysis,
|
||||
competitor_analysis=result.competitor_analysis,
|
||||
suggested_angles=result.suggested_angles,
|
||||
provider_used=result.provider_used,
|
||||
search_queries=result.search_queries,
|
||||
error_message=result.error_message,
|
||||
error_code=result.error_code,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Research API] Execute failed: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.post("/start", response_model=ResearchResponse)
|
||||
async def start_research(
|
||||
request: ResearchRequest,
|
||||
background_tasks: BackgroundTasks,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Start research asynchronously.
|
||||
|
||||
Returns a task_id that can be used to poll for status.
|
||||
Use this for comprehensive research that may take longer.
|
||||
"""
|
||||
try:
|
||||
if not current_user:
|
||||
raise HTTPException(status_code=401, detail="Authentication required")
|
||||
|
||||
user_id = str(current_user.get('id', ''))
|
||||
if not user_id:
|
||||
raise HTTPException(status_code=401, detail="Invalid user ID in authentication token")
|
||||
|
||||
logger.info(f"[Research API] Start async request: {request.query[:50]}...")
|
||||
|
||||
task_id = str(uuid.uuid4())
|
||||
|
||||
# Initialize task
|
||||
_research_tasks[task_id] = {
|
||||
"status": "pending",
|
||||
"progress_messages": [],
|
||||
"result": None,
|
||||
"error": None,
|
||||
}
|
||||
|
||||
# Start background task
|
||||
context = _convert_to_research_context(request, user_id)
|
||||
background_tasks.add_task(_run_research_task, task_id, context)
|
||||
|
||||
return ResearchResponse(
|
||||
success=True,
|
||||
task_id=task_id,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Research API] Start failed: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
async def _run_research_task(task_id: str, context: ResearchContext):
|
||||
"""Background task to run research."""
|
||||
try:
|
||||
_research_tasks[task_id]["status"] = "running"
|
||||
|
||||
def progress_callback(message: str):
|
||||
_research_tasks[task_id]["progress_messages"].append(message)
|
||||
|
||||
engine = ResearchEngine()
|
||||
result = await engine.research(context, progress_callback=progress_callback)
|
||||
|
||||
_research_tasks[task_id]["status"] = "completed"
|
||||
_research_tasks[task_id]["result"] = result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Research API] Task {task_id} failed: {e}")
|
||||
_research_tasks[task_id]["status"] = "failed"
|
||||
_research_tasks[task_id]["error"] = str(e)
|
||||
|
||||
|
||||
@router.get("/status/{task_id}")
|
||||
async def get_research_status(task_id: str):
|
||||
"""
|
||||
Get status of an async research task.
|
||||
|
||||
Poll this endpoint to get progress updates and final results.
|
||||
"""
|
||||
if task_id not in _research_tasks:
|
||||
raise HTTPException(status_code=404, detail="Task not found")
|
||||
|
||||
task = _research_tasks[task_id]
|
||||
|
||||
response = {
|
||||
"task_id": task_id,
|
||||
"status": task["status"],
|
||||
"progress_messages": task["progress_messages"],
|
||||
}
|
||||
|
||||
if task["status"] == "completed" and task["result"]:
|
||||
result = task["result"]
|
||||
response["result"] = {
|
||||
"success": result.success,
|
||||
"sources": result.sources,
|
||||
"keyword_analysis": result.keyword_analysis,
|
||||
"competitor_analysis": result.competitor_analysis,
|
||||
"suggested_angles": result.suggested_angles,
|
||||
"provider_used": result.provider_used,
|
||||
"search_queries": result.search_queries,
|
||||
}
|
||||
|
||||
# Clean up completed task after returning
|
||||
# In production, use Redis or database for persistence
|
||||
|
||||
elif task["status"] == "failed":
|
||||
response["error"] = task["error"]
|
||||
|
||||
return response
|
||||
|
||||
|
||||
@router.delete("/status/{task_id}")
|
||||
async def cancel_research(task_id: str):
|
||||
"""
|
||||
Cancel a running research task.
|
||||
"""
|
||||
if task_id not in _research_tasks:
|
||||
raise HTTPException(status_code=404, detail="Task not found")
|
||||
|
||||
task = _research_tasks[task_id]
|
||||
|
||||
if task["status"] in ["pending", "running"]:
|
||||
task["status"] = "cancelled"
|
||||
return {"message": "Task cancelled", "task_id": task_id}
|
||||
|
||||
return {"message": f"Task already {task['status']}", "task_id": task_id}
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Intent-Driven Research Endpoints
|
||||
# ============================================================================
|
||||
|
||||
class AnalyzeIntentRequest(BaseModel):
|
||||
"""Request to analyze user research intent."""
|
||||
user_input: str = Field(..., description="User's keywords, question, or goal")
|
||||
keywords: List[str] = Field(default_factory=list, description="Extracted keywords")
|
||||
use_persona: bool = Field(True, description="Use research persona for context")
|
||||
use_competitor_data: bool = Field(True, description="Use competitor data for context")
|
||||
|
||||
|
||||
class AnalyzeIntentResponse(BaseModel):
|
||||
"""Response from intent analysis."""
|
||||
success: bool
|
||||
intent: Dict[str, Any]
|
||||
analysis_summary: str
|
||||
suggested_queries: List[Dict[str, Any]]
|
||||
suggested_keywords: List[str]
|
||||
suggested_angles: List[str]
|
||||
quick_options: List[Dict[str, Any]]
|
||||
error_message: Optional[str] = None
|
||||
|
||||
|
||||
class IntentDrivenResearchRequest(BaseModel):
|
||||
"""Request for intent-driven research."""
|
||||
# Intent from previous analyze step, or minimal input for auto-inference
|
||||
user_input: str = Field(..., description="User's original input")
|
||||
|
||||
# Optional: Confirmed intent from UI (if user modified the inferred intent)
|
||||
confirmed_intent: Optional[Dict[str, Any]] = None
|
||||
|
||||
# Optional: Specific queries to run (if user selected from suggested)
|
||||
selected_queries: Optional[List[Dict[str, Any]]] = None
|
||||
|
||||
# Research configuration
|
||||
max_sources: int = Field(default=10, ge=1, le=25)
|
||||
include_domains: List[str] = Field(default_factory=list)
|
||||
exclude_domains: List[str] = Field(default_factory=list)
|
||||
|
||||
# Skip intent inference (for re-runs with same intent)
|
||||
skip_inference: bool = False
|
||||
|
||||
|
||||
class IntentDrivenResearchResponse(BaseModel):
|
||||
"""Response from intent-driven research."""
|
||||
success: bool
|
||||
|
||||
# Direct answers
|
||||
primary_answer: str = ""
|
||||
secondary_answers: Dict[str, str] = Field(default_factory=dict)
|
||||
|
||||
# Deliverables
|
||||
statistics: List[Dict[str, Any]] = Field(default_factory=list)
|
||||
expert_quotes: List[Dict[str, Any]] = Field(default_factory=list)
|
||||
case_studies: List[Dict[str, Any]] = Field(default_factory=list)
|
||||
trends: List[Dict[str, Any]] = Field(default_factory=list)
|
||||
comparisons: List[Dict[str, Any]] = Field(default_factory=list)
|
||||
best_practices: List[str] = Field(default_factory=list)
|
||||
step_by_step: List[str] = Field(default_factory=list)
|
||||
pros_cons: Optional[Dict[str, Any]] = None
|
||||
definitions: Dict[str, str] = Field(default_factory=dict)
|
||||
examples: List[str] = Field(default_factory=list)
|
||||
predictions: List[str] = Field(default_factory=list)
|
||||
|
||||
# Content-ready outputs
|
||||
executive_summary: str = ""
|
||||
key_takeaways: List[str] = Field(default_factory=list)
|
||||
suggested_outline: List[str] = Field(default_factory=list)
|
||||
|
||||
# Sources and metadata
|
||||
sources: List[Dict[str, Any]] = Field(default_factory=list)
|
||||
confidence: float = 0.8
|
||||
gaps_identified: List[str] = Field(default_factory=list)
|
||||
follow_up_queries: List[str] = Field(default_factory=list)
|
||||
|
||||
# The inferred/confirmed intent
|
||||
intent: Optional[Dict[str, Any]] = None
|
||||
|
||||
# Error handling
|
||||
error_message: Optional[str] = None
|
||||
|
||||
|
||||
@router.post("/intent/analyze", response_model=AnalyzeIntentResponse)
|
||||
async def analyze_research_intent(
|
||||
request: AnalyzeIntentRequest,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Analyze user input to understand research intent.
|
||||
|
||||
This endpoint uses AI to infer what the user really wants from their research:
|
||||
- What questions need answering
|
||||
- What deliverables they expect (statistics, quotes, case studies, etc.)
|
||||
- What depth and focus is appropriate
|
||||
|
||||
The response includes quick options that can be shown in the UI for user confirmation.
|
||||
"""
|
||||
try:
|
||||
if not current_user:
|
||||
raise HTTPException(status_code=401, detail="Authentication required")
|
||||
|
||||
user_id = str(current_user.get('id', ''))
|
||||
if not user_id:
|
||||
raise HTTPException(status_code=401, detail="Invalid user ID")
|
||||
|
||||
logger.info(f"[Intent API] Analyzing intent for: {request.user_input[:50]}...")
|
||||
|
||||
# Get research persona if requested
|
||||
research_persona = None
|
||||
competitor_data = None
|
||||
|
||||
if request.use_persona or request.use_competitor_data:
|
||||
from services.research.research_persona_service import ResearchPersonaService
|
||||
from services.onboarding_service import OnboardingService
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
# Get database session
|
||||
db = next(get_db())
|
||||
try:
|
||||
persona_service = ResearchPersonaService(db)
|
||||
onboarding_service = OnboardingService()
|
||||
|
||||
if request.use_persona:
|
||||
research_persona = persona_service.get_or_generate(user_id)
|
||||
|
||||
if request.use_competitor_data:
|
||||
competitor_data = onboarding_service.get_competitor_analysis(user_id, db)
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
# Infer intent
|
||||
intent_service = ResearchIntentInference()
|
||||
response = await intent_service.infer_intent(
|
||||
user_input=request.user_input,
|
||||
keywords=request.keywords,
|
||||
research_persona=research_persona,
|
||||
competitor_data=competitor_data,
|
||||
industry=research_persona.default_industry if research_persona else None,
|
||||
target_audience=research_persona.default_target_audience if research_persona else None,
|
||||
)
|
||||
|
||||
# Generate targeted queries
|
||||
query_generator = IntentQueryGenerator()
|
||||
query_result = await query_generator.generate_queries(
|
||||
intent=response.intent,
|
||||
research_persona=research_persona,
|
||||
)
|
||||
|
||||
# Update response with queries
|
||||
response.suggested_queries = [q.dict() for q in query_result.get("queries", [])]
|
||||
response.suggested_keywords = query_result.get("enhanced_keywords", [])
|
||||
response.suggested_angles = query_result.get("research_angles", [])
|
||||
|
||||
return AnalyzeIntentResponse(
|
||||
success=True,
|
||||
intent=response.intent.dict(),
|
||||
analysis_summary=response.analysis_summary,
|
||||
suggested_queries=response.suggested_queries,
|
||||
suggested_keywords=response.suggested_keywords,
|
||||
suggested_angles=response.suggested_angles,
|
||||
quick_options=response.quick_options,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Intent API] Analyze failed: {e}")
|
||||
return AnalyzeIntentResponse(
|
||||
success=False,
|
||||
intent={},
|
||||
analysis_summary="",
|
||||
suggested_queries=[],
|
||||
suggested_keywords=[],
|
||||
suggested_angles=[],
|
||||
quick_options=[],
|
||||
error_message=str(e),
|
||||
)
|
||||
|
||||
|
||||
@router.post("/intent/research", response_model=IntentDrivenResearchResponse)
|
||||
async def execute_intent_driven_research(
|
||||
request: IntentDrivenResearchRequest,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
):
|
||||
"""
|
||||
Execute research based on user intent.
|
||||
|
||||
This is the main endpoint for intent-driven research. It:
|
||||
1. Uses the confirmed intent (or infers from user_input if not provided)
|
||||
2. Generates targeted queries for each expected deliverable
|
||||
3. Executes research using Exa/Tavily/Google
|
||||
4. Analyzes results through the lens of user intent
|
||||
5. Returns exactly what the user needs
|
||||
|
||||
The response is organized by deliverable type (statistics, quotes, case studies, etc.)
|
||||
instead of generic search results.
|
||||
"""
|
||||
try:
|
||||
if not current_user:
|
||||
raise HTTPException(status_code=401, detail="Authentication required")
|
||||
|
||||
user_id = str(current_user.get('id', ''))
|
||||
if not user_id:
|
||||
raise HTTPException(status_code=401, detail="Invalid user ID")
|
||||
|
||||
logger.info(f"[Intent API] Executing intent-driven research for: {request.user_input[:50]}...")
|
||||
|
||||
# Get database session
|
||||
db = next(get_db())
|
||||
|
||||
try:
|
||||
# Get research persona
|
||||
from services.research.research_persona_service import ResearchPersonaService
|
||||
persona_service = ResearchPersonaService(db)
|
||||
research_persona = persona_service.get_or_generate(user_id)
|
||||
|
||||
# Determine intent
|
||||
if request.confirmed_intent:
|
||||
# Use confirmed intent from UI
|
||||
intent = ResearchIntent(**request.confirmed_intent)
|
||||
elif not request.skip_inference:
|
||||
# Infer intent from user input
|
||||
intent_service = ResearchIntentInference()
|
||||
intent_response = await intent_service.infer_intent(
|
||||
user_input=request.user_input,
|
||||
research_persona=research_persona,
|
||||
)
|
||||
intent = intent_response.intent
|
||||
else:
|
||||
# Create basic intent from input
|
||||
intent = ResearchIntent(
|
||||
primary_question=f"What are the key insights about: {request.user_input}?",
|
||||
purpose="learn",
|
||||
content_output="general",
|
||||
expected_deliverables=["key_statistics", "best_practices", "examples"],
|
||||
depth="detailed",
|
||||
original_input=request.user_input,
|
||||
confidence=0.6,
|
||||
)
|
||||
|
||||
# Generate or use provided queries
|
||||
if request.selected_queries:
|
||||
queries = [ResearchQuery(**q) for q in request.selected_queries]
|
||||
else:
|
||||
query_generator = IntentQueryGenerator()
|
||||
query_result = await query_generator.generate_queries(
|
||||
intent=intent,
|
||||
research_persona=research_persona,
|
||||
)
|
||||
queries = query_result.get("queries", [])
|
||||
|
||||
# Execute research using the Research Engine
|
||||
engine = ResearchEngine(db_session=db)
|
||||
|
||||
# Build context from intent
|
||||
personalization = ResearchPersonalizationContext(
|
||||
creator_id=user_id,
|
||||
industry=research_persona.default_industry if research_persona else None,
|
||||
target_audience=research_persona.default_target_audience if research_persona else None,
|
||||
)
|
||||
|
||||
# Use the highest priority query for the main search
|
||||
# (In a more advanced version, we could run multiple queries and merge)
|
||||
primary_query = queries[0] if queries else ResearchQuery(
|
||||
query=request.user_input,
|
||||
purpose=ExpectedDeliverable.KEY_STATISTICS,
|
||||
provider="exa",
|
||||
priority=5,
|
||||
expected_results="General research results",
|
||||
)
|
||||
|
||||
context = ResearchContext(
|
||||
query=primary_query.query,
|
||||
keywords=request.user_input.split()[:10],
|
||||
goal=_map_purpose_to_goal(intent.purpose),
|
||||
depth=_map_depth_to_engine_depth(intent.depth),
|
||||
provider_preference=_map_provider_to_preference(primary_query.provider),
|
||||
personalization=personalization,
|
||||
max_sources=request.max_sources,
|
||||
include_domains=request.include_domains,
|
||||
exclude_domains=request.exclude_domains,
|
||||
)
|
||||
|
||||
# Execute research
|
||||
raw_result = await engine.research(context)
|
||||
|
||||
# Analyze results using intent-aware analyzer
|
||||
analyzer = IntentAwareAnalyzer()
|
||||
analyzed_result = await analyzer.analyze(
|
||||
raw_results={
|
||||
"content": raw_result.raw_content or "",
|
||||
"sources": raw_result.sources,
|
||||
"grounding_metadata": raw_result.grounding_metadata,
|
||||
},
|
||||
intent=intent,
|
||||
research_persona=research_persona,
|
||||
)
|
||||
|
||||
# Build response
|
||||
return IntentDrivenResearchResponse(
|
||||
success=True,
|
||||
primary_answer=analyzed_result.primary_answer,
|
||||
secondary_answers=analyzed_result.secondary_answers,
|
||||
statistics=[s.dict() for s in analyzed_result.statistics],
|
||||
expert_quotes=[q.dict() for q in analyzed_result.expert_quotes],
|
||||
case_studies=[cs.dict() for cs in analyzed_result.case_studies],
|
||||
trends=[t.dict() for t in analyzed_result.trends],
|
||||
comparisons=[c.dict() for c in analyzed_result.comparisons],
|
||||
best_practices=analyzed_result.best_practices,
|
||||
step_by_step=analyzed_result.step_by_step,
|
||||
pros_cons=analyzed_result.pros_cons.dict() if analyzed_result.pros_cons else None,
|
||||
definitions=analyzed_result.definitions,
|
||||
examples=analyzed_result.examples,
|
||||
predictions=analyzed_result.predictions,
|
||||
executive_summary=analyzed_result.executive_summary,
|
||||
key_takeaways=analyzed_result.key_takeaways,
|
||||
suggested_outline=analyzed_result.suggested_outline,
|
||||
sources=[s.dict() for s in analyzed_result.sources],
|
||||
confidence=analyzed_result.confidence,
|
||||
gaps_identified=analyzed_result.gaps_identified,
|
||||
follow_up_queries=analyzed_result.follow_up_queries,
|
||||
intent=intent.dict(),
|
||||
)
|
||||
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Intent API] Research failed: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return IntentDrivenResearchResponse(
|
||||
success=False,
|
||||
error_message=str(e),
|
||||
)
|
||||
|
||||
|
||||
def _map_purpose_to_goal(purpose: str) -> ResearchGoal:
|
||||
"""Map intent purpose to research goal."""
|
||||
mapping = {
|
||||
"learn": ResearchGoal.EDUCATIONAL,
|
||||
"create_content": ResearchGoal.FACTUAL,
|
||||
"make_decision": ResearchGoal.FACTUAL,
|
||||
"compare": ResearchGoal.COMPETITIVE,
|
||||
"solve_problem": ResearchGoal.EDUCATIONAL,
|
||||
"find_data": ResearchGoal.FACTUAL,
|
||||
"explore_trends": ResearchGoal.TRENDING,
|
||||
"validate": ResearchGoal.FACTUAL,
|
||||
"generate_ideas": ResearchGoal.INSPIRATIONAL,
|
||||
}
|
||||
return mapping.get(purpose, ResearchGoal.FACTUAL)
|
||||
|
||||
|
||||
def _map_depth_to_engine_depth(depth: str) -> ResearchDepth:
|
||||
"""Map intent depth to research engine depth."""
|
||||
mapping = {
|
||||
"overview": ResearchDepth.QUICK,
|
||||
"detailed": ResearchDepth.STANDARD,
|
||||
"expert": ResearchDepth.COMPREHENSIVE,
|
||||
}
|
||||
return mapping.get(depth, ResearchDepth.STANDARD)
|
||||
|
||||
|
||||
def _map_provider_to_preference(provider: str) -> ProviderPreference:
|
||||
"""Map query provider to engine preference."""
|
||||
mapping = {
|
||||
"exa": ProviderPreference.EXA,
|
||||
"tavily": ProviderPreference.TAVILY,
|
||||
"google": ProviderPreference.GOOGLE,
|
||||
}
|
||||
return mapping.get(provider, ProviderPreference.AUTO)
|
||||
|
||||
@@ -33,11 +33,18 @@ class ProviderAvailability(BaseModel):
|
||||
|
||||
|
||||
class PersonaDefaults(BaseModel):
|
||||
"""Persona-aware research defaults."""
|
||||
"""Persona-aware research defaults for hyper-personalization."""
|
||||
industry: Optional[str] = None
|
||||
target_audience: Optional[str] = None
|
||||
suggested_domains: list[str] = []
|
||||
suggested_exa_category: Optional[str] = None
|
||||
has_research_persona: bool = False # Phase 2: Indicates if research persona exists
|
||||
|
||||
# Phase 2: Additional fields from research persona for pre-filling advanced options
|
||||
default_research_mode: Optional[str] = None # basic, comprehensive, targeted
|
||||
default_provider: Optional[str] = None # exa, tavily, google
|
||||
suggested_keywords: list[str] = [] # For keyword suggestions
|
||||
research_angles: list[str] = [] # Alternative research focuses
|
||||
|
||||
|
||||
class ResearchConfigResponse(BaseModel):
|
||||
@@ -106,7 +113,12 @@ async def get_persona_defaults(
|
||||
"""
|
||||
Get persona-aware research defaults for the current user.
|
||||
|
||||
Returns industry, target audience, and smart suggestions based on onboarding data.
|
||||
Phase 2: Prioritizes research persona fields (richer defaults) over core persona.
|
||||
Since onboarding is mandatory, we always have core persona data - never return "General".
|
||||
|
||||
Returns industry, target audience, and smart suggestions based on:
|
||||
1. Research persona (if exists) - has suggested domains, Exa category, etc.
|
||||
2. Core persona (fallback) - industry and target audience from onboarding
|
||||
"""
|
||||
try:
|
||||
user_id = str(current_user.get('id'))
|
||||
@@ -114,54 +126,114 @@ async def get_persona_defaults(
|
||||
# Add explicit null check for database session
|
||||
if not db:
|
||||
logger.error(f"[ResearchConfig] Database session is None for user {user_id} in get_persona_defaults")
|
||||
# Return defaults rather than error
|
||||
# Return minimal defaults - but onboarding guarantees this won't happen
|
||||
return PersonaDefaults()
|
||||
|
||||
db_service = OnboardingDatabaseService(db=db)
|
||||
|
||||
# Try to get persona data first (most reliable source for industry/target_audience)
|
||||
# Phase 2: First check if research persona exists (cached only - don't generate here)
|
||||
# Generation happens in ResearchEngine.research() on first use
|
||||
research_persona = None
|
||||
try:
|
||||
persona_service = ResearchPersonaService(db_session=db)
|
||||
research_persona = persona_service.get_cached_only(user_id)
|
||||
except Exception as e:
|
||||
logger.debug(f"[ResearchConfig] Could not get research persona for {user_id}: {e}")
|
||||
|
||||
# If research persona exists, use its richer defaults (Phase 2: hyper-personalization)
|
||||
if research_persona:
|
||||
logger.info(f"[ResearchConfig] Using research persona defaults for user {user_id}")
|
||||
|
||||
# Ensure we never return "General" - provide meaningful defaults
|
||||
industry = research_persona.default_industry
|
||||
target_audience = research_persona.default_target_audience
|
||||
|
||||
# If persona has generic defaults, provide better ones
|
||||
if industry == "General" or not industry:
|
||||
industry = "Technology" # Safe default for content creators
|
||||
logger.info(f"[ResearchConfig] Upgrading generic industry to '{industry}' for user {user_id}")
|
||||
|
||||
if target_audience == "General" or not target_audience:
|
||||
target_audience = "Professionals and content consumers" # Better than "General"
|
||||
logger.info(f"[ResearchConfig] Upgrading generic target_audience to '{target_audience}' for user {user_id}")
|
||||
|
||||
return PersonaDefaults(
|
||||
industry=industry,
|
||||
target_audience=target_audience,
|
||||
suggested_domains=research_persona.suggested_exa_domains or [],
|
||||
suggested_exa_category=research_persona.suggested_exa_category,
|
||||
has_research_persona=True, # Frontend can use this
|
||||
# Phase 2: Additional pre-fill fields
|
||||
default_research_mode=research_persona.default_research_mode,
|
||||
default_provider=research_persona.default_provider,
|
||||
suggested_keywords=research_persona.suggested_keywords or [],
|
||||
research_angles=research_persona.research_angles or [],
|
||||
# Phase 2+: Enhanced provider-specific defaults
|
||||
suggested_exa_search_type=getattr(research_persona, 'suggested_exa_search_type', None),
|
||||
suggested_tavily_topic=getattr(research_persona, 'suggested_tavily_topic', None),
|
||||
suggested_tavily_search_depth=getattr(research_persona, 'suggested_tavily_search_depth', None),
|
||||
suggested_tavily_include_answer=getattr(research_persona, 'suggested_tavily_include_answer', None),
|
||||
suggested_tavily_time_range=getattr(research_persona, 'suggested_tavily_time_range', None),
|
||||
suggested_tavily_raw_content_format=getattr(research_persona, 'suggested_tavily_raw_content_format', None),
|
||||
provider_recommendations=getattr(research_persona, 'provider_recommendations', {}),
|
||||
)
|
||||
|
||||
# Fallback to core persona from onboarding (guaranteed to exist after onboarding)
|
||||
persona_data = db_service.get_persona_data(user_id, db)
|
||||
industry = 'General'
|
||||
target_audience = 'General'
|
||||
industry = None
|
||||
target_audience = None
|
||||
|
||||
if persona_data:
|
||||
core_persona = persona_data.get('corePersona') or persona_data.get('core_persona')
|
||||
if core_persona:
|
||||
if core_persona.get('industry'):
|
||||
industry = core_persona['industry']
|
||||
if core_persona.get('target_audience'):
|
||||
target_audience = core_persona['target_audience']
|
||||
industry = core_persona.get('industry')
|
||||
target_audience = core_persona.get('target_audience')
|
||||
|
||||
# Fallback to website analysis if persona data doesn't have industry info
|
||||
if industry == 'General':
|
||||
# Fallback to website analysis if core persona doesn't have industry
|
||||
if not industry:
|
||||
website_analysis = db_service.get_website_analysis(user_id, db)
|
||||
if website_analysis:
|
||||
target_audience_data = website_analysis.get('target_audience', {})
|
||||
if isinstance(target_audience_data, dict):
|
||||
# Extract from target_audience JSON field
|
||||
industry_focus = target_audience_data.get('industry_focus')
|
||||
if industry_focus:
|
||||
industry = industry_focus
|
||||
industry = target_audience_data.get('industry_focus')
|
||||
demographics = target_audience_data.get('demographics')
|
||||
if demographics:
|
||||
if demographics and not target_audience:
|
||||
target_audience = demographics if isinstance(demographics, str) else str(demographics)
|
||||
|
||||
# Phase 2: Never return "General" - use sensible defaults from onboarding or fallback
|
||||
# Since onboarding is mandatory, we should always have real data
|
||||
if not industry:
|
||||
industry = "Technology" # Safe default for content creators
|
||||
logger.warning(f"[ResearchConfig] No industry found for user {user_id}, using default")
|
||||
if not target_audience:
|
||||
target_audience = "Professionals" # Safe default
|
||||
logger.warning(f"[ResearchConfig] No target_audience found for user {user_id}, using default")
|
||||
|
||||
# Suggest domains based on industry
|
||||
suggested_domains = _get_domain_suggestions(industry)
|
||||
|
||||
# Suggest Exa category based on industry
|
||||
suggested_exa_category = _get_exa_category_suggestion(industry)
|
||||
|
||||
logger.info(f"[ResearchConfig] Using core persona defaults for user {user_id}: industry={industry}")
|
||||
|
||||
return PersonaDefaults(
|
||||
industry=industry,
|
||||
target_audience=target_audience,
|
||||
suggested_domains=suggested_domains,
|
||||
suggested_exa_category=suggested_exa_category
|
||||
suggested_exa_category=suggested_exa_category,
|
||||
has_research_persona=False # Frontend knows to trigger generation
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"[ResearchConfig] Error getting persona defaults for user {user_id if 'user_id' in locals() else 'unknown'}: {e}", exc_info=True)
|
||||
# Return defaults rather than error
|
||||
return PersonaDefaults()
|
||||
# Return sensible defaults - never "General"
|
||||
return PersonaDefaults(
|
||||
industry="Technology",
|
||||
target_audience="Professionals",
|
||||
suggested_domains=[],
|
||||
suggested_exa_category=None,
|
||||
has_research_persona=False
|
||||
)
|
||||
|
||||
|
||||
@router.get("/research-persona")
|
||||
@@ -430,7 +502,7 @@ async def get_competitor_analysis(
|
||||
success=False,
|
||||
error="Onboarding step 3 (Competitor Analysis) is not completed. Please complete onboarding step 3 first."
|
||||
)
|
||||
|
||||
|
||||
print(f"[COMPETITOR_ANALYSIS] ✅ Step 3 is completed (current_step={session.current_step} or research_preferences exists)")
|
||||
|
||||
# Try Method 1: Get competitor data from CompetitorAnalysis table using OnboardingDatabaseService
|
||||
@@ -438,11 +510,11 @@ async def get_competitor_analysis(
|
||||
print(f"[COMPETITOR_ANALYSIS] 🔍 Method 1: Querying CompetitorAnalysis table using OnboardingDatabaseService...")
|
||||
try:
|
||||
competitors = db_service.get_competitor_analysis(user_id, db)
|
||||
|
||||
|
||||
if competitors:
|
||||
print(f"[COMPETITOR_ANALYSIS] ✅ Found {len(competitors)} competitor records from CompetitorAnalysis table")
|
||||
logger.info(f"[ResearchConfig] Found {len(competitors)} competitors from CompetitorAnalysis table for user {user_id}")
|
||||
|
||||
|
||||
# Map competitor fields to match frontend expectations
|
||||
mapped_competitors = []
|
||||
for comp in competitors:
|
||||
@@ -453,7 +525,7 @@ async def get_competitor_analysis(
|
||||
"similarity_score": comp.get("relevance_score") or comp.get("similarity_score", 0.5)
|
||||
}
|
||||
mapped_competitors.append(mapped_comp)
|
||||
|
||||
|
||||
print(f"[COMPETITOR_ANALYSIS] ✅ SUCCESS: Returning {len(mapped_competitors)} competitors for user_id={user_id}")
|
||||
return CompetitorAnalysisResponse(
|
||||
success=True,
|
||||
@@ -468,7 +540,7 @@ async def get_competitor_analysis(
|
||||
)
|
||||
else:
|
||||
print(f"[COMPETITOR_ANALYSIS] ⚠️ No competitor records found in CompetitorAnalysis table for user_id={user_id}")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"[COMPETITOR_ANALYSIS] ❌ EXCEPTION in Method 1: {e}")
|
||||
import traceback
|
||||
@@ -487,12 +559,12 @@ async def get_competitor_analysis(
|
||||
research_data_result = await step3_service.get_research_data(str(session.id))
|
||||
|
||||
print(f"[COMPETITOR_ANALYSIS] Step3ResearchService.get_research_data() result: success={research_data_result.get('success')}")
|
||||
|
||||
|
||||
if research_data_result.get('success'):
|
||||
# Handle both 'research_data' and 'step3_research_data' keys
|
||||
# Handle both 'research_data' and 'step3_research_data' keys
|
||||
research_data = research_data_result.get('step3_research_data') or research_data_result.get('research_data', {})
|
||||
print(f"[COMPETITOR_ANALYSIS] Research data keys: {list(research_data.keys()) if isinstance(research_data, dict) else 'Not a dict'}")
|
||||
|
||||
|
||||
if isinstance(research_data, dict) and research_data.get('competitors'):
|
||||
competitors_list = research_data.get('competitors', [])
|
||||
print(f"[COMPETITOR_ANALYSIS] ✅ Found {len(competitors_list)} competitors in step_data via Step3ResearchService")
|
||||
@@ -500,8 +572,8 @@ async def get_competitor_analysis(
|
||||
if competitors_list:
|
||||
analysis_metadata = research_data.get('analysis_metadata', {})
|
||||
social_media_data = analysis_metadata.get('social_media_data', {})
|
||||
|
||||
# Map competitor fields to match frontend expectations
|
||||
|
||||
# Map competitor fields to match frontend expectations
|
||||
mapped_competitors = []
|
||||
for comp in competitors_list:
|
||||
mapped_comp = {
|
||||
@@ -511,7 +583,7 @@ async def get_competitor_analysis(
|
||||
"similarity_score": comp.get("relevance_score") or comp.get("similarity_score", 0.5)
|
||||
}
|
||||
mapped_competitors.append(mapped_comp)
|
||||
|
||||
|
||||
print(f"[COMPETITOR_ANALYSIS] ✅ SUCCESS: Returning {len(mapped_competitors)} competitors from step_data for user_id={user_id}")
|
||||
logger.info(f"[ResearchConfig] Found {len(mapped_competitors)} competitors from step_data via Step3ResearchService for user {user_id}")
|
||||
return CompetitorAnalysisResponse(
|
||||
@@ -561,6 +633,114 @@ async def get_competitor_analysis(
|
||||
print(f"[COMPETITOR_ANALYSIS] ===== END: Getting competitor analysis for user_id={user_id} =====\n")
|
||||
|
||||
|
||||
@router.post("/competitor-analysis/refresh", response_model=CompetitorAnalysisResponse)
|
||||
async def refresh_competitor_analysis(
|
||||
current_user: Dict = Depends(get_current_user),
|
||||
db: Session = Depends(get_db)
|
||||
):
|
||||
"""
|
||||
Refresh competitor analysis by re-running competitor discovery from onboarding.
|
||||
|
||||
This endpoint re-triggers the competitor discovery process and saves the results
|
||||
to the database, allowing users to update their competitor analysis data.
|
||||
"""
|
||||
user_id = None
|
||||
try:
|
||||
user_id = str(current_user.get('id'))
|
||||
logger.info(f"[ResearchConfig] Refreshing competitor analysis for user {user_id}")
|
||||
|
||||
if not db:
|
||||
raise HTTPException(status_code=500, detail="Database session not available")
|
||||
|
||||
db_service = OnboardingDatabaseService(db=db)
|
||||
|
||||
# Get onboarding session
|
||||
session = db_service.get_session_by_user(user_id, db)
|
||||
if not session:
|
||||
return CompetitorAnalysisResponse(
|
||||
success=False,
|
||||
error="No onboarding session found. Please complete onboarding first."
|
||||
)
|
||||
|
||||
# Get website URL from website analysis
|
||||
website_analysis = db_service.get_website_analysis(user_id, db)
|
||||
if not website_analysis or not website_analysis.get('website_url'):
|
||||
return CompetitorAnalysisResponse(
|
||||
success=False,
|
||||
error="No website URL found. Please complete onboarding step 2 (Website Analysis) first."
|
||||
)
|
||||
|
||||
user_url = website_analysis.get('website_url')
|
||||
if not user_url or user_url.strip() == '':
|
||||
return CompetitorAnalysisResponse(
|
||||
success=False,
|
||||
error="Website URL is empty. Please complete onboarding step 2 (Website Analysis) first."
|
||||
)
|
||||
|
||||
# Get industry context from research preferences or persona
|
||||
research_prefs = db_service.get_research_preferences(user_id, db) or {}
|
||||
persona_data = db_service.get_persona_data(user_id, db) or {}
|
||||
core_persona = persona_data.get('corePersona') or persona_data.get('core_persona') or {}
|
||||
industry_context = core_persona.get('industry') or research_prefs.get('industry') or None
|
||||
|
||||
# Import and use Step3ResearchService to re-run competitor discovery
|
||||
from api.onboarding_utils.step3_research_service import Step3ResearchService
|
||||
|
||||
step3_service = Step3ResearchService()
|
||||
result = await step3_service.discover_competitors_for_onboarding(
|
||||
user_url=user_url,
|
||||
user_id=user_id,
|
||||
industry_context=industry_context,
|
||||
num_results=25,
|
||||
website_analysis_data=website_analysis
|
||||
)
|
||||
|
||||
if result.get("success"):
|
||||
# Get the updated competitor data from database
|
||||
competitors = db_service.get_competitor_analysis(user_id, db)
|
||||
|
||||
if competitors:
|
||||
# Map competitor fields
|
||||
mapped_competitors = []
|
||||
for comp in competitors:
|
||||
mapped_comp = {
|
||||
**comp,
|
||||
"name": comp.get("title") or comp.get("name") or comp.get("domain", ""),
|
||||
"description": comp.get("summary") or comp.get("description", ""),
|
||||
"similarity_score": comp.get("relevance_score") or comp.get("similarity_score", 0.5)
|
||||
}
|
||||
mapped_competitors.append(mapped_comp)
|
||||
|
||||
logger.info(f"[ResearchConfig] Successfully refreshed competitor analysis: {len(mapped_competitors)} competitors")
|
||||
return CompetitorAnalysisResponse(
|
||||
success=True,
|
||||
competitors=mapped_competitors,
|
||||
social_media_accounts=result.get("social_media_accounts", {}),
|
||||
social_media_citations=result.get("social_media_citations", []),
|
||||
research_summary=result.get("research_summary", {}),
|
||||
analysis_timestamp=result.get("analysis_timestamp")
|
||||
)
|
||||
else:
|
||||
return CompetitorAnalysisResponse(
|
||||
success=False,
|
||||
error="Competitor discovery completed but no data was saved. Please try again."
|
||||
)
|
||||
else:
|
||||
return CompetitorAnalysisResponse(
|
||||
success=False,
|
||||
error=result.get("error", "Failed to refresh competitor analysis")
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[ResearchConfig] Error refreshing competitor analysis for user {user_id if user_id else 'unknown'}: {e}", exc_info=True)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to refresh competitor analysis: {str(e)}"
|
||||
)
|
||||
|
||||
|
||||
# Helper functions from RESEARCH_AI_HYPERPERSONALIZATION.md
|
||||
|
||||
def _get_domain_suggestions(industry: str) -> list[str]:
|
||||
|
||||
@@ -56,7 +56,9 @@ class TaskManager:
|
||||
self.cleanup_old_tasks()
|
||||
|
||||
if task_id not in self.task_storage:
|
||||
logger.warning(f"[StoryWriter] Task not found: {task_id}")
|
||||
# Log at DEBUG level - task not found is expected when tasks expire or are cleaned up
|
||||
# This prevents log spam from frontend polling for expired/completed tasks
|
||||
logger.debug(f"[StoryWriter] Task not found: {task_id} (may have expired or been cleaned up)")
|
||||
return None
|
||||
|
||||
task = self.task_storage[task_id]
|
||||
|
||||
@@ -31,17 +31,21 @@ def generate_hd_video_payload(request: Any, user_id: str) -> Dict[str, Any]:
|
||||
kwargs["seed"] = request.seed
|
||||
|
||||
logger.info(f"[StoryWriter] Generating HD video via {getattr(request, 'provider', 'huggingface')} for user {user_id}")
|
||||
raw_bytes = ai_video_generate(
|
||||
result = ai_video_generate(
|
||||
prompt=request.prompt,
|
||||
operation_type="text-to-video",
|
||||
provider=getattr(request, "provider", None) or "huggingface",
|
||||
user_id=user_id,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Extract video bytes from result dict
|
||||
video_bytes = result["video_bytes"]
|
||||
|
||||
filename = f"hd_{uuid4().hex}.mp4"
|
||||
file_path = output_dir / filename
|
||||
with open(file_path, "wb") as fh:
|
||||
fh.write(raw_bytes)
|
||||
fh.write(video_bytes)
|
||||
|
||||
logger.info(f"[StoryWriter] HD video saved to {file_path}")
|
||||
return {
|
||||
@@ -111,16 +115,20 @@ def generate_hd_video_scene_payload(request: Any, user_id: str) -> Dict[str, Any
|
||||
if getattr(request, "seed", None) is not None:
|
||||
kwargs["seed"] = request.seed
|
||||
|
||||
raw_bytes = ai_video_generate(
|
||||
result = ai_video_generate(
|
||||
prompt=enhanced_prompt,
|
||||
operation_type="text-to-video",
|
||||
provider=getattr(request, "provider", None) or "huggingface",
|
||||
user_id=user_id,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Extract video bytes from result dict
|
||||
video_bytes = result["video_bytes"]
|
||||
|
||||
video_service = StoryVideoGenerationService()
|
||||
save_result = video_service.save_scene_video(
|
||||
video_bytes=raw_bytes,
|
||||
video_bytes=video_bytes,
|
||||
scene_number=scene_number,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
@@ -26,6 +26,76 @@ YOUTUBE_AUDIO_DIR.mkdir(parents=True, exist_ok=True)
|
||||
# Initialize audio service
|
||||
audio_service = StoryAudioGenerationService(output_dir=str(YOUTUBE_AUDIO_DIR))
|
||||
|
||||
# WaveSpeed Minimax Speech voice ids include language-specific voices
|
||||
# Ref: https://wavespeed.ai/docs/docs-api/minimax/minimax_speech_voice_id
|
||||
LANGUAGE_CODE_TO_LANGUAGE_BOOST = {
|
||||
"en": "English",
|
||||
"es": "Spanish",
|
||||
"fr": "French",
|
||||
"de": "German",
|
||||
"pt": "Portuguese",
|
||||
"it": "Italian",
|
||||
"hi": "Hindi",
|
||||
"ar": "Arabic",
|
||||
"ru": "Russian",
|
||||
"ja": "Japanese",
|
||||
"ko": "Korean",
|
||||
"zh": "Chinese",
|
||||
"vi": "Vietnamese",
|
||||
"id": "Indonesian",
|
||||
"tr": "Turkish",
|
||||
"nl": "Dutch",
|
||||
"pl": "Polish",
|
||||
"th": "Thai",
|
||||
"uk": "Ukrainian",
|
||||
"el": "Greek",
|
||||
"cs": "Czech",
|
||||
"fi": "Finnish",
|
||||
"ro": "Romanian",
|
||||
}
|
||||
|
||||
# Default language-specific Minimax voices (first-choice). We keep English on the existing "persona" voices.
|
||||
LANGUAGE_BOOST_TO_DEFAULT_VOICE_ID = {
|
||||
"Spanish": "Spanish_male_1_v1",
|
||||
"French": "French_male_1_v1",
|
||||
"German": "German_male_1_v1",
|
||||
"Portuguese": "Portuguese_male_1_v1",
|
||||
"Italian": "Italian_male_1_v1",
|
||||
"Hindi": "Hindi_male_1_v1",
|
||||
"Arabic": "Arabic_male_1_v1",
|
||||
"Russian": "Russian_male_1_v1",
|
||||
"Japanese": "Japanese_male_1_v1",
|
||||
"Korean": "Korean_male_1_v1",
|
||||
"Chinese": "Chinese_male_1_v1",
|
||||
"Vietnamese": "Vietnamese_male_1_v1",
|
||||
"Indonesian": "Indonesian_male_1_v1",
|
||||
"Turkish": "Turkish_male_1_v1",
|
||||
"Dutch": "Dutch_male_1_v1",
|
||||
"Polish": "Polish_male_1_v1",
|
||||
"Thai": "Thai_male_1_v1",
|
||||
"Ukrainian": "Ukrainian_male_1_v1",
|
||||
"Greek": "Greek_male_1_v1",
|
||||
"Czech": "Czech_male_1_v1",
|
||||
"Finnish": "Finnish_male_1_v1",
|
||||
"Romanian": "Romanian_male_1_v1",
|
||||
}
|
||||
|
||||
|
||||
def _resolve_language_boost(language: Optional[str], explicit_language_boost: Optional[str]) -> str:
|
||||
"""
|
||||
Determine the effective WaveSpeed `language_boost`.
|
||||
- If user explicitly provided language_boost, use it (including "auto").
|
||||
- Else if language code provided, map to the WaveSpeed boost label.
|
||||
- Else default to English (backwards compatible).
|
||||
"""
|
||||
if explicit_language_boost is not None and str(explicit_language_boost).strip() != "":
|
||||
return str(explicit_language_boost).strip()
|
||||
|
||||
if language is not None and str(language).strip() != "":
|
||||
lang_code = str(language).strip().lower()
|
||||
return LANGUAGE_CODE_TO_LANGUAGE_BOOST.get(lang_code, "auto")
|
||||
|
||||
return "English"
|
||||
|
||||
def select_optimal_emotion(scene_title: str, narration: str, video_plan_context: Optional[Dict[str, Any]] = None) -> str:
|
||||
"""
|
||||
@@ -153,6 +223,7 @@ class YouTubeAudioRequest(BaseModel):
|
||||
scene_title: str
|
||||
text: str
|
||||
voice_id: Optional[str] = None # Will auto-select based on content if not provided
|
||||
language: Optional[str] = None # Language code for multilingual audio (e.g., "en", "es", "fr")
|
||||
speed: float = 1.0
|
||||
volume: float = 1.0
|
||||
pitch: float = 0.0
|
||||
@@ -164,7 +235,7 @@ class YouTubeAudioRequest(BaseModel):
|
||||
bitrate: int = 256000 # Highest quality: 256kbps (valid values: 32000, 64000, 128000, 256000)
|
||||
channel: Optional[str] = "2" # Stereo for richer audio (valid values: "1" or "2")
|
||||
format: Optional[str] = "mp3" # Universal format for web
|
||||
language_boost: Optional[str] = "English" # Optimize for English content
|
||||
language_boost: Optional[str] = None # If not provided, inferred from `language` (or defaults to English)
|
||||
enable_sync_mode: bool = True
|
||||
# Context for intelligent voice/emotion selection
|
||||
video_plan_context: Optional[Dict[str, Any]] = None # Optional video plan for context-aware voice selection
|
||||
@@ -224,13 +295,24 @@ async def generate_youtube_scene_audio(
|
||||
|
||||
logger.info(f"[YouTubeAudio] Text preprocessing: {len(request.text)} -> {len(processed_text)} characters")
|
||||
|
||||
effective_language_boost = _resolve_language_boost(request.language, request.language_boost)
|
||||
|
||||
# Intelligent voice and emotion selection based on content analysis
|
||||
if not request.voice_id:
|
||||
selected_voice = select_optimal_voice(
|
||||
request.scene_title,
|
||||
processed_text,
|
||||
request.video_plan_context
|
||||
)
|
||||
# If non-English language is selected, default to the language-specific Minimax voice_id.
|
||||
# Otherwise keep the existing English persona voice selection logic.
|
||||
if effective_language_boost in LANGUAGE_BOOST_TO_DEFAULT_VOICE_ID and effective_language_boost not in ["English", "auto"]:
|
||||
selected_voice = LANGUAGE_BOOST_TO_DEFAULT_VOICE_ID[effective_language_boost]
|
||||
logger.info(
|
||||
f"[VoiceSelection] Using language-specific default voice '{selected_voice}' "
|
||||
f"(language_boost={effective_language_boost}, language={request.language})"
|
||||
)
|
||||
else:
|
||||
selected_voice = select_optimal_voice(
|
||||
request.scene_title,
|
||||
processed_text,
|
||||
request.video_plan_context
|
||||
)
|
||||
else:
|
||||
selected_voice = request.voice_id
|
||||
|
||||
@@ -244,7 +326,10 @@ async def generate_youtube_scene_audio(
|
||||
else:
|
||||
selected_emotion = request.emotion
|
||||
|
||||
logger.info(f"[YouTubeAudio] Voice selection: {selected_voice}, Emotion: {selected_emotion}")
|
||||
logger.info(
|
||||
f"[YouTubeAudio] Voice selection: {selected_voice}, Emotion: {selected_emotion}, "
|
||||
f"language={request.language}, language_boost={effective_language_boost}"
|
||||
)
|
||||
|
||||
# Build kwargs for optional parameters - use defaults if None
|
||||
# WaveSpeed API requires specific values, so we provide sensible defaults
|
||||
@@ -252,7 +337,11 @@ async def generate_youtube_scene_audio(
|
||||
optional_kwargs = {}
|
||||
|
||||
# DEBUG: Log what values we received
|
||||
logger.info(f"[YouTubeAudio] Request parameters: sample_rate={request.sample_rate}, bitrate={request.bitrate}, channel={request.channel}, format={request.format}, language_boost={request.language_boost}")
|
||||
logger.info(
|
||||
f"[YouTubeAudio] Request parameters: sample_rate={request.sample_rate}, bitrate={request.bitrate}, "
|
||||
f"channel={request.channel}, format={request.format}, language_boost={request.language_boost}, "
|
||||
f"effective_language_boost={effective_language_boost}, language={request.language}"
|
||||
)
|
||||
|
||||
# sample_rate: Use provided value or omit (WaveSpeed will use default)
|
||||
if request.sample_rate is not None:
|
||||
@@ -276,9 +365,9 @@ async def generate_youtube_scene_audio(
|
||||
if request.format is not None:
|
||||
optional_kwargs["format"] = request.format
|
||||
|
||||
# language_boost: Use provided value or omit (WaveSpeed will use default)
|
||||
if request.language_boost is not None:
|
||||
optional_kwargs["language_boost"] = request.language_boost
|
||||
# language_boost: always send resolved value (improves pronunciation and helps multilingual voices)
|
||||
if effective_language_boost is not None and str(effective_language_boost).strip() != "":
|
||||
optional_kwargs["language_boost"] = effective_language_boost
|
||||
|
||||
logger.info(f"[YouTubeAudio] Final optional_kwargs: {optional_kwargs}")
|
||||
|
||||
|
||||
@@ -287,7 +287,7 @@ async def create_video_plan(
|
||||
|
||||
# Check for existing YouTube creator avatar in asset library
|
||||
asset_service = ContentAssetService(db)
|
||||
existing_avatars = asset_service.get_assets(
|
||||
existing_avatars, _ = asset_service.get_user_assets(
|
||||
user_id=user_id,
|
||||
asset_type=AssetType.IMAGE,
|
||||
source_module=AssetSource.YOUTUBE_CREATOR,
|
||||
@@ -685,11 +685,12 @@ async def render_single_scene_video(
|
||||
async def get_render_status(
|
||||
task_id: str,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
Get the status of a video rendering task.
|
||||
|
||||
Returns current progress, status, and result when complete.
|
||||
Returns None if task not found (matches podcast pattern for graceful handling).
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
@@ -697,24 +698,17 @@ async def get_render_status(
|
||||
logger.debug(f"[YouTubeAPI] Getting render status for task: {task_id}")
|
||||
task_status = task_manager.get_task_status(task_id)
|
||||
if not task_status:
|
||||
logger.warning(
|
||||
f"[YouTubeAPI] Task {task_id} not found. "
|
||||
f"Available tasks: {list(task_manager.task_storage.keys())[:5]}..."
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail={
|
||||
"error": "Task not found",
|
||||
"message": "The render task was not found. It may have expired, been cleaned up, or the server may have restarted.",
|
||||
"task_id": task_id,
|
||||
"user_action": "Please try rendering again."
|
||||
}
|
||||
# Log at DEBUG level - null is expected when tasks expire or server restarts
|
||||
# This prevents log spam from frontend polling for expired/completed tasks
|
||||
# Return None instead of raising 404 to match podcast pattern for graceful frontend handling
|
||||
logger.debug(
|
||||
f"[YouTubeAPI] Task {task_id} not found (may have expired or been cleaned up). "
|
||||
f"Available tasks: {len(task_manager.task_storage)}"
|
||||
)
|
||||
return None
|
||||
|
||||
return task_status
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[YouTubeAPI] Error getting render status: {e}", exc_info=True)
|
||||
raise HTTPException(
|
||||
@@ -1201,6 +1195,12 @@ def _execute_scene_video_render_task(
|
||||
result=result,
|
||||
)
|
||||
|
||||
# Verify the task status was updated correctly (matches podcast pattern)
|
||||
updated_status = task_manager.get_task_status(task_id)
|
||||
logger.info(
|
||||
f"[YouTubeRenderer] Task status after update: task_id={task_id}, status={updated_status.get('status') if updated_status else 'None'}, has_result={bool(updated_status.get('result') if updated_status else False)}, video_url={updated_status.get('result', {}).get('video_url') if updated_status else 'N/A'}"
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"[YouTubeRenderer] ✅ Single-scene render {task_id} completed (scene {scene_num}), cost=${total_cost:.2f}"
|
||||
)
|
||||
@@ -1348,27 +1348,37 @@ async def list_videos(
|
||||
List videos for the current user from the asset library (source: youtube_creator).
|
||||
Used to rescue/persist scene videos after reloads.
|
||||
"""
|
||||
user_id = require_authenticated_user(current_user)
|
||||
asset_service = ContentAssetService(db)
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
assets = asset_service.get_assets(
|
||||
user_id=user_id,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.YOUTUBE_CREATOR,
|
||||
limit=100,
|
||||
)
|
||||
assets, _ = asset_service.get_user_assets(
|
||||
user_id=user_id,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.YOUTUBE_CREATOR,
|
||||
limit=100,
|
||||
)
|
||||
|
||||
videos = []
|
||||
for asset in assets:
|
||||
videos.append({
|
||||
"scene_number": asset.asset_metadata.get("scene_number") if asset.asset_metadata else None,
|
||||
"video_url": asset.file_url,
|
||||
"filename": asset.filename,
|
||||
"created_at": asset.created_at,
|
||||
"resolution": asset.asset_metadata.get("resolution") if asset.asset_metadata else None,
|
||||
})
|
||||
videos = []
|
||||
for asset in assets:
|
||||
try:
|
||||
videos.append({
|
||||
"scene_number": asset.asset_metadata.get("scene_number") if asset.asset_metadata else None,
|
||||
"video_url": asset.file_url,
|
||||
"filename": asset.filename,
|
||||
"created_at": asset.created_at.isoformat() if asset.created_at else None,
|
||||
"resolution": asset.asset_metadata.get("resolution") if asset.asset_metadata else None,
|
||||
})
|
||||
except Exception as asset_error:
|
||||
logger.warning(f"[YouTubeAPI] Error processing asset {asset.id if hasattr(asset, 'id') else 'unknown'}: {asset_error}")
|
||||
continue # Skip this asset and continue with others
|
||||
|
||||
return VideoListResponse(videos=videos)
|
||||
logger.info(f"[YouTubeAPI] Listed {len(videos)} videos for user {user_id}")
|
||||
return VideoListResponse(videos=videos)
|
||||
except Exception as e:
|
||||
logger.error(f"[YouTubeAPI] Error listing videos: {e}", exc_info=True)
|
||||
# Return empty list on error rather than failing completely
|
||||
return VideoListResponse(videos=[], success=False, message=f"Failed to list videos: {str(e)}")
|
||||
|
||||
|
||||
def _execute_combine_video_task(
|
||||
|
||||
@@ -316,6 +316,10 @@ app.include_router(youtube_router, prefix="/api")
|
||||
# Include research configuration router
|
||||
app.include_router(research_config_router, prefix="/api/research", tags=["research"])
|
||||
|
||||
# Include Research Engine router (standalone AI research module)
|
||||
from api.research.router import router as research_engine_router
|
||||
app.include_router(research_engine_router, tags=["Research Engine"])
|
||||
|
||||
# Scheduler dashboard routes
|
||||
from api.scheduler_dashboard import router as scheduler_dashboard_router
|
||||
app.include_router(scheduler_dashboard_router)
|
||||
|
||||
@@ -208,12 +208,18 @@ class ClerkAuthMiddleware:
|
||||
clerk_auth = ClerkAuthMiddleware()
|
||||
|
||||
async def get_current_user(
|
||||
request: Request,
|
||||
credentials: Optional[HTTPAuthorizationCredentials] = Depends(security)
|
||||
) -> Dict[str, Any]:
|
||||
"""Get current authenticated user."""
|
||||
try:
|
||||
if not credentials:
|
||||
logger.warning("No credentials provided")
|
||||
# CRITICAL: Log as ERROR since this is a security issue - authenticated endpoint accessed without credentials
|
||||
endpoint_path = f"{request.method} {request.url.path}"
|
||||
logger.error(
|
||||
f"🔒 AUTHENTICATION ERROR: No credentials provided for authenticated endpoint: {endpoint_path} "
|
||||
f"(client_ip={request.client.host if request.client else 'unknown'})"
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Not authenticated",
|
||||
@@ -223,9 +229,12 @@ async def get_current_user(
|
||||
token = credentials.credentials
|
||||
user = await clerk_auth.verify_token(token)
|
||||
if not user:
|
||||
# Token verification failed (likely expired) - log at debug level to reduce noise
|
||||
# The HTTPException will still be raised, but we don't need to spam logs
|
||||
logger.debug("Token verification failed (likely expired token)")
|
||||
# Token verification failed - log with endpoint context for debugging
|
||||
endpoint_path = f"{request.method} {request.url.path}"
|
||||
logger.error(
|
||||
f"🔒 AUTHENTICATION ERROR: Token verification failed for endpoint: {endpoint_path} "
|
||||
f"(client_ip={request.client.host if request.client else 'unknown'})"
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Authentication failed",
|
||||
@@ -237,7 +246,11 @@ async def get_current_user(
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Authentication error: {e}")
|
||||
endpoint_path = f"{request.method} {request.url.path}"
|
||||
logger.error(
|
||||
f"🔒 AUTHENTICATION ERROR: Unexpected error during authentication for endpoint: {endpoint_path}: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Authentication failed",
|
||||
@@ -291,7 +304,13 @@ async def get_current_user_with_query_token(
|
||||
token_to_verify = query_token
|
||||
|
||||
if not token_to_verify:
|
||||
logger.warning("No credentials provided (neither header nor query parameter)")
|
||||
# CRITICAL: Log as ERROR since this is a security issue
|
||||
endpoint_path = f"{request.method} {request.url.path}"
|
||||
logger.error(
|
||||
f"🔒 AUTHENTICATION ERROR: No credentials provided (neither header nor query parameter) "
|
||||
f"for authenticated endpoint: {endpoint_path} "
|
||||
f"(client_ip={request.client.host if request.client else 'unknown'})"
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Not authenticated",
|
||||
@@ -300,8 +319,12 @@ async def get_current_user_with_query_token(
|
||||
|
||||
user = await clerk_auth.verify_token(token_to_verify)
|
||||
if not user:
|
||||
# Token verification failed (likely expired) - log at debug level to reduce noise
|
||||
logger.debug("Token verification failed (likely expired token)")
|
||||
# Token verification failed - log with endpoint context
|
||||
endpoint_path = f"{request.method} {request.url.path}"
|
||||
logger.error(
|
||||
f"🔒 AUTHENTICATION ERROR: Token verification failed for endpoint: {endpoint_path} "
|
||||
f"(client_ip={request.client.host if request.client else 'unknown'})"
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Authentication failed",
|
||||
@@ -313,7 +336,11 @@ async def get_current_user_with_query_token(
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Authentication error: {e}")
|
||||
endpoint_path = f"{request.method} {request.url.path}"
|
||||
logger.error(
|
||||
f"🔒 AUTHENTICATION ERROR: Unexpected error during authentication for endpoint: {endpoint_path}: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Authentication failed",
|
||||
|
||||
355
backend/models/research_intent_models.py
Normal file
355
backend/models/research_intent_models.py
Normal file
@@ -0,0 +1,355 @@
|
||||
"""
|
||||
Research Intent Models
|
||||
|
||||
Pydantic models for understanding user research intent.
|
||||
These models capture what the user actually wants to accomplish from their research,
|
||||
enabling targeted query generation and intent-aware result analysis.
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 1.0
|
||||
"""
|
||||
|
||||
from enum import Enum
|
||||
from typing import Dict, Any, List, Optional, Union
|
||||
from pydantic import BaseModel, Field
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class ResearchPurpose(str, Enum):
|
||||
"""Why is the user researching?"""
|
||||
LEARN = "learn" # Understand a topic for personal knowledge
|
||||
CREATE_CONTENT = "create_content" # Write article/blog/podcast/video
|
||||
MAKE_DECISION = "make_decision" # Choose between options
|
||||
COMPARE = "compare" # Compare alternatives/competitors
|
||||
SOLVE_PROBLEM = "solve_problem" # Find solution to a problem
|
||||
FIND_DATA = "find_data" # Get statistics/facts/citations
|
||||
EXPLORE_TRENDS = "explore_trends" # Understand market/industry trends
|
||||
VALIDATE = "validate" # Verify claims/information
|
||||
GENERATE_IDEAS = "generate_ideas" # Brainstorm content ideas
|
||||
|
||||
|
||||
class ContentOutput(str, Enum):
|
||||
"""What content type will be created from this research?"""
|
||||
BLOG = "blog"
|
||||
PODCAST = "podcast"
|
||||
VIDEO = "video"
|
||||
SOCIAL_POST = "social_post"
|
||||
NEWSLETTER = "newsletter"
|
||||
PRESENTATION = "presentation"
|
||||
REPORT = "report"
|
||||
WHITEPAPER = "whitepaper"
|
||||
EMAIL = "email"
|
||||
GENERAL = "general" # No specific output
|
||||
|
||||
|
||||
class ExpectedDeliverable(str, Enum):
|
||||
"""What specific outputs the user expects from research."""
|
||||
KEY_STATISTICS = "key_statistics" # Numbers, data points, percentages
|
||||
EXPERT_QUOTES = "expert_quotes" # Authoritative statements
|
||||
CASE_STUDIES = "case_studies" # Real examples and success stories
|
||||
COMPARISONS = "comparisons" # Side-by-side analysis
|
||||
TRENDS = "trends" # Market/industry trends
|
||||
BEST_PRACTICES = "best_practices" # Recommendations and guidelines
|
||||
STEP_BY_STEP = "step_by_step" # Process/how-to instructions
|
||||
PROS_CONS = "pros_cons" # Advantages/disadvantages
|
||||
DEFINITIONS = "definitions" # Clear explanations of concepts
|
||||
CITATIONS = "citations" # Authoritative sources
|
||||
EXAMPLES = "examples" # Concrete examples
|
||||
PREDICTIONS = "predictions" # Future outlook
|
||||
|
||||
|
||||
class ResearchDepthLevel(str, Enum):
|
||||
"""How deep the research should go."""
|
||||
OVERVIEW = "overview" # Quick summary, surface level
|
||||
DETAILED = "detailed" # In-depth analysis
|
||||
EXPERT = "expert" # Comprehensive, expert-level research
|
||||
|
||||
|
||||
class InputType(str, Enum):
|
||||
"""Type of user input detected."""
|
||||
KEYWORDS = "keywords" # Simple keywords: "AI healthcare 2025"
|
||||
QUESTION = "question" # A question: "What are the best AI tools?"
|
||||
GOAL = "goal" # Goal statement: "I need to write a blog about..."
|
||||
MIXED = "mixed" # Combination of above
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Structured Deliverable Models
|
||||
# ============================================================================
|
||||
|
||||
class StatisticWithCitation(BaseModel):
|
||||
"""A statistic with full attribution."""
|
||||
statistic: str = Field(..., description="The full statistical statement")
|
||||
value: Optional[str] = Field(None, description="The numeric value (e.g., '72%')")
|
||||
context: str = Field(..., description="Context of when/where this was measured")
|
||||
source: str = Field(..., description="Source name/publication")
|
||||
url: str = Field(..., description="Source URL")
|
||||
credibility: float = Field(0.8, ge=0.0, le=1.0, description="Credibility score 0-1")
|
||||
recency: Optional[str] = Field(None, description="How recent the data is")
|
||||
|
||||
|
||||
class ExpertQuote(BaseModel):
|
||||
"""A quote from an authoritative source."""
|
||||
quote: str = Field(..., description="The actual quote")
|
||||
speaker: str = Field(..., description="Name of the speaker")
|
||||
title: Optional[str] = Field(None, description="Title/role of the speaker")
|
||||
organization: Optional[str] = Field(None, description="Organization/company")
|
||||
context: Optional[str] = Field(None, description="Context of the quote")
|
||||
source: str = Field(..., description="Source name")
|
||||
url: str = Field(..., description="Source URL")
|
||||
|
||||
|
||||
class CaseStudySummary(BaseModel):
|
||||
"""Summary of a case study."""
|
||||
title: str = Field(..., description="Case study title")
|
||||
organization: str = Field(..., description="Organization featured")
|
||||
challenge: str = Field(..., description="The challenge/problem faced")
|
||||
solution: str = Field(..., description="The solution implemented")
|
||||
outcome: str = Field(..., description="The results achieved")
|
||||
key_metrics: List[str] = Field(default_factory=list, description="Key metrics/numbers")
|
||||
source: str = Field(..., description="Source name")
|
||||
url: str = Field(..., description="Source URL")
|
||||
|
||||
|
||||
class TrendAnalysis(BaseModel):
|
||||
"""Analysis of a trend."""
|
||||
trend: str = Field(..., description="The trend description")
|
||||
direction: str = Field(..., description="growing, declining, emerging, stable")
|
||||
evidence: List[str] = Field(default_factory=list, description="Supporting evidence")
|
||||
impact: Optional[str] = Field(None, description="Potential impact")
|
||||
timeline: Optional[str] = Field(None, description="Timeline of the trend")
|
||||
sources: List[str] = Field(default_factory=list, description="Source URLs")
|
||||
|
||||
|
||||
class ComparisonItem(BaseModel):
|
||||
"""An item in a comparison."""
|
||||
name: str
|
||||
description: Optional[str] = None
|
||||
pros: List[str] = Field(default_factory=list)
|
||||
cons: List[str] = Field(default_factory=list)
|
||||
features: Dict[str, str] = Field(default_factory=dict)
|
||||
rating: Optional[float] = None
|
||||
source: Optional[str] = None
|
||||
|
||||
|
||||
class ComparisonTable(BaseModel):
|
||||
"""Comparison between options."""
|
||||
title: str = Field(..., description="Comparison title")
|
||||
criteria: List[str] = Field(default_factory=list, description="Comparison criteria")
|
||||
items: List[ComparisonItem] = Field(default_factory=list, description="Items being compared")
|
||||
winner: Optional[str] = Field(None, description="Recommended option if applicable")
|
||||
verdict: Optional[str] = Field(None, description="Summary verdict")
|
||||
|
||||
|
||||
class ProsCons(BaseModel):
|
||||
"""Pros and cons analysis."""
|
||||
subject: str = Field(..., description="What is being analyzed")
|
||||
pros: List[str] = Field(default_factory=list, description="Advantages")
|
||||
cons: List[str] = Field(default_factory=list, description="Disadvantages")
|
||||
balanced_verdict: str = Field(..., description="Balanced conclusion")
|
||||
|
||||
|
||||
class SourceWithRelevance(BaseModel):
|
||||
"""A source with relevance information."""
|
||||
title: str
|
||||
url: str
|
||||
excerpt: Optional[str] = None
|
||||
relevance_score: float = Field(0.8, ge=0.0, le=1.0)
|
||||
relevance_reason: Optional[str] = None
|
||||
content_type: Optional[str] = None # article, research paper, news, etc.
|
||||
published_date: Optional[str] = None
|
||||
credibility_score: float = Field(0.8, ge=0.0, le=1.0)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Intent Models
|
||||
# ============================================================================
|
||||
|
||||
class ResearchIntent(BaseModel):
|
||||
"""
|
||||
What the user actually wants from their research.
|
||||
This is inferred from user input + research persona.
|
||||
"""
|
||||
|
||||
# Core understanding
|
||||
primary_question: str = Field(..., description="The main question to answer")
|
||||
secondary_questions: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Related questions that should be answered"
|
||||
)
|
||||
|
||||
# Purpose classification
|
||||
purpose: ResearchPurpose = Field(
|
||||
ResearchPurpose.LEARN,
|
||||
description="Why the user is researching"
|
||||
)
|
||||
content_output: ContentOutput = Field(
|
||||
ContentOutput.GENERAL,
|
||||
description="What content type will be created"
|
||||
)
|
||||
|
||||
# What they need from results
|
||||
expected_deliverables: List[ExpectedDeliverable] = Field(
|
||||
default_factory=list,
|
||||
description="Specific outputs the user expects"
|
||||
)
|
||||
|
||||
# Depth and focus
|
||||
depth: ResearchDepthLevel = Field(
|
||||
ResearchDepthLevel.DETAILED,
|
||||
description="How deep the research should go"
|
||||
)
|
||||
focus_areas: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Specific aspects to focus on"
|
||||
)
|
||||
|
||||
# Constraints
|
||||
perspective: Optional[str] = Field(
|
||||
None,
|
||||
description="Perspective to research from (e.g., 'hospital administrator')"
|
||||
)
|
||||
time_sensitivity: Optional[str] = Field(
|
||||
None,
|
||||
description="Time constraint: 'real_time', 'recent', 'historical', 'evergreen'"
|
||||
)
|
||||
|
||||
# Detected input type
|
||||
input_type: InputType = Field(
|
||||
InputType.KEYWORDS,
|
||||
description="Type of user input detected"
|
||||
)
|
||||
|
||||
# Original user input (for reference)
|
||||
original_input: str = Field(..., description="The original user input")
|
||||
|
||||
# Confidence in inference
|
||||
confidence: float = Field(
|
||||
0.8,
|
||||
ge=0.0,
|
||||
le=1.0,
|
||||
description="Confidence in the intent inference"
|
||||
)
|
||||
needs_clarification: bool = Field(
|
||||
False,
|
||||
description="True if AI is uncertain and needs user clarification"
|
||||
)
|
||||
clarifying_questions: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Questions to ask user if uncertain"
|
||||
)
|
||||
|
||||
class Config:
|
||||
use_enum_values = True
|
||||
|
||||
|
||||
class ResearchQuery(BaseModel):
|
||||
"""A targeted research query with purpose."""
|
||||
query: str = Field(..., description="The search query")
|
||||
purpose: ExpectedDeliverable = Field(..., description="What this query targets")
|
||||
provider: str = Field("exa", description="Preferred provider: exa, tavily, google")
|
||||
priority: int = Field(1, ge=1, le=5, description="Priority 1-5, higher = more important")
|
||||
expected_results: str = Field(..., description="What we expect to find with this query")
|
||||
|
||||
|
||||
class IntentInferenceRequest(BaseModel):
|
||||
"""Request to infer research intent from user input."""
|
||||
user_input: str = Field(..., description="User's keywords, question, or goal")
|
||||
keywords: List[str] = Field(default_factory=list, description="Extracted keywords")
|
||||
use_persona: bool = Field(True, description="Use research persona for context")
|
||||
use_competitor_data: bool = Field(True, description="Use competitor data for context")
|
||||
|
||||
|
||||
class IntentInferenceResponse(BaseModel):
|
||||
"""Response from intent inference."""
|
||||
success: bool = True
|
||||
intent: ResearchIntent
|
||||
analysis_summary: str = Field(..., description="AI's understanding of user intent")
|
||||
suggested_queries: List[ResearchQuery] = Field(
|
||||
default_factory=list,
|
||||
description="Generated research queries based on intent"
|
||||
)
|
||||
suggested_keywords: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Enhanced/expanded keywords"
|
||||
)
|
||||
suggested_angles: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Research angles to explore"
|
||||
)
|
||||
quick_options: List[Dict[str, Any]] = Field(
|
||||
default_factory=list,
|
||||
description="Quick options for user to confirm/modify intent"
|
||||
)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Intent-Driven Research Result
|
||||
# ============================================================================
|
||||
|
||||
class IntentDrivenResearchResult(BaseModel):
|
||||
"""
|
||||
Research results organized by what user needs.
|
||||
This is the final output after intent-aware analysis.
|
||||
"""
|
||||
|
||||
success: bool = True
|
||||
|
||||
# Direct answers
|
||||
primary_answer: str = Field(..., description="Direct answer to primary question")
|
||||
secondary_answers: Dict[str, str] = Field(
|
||||
default_factory=dict,
|
||||
description="Answers to secondary questions (question → answer)"
|
||||
)
|
||||
|
||||
# Deliverables (populated based on user's expected_deliverables)
|
||||
statistics: List[StatisticWithCitation] = Field(default_factory=list)
|
||||
expert_quotes: List[ExpertQuote] = Field(default_factory=list)
|
||||
case_studies: List[CaseStudySummary] = Field(default_factory=list)
|
||||
comparisons: List[ComparisonTable] = Field(default_factory=list)
|
||||
trends: List[TrendAnalysis] = Field(default_factory=list)
|
||||
best_practices: List[str] = Field(default_factory=list)
|
||||
step_by_step: List[str] = Field(default_factory=list)
|
||||
pros_cons: Optional[ProsCons] = None
|
||||
definitions: Dict[str, str] = Field(
|
||||
default_factory=dict,
|
||||
description="Term → definition mappings"
|
||||
)
|
||||
examples: List[str] = Field(default_factory=list)
|
||||
predictions: List[str] = Field(default_factory=list)
|
||||
|
||||
# Content-ready outputs
|
||||
executive_summary: str = Field("", description="2-3 sentence summary")
|
||||
key_takeaways: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="5-7 key bullet points"
|
||||
)
|
||||
suggested_outline: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Suggested content outline if creating content"
|
||||
)
|
||||
|
||||
# Supporting data
|
||||
sources: List[SourceWithRelevance] = Field(default_factory=list)
|
||||
raw_content: Optional[str] = Field(None, description="Raw content for further processing")
|
||||
|
||||
# Research quality metadata
|
||||
confidence: float = Field(0.8, ge=0.0, le=1.0)
|
||||
gaps_identified: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="What we couldn't find"
|
||||
)
|
||||
follow_up_queries: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Suggested additional research"
|
||||
)
|
||||
|
||||
# Original intent for reference
|
||||
original_intent: Optional[ResearchIntent] = None
|
||||
|
||||
# Error handling
|
||||
error_message: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
use_enum_values = True
|
||||
|
||||
@@ -39,13 +39,45 @@ class ResearchPersona(BaseModel):
|
||||
|
||||
# Domain & Source Intelligence
|
||||
suggested_exa_domains: List[str] = Field(
|
||||
default_factory=list,
|
||||
default_factory=list,
|
||||
description="4-6 authoritative domains for the industry"
|
||||
)
|
||||
suggested_exa_category: Optional[str] = Field(
|
||||
None,
|
||||
None,
|
||||
description="Suggested Exa category based on industry"
|
||||
)
|
||||
suggested_exa_search_type: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Exa search algorithm: auto, neural, keyword, fast, deep"
|
||||
)
|
||||
|
||||
# Tavily Provider Intelligence
|
||||
suggested_tavily_topic: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily topic: general, news, finance"
|
||||
)
|
||||
suggested_tavily_search_depth: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily search depth: basic, advanced, fast, ultra-fast"
|
||||
)
|
||||
suggested_tavily_include_answer: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily answer type: false, basic, advanced"
|
||||
)
|
||||
suggested_tavily_time_range: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily time range: day, week, month, year"
|
||||
)
|
||||
suggested_tavily_raw_content_format: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily raw content format: false, markdown, text"
|
||||
)
|
||||
|
||||
# Provider Selection Logic
|
||||
provider_recommendations: Dict[str, str] = Field(
|
||||
default_factory=dict,
|
||||
description="Provider recommendations by use case: {'trends': 'tavily', 'deep_research': 'exa', 'factual': 'google'}"
|
||||
)
|
||||
|
||||
# Query Enhancement Intelligence
|
||||
research_angles: List[str] = Field(
|
||||
@@ -88,6 +120,19 @@ class ResearchPersona(BaseModel):
|
||||
},
|
||||
"suggested_exa_domains": ["pubmed.gov", "nejm.org", "thelancet.com"],
|
||||
"suggested_exa_category": "research paper",
|
||||
"suggested_exa_search_type": "neural",
|
||||
"suggested_tavily_topic": "news",
|
||||
"suggested_tavily_search_depth": "advanced",
|
||||
"suggested_tavily_include_answer": "advanced",
|
||||
"suggested_tavily_time_range": "month",
|
||||
"suggested_tavily_raw_content_format": "markdown",
|
||||
"provider_recommendations": {
|
||||
"trends": "tavily",
|
||||
"deep_research": "exa",
|
||||
"factual": "google",
|
||||
"news": "tavily",
|
||||
"academic": "exa"
|
||||
},
|
||||
"research_angles": [
|
||||
"Compare telemedicine platforms",
|
||||
"Telemedicine ROI analysis",
|
||||
|
||||
11
backend/routers/video_studio.py
Normal file
11
backend/routers/video_studio.py
Normal file
@@ -0,0 +1,11 @@
|
||||
"""
|
||||
Video Studio Router (Legacy Import)
|
||||
|
||||
This file is kept for backward compatibility.
|
||||
All functionality has been moved to backend/routers/video_studio/ module.
|
||||
"""
|
||||
|
||||
# Re-export from the new modular structure
|
||||
from routers.video_studio import router
|
||||
|
||||
__all__ = ["router"]
|
||||
38
backend/routers/video_studio/__init__.py
Normal file
38
backend/routers/video_studio/__init__.py
Normal file
@@ -0,0 +1,38 @@
|
||||
"""
|
||||
Video Studio Router
|
||||
|
||||
Provides AI video generation capabilities including:
|
||||
- Text-to-video generation
|
||||
- Image-to-video transformation
|
||||
- Avatar/face generation
|
||||
- Video enhancement and editing
|
||||
|
||||
Uses WaveSpeed AI models for high-quality video generation.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter
|
||||
|
||||
from .endpoints import create, avatar, enhance, extend, transform, models, serve, tasks, prompt, social, face_swap, video_translate, video_background_remover, add_audio_to_video
|
||||
|
||||
# Create main router
|
||||
router = APIRouter(
|
||||
prefix="/video-studio",
|
||||
tags=["video-studio"],
|
||||
responses={404: {"description": "Not found"}},
|
||||
)
|
||||
|
||||
# Include all endpoint routers
|
||||
router.include_router(create.router)
|
||||
router.include_router(avatar.router)
|
||||
router.include_router(enhance.router)
|
||||
router.include_router(extend.router)
|
||||
router.include_router(transform.router)
|
||||
router.include_router(social.router)
|
||||
router.include_router(face_swap.router)
|
||||
router.include_router(video_translate.router)
|
||||
router.include_router(video_background_remover.router)
|
||||
router.include_router(add_audio_to_video.router)
|
||||
router.include_router(models.router)
|
||||
router.include_router(serve.router)
|
||||
router.include_router(tasks.router)
|
||||
router.include_router(prompt.router)
|
||||
1
backend/routers/video_studio/endpoints/__init__.py
Normal file
1
backend/routers/video_studio/endpoints/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
"""Video Studio endpoint modules."""
|
||||
159
backend/routers/video_studio/endpoints/add_audio_to_video.py
Normal file
159
backend/routers/video_studio/endpoints/add_audio_to_video.py
Normal file
@@ -0,0 +1,159 @@
|
||||
"""
|
||||
Add Audio to Video endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio.add_audio_to_video_service import AddAudioToVideoService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.add_audio_to_video")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/add-audio-to-video")
|
||||
async def add_audio_to_video(
|
||||
background_tasks: BackgroundTasks,
|
||||
video_file: UploadFile = File(..., description="Source video for audio addition"),
|
||||
model: str = Form("hunyuan-video-foley", description="AI model to use: 'hunyuan-video-foley' or 'think-sound'"),
|
||||
prompt: Optional[str] = Form(None, description="Optional text prompt describing desired sounds (Hunyuan Video Foley)"),
|
||||
seed: Optional[int] = Form(None, description="Random seed for reproducibility (-1 for random)"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Add audio to video using AI models.
|
||||
|
||||
Supports:
|
||||
1. Hunyuan Video Foley - Generate realistic Foley and ambient audio from video
|
||||
- Optional text prompt to describe desired sounds
|
||||
- Seed control for reproducibility
|
||||
|
||||
2. Think Sound - (To be added)
|
||||
|
||||
Args:
|
||||
video_file: Source video file
|
||||
model: AI model to use
|
||||
prompt: Optional text prompt describing desired sounds
|
||||
seed: Random seed for reproducibility
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
if not video_file.content_type.startswith('video/'):
|
||||
raise HTTPException(status_code=400, detail="File must be a video")
|
||||
|
||||
# Initialize services
|
||||
add_audio_service = AddAudioToVideoService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(f"[AddAudioToVideo] Audio addition request: user={user_id}, model={model}, has_prompt={prompt is not None}")
|
||||
|
||||
# Read video file
|
||||
video_data = await video_file.read()
|
||||
|
||||
# Add audio to video
|
||||
result = await add_audio_service.add_audio(
|
||||
video_data=video_data,
|
||||
model=model,
|
||||
prompt=prompt,
|
||||
seed=seed,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Adding audio failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store processed video in asset library
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"original_file": video_file.filename,
|
||||
"model": result.get("model_used", model),
|
||||
"has_prompt": prompt is not None,
|
||||
"prompt": prompt,
|
||||
"generation_type": "add_audio",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"audio_added_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "audio_addition", "ai-processed"]
|
||||
)
|
||||
|
||||
logger.info(f"[AddAudioToVideo] Audio addition successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"model_used": result.get("model_used", model),
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[AddAudioToVideo] Audio addition error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Adding audio failed: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/add-audio-to-video/estimate-cost")
|
||||
async def estimate_add_audio_cost(
|
||||
model: str = Form("hunyuan-video-foley", description="AI model to use"),
|
||||
estimated_duration: float = Form(10.0, description="Estimated video duration in seconds", ge=0.0),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Estimate cost for adding audio to video operation.
|
||||
|
||||
Returns estimated cost based on model and duration.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
add_audio_service = AddAudioToVideoService()
|
||||
estimated_cost = add_audio_service.calculate_cost(model, estimated_duration)
|
||||
|
||||
# Build response based on model pricing
|
||||
if model == "think-sound":
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"model": model,
|
||||
"estimated_duration": estimated_duration,
|
||||
"pricing_model": "per_video",
|
||||
"flat_rate": 0.05,
|
||||
}
|
||||
else:
|
||||
# Hunyuan Video Foley (per-second pricing)
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"model": model,
|
||||
"estimated_duration": estimated_duration,
|
||||
"cost_per_second": 0.02, # Estimated pricing
|
||||
"pricing_model": "per_second",
|
||||
"min_duration": 5.0,
|
||||
"max_duration": 600.0, # 10 minutes max
|
||||
"min_charge": 0.02 * 5.0,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[AddAudioToVideo] Failed to estimate cost: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to estimate cost: {str(e)}")
|
||||
293
backend/routers/video_studio/endpoints/avatar.py
Normal file
293
backend/routers/video_studio/endpoints/avatar.py
Normal file
@@ -0,0 +1,293 @@
|
||||
"""
|
||||
Avatar generation endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import base64
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...services.video_studio.avatar_service import AvatarStudioService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
from api.story_writer.task_manager import task_manager
|
||||
from ..tasks.avatar_generation import execute_avatar_generation_task
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.avatar")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/avatars")
|
||||
async def generate_avatar_video(
|
||||
background_tasks: BackgroundTasks,
|
||||
avatar_file: UploadFile = File(..., description="Avatar/face image"),
|
||||
audio_file: Optional[UploadFile] = File(None, description="Audio file for lip sync"),
|
||||
video_file: Optional[UploadFile] = File(None, description="Source video for face swap"),
|
||||
text: Optional[str] = Form(None, description="Text to speak (alternative to audio)"),
|
||||
language: str = Form("en", description="Language for text-to-speech"),
|
||||
provider: str = Form("wavespeed", description="AI provider to use"),
|
||||
model: str = Form("wavespeed/mocha", description="Specific AI model to use"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate talking avatar video or perform face swap.
|
||||
|
||||
Supports both text-to-speech and audio input for natural lip sync.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
# Validate inputs
|
||||
if not avatar_file.content_type.startswith('image/'):
|
||||
raise HTTPException(status_code=400, detail="Avatar file must be an image")
|
||||
|
||||
if not any([audio_file, video_file, text]):
|
||||
raise HTTPException(status_code=400, detail="Must provide audio file, video file, or text")
|
||||
|
||||
# Initialize services
|
||||
video_service = VideoStudioService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(f"[VideoStudio] Avatar generation request: user={user_id}, model={model}")
|
||||
|
||||
# Read files
|
||||
avatar_data = await avatar_file.read()
|
||||
audio_data = await audio_file.read() if audio_file else None
|
||||
video_data = await video_file.read() if video_file else None
|
||||
|
||||
# Generate avatar video
|
||||
result = await video_service.generate_avatar_video(
|
||||
avatar_data=avatar_data,
|
||||
audio_data=audio_data,
|
||||
video_data=video_data,
|
||||
text=text,
|
||||
language=language,
|
||||
provider=provider,
|
||||
model=model,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Avatar generation failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store in asset library if successful
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"avatar_file": avatar_file.filename,
|
||||
"audio_file": audio_file.filename if audio_file else None,
|
||||
"video_file": video_file.filename if video_file else None,
|
||||
"text": text,
|
||||
"language": language,
|
||||
"provider": provider,
|
||||
"model": model,
|
||||
"generation_type": "avatar",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"avatar_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "avatar", "ai-generated"]
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Avatar generation successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"model_used": model,
|
||||
"provider": provider,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Avatar generation error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Avatar generation failed: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/avatar/create-async")
|
||||
async def create_avatar_async(
|
||||
background_tasks: BackgroundTasks,
|
||||
image: UploadFile = File(..., description="Image file for avatar"),
|
||||
audio: UploadFile = File(..., description="Audio file for lip-sync"),
|
||||
resolution: str = Form("720p", description="Video resolution (480p or 720p)"),
|
||||
prompt: Optional[str] = Form(None, description="Optional prompt for expression/style"),
|
||||
mask_image: Optional[UploadFile] = File(None, description="Optional mask image (InfiniteTalk only)"),
|
||||
seed: Optional[int] = Form(None, description="Optional random seed"),
|
||||
model: str = Form("infinitetalk", description="Model to use: 'infinitetalk' or 'hunyuan-avatar'"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Create talking avatar asynchronously with polling support.
|
||||
|
||||
Upload a photo and audio to create a talking avatar with perfect lip-sync.
|
||||
Supports resolutions of 480p and 720p.
|
||||
- InfiniteTalk: up to 10 minutes long
|
||||
- Hunyuan Avatar: up to 2 minutes (120 seconds) long
|
||||
|
||||
Returns task_id for polling. Frontend can poll /api/video-studio/task/{task_id}/status
|
||||
to get progress updates and final result.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
# Validate resolution
|
||||
if resolution not in ["480p", "720p"]:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Resolution must be '480p' or '720p'"
|
||||
)
|
||||
|
||||
# Read image data
|
||||
image_data = await image.read()
|
||||
if len(image_data) == 0:
|
||||
raise HTTPException(status_code=400, detail="Image file is empty")
|
||||
|
||||
# Read audio data
|
||||
audio_data = await audio.read()
|
||||
if len(audio_data) == 0:
|
||||
raise HTTPException(status_code=400, detail="Audio file is empty")
|
||||
|
||||
# Convert to base64
|
||||
image_base64 = base64.b64encode(image_data).decode('utf-8')
|
||||
# Add data URI prefix
|
||||
image_mime = image.content_type or "image/png"
|
||||
image_base64 = f"data:{image_mime};base64,{image_base64}"
|
||||
|
||||
audio_base64 = base64.b64encode(audio_data).decode('utf-8')
|
||||
audio_mime = audio.content_type or "audio/mpeg"
|
||||
audio_base64 = f"data:{audio_mime};base64,{audio_base64}"
|
||||
|
||||
# Handle optional mask image
|
||||
mask_image_base64 = None
|
||||
if mask_image:
|
||||
mask_data = await mask_image.read()
|
||||
if len(mask_data) > 0:
|
||||
mask_base64 = base64.b64encode(mask_data).decode('utf-8')
|
||||
mask_mime = mask_image.content_type or "image/png"
|
||||
mask_image_base64 = f"data:{mask_mime};base64,{mask_base64}"
|
||||
|
||||
# Create task
|
||||
task_id = task_manager.create_task("avatar_generation")
|
||||
|
||||
# Validate model
|
||||
if model not in ["infinitetalk", "hunyuan-avatar"]:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Model must be 'infinitetalk' or 'hunyuan-avatar'"
|
||||
)
|
||||
|
||||
# Start background task
|
||||
background_tasks.add_task(
|
||||
execute_avatar_generation_task,
|
||||
task_id=task_id,
|
||||
user_id=user_id,
|
||||
image_base64=image_base64,
|
||||
audio_base64=audio_base64,
|
||||
resolution=resolution,
|
||||
prompt=prompt,
|
||||
mask_image_base64=mask_image_base64,
|
||||
seed=seed,
|
||||
model=model,
|
||||
)
|
||||
|
||||
logger.info(f"[AvatarStudio] Started async avatar generation: task_id={task_id}, user={user_id}")
|
||||
|
||||
return {
|
||||
"task_id": task_id,
|
||||
"status": "pending",
|
||||
"message": f"Avatar generation started. This may take several minutes. Poll /api/video-studio/task/{task_id}/status for updates."
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[AvatarStudio] Failed to start async avatar generation: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to start avatar generation: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/avatar/estimate-cost")
|
||||
async def estimate_avatar_cost(
|
||||
resolution: str = Form("720p", description="Video resolution (480p or 720p)"),
|
||||
estimated_duration: float = Form(10.0, description="Estimated video duration in seconds", ge=5.0, le=600.0),
|
||||
model: str = Form("infinitetalk", description="Model to use: 'infinitetalk' or 'hunyuan-avatar'"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Estimate cost for talking avatar generation.
|
||||
|
||||
Returns estimated cost based on resolution, duration, and model.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
# Validate resolution
|
||||
if resolution not in ["480p", "720p"]:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Resolution must be '480p' or '720p'"
|
||||
)
|
||||
|
||||
# Validate model
|
||||
if model not in ["infinitetalk", "hunyuan-avatar"]:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Model must be 'infinitetalk' or 'hunyuan-avatar'"
|
||||
)
|
||||
|
||||
# Validate duration for Hunyuan Avatar (max 120 seconds)
|
||||
if model == "hunyuan-avatar" and estimated_duration > 120:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Hunyuan Avatar supports maximum 120 seconds (2 minutes)"
|
||||
)
|
||||
|
||||
avatar_service = AvatarStudioService()
|
||||
estimated_cost = avatar_service.calculate_cost_estimate(resolution, estimated_duration, model)
|
||||
|
||||
# Return pricing info based on model
|
||||
if model == "hunyuan-avatar":
|
||||
cost_per_5_seconds = 0.15 if resolution == "480p" else 0.30
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"resolution": resolution,
|
||||
"estimated_duration": estimated_duration,
|
||||
"model": model,
|
||||
"cost_per_5_seconds": cost_per_5_seconds,
|
||||
"pricing_model": "per_5_seconds",
|
||||
"max_duration": 120,
|
||||
}
|
||||
else:
|
||||
cost_per_second = 0.03 if resolution == "480p" else 0.06
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"resolution": resolution,
|
||||
"estimated_duration": estimated_duration,
|
||||
"model": model,
|
||||
"cost_per_second": cost_per_second,
|
||||
"pricing_model": "per_second",
|
||||
"max_duration": 600,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[AvatarStudio] Failed to estimate cost: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to estimate cost: {str(e)}")
|
||||
304
backend/routers/video_studio/endpoints/create.py
Normal file
304
backend/routers/video_studio/endpoints/create.py
Normal file
@@ -0,0 +1,304 @@
|
||||
"""
|
||||
Create video endpoints: text-to-video and image-to-video generation.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
from api.story_writer.task_manager import task_manager
|
||||
from ..tasks.video_generation import execute_video_generation_task
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.create")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/generate")
|
||||
async def generate_video(
|
||||
background_tasks: BackgroundTasks,
|
||||
prompt: str = Form(..., description="Text description for video generation"),
|
||||
negative_prompt: Optional[str] = Form(None, description="What to avoid in the video"),
|
||||
duration: int = Form(5, description="Video duration in seconds", ge=1, le=10),
|
||||
resolution: str = Form("720p", description="Video resolution"),
|
||||
aspect_ratio: str = Form("16:9", description="Video aspect ratio"),
|
||||
motion_preset: str = Form("medium", description="Motion intensity"),
|
||||
provider: str = Form("wavespeed", description="AI provider to use"),
|
||||
model: str = Form("hunyuan-video-1.5", description="Specific AI model to use"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate video from text description using AI models.
|
||||
|
||||
Supports multiple providers and models for optimal quality and cost.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
# Initialize services
|
||||
video_service = VideoStudioService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(f"[VideoStudio] Text-to-video request: user={user_id}, model={model}, duration={duration}s")
|
||||
|
||||
# Generate video
|
||||
result = await video_service.generate_text_to_video(
|
||||
prompt=prompt,
|
||||
negative_prompt=negative_prompt,
|
||||
duration=duration,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
motion_preset=motion_preset,
|
||||
provider=provider,
|
||||
model=model,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Video generation failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store in asset library if successful
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"prompt": prompt,
|
||||
"negative_prompt": negative_prompt,
|
||||
"duration": duration,
|
||||
"resolution": resolution,
|
||||
"aspect_ratio": aspect_ratio,
|
||||
"motion_preset": motion_preset,
|
||||
"provider": provider,
|
||||
"model": model,
|
||||
"generation_type": "text-to-video",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"video_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "text-to-video", "ai-generated"]
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Video generated successfully: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"estimated_duration": result.get("estimated_duration", duration),
|
||||
"model_used": model,
|
||||
"provider": provider,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Text-to-video error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Video generation failed: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/transform")
|
||||
async def transform_to_video(
|
||||
background_tasks: BackgroundTasks,
|
||||
file: UploadFile = File(..., description="Image file to transform"),
|
||||
prompt: Optional[str] = Form(None, description="Optional text prompt to guide transformation"),
|
||||
duration: int = Form(5, description="Video duration in seconds", ge=1, le=10),
|
||||
resolution: str = Form("720p", description="Video resolution"),
|
||||
aspect_ratio: str = Form("16:9", description="Video aspect ratio"),
|
||||
motion_preset: str = Form("medium", description="Motion intensity"),
|
||||
provider: str = Form("wavespeed", description="AI provider to use"),
|
||||
model: str = Form("alibaba/wan-2.5", description="Specific AI model to use"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Transform image to video using AI models.
|
||||
|
||||
Supports various motion presets and durations for dynamic video creation.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
# Validate file type
|
||||
if not file.content_type.startswith('image/'):
|
||||
raise HTTPException(status_code=400, detail="File must be an image")
|
||||
|
||||
# Initialize services
|
||||
video_service = VideoStudioService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(f"[VideoStudio] Image-to-video request: user={user_id}, model={model}, duration={duration}s")
|
||||
|
||||
# Read image file
|
||||
image_data = await file.read()
|
||||
|
||||
# Generate video
|
||||
result = await video_service.generate_image_to_video(
|
||||
image_data=image_data,
|
||||
prompt=prompt,
|
||||
duration=duration,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
motion_preset=motion_preset,
|
||||
provider=provider,
|
||||
model=model,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Video transformation failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store in asset library if successful
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"original_image": file.filename,
|
||||
"prompt": prompt,
|
||||
"duration": duration,
|
||||
"resolution": resolution,
|
||||
"aspect_ratio": aspect_ratio,
|
||||
"motion_preset": motion_preset,
|
||||
"provider": provider,
|
||||
"model": model,
|
||||
"generation_type": "image-to-video",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"video_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "image-to-video", "ai-generated"]
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Video transformation successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"estimated_duration": result.get("estimated_duration", duration),
|
||||
"model_used": model,
|
||||
"provider": provider,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Image-to-video error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Video transformation failed: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/generate-async")
|
||||
async def generate_video_async(
|
||||
background_tasks: BackgroundTasks,
|
||||
prompt: Optional[str] = Form(None, description="Text description for video generation"),
|
||||
image: Optional[UploadFile] = File(None, description="Image file for image-to-video"),
|
||||
operation_type: str = Form("text-to-video", description="Operation type: text-to-video or image-to-video"),
|
||||
negative_prompt: Optional[str] = Form(None, description="What to avoid in the video"),
|
||||
duration: int = Form(5, description="Video duration in seconds", ge=1, le=10),
|
||||
resolution: str = Form("720p", description="Video resolution"),
|
||||
aspect_ratio: str = Form("16:9", description="Video aspect ratio"),
|
||||
motion_preset: str = Form("medium", description="Motion intensity"),
|
||||
provider: str = Form("wavespeed", description="AI provider to use"),
|
||||
model: str = Form("alibaba/wan-2.5", description="Specific AI model to use"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate video asynchronously with polling support.
|
||||
|
||||
Returns task_id for polling. Frontend can poll /api/video-studio/task/{task_id}/status
|
||||
to get progress updates and final result.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
# Validate operation type
|
||||
if operation_type not in ["text-to-video", "image-to-video"]:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Invalid operation_type: {operation_type}. Must be 'text-to-video' or 'image-to-video'"
|
||||
)
|
||||
|
||||
# Validate inputs based on operation type
|
||||
if operation_type == "text-to-video" and not prompt:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="prompt is required for text-to-video generation"
|
||||
)
|
||||
|
||||
if operation_type == "image-to-video" and not image:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="image file is required for image-to-video generation"
|
||||
)
|
||||
|
||||
# Read image data if provided
|
||||
image_data = None
|
||||
if image:
|
||||
image_data = await image.read()
|
||||
if len(image_data) == 0:
|
||||
raise HTTPException(status_code=400, detail="Image file is empty")
|
||||
|
||||
# Create task
|
||||
task_id = task_manager.create_task("video_generation")
|
||||
|
||||
# Prepare kwargs
|
||||
kwargs = {
|
||||
"duration": duration,
|
||||
"resolution": resolution,
|
||||
"model": model,
|
||||
}
|
||||
if negative_prompt:
|
||||
kwargs["negative_prompt"] = negative_prompt
|
||||
if aspect_ratio:
|
||||
kwargs["aspect_ratio"] = aspect_ratio
|
||||
if motion_preset:
|
||||
kwargs["motion_preset"] = motion_preset
|
||||
|
||||
# Start background task
|
||||
background_tasks.add_task(
|
||||
execute_video_generation_task,
|
||||
task_id=task_id,
|
||||
operation_type=operation_type,
|
||||
user_id=user_id,
|
||||
prompt=prompt,
|
||||
image_data=image_data,
|
||||
provider=provider,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Started async video generation: task_id={task_id}, operation={operation_type}, user={user_id}")
|
||||
|
||||
return {
|
||||
"task_id": task_id,
|
||||
"status": "pending",
|
||||
"message": f"Video generation started. This may take several minutes. Poll /api/video-studio/task/{task_id}/status for updates."
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Failed to start async video generation: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to start video generation: {str(e)}")
|
||||
157
backend/routers/video_studio/endpoints/enhance.py
Normal file
157
backend/routers/video_studio/endpoints/enhance.py
Normal file
@@ -0,0 +1,157 @@
|
||||
"""
|
||||
Video enhancement endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.enhance")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/enhance")
|
||||
async def enhance_video(
|
||||
background_tasks: BackgroundTasks,
|
||||
file: UploadFile = File(..., description="Video file to enhance"),
|
||||
enhancement_type: str = Form(..., description="Type of enhancement: upscale, stabilize, colorize, etc"),
|
||||
target_resolution: Optional[str] = Form(None, description="Target resolution for upscale"),
|
||||
provider: str = Form("wavespeed", description="AI provider to use"),
|
||||
model: str = Form("wavespeed/flashvsr", description="Specific AI model to use"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Enhance existing video using AI models.
|
||||
|
||||
Supports upscaling, stabilization, colorization, and other enhancements.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
if not file.content_type.startswith('video/'):
|
||||
raise HTTPException(status_code=400, detail="File must be a video")
|
||||
|
||||
# Initialize services
|
||||
video_service = VideoStudioService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(f"[VideoStudio] Video enhancement request: user={user_id}, type={enhancement_type}, model={model}")
|
||||
|
||||
# Read video file
|
||||
video_data = await file.read()
|
||||
|
||||
# Enhance video
|
||||
result = await video_service.enhance_video(
|
||||
video_data=video_data,
|
||||
enhancement_type=enhancement_type,
|
||||
target_resolution=target_resolution,
|
||||
provider=provider,
|
||||
model=model,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Video enhancement failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store enhanced version in asset library
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"original_file": file.filename,
|
||||
"enhancement_type": enhancement_type,
|
||||
"target_resolution": target_resolution,
|
||||
"provider": provider,
|
||||
"model": model,
|
||||
"generation_type": "enhancement",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"enhanced_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "enhancement", "ai-enhanced"]
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Video enhancement successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"enhancement_type": enhancement_type,
|
||||
"model_used": model,
|
||||
"provider": provider,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Video enhancement error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Video enhancement failed: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/enhance/estimate-cost")
|
||||
async def estimate_enhance_cost(
|
||||
target_resolution: str = Form("1080p", description="Target resolution (720p, 1080p, 2k, 4k)"),
|
||||
estimated_duration: float = Form(10.0, description="Estimated video duration in seconds", ge=5.0),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Estimate cost for video enhancement operation.
|
||||
|
||||
Returns estimated cost based on target resolution and duration.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
# Validate resolution
|
||||
if target_resolution not in ("720p", "1080p", "2k", "4k"):
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Target resolution must be '720p', '1080p', '2k', or '4k'"
|
||||
)
|
||||
|
||||
# FlashVSR pricing: $0.06-$0.16 per 5 seconds based on resolution
|
||||
pricing = {
|
||||
"720p": 0.06 / 5, # $0.012 per second
|
||||
"1080p": 0.09 / 5, # $0.018 per second
|
||||
"2k": 0.12 / 5, # $0.024 per second
|
||||
"4k": 0.16 / 5, # $0.032 per second
|
||||
}
|
||||
|
||||
cost_per_second = pricing.get(target_resolution.lower(), pricing["1080p"])
|
||||
estimated_cost = max(5.0, estimated_duration) * cost_per_second # Minimum 5 seconds
|
||||
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"target_resolution": target_resolution,
|
||||
"estimated_duration": estimated_duration,
|
||||
"cost_per_second": cost_per_second,
|
||||
"pricing_model": "per_second",
|
||||
"min_duration": 5.0,
|
||||
"max_duration": 600.0, # 10 minutes max
|
||||
"min_charge": cost_per_second * 5.0,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Failed to estimate cost: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to estimate cost: {str(e)}")
|
||||
158
backend/routers/video_studio/endpoints/extend.py
Normal file
158
backend/routers/video_studio/endpoints/extend.py
Normal file
@@ -0,0 +1,158 @@
|
||||
"""
|
||||
Video extension endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.extend")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/extend")
|
||||
async def extend_video(
|
||||
background_tasks: BackgroundTasks,
|
||||
file: UploadFile = File(..., description="Video file to extend"),
|
||||
prompt: str = Form(..., description="Text prompt describing how to extend the video"),
|
||||
model: str = Form("wan-2.5", description="Model to use: 'wan-2.5', 'wan-2.2-spicy', or 'seedance-1.5-pro'"),
|
||||
audio: Optional[UploadFile] = File(None, description="Optional audio file to guide generation (WAN 2.5 only)"),
|
||||
negative_prompt: Optional[str] = Form(None, description="Negative prompt (WAN 2.5 only)"),
|
||||
resolution: str = Form("720p", description="Output resolution: 480p, 720p, or 1080p (1080p WAN 2.5 only)"),
|
||||
duration: int = Form(5, description="Duration of extended video in seconds (varies by model)"),
|
||||
enable_prompt_expansion: bool = Form(False, description="Enable prompt optimizer (WAN 2.5 only)"),
|
||||
generate_audio: bool = Form(True, description="Generate audio for extended video (Seedance 1.5 Pro only)"),
|
||||
camera_fixed: bool = Form(False, description="Fix camera position (Seedance 1.5 Pro only)"),
|
||||
seed: Optional[int] = Form(None, description="Random seed for reproducibility"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Extend video duration using WAN 2.5, WAN 2.2 Spicy, or Seedance 1.5 Pro video-extend.
|
||||
|
||||
Takes a short video clip and extends it with motion/audio continuity.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
if not file.content_type.startswith('video/'):
|
||||
raise HTTPException(status_code=400, detail="File must be a video")
|
||||
|
||||
# Validate model-specific constraints
|
||||
if model in ("wan-2.2-spicy", "wavespeed-ai/wan-2.2-spicy/video-extend"):
|
||||
if duration not in [5, 8]:
|
||||
raise HTTPException(status_code=400, detail="WAN 2.2 Spicy only supports 5 or 8 second durations")
|
||||
if resolution not in ["480p", "720p"]:
|
||||
raise HTTPException(status_code=400, detail="WAN 2.2 Spicy only supports 480p or 720p resolution")
|
||||
if audio:
|
||||
raise HTTPException(status_code=400, detail="Audio is not supported for WAN 2.2 Spicy")
|
||||
elif model in ("seedance-1.5-pro", "bytedance/seedance-v1.5-pro/video-extend"):
|
||||
if duration < 4 or duration > 12:
|
||||
raise HTTPException(status_code=400, detail="Seedance 1.5 Pro only supports 4-12 second durations")
|
||||
if resolution not in ["480p", "720p"]:
|
||||
raise HTTPException(status_code=400, detail="Seedance 1.5 Pro only supports 480p or 720p resolution")
|
||||
if audio:
|
||||
raise HTTPException(status_code=400, detail="Audio upload is not supported for Seedance 1.5 Pro (use generate_audio instead)")
|
||||
else:
|
||||
# WAN 2.5 validation
|
||||
if duration < 3 or duration > 10:
|
||||
raise HTTPException(status_code=400, detail="WAN 2.5 duration must be between 3 and 10 seconds")
|
||||
if resolution not in ["480p", "720p", "1080p"]:
|
||||
raise HTTPException(status_code=400, detail="WAN 2.5 resolution must be 480p, 720p, or 1080p")
|
||||
|
||||
# Initialize services
|
||||
video_service = VideoStudioService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(f"[VideoStudio] Video extension request: user={user_id}, model={model}, duration={duration}s, resolution={resolution}")
|
||||
|
||||
# Read video file
|
||||
video_data = await file.read()
|
||||
|
||||
# Read audio file if provided (WAN 2.5 only)
|
||||
audio_data = None
|
||||
if audio:
|
||||
if model in ("wan-2.2-spicy", "wavespeed-ai/wan-2.2-spicy/video-extend", "seedance-1.5-pro", "bytedance/seedance-v1.5-pro/video-extend"):
|
||||
raise HTTPException(status_code=400, detail=f"Audio upload is not supported for {model} model")
|
||||
|
||||
if not audio.content_type.startswith('audio/'):
|
||||
raise HTTPException(status_code=400, detail="Audio file must be an audio file")
|
||||
|
||||
# Validate audio file size (max 15MB per documentation)
|
||||
audio_data = await audio.read()
|
||||
if len(audio_data) > 15 * 1024 * 1024:
|
||||
raise HTTPException(status_code=400, detail="Audio file must be less than 15MB")
|
||||
|
||||
# Note: Audio duration validation (3-30s) would require parsing the audio file
|
||||
# This is handled by the API, but we could add it here if needed
|
||||
|
||||
# Extend video
|
||||
result = await video_service.extend_video(
|
||||
video_data=video_data,
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
audio_data=audio_data,
|
||||
negative_prompt=negative_prompt,
|
||||
resolution=resolution,
|
||||
duration=duration,
|
||||
enable_prompt_expansion=enable_prompt_expansion,
|
||||
generate_audio=generate_audio,
|
||||
camera_fixed=camera_fixed,
|
||||
seed=seed,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Video extension failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store extended version in asset library
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"original_file": file.filename,
|
||||
"prompt": prompt,
|
||||
"duration": duration,
|
||||
"resolution": resolution,
|
||||
"generation_type": "extend",
|
||||
"model": result.get("model_used", "alibaba/wan-2.5/video-extend"),
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"extended_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "extend", "ai-extended"]
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Video extension successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"duration": duration,
|
||||
"resolution": resolution,
|
||||
"model_used": result.get("model_used", "alibaba/wan-2.5/video-extend"),
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Video extension error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Video extension failed: {str(e)}")
|
||||
237
backend/routers/video_studio/endpoints/face_swap.py
Normal file
237
backend/routers/video_studio/endpoints/face_swap.py
Normal file
@@ -0,0 +1,237 @@
|
||||
"""
|
||||
Face Swap endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...services.video_studio.face_swap_service import FaceSwapService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.face_swap")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/face-swap")
|
||||
async def swap_face(
|
||||
background_tasks: BackgroundTasks,
|
||||
image_file: UploadFile = File(..., description="Reference image for character swap"),
|
||||
video_file: UploadFile = File(..., description="Source video for face swap"),
|
||||
model: str = Form("mocha", description="AI model to use: 'mocha' or 'video-face-swap'"),
|
||||
prompt: Optional[str] = Form(None, description="Optional prompt to guide the swap (MoCha only)"),
|
||||
resolution: str = Form("480p", description="Output resolution for MoCha (480p or 720p)"),
|
||||
seed: Optional[int] = Form(None, description="Random seed for reproducibility (MoCha only, -1 for random)"),
|
||||
target_gender: str = Form("all", description="Filter which faces to swap (video-face-swap only: all, female, male)"),
|
||||
target_index: int = Form(0, description="Select which face to swap (video-face-swap only: 0 = largest)"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Perform face/character swap using MoCha or Video Face Swap.
|
||||
|
||||
Supports two models:
|
||||
1. MoCha (wavespeed-ai/wan-2.1/mocha) - Character replacement with motion preservation
|
||||
- Resolution: 480p ($0.04/s) or 720p ($0.08/s)
|
||||
- Max length: 120 seconds
|
||||
- Features: Prompt guidance, seed control
|
||||
|
||||
2. Video Face Swap (wavespeed-ai/video-face-swap) - Simple face swap with multi-face support
|
||||
- Pricing: $0.01/s
|
||||
- Max length: 10 minutes (600 seconds)
|
||||
- Features: Gender filter, face index selection
|
||||
|
||||
Requirements:
|
||||
- Image: Clear reference image (JPG/PNG, avoid WEBP)
|
||||
- Video: Source video (max 120s for MoCha, max 600s for video-face-swap)
|
||||
- Minimum charge: 5 seconds for both models
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
# Validate file types
|
||||
if not image_file.content_type.startswith('image/'):
|
||||
raise HTTPException(status_code=400, detail="Image file must be an image")
|
||||
|
||||
if not video_file.content_type.startswith('video/'):
|
||||
raise HTTPException(status_code=400, detail="Video file must be a video")
|
||||
|
||||
# Validate resolution
|
||||
if resolution not in ("480p", "720p"):
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Resolution must be '480p' or '720p'"
|
||||
)
|
||||
|
||||
# Initialize services
|
||||
face_swap_service = FaceSwapService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(
|
||||
f"[FaceSwap] Face swap request: user={user_id}, "
|
||||
f"resolution={resolution}"
|
||||
)
|
||||
|
||||
# Read files
|
||||
image_data = await image_file.read()
|
||||
video_data = await video_file.read()
|
||||
|
||||
# Validate file sizes
|
||||
if len(image_data) > 10 * 1024 * 1024: # 10MB
|
||||
raise HTTPException(status_code=400, detail="Image file must be less than 10MB")
|
||||
|
||||
if len(video_data) > 500 * 1024 * 1024: # 500MB
|
||||
raise HTTPException(status_code=400, detail="Video file must be less than 500MB")
|
||||
|
||||
# Perform face swap
|
||||
result = await face_swap_service.swap_face(
|
||||
image_data=image_data,
|
||||
video_data=video_data,
|
||||
model=model,
|
||||
prompt=prompt,
|
||||
resolution=resolution,
|
||||
seed=seed,
|
||||
target_gender=target_gender,
|
||||
target_index=target_index,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Face swap failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store in asset library
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
model_name = "wavespeed-ai/wan-2.1/mocha" if model == "mocha" else "wavespeed-ai/video-face-swap"
|
||||
|
||||
asset_metadata = {
|
||||
"image_file": image_file.filename,
|
||||
"video_file": video_file.filename,
|
||||
"model": model,
|
||||
"operation_type": "face_swap",
|
||||
}
|
||||
|
||||
if model == "mocha":
|
||||
asset_metadata.update({
|
||||
"prompt": prompt,
|
||||
"resolution": resolution,
|
||||
"seed": seed,
|
||||
})
|
||||
else: # video-face-swap
|
||||
asset_metadata.update({
|
||||
"target_gender": target_gender,
|
||||
"target_index": target_index,
|
||||
})
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"face_swap_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "face_swap", "ai-generated"],
|
||||
)
|
||||
|
||||
logger.info(f"[FaceSwap] Face swap successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"model": model,
|
||||
"resolution": result.get("resolution"),
|
||||
"metadata": result.get("metadata", {}),
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[FaceSwap] Face swap error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Face swap failed: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/face-swap/estimate-cost")
|
||||
async def estimate_face_swap_cost(
|
||||
model: str = Form("mocha", description="AI model to use: 'mocha' or 'video-face-swap'"),
|
||||
resolution: str = Form("480p", description="Output resolution for MoCha (480p or 720p)"),
|
||||
estimated_duration: float = Form(10.0, description="Estimated video duration in seconds", ge=5.0),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Estimate cost for face swap operation.
|
||||
|
||||
Returns estimated cost based on model, resolution (for MoCha), and duration.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
# Validate model
|
||||
if model not in ("mocha", "video-face-swap"):
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Model must be 'mocha' or 'video-face-swap'"
|
||||
)
|
||||
|
||||
# Validate resolution (only for MoCha)
|
||||
if model == "mocha":
|
||||
if resolution not in ("480p", "720p"):
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Resolution must be '480p' or '720p' for MoCha"
|
||||
)
|
||||
max_duration = 120.0
|
||||
else:
|
||||
max_duration = 600.0 # 10 minutes for video-face-swap
|
||||
|
||||
if estimated_duration > max_duration:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Estimated duration must be <= {max_duration} seconds for {model}"
|
||||
)
|
||||
|
||||
face_swap_service = FaceSwapService()
|
||||
estimated_cost = face_swap_service.calculate_cost(model, resolution if model == "mocha" else None, estimated_duration)
|
||||
|
||||
# Pricing info
|
||||
if model == "mocha":
|
||||
cost_per_second = 0.04 if resolution == "480p" else 0.08
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"model": model,
|
||||
"resolution": resolution,
|
||||
"estimated_duration": estimated_duration,
|
||||
"cost_per_second": cost_per_second,
|
||||
"pricing_model": "per_second",
|
||||
"min_duration": 5.0,
|
||||
"max_duration": 120.0,
|
||||
"min_charge": cost_per_second * 5.0,
|
||||
}
|
||||
else: # video-face-swap
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"model": model,
|
||||
"estimated_duration": estimated_duration,
|
||||
"cost_per_second": 0.01,
|
||||
"pricing_model": "per_second",
|
||||
"min_duration": 5.0,
|
||||
"max_duration": 600.0,
|
||||
"min_charge": 0.05, # $0.01 * 5 seconds
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[FaceSwap] Failed to estimate cost: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to estimate cost: {str(e)}")
|
||||
82
backend/routers/video_studio/endpoints/models.py
Normal file
82
backend/routers/video_studio/endpoints/models.py
Normal file
@@ -0,0 +1,82 @@
|
||||
"""
|
||||
Model listing and cost estimation endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from typing import Optional, Dict, Any
|
||||
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.models")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.get("/models")
|
||||
async def list_available_models(
|
||||
operation_type: Optional[str] = None,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
List available AI models for video generation.
|
||||
|
||||
Optionally filter by operation type (text-to-video, image-to-video, avatar, enhancement).
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
video_service = VideoStudioService()
|
||||
|
||||
models = video_service.get_available_models(operation_type)
|
||||
|
||||
logger.info(f"[VideoStudio] Listed models for user={user_id}, operation={operation_type}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"models": models,
|
||||
"operation_type": operation_type,
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Error listing models: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to list models: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/cost-estimate")
|
||||
async def estimate_cost(
|
||||
operation_type: str,
|
||||
duration: Optional[int] = None,
|
||||
resolution: Optional[str] = None,
|
||||
model: Optional[str] = None,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Estimate cost for video generation operations.
|
||||
|
||||
Provides real-time cost estimates before generation.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
video_service = VideoStudioService()
|
||||
|
||||
estimate = video_service.estimate_cost(
|
||||
operation_type=operation_type,
|
||||
duration=duration,
|
||||
resolution=resolution,
|
||||
model=model,
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Cost estimate for user={user_id}: {estimate}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"estimate": estimate,
|
||||
"operation_type": operation_type,
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Error estimating cost: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to estimate cost: {str(e)}")
|
||||
89
backend/routers/video_studio/endpoints/prompt.py
Normal file
89
backend/routers/video_studio/endpoints/prompt.py
Normal file
@@ -0,0 +1,89 @@
|
||||
"""
|
||||
Prompt optimization endpoints for Video Studio.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import Optional, Dict, Any
|
||||
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
from services.wavespeed.client import WaveSpeedClient
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.prompt")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
class PromptOptimizeRequest(BaseModel):
|
||||
text: str = Field(..., description="The prompt text to optimize")
|
||||
mode: Optional[str] = Field(
|
||||
default="video",
|
||||
pattern="^(image|video)$",
|
||||
description="Optimization mode: 'image' or 'video' (default: 'video' for Video Studio)"
|
||||
)
|
||||
style: Optional[str] = Field(
|
||||
default="default",
|
||||
pattern="^(default|artistic|photographic|technical|anime|realistic)$",
|
||||
description="Style: 'default', 'artistic', 'photographic', 'technical', 'anime', or 'realistic'"
|
||||
)
|
||||
image: Optional[str] = Field(None, description="Base64-encoded image for context (optional)")
|
||||
|
||||
|
||||
class PromptOptimizeResponse(BaseModel):
|
||||
optimized_prompt: str
|
||||
success: bool
|
||||
|
||||
|
||||
@router.post("/optimize-prompt")
|
||||
async def optimize_prompt(
|
||||
request: PromptOptimizeRequest,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> PromptOptimizeResponse:
|
||||
"""
|
||||
Optimize a prompt using WaveSpeed prompt optimizer.
|
||||
|
||||
The WaveSpeedAI Prompt Optimizer enhances prompts specifically for image and video
|
||||
generation workflows. It restructures and enriches your input prompt to improve:
|
||||
- Visual clarity and composition
|
||||
- Cinematic framing and lighting
|
||||
- Camera movement and style consistency
|
||||
- Motion dynamics for video generation
|
||||
|
||||
Produces significantly better outputs across video generation models like FLUX, Wan,
|
||||
Kling, Veo, Seedance, and more.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
if not request.text or not request.text.strip():
|
||||
raise HTTPException(status_code=400, detail="Prompt text is required")
|
||||
|
||||
# Default to "video" mode for Video Studio
|
||||
mode = request.mode or "video"
|
||||
style = request.style or "default"
|
||||
|
||||
logger.info(f"[VideoStudio] Optimizing prompt for user {user_id} (mode={mode}, style={style})")
|
||||
|
||||
client = WaveSpeedClient()
|
||||
optimized_prompt = client.optimize_prompt(
|
||||
text=request.text.strip(),
|
||||
mode=mode,
|
||||
style=style,
|
||||
image=request.image, # Optional base64 image
|
||||
enable_sync_mode=True,
|
||||
timeout=30
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Prompt optimized successfully for user {user_id}")
|
||||
|
||||
return PromptOptimizeResponse(
|
||||
optimized_prompt=optimized_prompt,
|
||||
success=True
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as exc:
|
||||
logger.error(f"[VideoStudio] Failed to optimize prompt: {exc}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to optimize prompt: {str(exc)}")
|
||||
74
backend/routers/video_studio/endpoints/serve.py
Normal file
74
backend/routers/video_studio/endpoints/serve.py
Normal file
@@ -0,0 +1,74 @@
|
||||
"""
|
||||
Video serving endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from fastapi.responses import FileResponse
|
||||
from typing import Dict, Any
|
||||
from pathlib import Path
|
||||
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.serve")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.get("/videos/{user_id}/{video_filename:path}", summary="Serve Video Studio Video")
|
||||
async def serve_video_studio_video(
|
||||
user_id: str,
|
||||
video_filename: str,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> FileResponse:
|
||||
"""
|
||||
Serve a generated Video Studio video file.
|
||||
|
||||
Security: Only the video owner can access their videos.
|
||||
"""
|
||||
try:
|
||||
# Verify the requesting user matches the video owner
|
||||
authenticated_user_id = require_authenticated_user(current_user)
|
||||
if authenticated_user_id != user_id:
|
||||
raise HTTPException(
|
||||
status_code=403,
|
||||
detail="You can only access your own videos"
|
||||
)
|
||||
|
||||
# Get base directory
|
||||
base_dir = Path(__file__).parent.parent.parent.parent
|
||||
video_studio_videos_dir = base_dir / "video_studio_videos"
|
||||
video_path = video_studio_videos_dir / user_id / video_filename
|
||||
|
||||
# Security: Ensure path is within video_studio_videos directory
|
||||
try:
|
||||
resolved_path = video_path.resolve()
|
||||
resolved_base = video_studio_videos_dir.resolve()
|
||||
if not str(resolved_path).startswith(str(resolved_base)):
|
||||
raise HTTPException(
|
||||
status_code=403,
|
||||
detail="Invalid video path"
|
||||
)
|
||||
except (OSError, ValueError) as e:
|
||||
logger.error(f"[VideoStudio] Path resolution error: {e}")
|
||||
raise HTTPException(status_code=403, detail="Invalid video path")
|
||||
|
||||
# Check if file exists
|
||||
if not video_path.exists() or not video_path.is_file():
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail=f"Video not found: {video_filename}"
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Serving video: {video_path}")
|
||||
return FileResponse(
|
||||
path=str(video_path),
|
||||
media_type="video/mp4",
|
||||
filename=video_filename,
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Failed to serve video: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to serve video: {str(e)}")
|
||||
195
backend/routers/video_studio/endpoints/social.py
Normal file
195
backend/routers/video_studio/endpoints/social.py
Normal file
@@ -0,0 +1,195 @@
|
||||
"""
|
||||
Social Optimizer endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any, List
|
||||
import json
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...services.video_studio.social_optimizer_service import (
|
||||
SocialOptimizerService,
|
||||
OptimizationOptions,
|
||||
)
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.social")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/social/optimize")
|
||||
async def optimize_for_social(
|
||||
background_tasks: BackgroundTasks,
|
||||
file: UploadFile = File(..., description="Source video file"),
|
||||
platforms: str = Form(..., description="Comma-separated list of platforms (instagram,tiktok,youtube,linkedin,facebook,twitter)"),
|
||||
auto_crop: bool = Form(True, description="Auto-crop to platform aspect ratio"),
|
||||
generate_thumbnails: bool = Form(True, description="Generate thumbnails"),
|
||||
compress: bool = Form(True, description="Compress for file size limits"),
|
||||
trim_mode: str = Form("beginning", description="Trim mode if video exceeds duration (beginning, middle, end)"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Optimize video for multiple social media platforms.
|
||||
|
||||
Creates platform-optimized versions with:
|
||||
- Aspect ratio conversion
|
||||
- Duration trimming
|
||||
- File size compression
|
||||
- Thumbnail generation
|
||||
|
||||
Returns optimized videos for each selected platform.
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
if not file.content_type.startswith('video/'):
|
||||
raise HTTPException(status_code=400, detail="File must be a video")
|
||||
|
||||
# Parse platforms
|
||||
platform_list = [p.strip().lower() for p in platforms.split(",") if p.strip()]
|
||||
if not platform_list:
|
||||
raise HTTPException(status_code=400, detail="At least one platform must be specified")
|
||||
|
||||
# Validate platforms
|
||||
valid_platforms = ["instagram", "tiktok", "youtube", "linkedin", "facebook", "twitter"]
|
||||
invalid_platforms = [p for p in platform_list if p not in valid_platforms]
|
||||
if invalid_platforms:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Invalid platforms: {', '.join(invalid_platforms)}. Valid platforms: {', '.join(valid_platforms)}"
|
||||
)
|
||||
|
||||
# Validate trim_mode
|
||||
valid_trim_modes = ["beginning", "middle", "end"]
|
||||
if trim_mode not in valid_trim_modes:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Invalid trim_mode. Must be one of: {', '.join(valid_trim_modes)}"
|
||||
)
|
||||
|
||||
# Initialize services
|
||||
video_service = VideoStudioService()
|
||||
social_optimizer = SocialOptimizerService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(
|
||||
f"[SocialOptimizer] Optimization request: "
|
||||
f"user={user_id}, platforms={platform_list}"
|
||||
)
|
||||
|
||||
# Read video file
|
||||
video_data = await file.read()
|
||||
|
||||
# Create optimization options
|
||||
options = OptimizationOptions(
|
||||
auto_crop=auto_crop,
|
||||
generate_thumbnails=generate_thumbnails,
|
||||
compress=compress,
|
||||
trim_mode=trim_mode,
|
||||
)
|
||||
|
||||
# Optimize for platforms
|
||||
result = await social_optimizer.optimize_for_platforms(
|
||||
video_bytes=video_data,
|
||||
platforms=platform_list,
|
||||
options=options,
|
||||
user_id=user_id,
|
||||
video_studio_service=video_service,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Optimization failed: {result.get('errors', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store results in asset library
|
||||
for platform_result in result.get("results", []):
|
||||
asset_metadata = {
|
||||
"platform": platform_result["platform"],
|
||||
"name": platform_result["name"],
|
||||
"aspect_ratio": platform_result["aspect_ratio"],
|
||||
"duration": platform_result["duration"],
|
||||
"file_size": platform_result["file_size"],
|
||||
"width": platform_result["width"],
|
||||
"height": platform_result["height"],
|
||||
"optimization_type": "social_optimizer",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"social_{platform_result['platform']}_{platform_result['name'].replace(' ', '_').lower()}.mp4",
|
||||
file_url=platform_result["video_url"],
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=0.0, # Free (FFmpeg processing)
|
||||
tags=["video_studio", "social_optimizer", platform_result["platform"]],
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"[SocialOptimizer] Optimization successful: "
|
||||
f"user={user_id}, platforms={len(result.get('results', []))}"
|
||||
)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"results": result.get("results", []),
|
||||
"errors": result.get("errors", []),
|
||||
"cost": result.get("cost", 0.0),
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[SocialOptimizer] Optimization error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Optimization failed: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/social/platforms")
|
||||
async def get_platforms(
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get list of available platforms and their specifications.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
from ...services.video_studio.platform_specs import (
|
||||
PLATFORM_SPECS,
|
||||
Platform,
|
||||
)
|
||||
|
||||
platforms_data = {}
|
||||
for platform in Platform:
|
||||
specs = [spec for spec in PLATFORM_SPECS if spec.platform == platform]
|
||||
platforms_data[platform.value] = [
|
||||
{
|
||||
"name": spec.name,
|
||||
"aspect_ratio": spec.aspect_ratio,
|
||||
"width": spec.width,
|
||||
"height": spec.height,
|
||||
"max_duration": spec.max_duration,
|
||||
"max_file_size_mb": spec.max_file_size_mb,
|
||||
"formats": spec.formats,
|
||||
"description": spec.description,
|
||||
}
|
||||
for spec in specs
|
||||
]
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"platforms": platforms_data,
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[SocialOptimizer] Failed to get platforms: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get platforms: {str(e)}")
|
||||
40
backend/routers/video_studio/endpoints/tasks.py
Normal file
40
backend/routers/video_studio/endpoints/tasks.py
Normal file
@@ -0,0 +1,40 @@
|
||||
"""
|
||||
Async task status endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from typing import Dict, Any
|
||||
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
from api.story_writer.task_manager import task_manager
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.tasks")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.get("/task/{task_id}/status")
|
||||
async def get_task_status(
|
||||
task_id: str,
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Poll for video generation task status.
|
||||
|
||||
Returns task status, progress, and result when complete.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
status = task_manager.get_task_status(task_id)
|
||||
if not status:
|
||||
raise HTTPException(status_code=404, detail="Task not found or expired")
|
||||
|
||||
return status
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Failed to get task status: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get task status: {str(e)}")
|
||||
144
backend/routers/video_studio/endpoints/transform.py
Normal file
144
backend/routers/video_studio/endpoints/transform.py
Normal file
@@ -0,0 +1,144 @@
|
||||
"""
|
||||
Video transformation endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.transform")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/transform")
|
||||
async def transform_video(
|
||||
background_tasks: BackgroundTasks,
|
||||
file: UploadFile = File(..., description="Video file to transform"),
|
||||
transform_type: str = Form(..., description="Type of transformation: format, aspect, speed, resolution, compress"),
|
||||
# Format conversion parameters
|
||||
output_format: Optional[str] = Form(None, description="Output format for format conversion (mp4, mov, webm, gif)"),
|
||||
codec: Optional[str] = Form(None, description="Video codec (libx264, libvpx-vp9, etc.)"),
|
||||
quality: Optional[str] = Form(None, description="Quality preset (high, medium, low)"),
|
||||
audio_codec: Optional[str] = Form(None, description="Audio codec (aac, mp3, opus, etc.)"),
|
||||
# Aspect ratio parameters
|
||||
target_aspect: Optional[str] = Form(None, description="Target aspect ratio (16:9, 9:16, 1:1, 4:5, 21:9)"),
|
||||
crop_mode: Optional[str] = Form("center", description="Crop mode for aspect conversion (center, letterbox)"),
|
||||
# Speed parameters
|
||||
speed_factor: Optional[float] = Form(None, description="Speed multiplier (0.25, 0.5, 1.0, 1.5, 2.0, 4.0)"),
|
||||
# Resolution parameters
|
||||
target_resolution: Optional[str] = Form(None, description="Target resolution (480p, 720p, 1080p, 1440p, 4k)"),
|
||||
maintain_aspect: bool = Form(True, description="Whether to maintain aspect ratio when scaling"),
|
||||
# Compression parameters
|
||||
target_size_mb: Optional[float] = Form(None, description="Target file size in MB for compression"),
|
||||
compress_quality: Optional[str] = Form(None, description="Quality preset for compression (high, medium, low)"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Transform video using FFmpeg/MoviePy (format, aspect, speed, resolution, compression).
|
||||
|
||||
Supports:
|
||||
- Format conversion (MP4, MOV, WebM, GIF)
|
||||
- Aspect ratio conversion (16:9, 9:16, 1:1, 4:5, 21:9)
|
||||
- Speed adjustment (0.25x - 4x)
|
||||
- Resolution scaling (480p - 4K)
|
||||
- Compression (file size optimization)
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
if not file.content_type.startswith('video/'):
|
||||
raise HTTPException(status_code=400, detail="File must be a video")
|
||||
|
||||
# Initialize services
|
||||
video_service = VideoStudioService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(
|
||||
f"[VideoStudio] Video transformation request: "
|
||||
f"user={user_id}, type={transform_type}"
|
||||
)
|
||||
|
||||
# Read video file
|
||||
video_data = await file.read()
|
||||
|
||||
# Validate transform type
|
||||
valid_transform_types = ["format", "aspect", "speed", "resolution", "compress"]
|
||||
if transform_type not in valid_transform_types:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Invalid transform_type. Must be one of: {', '.join(valid_transform_types)}"
|
||||
)
|
||||
|
||||
# Transform video
|
||||
result = await video_service.transform_video(
|
||||
video_data=video_data,
|
||||
transform_type=transform_type,
|
||||
user_id=user_id,
|
||||
output_format=output_format,
|
||||
codec=codec,
|
||||
quality=quality,
|
||||
audio_codec=audio_codec,
|
||||
target_aspect=target_aspect,
|
||||
crop_mode=crop_mode,
|
||||
speed_factor=speed_factor,
|
||||
target_resolution=target_resolution,
|
||||
maintain_aspect=maintain_aspect,
|
||||
target_size_mb=target_size_mb,
|
||||
compress_quality=compress_quality,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Video transformation failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store transformed version in asset library
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"original_file": file.filename,
|
||||
"transform_type": transform_type,
|
||||
"output_format": output_format,
|
||||
"target_aspect": target_aspect,
|
||||
"speed_factor": speed_factor,
|
||||
"target_resolution": target_resolution,
|
||||
"generation_type": "transformation",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"transformed_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "transform", transform_type]
|
||||
)
|
||||
|
||||
logger.info(f"[VideoStudio] Video transformation successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"transform_type": transform_type,
|
||||
"metadata": result.get("metadata", {}),
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Video transformation error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Video transformation failed: {str(e)}")
|
||||
@@ -0,0 +1,146 @@
|
||||
"""
|
||||
Video Background Remover endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio.video_background_remover_service import VideoBackgroundRemoverService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.video_background_remover")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/video-background-remover")
|
||||
async def remove_background(
|
||||
background_tasks: BackgroundTasks,
|
||||
video_file: UploadFile = File(..., description="Source video for background removal"),
|
||||
background_image_file: Optional[UploadFile] = File(None, description="Optional background image for replacement"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Remove or replace video background using WaveSpeed Video Background Remover.
|
||||
|
||||
Features:
|
||||
- Clean matting and edge-aware blending
|
||||
- Natural compositing for realistic results
|
||||
- Optional background image replacement
|
||||
- Supports videos up to 10 minutes
|
||||
|
||||
Args:
|
||||
video_file: Source video file
|
||||
background_image_file: Optional replacement background image
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
if not video_file.content_type.startswith('video/'):
|
||||
raise HTTPException(status_code=400, detail="File must be a video")
|
||||
|
||||
# Initialize services
|
||||
background_remover_service = VideoBackgroundRemoverService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(f"[VideoBackgroundRemover] Background removal request: user={user_id}, has_background={background_image_file is not None}")
|
||||
|
||||
# Read video file
|
||||
video_data = await video_file.read()
|
||||
|
||||
# Read background image if provided
|
||||
background_image_data = None
|
||||
if background_image_file:
|
||||
if not background_image_file.content_type.startswith('image/'):
|
||||
raise HTTPException(status_code=400, detail="Background file must be an image")
|
||||
background_image_data = await background_image_file.read()
|
||||
|
||||
# Remove/replace background
|
||||
result = await background_remover_service.remove_background(
|
||||
video_data=video_data,
|
||||
background_image_data=background_image_data,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Background removal failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store processed video in asset library
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"original_file": video_file.filename,
|
||||
"has_background_replacement": result.get("has_background_replacement", False),
|
||||
"background_file": background_image_file.filename if background_image_file else None,
|
||||
"generation_type": "background_removal",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"bg_removed_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "background_removal", "ai-processed"]
|
||||
)
|
||||
|
||||
logger.info(f"[VideoBackgroundRemover] Background removal successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"has_background_replacement": result.get("has_background_replacement", False),
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoBackgroundRemover] Background removal error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Background removal failed: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/video-background-remover/estimate-cost")
|
||||
async def estimate_background_removal_cost(
|
||||
estimated_duration: float = Form(10.0, description="Estimated video duration in seconds", ge=5.0),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Estimate cost for video background removal operation.
|
||||
|
||||
Returns estimated cost based on duration.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
background_remover_service = VideoBackgroundRemoverService()
|
||||
estimated_cost = background_remover_service.calculate_cost(estimated_duration)
|
||||
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"estimated_duration": estimated_duration,
|
||||
"cost_per_second": 0.01,
|
||||
"pricing_model": "per_second",
|
||||
"min_duration": 0.0,
|
||||
"max_duration": 600.0, # 10 minutes max
|
||||
"min_charge": 0.05, # Minimum $0.05 for ≤5 seconds
|
||||
"max_charge": 6.00, # Maximum $6.00 for 600 seconds
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoBackgroundRemover] Failed to estimate cost: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to estimate cost: {str(e)}")
|
||||
260
backend/routers/video_studio/endpoints/video_translate.py
Normal file
260
backend/routers/video_studio/endpoints/video_translate.py
Normal file
@@ -0,0 +1,260 @@
|
||||
"""
|
||||
Video Translate endpoints.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
from ...database import get_db
|
||||
from ...models.content_asset_models import AssetSource, AssetType
|
||||
from ...services.video_studio import VideoStudioService
|
||||
from ...services.video_studio.video_translate_service import VideoTranslateService
|
||||
from ...services.asset_service import ContentAssetService
|
||||
from ...utils.auth import get_current_user, require_authenticated_user
|
||||
from ...utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.endpoints.video_translate")
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.post("/video-translate")
|
||||
async def translate_video(
|
||||
background_tasks: BackgroundTasks,
|
||||
video_file: UploadFile = File(..., description="Source video to translate"),
|
||||
output_language: str = Form("English", description="Target language for translation"),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Translate video to target language using HeyGen Video Translate.
|
||||
|
||||
Supports 70+ languages and 175+ dialects. Translates both audio and video
|
||||
with lip-sync preservation.
|
||||
|
||||
Requirements:
|
||||
- Video: Source video file (MP4, WebM, etc.)
|
||||
- Output Language: Target language (default: "English")
|
||||
- Pricing: $0.0375/second
|
||||
|
||||
Supported languages include:
|
||||
- English, Spanish, French, Hindi, Italian, German, Polish, Portuguese
|
||||
- Chinese, Japanese, Korean, Arabic, Russian, and many more
|
||||
- Regional variants (e.g., "English (United States)", "Spanish (Mexico)")
|
||||
"""
|
||||
try:
|
||||
user_id = require_authenticated_user(current_user)
|
||||
|
||||
# Validate file type
|
||||
if not video_file.content_type.startswith('video/'):
|
||||
raise HTTPException(status_code=400, detail="File must be a video")
|
||||
|
||||
# Initialize services
|
||||
video_translate_service = VideoTranslateService()
|
||||
asset_service = ContentAssetService(db)
|
||||
|
||||
logger.info(
|
||||
f"[VideoTranslate] Video translate request: user={user_id}, "
|
||||
f"output_language={output_language}"
|
||||
)
|
||||
|
||||
# Read file
|
||||
video_data = await video_file.read()
|
||||
|
||||
# Validate file size (reasonable limit)
|
||||
if len(video_data) > 500 * 1024 * 1024: # 500MB
|
||||
raise HTTPException(status_code=400, detail="Video file must be less than 500MB")
|
||||
|
||||
# Perform video translation
|
||||
result = await video_translate_service.translate_video(
|
||||
video_data=video_data,
|
||||
output_language=output_language,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
if not result.get("success"):
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Video translation failed: {result.get('error', 'Unknown error')}"
|
||||
)
|
||||
|
||||
# Store in asset library
|
||||
video_url = result.get("video_url")
|
||||
if video_url:
|
||||
asset_metadata = {
|
||||
"video_file": video_file.filename,
|
||||
"output_language": output_language,
|
||||
"operation_type": "video_translate",
|
||||
"model": "heygen/video-translate",
|
||||
}
|
||||
|
||||
asset_service.create_asset(
|
||||
user_id=user_id,
|
||||
filename=f"video_translate_{uuid.uuid4().hex[:8]}.mp4",
|
||||
file_url=video_url,
|
||||
asset_type=AssetType.VIDEO,
|
||||
source_module=AssetSource.VIDEO_STUDIO,
|
||||
asset_metadata=asset_metadata,
|
||||
cost=result.get("cost", 0),
|
||||
tags=["video_studio", "video_translate", "ai-generated"],
|
||||
)
|
||||
|
||||
logger.info(f"[VideoTranslate] Video translate successful: user={user_id}, url={video_url}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": video_url,
|
||||
"cost": result.get("cost", 0),
|
||||
"output_language": output_language,
|
||||
"metadata": result.get("metadata", {}),
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoTranslate] Video translate error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Video translation failed: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/video-translate/estimate-cost")
|
||||
async def estimate_video_translate_cost(
|
||||
estimated_duration: float = Form(10.0, description="Estimated video duration in seconds", ge=1.0),
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Estimate cost for video translation operation.
|
||||
|
||||
Returns estimated cost based on duration.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
video_translate_service = VideoTranslateService()
|
||||
estimated_cost = video_translate_service.calculate_cost(estimated_duration)
|
||||
|
||||
return {
|
||||
"estimated_cost": estimated_cost,
|
||||
"estimated_duration": estimated_duration,
|
||||
"cost_per_second": 0.0375,
|
||||
"pricing_model": "per_second",
|
||||
"min_duration": 1.0,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoTranslate] Failed to estimate cost: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to estimate cost: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/video-translate/languages")
|
||||
async def get_supported_languages(
|
||||
current_user: Dict[str, Any] = Depends(get_current_user),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get list of supported languages for video translation.
|
||||
|
||||
Returns a categorized list of 70+ languages and 175+ dialects.
|
||||
"""
|
||||
try:
|
||||
require_authenticated_user(current_user)
|
||||
|
||||
# Common languages (simplified list - full list has 175+ dialects)
|
||||
languages = [
|
||||
"English",
|
||||
"English (United States)",
|
||||
"English (UK)",
|
||||
"English (Australia)",
|
||||
"English (Canada)",
|
||||
"Spanish",
|
||||
"Spanish (Spain)",
|
||||
"Spanish (Mexico)",
|
||||
"Spanish (Argentina)",
|
||||
"French",
|
||||
"French (France)",
|
||||
"French (Canada)",
|
||||
"German",
|
||||
"German (Germany)",
|
||||
"Italian",
|
||||
"Italian (Italy)",
|
||||
"Portuguese",
|
||||
"Portuguese (Brazil)",
|
||||
"Portuguese (Portugal)",
|
||||
"Chinese",
|
||||
"Chinese (Mandarin, Simplified)",
|
||||
"Chinese (Cantonese, Traditional)",
|
||||
"Japanese",
|
||||
"Japanese (Japan)",
|
||||
"Korean",
|
||||
"Korean (Korea)",
|
||||
"Hindi",
|
||||
"Hindi (India)",
|
||||
"Arabic",
|
||||
"Arabic (Saudi Arabia)",
|
||||
"Arabic (Egypt)",
|
||||
"Russian",
|
||||
"Russian (Russia)",
|
||||
"Polish",
|
||||
"Polish (Poland)",
|
||||
"Dutch",
|
||||
"Dutch (Netherlands)",
|
||||
"Turkish",
|
||||
"Turkish (Türkiye)",
|
||||
"Thai",
|
||||
"Thai (Thailand)",
|
||||
"Vietnamese",
|
||||
"Vietnamese (Vietnam)",
|
||||
"Indonesian",
|
||||
"Indonesian (Indonesia)",
|
||||
"Malay",
|
||||
"Malay (Malaysia)",
|
||||
"Filipino",
|
||||
"Filipino (Philippines)",
|
||||
"Bengali (India)",
|
||||
"Tamil (India)",
|
||||
"Telugu (India)",
|
||||
"Marathi (India)",
|
||||
"Gujarati (India)",
|
||||
"Kannada (India)",
|
||||
"Malayalam (India)",
|
||||
"Urdu (India)",
|
||||
"Urdu (Pakistan)",
|
||||
"Swedish",
|
||||
"Swedish (Sweden)",
|
||||
"Norwegian Bokmål (Norway)",
|
||||
"Danish",
|
||||
"Danish (Denmark)",
|
||||
"Finnish",
|
||||
"Finnish (Finland)",
|
||||
"Greek",
|
||||
"Greek (Greece)",
|
||||
"Hebrew (Israel)",
|
||||
"Czech",
|
||||
"Czech (Czechia)",
|
||||
"Romanian",
|
||||
"Romanian (Romania)",
|
||||
"Hungarian",
|
||||
"Hungarian (Hungary)",
|
||||
"Bulgarian",
|
||||
"Bulgarian (Bulgaria)",
|
||||
"Croatian",
|
||||
"Croatian (Croatia)",
|
||||
"Ukrainian",
|
||||
"Ukrainian (Ukraine)",
|
||||
"English - Your Accent",
|
||||
"English - American Accent",
|
||||
]
|
||||
|
||||
return {
|
||||
"languages": sorted(languages),
|
||||
"total_count": len(languages),
|
||||
"note": "This is a simplified list. Full API supports 70+ languages and 175+ dialects. See documentation for complete list.",
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoTranslate] Failed to get languages: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get languages: {str(e)}")
|
||||
1
backend/routers/video_studio/tasks/__init__.py
Normal file
1
backend/routers/video_studio/tasks/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
"""Background tasks for Video Studio."""
|
||||
147
backend/routers/video_studio/tasks/avatar_generation.py
Normal file
147
backend/routers/video_studio/tasks/avatar_generation.py
Normal file
@@ -0,0 +1,147 @@
|
||||
"""
|
||||
Background task for async avatar generation.
|
||||
"""
|
||||
|
||||
from typing import Optional
|
||||
from api.story_writer.task_manager import task_manager
|
||||
from services.video_studio.avatar_service import AvatarStudioService
|
||||
from services.video_studio import VideoStudioService
|
||||
from utils.asset_tracker import save_asset_to_library
|
||||
from utils.logger_utils import get_service_logger
|
||||
from ..utils import extract_error_message
|
||||
|
||||
logger = get_service_logger("video_studio.tasks.avatar")
|
||||
|
||||
|
||||
async def execute_avatar_generation_task(
|
||||
task_id: str,
|
||||
user_id: str,
|
||||
image_base64: str,
|
||||
audio_base64: str,
|
||||
resolution: str = "720p",
|
||||
prompt: Optional[str] = None,
|
||||
mask_image_base64: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
model: str = "infinitetalk",
|
||||
):
|
||||
"""Background task for async avatar generation with progress updates."""
|
||||
try:
|
||||
# Progress callback that updates task status
|
||||
def progress_callback(progress: float, message: str):
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"processing",
|
||||
progress=progress,
|
||||
message=message
|
||||
)
|
||||
|
||||
# Update initial status
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"processing",
|
||||
progress=5.0,
|
||||
message="Initializing avatar generation..."
|
||||
)
|
||||
|
||||
# Create avatar service
|
||||
avatar_service = AvatarStudioService()
|
||||
|
||||
# Generate avatar video
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"processing",
|
||||
progress=20.0,
|
||||
message=f"Submitting request to {model}..."
|
||||
)
|
||||
|
||||
result = await avatar_service.create_talking_avatar(
|
||||
image_base64=image_base64,
|
||||
audio_base64=audio_base64,
|
||||
resolution=resolution,
|
||||
prompt=prompt,
|
||||
mask_image_base64=mask_image_base64,
|
||||
seed=seed,
|
||||
user_id=user_id,
|
||||
model=model,
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"processing",
|
||||
progress=90.0,
|
||||
message="Saving video file..."
|
||||
)
|
||||
|
||||
# Save file
|
||||
video_service = VideoStudioService()
|
||||
save_result = video_service._save_video_file(
|
||||
video_bytes=result["video_bytes"],
|
||||
operation_type="talking-avatar",
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
# Save to asset library
|
||||
try:
|
||||
from services.database import get_db
|
||||
db = next(get_db())
|
||||
try:
|
||||
save_asset_to_library(
|
||||
db=db,
|
||||
user_id=user_id,
|
||||
asset_type="video",
|
||||
source_module="video_studio",
|
||||
filename=save_result["filename"],
|
||||
file_url=save_result["file_url"],
|
||||
file_path=save_result["file_path"],
|
||||
file_size=save_result["file_size"],
|
||||
mime_type="video/mp4",
|
||||
title="Video Studio: Talking Avatar",
|
||||
description=f"Talking avatar video: {prompt[:100] if prompt else 'No prompt'}",
|
||||
prompt=result.get("prompt", prompt or ""),
|
||||
tags=["video_studio", "avatar", "talking_avatar"],
|
||||
provider=result.get("provider", "wavespeed"),
|
||||
model=result.get("model_name", "wavespeed-ai/infinitetalk"),
|
||||
cost=result.get("cost", 0.0),
|
||||
asset_metadata={
|
||||
"resolution": result.get("resolution", resolution),
|
||||
"duration": result.get("duration", 5.0),
|
||||
"operation": "talking-avatar",
|
||||
"width": result.get("width", 1280),
|
||||
"height": result.get("height", 720),
|
||||
}
|
||||
)
|
||||
logger.info(f"[AvatarStudio] Video saved to asset library")
|
||||
finally:
|
||||
db.close()
|
||||
except Exception as e:
|
||||
logger.warning(f"[AvatarStudio] Failed to save to asset library: {e}")
|
||||
|
||||
# Update task with final result
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"completed",
|
||||
progress=100.0,
|
||||
message="Avatar generation complete!",
|
||||
result={
|
||||
"video_url": save_result["file_url"],
|
||||
"cost": result.get("cost", 0.0),
|
||||
"duration": result.get("duration", 5.0),
|
||||
"model": result.get("model_name", "wavespeed-ai/infinitetalk"),
|
||||
"provider": result.get("provider", "wavespeed"),
|
||||
"resolution": result.get("resolution", resolution),
|
||||
"width": result.get("width", 1280),
|
||||
"height": result.get("height", 720),
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as exc:
|
||||
error_message = extract_error_message(exc)
|
||||
logger.error(f"[AvatarStudio] Avatar generation failed: {error_message}", exc_info=True)
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"failed",
|
||||
progress=0.0,
|
||||
message=f"Avatar generation failed: {error_message}",
|
||||
error=error_message
|
||||
)
|
||||
128
backend/routers/video_studio/tasks/video_generation.py
Normal file
128
backend/routers/video_studio/tasks/video_generation.py
Normal file
@@ -0,0 +1,128 @@
|
||||
"""
|
||||
Background task for async video generation.
|
||||
"""
|
||||
|
||||
from typing import Optional, Dict, Any
|
||||
from api.story_writer.task_manager import task_manager
|
||||
from services.video_studio import VideoStudioService
|
||||
from utils.asset_tracker import save_asset_to_library
|
||||
from utils.logger_utils import get_service_logger
|
||||
from ..utils import extract_error_message
|
||||
|
||||
logger = get_service_logger("video_studio.tasks")
|
||||
|
||||
|
||||
def execute_video_generation_task(
|
||||
task_id: str,
|
||||
operation_type: str,
|
||||
user_id: str,
|
||||
prompt: Optional[str] = None,
|
||||
image_data: Optional[bytes] = None,
|
||||
image_base64: Optional[str] = None,
|
||||
provider: str = "wavespeed",
|
||||
**kwargs,
|
||||
):
|
||||
"""Background task for async video generation with progress updates."""
|
||||
try:
|
||||
from services.llm_providers.main_video_generation import ai_video_generate
|
||||
|
||||
# Progress callback that updates task status
|
||||
def progress_callback(progress: float, message: str):
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"processing",
|
||||
progress=progress,
|
||||
message=message
|
||||
)
|
||||
|
||||
# Update initial status
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"processing",
|
||||
progress=5.0,
|
||||
message="Initializing video generation..."
|
||||
)
|
||||
|
||||
# Call unified video generation with progress callback
|
||||
result = ai_video_generate(
|
||||
prompt=prompt,
|
||||
image_data=image_data,
|
||||
image_base64=image_base64,
|
||||
operation_type=operation_type,
|
||||
provider=provider,
|
||||
user_id=user_id,
|
||||
progress_callback=progress_callback,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
# Save file
|
||||
video_service = VideoStudioService()
|
||||
save_result = video_service._save_video_file(
|
||||
video_bytes=result["video_bytes"],
|
||||
operation_type=operation_type,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
# Save to asset library
|
||||
try:
|
||||
from services.database import get_db
|
||||
db = next(get_db())
|
||||
try:
|
||||
save_asset_to_library(
|
||||
db=db,
|
||||
user_id=user_id,
|
||||
asset_type="video",
|
||||
source_module="video_studio",
|
||||
filename=save_result["filename"],
|
||||
file_url=save_result["file_url"],
|
||||
file_path=save_result["file_path"],
|
||||
file_size=save_result["file_size"],
|
||||
mime_type="video/mp4",
|
||||
title=f"Video Studio: {operation_type.replace('-', ' ').title()}",
|
||||
description=f"Generated video: {prompt[:100] if prompt else 'No prompt'}",
|
||||
prompt=result.get("prompt", prompt or ""),
|
||||
tags=["video_studio", operation_type],
|
||||
provider=result.get("provider", provider),
|
||||
model=result.get("model_name", kwargs.get("model", "unknown")),
|
||||
cost=result.get("cost", 0.0),
|
||||
asset_metadata={
|
||||
"resolution": result.get("resolution", kwargs.get("resolution", "720p")),
|
||||
"duration": result.get("duration", float(kwargs.get("duration", 5))),
|
||||
"operation": operation_type,
|
||||
"width": result.get("width", 1280),
|
||||
"height": result.get("height", 720),
|
||||
}
|
||||
)
|
||||
logger.info(f"[VideoStudio] Video saved to asset library")
|
||||
finally:
|
||||
db.close()
|
||||
except Exception as e:
|
||||
logger.warning(f"[VideoStudio] Failed to save to asset library: {e}")
|
||||
|
||||
# Update task with final result
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"completed",
|
||||
progress=100.0,
|
||||
message="Video generation complete!",
|
||||
result={
|
||||
"video_url": save_result["file_url"],
|
||||
"cost": result.get("cost", 0.0),
|
||||
"duration": result.get("duration", float(kwargs.get("duration", 5))),
|
||||
"model": result.get("model_name", kwargs.get("model", "unknown")),
|
||||
"provider": result.get("provider", provider),
|
||||
"resolution": result.get("resolution", kwargs.get("resolution", "720p")),
|
||||
"width": result.get("width", 1280),
|
||||
"height": result.get("height", 720),
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as exc:
|
||||
logger.exception(f"[VideoStudio] Video generation failed: {exc}")
|
||||
error_msg = extract_error_message(exc)
|
||||
task_manager.update_task_status(
|
||||
task_id,
|
||||
"failed",
|
||||
error=error_msg,
|
||||
message=f"Video generation failed: {error_msg}"
|
||||
)
|
||||
54
backend/routers/video_studio/utils.py
Normal file
54
backend/routers/video_studio/utils.py
Normal file
@@ -0,0 +1,54 @@
|
||||
"""
|
||||
Utility functions for Video Studio router.
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
from typing import Any
|
||||
from fastapi import HTTPException
|
||||
from utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio_router")
|
||||
|
||||
|
||||
def extract_error_message(exc: Exception) -> str:
|
||||
"""
|
||||
Extract user-friendly error message from exception.
|
||||
Handles HTTPException with nested error details from WaveSpeed API.
|
||||
"""
|
||||
if isinstance(exc, HTTPException):
|
||||
detail = exc.detail
|
||||
# If detail is a dict (from WaveSpeed client)
|
||||
if isinstance(detail, dict):
|
||||
# Try to extract message from nested response JSON
|
||||
response_str = detail.get("response", "")
|
||||
if response_str:
|
||||
try:
|
||||
response_json = json.loads(response_str)
|
||||
if isinstance(response_json, dict) and "message" in response_json:
|
||||
return response_json["message"]
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
# Fall back to error field
|
||||
if "error" in detail:
|
||||
return detail["error"]
|
||||
# If detail is a string
|
||||
elif isinstance(detail, str):
|
||||
return detail
|
||||
|
||||
# For other exceptions, use string representation
|
||||
error_str = str(exc)
|
||||
|
||||
# Try to extract meaningful message from HTTPException string format
|
||||
if "Insufficient credits" in error_str or "insufficient credits" in error_str.lower():
|
||||
return "Insufficient WaveSpeed credits. Please top up your account."
|
||||
|
||||
# Try to extract JSON message from string
|
||||
try:
|
||||
json_match = re.search(r'"message"\s*:\s*"([^"]+)"', error_str)
|
||||
if json_match:
|
||||
return json_match.group(1)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return error_str
|
||||
@@ -10,7 +10,7 @@ from loguru import logger
|
||||
|
||||
from .wan25_service import WAN25Service
|
||||
from .infinitetalk_adapter import InfiniteTalkService
|
||||
from services.llm_providers.main_video_generation import track_video_usage
|
||||
from services.llm_providers.main_video_generation import ai_video_generate
|
||||
from utils.logger_utils import get_service_logger
|
||||
from utils.file_storage import save_file_safely, sanitize_filename
|
||||
|
||||
@@ -114,7 +114,7 @@ class TransformStudioService:
|
||||
request: TransformImageToVideoRequest,
|
||||
user_id: str,
|
||||
) -> Dict[str, Any]:
|
||||
"""Transform image to video using WAN 2.5.
|
||||
"""Transform image to video using unified video generation entry point.
|
||||
|
||||
Args:
|
||||
request: Transform request
|
||||
@@ -128,43 +128,34 @@ class TransformStudioService:
|
||||
f"resolution={request.resolution}, duration={request.duration}s"
|
||||
)
|
||||
|
||||
# Generate video using WAN 2.5
|
||||
result = await self.wan25_service.generate_video(
|
||||
# Use unified video generation entry point
|
||||
# This handles pre-flight validation, generation, and usage tracking
|
||||
# Returns dict with video_bytes and full metadata
|
||||
result = ai_video_generate(
|
||||
image_base64=request.image_base64,
|
||||
prompt=request.prompt,
|
||||
audio_base64=request.audio_base64,
|
||||
resolution=request.resolution,
|
||||
operation_type="image-to-video",
|
||||
provider="wavespeed",
|
||||
user_id=user_id,
|
||||
duration=request.duration,
|
||||
resolution=request.resolution,
|
||||
negative_prompt=request.negative_prompt,
|
||||
seed=request.seed,
|
||||
audio_base64=request.audio_base64,
|
||||
enable_prompt_expansion=request.enable_prompt_expansion,
|
||||
model="alibaba/wan-2.5/image-to-video",
|
||||
)
|
||||
|
||||
# Extract video bytes and metadata from result
|
||||
video_bytes = result["video_bytes"]
|
||||
|
||||
# Save video to disk
|
||||
save_result = self._save_video_file(
|
||||
video_bytes=result["video_bytes"],
|
||||
video_bytes=video_bytes,
|
||||
operation_type="image-to-video",
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
# Track usage
|
||||
try:
|
||||
usage_info = track_video_usage(
|
||||
user_id=user_id,
|
||||
provider=result["provider"],
|
||||
model_name=result["model_name"],
|
||||
prompt=result["prompt"],
|
||||
video_bytes=result["video_bytes"],
|
||||
cost_override=result["cost"],
|
||||
)
|
||||
logger.info(
|
||||
f"[Transform Studio] Usage tracked: {usage_info.get('current_calls', 0)} / "
|
||||
f"{usage_info.get('video_limit_display', '∞')} videos, "
|
||||
f"cost=${result['cost']:.2f}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"[Transform Studio] Failed to track usage: {e}")
|
||||
|
||||
# Save to asset library
|
||||
try:
|
||||
from services.database import get_db
|
||||
@@ -184,17 +175,17 @@ class TransformStudioService:
|
||||
mime_type="video/mp4",
|
||||
title=f"Transform: Image-to-Video ({request.resolution})",
|
||||
description=f"Generated video using WAN 2.5: {request.prompt[:100]}",
|
||||
prompt=result["prompt"],
|
||||
prompt=result.get("prompt", request.prompt),
|
||||
tags=["image_studio", "transform", "video", "image-to-video", request.resolution],
|
||||
provider=result["provider"],
|
||||
model=result["model_name"],
|
||||
cost=result["cost"],
|
||||
provider=result.get("provider", "wavespeed"),
|
||||
model=result.get("model_name", "alibaba/wan-2.5/image-to-video"),
|
||||
cost=result.get("cost", 0.0),
|
||||
asset_metadata={
|
||||
"resolution": request.resolution,
|
||||
"duration": result["duration"],
|
||||
"duration": result.get("duration", float(request.duration)),
|
||||
"operation": "image-to-video",
|
||||
"width": result["width"],
|
||||
"height": result["height"],
|
||||
"width": result.get("width", 1280),
|
||||
"height": result.get("height", 720),
|
||||
}
|
||||
)
|
||||
logger.info(f"[Transform Studio] Video saved to asset library")
|
||||
@@ -207,14 +198,14 @@ class TransformStudioService:
|
||||
"success": True,
|
||||
"video_url": save_result["file_url"],
|
||||
"video_base64": None, # Don't include base64 for large videos
|
||||
"duration": result["duration"],
|
||||
"resolution": result["resolution"],
|
||||
"width": result["width"],
|
||||
"height": result["height"],
|
||||
"duration": result.get("duration", float(request.duration)),
|
||||
"resolution": result.get("resolution", request.resolution),
|
||||
"width": result.get("width", 1280),
|
||||
"height": result.get("height", 720),
|
||||
"file_size": save_result["file_size"],
|
||||
"cost": result["cost"],
|
||||
"provider": result["provider"],
|
||||
"model": result["model_name"],
|
||||
"cost": result.get("cost", 0.0),
|
||||
"provider": result.get("provider", "wavespeed"),
|
||||
"model": result.get("model_name", "alibaba/wan-2.5/image-to-video"),
|
||||
"metadata": result.get("metadata", {}),
|
||||
}
|
||||
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
import base64
|
||||
import asyncio
|
||||
from typing import Any, Dict, Optional
|
||||
from typing import Any, Dict, Optional, Callable
|
||||
import requests
|
||||
from fastapi import HTTPException
|
||||
from loguru import logger
|
||||
@@ -103,6 +103,7 @@ class WAN25Service:
|
||||
negative_prompt: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
enable_prompt_expansion: bool = True,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Generate video using WAN 2.5.
|
||||
|
||||
@@ -217,7 +218,8 @@ class WAN25Service:
|
||||
result = self.client.poll_until_complete(
|
||||
prediction_id,
|
||||
timeout_seconds=180, # 3 minutes max
|
||||
interval_seconds=2.0
|
||||
interval_seconds=2.0,
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
except HTTPException as e:
|
||||
detail = e.detail or {}
|
||||
|
||||
@@ -2,7 +2,9 @@
|
||||
Main Video Generation Service
|
||||
|
||||
Provides a unified interface for AI video generation providers.
|
||||
Initial support: Hugging Face Inference Providers (text-to-video).
|
||||
Supports:
|
||||
- Text-to-video: Hugging Face Inference Providers, WaveSpeed models
|
||||
- Image-to-video: WaveSpeed WAN 2.5, Kandinsky 5 Pro
|
||||
Stubs included for Gemini (Veo 3) and OpenAI (Sora) for future use.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
@@ -11,7 +13,8 @@ import os
|
||||
import base64
|
||||
import io
|
||||
import sys
|
||||
from typing import Any, Dict, Optional, Union
|
||||
import asyncio
|
||||
from typing import Any, Dict, Optional, Union, Callable
|
||||
|
||||
from fastapi import HTTPException
|
||||
|
||||
@@ -37,6 +40,7 @@ def _get_api_key(provider: str) -> Optional[str]:
|
||||
manager = APIKeyManager()
|
||||
mapping = {
|
||||
"huggingface": "hf_token",
|
||||
"wavespeed": "wavespeed", # WaveSpeed API key
|
||||
"gemini": "gemini", # placeholder for Veo 3
|
||||
"openai": "openai_api_key", # placeholder for Sora
|
||||
}
|
||||
@@ -211,6 +215,115 @@ def _generate_with_huggingface(
|
||||
})
|
||||
|
||||
|
||||
async def _generate_image_to_video_wavespeed(
|
||||
image_data: Optional[bytes] = None,
|
||||
image_base64: Optional[str] = None,
|
||||
prompt: str = "",
|
||||
duration: int = 5,
|
||||
resolution: str = "720p",
|
||||
model: str = "alibaba/wan-2.5/image-to-video",
|
||||
negative_prompt: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
audio_base64: Optional[str] = None,
|
||||
enable_prompt_expansion: bool = True,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
**kwargs
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate video from image using WaveSpeed (WAN 2.5 or Kandinsky 5 Pro).
|
||||
|
||||
Args:
|
||||
image_data: Image bytes (required if image_base64 not provided)
|
||||
image_base64: Image in base64 or data URI format (required if image_data not provided)
|
||||
prompt: Text prompt describing the video motion
|
||||
duration: Video duration in seconds (5 or 10)
|
||||
resolution: Output resolution (480p, 720p, 1080p)
|
||||
model: Model to use (alibaba/wan-2.5/image-to-video, wavespeed/kandinsky5-pro/image-to-video)
|
||||
negative_prompt: Optional negative prompt
|
||||
seed: Optional random seed
|
||||
audio_base64: Optional audio file for synchronization
|
||||
enable_prompt_expansion: Enable prompt optimization
|
||||
|
||||
Returns:
|
||||
Dictionary with video_bytes and metadata (cost, duration, resolution, width, height, etc.)
|
||||
"""
|
||||
# Import here to avoid circular dependencies
|
||||
from services.image_studio.wan25_service import WAN25Service
|
||||
|
||||
logger.info(f"[video_gen] WaveSpeed image-to-video: model={model}, resolution={resolution}, duration={duration}s")
|
||||
|
||||
# Validate inputs
|
||||
if not image_data and not image_base64:
|
||||
raise ValueError("Either image_data or image_base64 must be provided for image-to-video")
|
||||
|
||||
# Convert image_data to base64 if needed
|
||||
if image_data and not image_base64:
|
||||
image_base64 = base64.b64encode(image_data).decode('utf-8')
|
||||
# Add data URI prefix if not present
|
||||
if not image_base64.startswith("data:"):
|
||||
image_base64 = f"data:image/png;base64,{image_base64}"
|
||||
|
||||
# Initialize WAN25Service (handles both WAN 2.5 and Kandinsky 5 Pro)
|
||||
wan25_service = WAN25Service()
|
||||
|
||||
try:
|
||||
# Generate video using WAN25Service (returns full metadata)
|
||||
result = await wan25_service.generate_video(
|
||||
image_base64=image_base64,
|
||||
prompt=prompt,
|
||||
audio_base64=audio_base64,
|
||||
resolution=resolution,
|
||||
duration=duration,
|
||||
negative_prompt=negative_prompt,
|
||||
seed=seed,
|
||||
enable_prompt_expansion=enable_prompt_expansion,
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
|
||||
video_bytes = result.get("video_bytes")
|
||||
if not video_bytes:
|
||||
raise ValueError("WAN25Service returned no video bytes")
|
||||
|
||||
if not isinstance(video_bytes, bytes):
|
||||
raise TypeError(f"Expected bytes from WAN25Service, got {type(video_bytes)}")
|
||||
|
||||
if len(video_bytes) == 0:
|
||||
raise ValueError("Received empty video bytes from WaveSpeed API")
|
||||
|
||||
logger.info(f"[video_gen] Successfully generated image-to-video: {len(video_bytes)} bytes")
|
||||
|
||||
# Return video bytes with metadata
|
||||
return {
|
||||
"video_bytes": video_bytes,
|
||||
"prompt": result.get("prompt", prompt),
|
||||
"duration": result.get("duration", float(duration)),
|
||||
"model_name": result.get("model_name", model),
|
||||
"cost": result.get("cost", 0.0),
|
||||
"provider": result.get("provider", "wavespeed"),
|
||||
"resolution": result.get("resolution", resolution),
|
||||
"width": result.get("width", 1280),
|
||||
"height": result.get("height", 720),
|
||||
"metadata": result.get("metadata", {}),
|
||||
"source_video_url": result.get("source_video_url"),
|
||||
"prediction_id": result.get("prediction_id"),
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
# Re-raise HTTPExceptions from WAN25Service
|
||||
raise
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
error_type = type(e).__name__
|
||||
logger.error(f"[video_gen] WaveSpeed image-to-video error ({error_type}): {error_msg}", exc_info=True)
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": f"WaveSpeed image-to-video generation failed: {error_msg}",
|
||||
"error_type": error_type
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def _generate_with_gemini(prompt: str, **kwargs) -> bytes:
|
||||
raise VideoProviderNotImplemented("Gemini Veo 3 integration coming soon.")
|
||||
|
||||
@@ -218,26 +331,154 @@ def _generate_with_openai(prompt: str, **kwargs) -> bytes:
|
||||
raise VideoProviderNotImplemented("OpenAI Sora integration coming soon.")
|
||||
|
||||
|
||||
def ai_video_generate(
|
||||
async def _generate_text_to_video_wavespeed(
|
||||
prompt: str,
|
||||
duration: int = 5,
|
||||
resolution: str = "720p",
|
||||
model: str = "hunyuan-video-1.5",
|
||||
negative_prompt: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
audio_base64: Optional[str] = None,
|
||||
enable_prompt_expansion: bool = True,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
**kwargs
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate text-to-video using WaveSpeed models.
|
||||
|
||||
Args:
|
||||
prompt: Text prompt describing the video
|
||||
duration: Video duration in seconds
|
||||
resolution: Output resolution (480p, 720p)
|
||||
model: Model identifier (e.g., "hunyuan-video-1.5")
|
||||
negative_prompt: Optional negative prompt
|
||||
seed: Optional random seed
|
||||
audio_base64: Optional audio (not supported by all models)
|
||||
enable_prompt_expansion: Enable prompt optimization (not supported by all models)
|
||||
progress_callback: Optional progress callback function
|
||||
**kwargs: Additional model-specific parameters
|
||||
|
||||
Returns:
|
||||
Dictionary with video_bytes, prompt, duration, model_name, cost, etc.
|
||||
"""
|
||||
from .video_generation.wavespeed_provider import get_wavespeed_text_to_video_service
|
||||
|
||||
logger.info(f"[video_gen] WaveSpeed text-to-video: model={model}, resolution={resolution}, duration={duration}s")
|
||||
|
||||
# Get the appropriate service for the model
|
||||
try:
|
||||
service = get_wavespeed_text_to_video_service(model)
|
||||
except ValueError as e:
|
||||
logger.error(f"[video_gen] Unsupported WaveSpeed text-to-video model: {model}")
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=str(e)
|
||||
)
|
||||
|
||||
# Generate video using the service
|
||||
try:
|
||||
result = await service.generate_video(
|
||||
prompt=prompt,
|
||||
duration=duration,
|
||||
resolution=resolution,
|
||||
negative_prompt=negative_prompt,
|
||||
seed=seed,
|
||||
audio_base64=audio_base64,
|
||||
enable_prompt_expansion=enable_prompt_expansion,
|
||||
progress_callback=progress_callback,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
logger.info(f"[video_gen] Successfully generated text-to-video: {len(result.get('video_bytes', b''))} bytes")
|
||||
return result
|
||||
|
||||
except HTTPException:
|
||||
# Re-raise HTTPExceptions from service
|
||||
raise
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
error_type = type(e).__name__
|
||||
logger.error(f"[video_gen] WaveSpeed text-to-video error ({error_type}): {error_msg}", exc_info=True)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail={
|
||||
"error": f"WaveSpeed text-to-video generation failed: {error_msg}",
|
||||
"type": error_type,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
async def ai_video_generate(
|
||||
prompt: Optional[str] = None,
|
||||
image_data: Optional[bytes] = None,
|
||||
image_base64: Optional[str] = None,
|
||||
operation_type: str = "text-to-video",
|
||||
provider: str = "huggingface",
|
||||
user_id: Optional[str] = None,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
**kwargs,
|
||||
) -> bytes:
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Unified video generation entry point.
|
||||
|
||||
- provider: 'huggingface' (default), 'gemini' (veo3 stub), 'openai' (sora stub)
|
||||
- kwargs: num_frames, guidance_scale, num_inference_steps, negative_prompt, seed, model
|
||||
|
||||
Returns raw video bytes (mp4/webm depending on provider).
|
||||
Unified video generation entry point for ALL video operations.
|
||||
|
||||
Supports:
|
||||
- text-to-video: prompt required, provider: 'huggingface', 'wavespeed', 'gemini' (stub), 'openai' (stub)
|
||||
- image-to-video: image_data or image_base64 required, provider: 'wavespeed'
|
||||
|
||||
Args:
|
||||
prompt: Text prompt (required for text-to-video)
|
||||
image_data: Image bytes (required for image-to-video if image_base64 not provided)
|
||||
image_base64: Image base64 string (required for image-to-video if image_data not provided)
|
||||
operation_type: "text-to-video" or "image-to-video" (default: "text-to-video")
|
||||
provider: Provider name (default: "huggingface" for text-to-video, "wavespeed" for image-to-video)
|
||||
user_id: Required for subscription/usage tracking
|
||||
progress_callback: Optional function(progress: float, message: str) -> None
|
||||
Called at key stages: submission (10%), polling (20-80%), completion (100%)
|
||||
**kwargs: Model-specific parameters:
|
||||
- For text-to-video: num_frames, guidance_scale, num_inference_steps, negative_prompt, seed, model
|
||||
- For image-to-video: duration, resolution, negative_prompt, seed, audio_base64, enable_prompt_expansion, model
|
||||
|
||||
Returns:
|
||||
Dictionary with:
|
||||
- video_bytes: Raw video bytes (mp4/webm depending on provider)
|
||||
- prompt: The prompt used (may be enhanced)
|
||||
- duration: Video duration in seconds
|
||||
- model_name: Model used for generation
|
||||
- cost: Cost of generation
|
||||
- provider: Provider name
|
||||
- resolution: Video resolution (for image-to-video)
|
||||
- width: Video width in pixels (for image-to-video)
|
||||
- height: Video height in pixels (for image-to-video)
|
||||
- metadata: Additional metadata dict
|
||||
"""
|
||||
logger.info(f"[video_gen] provider={provider}")
|
||||
logger.info(f"[video_gen] operation={operation_type}, provider={provider}")
|
||||
|
||||
# Enforce authentication usage like text gen does
|
||||
if not user_id:
|
||||
raise RuntimeError("user_id is required for subscription/usage tracking.")
|
||||
|
||||
# Validate operation type and required inputs
|
||||
if operation_type == "text-to-video":
|
||||
if not prompt:
|
||||
raise ValueError("prompt is required for text-to-video generation")
|
||||
# Set default provider if not specified
|
||||
if provider == "huggingface" and "model" not in kwargs:
|
||||
kwargs.setdefault("model", "tencent/HunyuanVideo")
|
||||
elif operation_type == "image-to-video":
|
||||
if not image_data and not image_base64:
|
||||
raise ValueError("image_data or image_base64 is required for image-to-video generation")
|
||||
# Set default provider and model for image-to-video
|
||||
if provider not in ["wavespeed"]:
|
||||
logger.warning(f"[video_gen] Provider {provider} not supported for image-to-video, defaulting to wavespeed")
|
||||
provider = "wavespeed"
|
||||
if "model" not in kwargs:
|
||||
kwargs.setdefault("model", "alibaba/wan-2.5/image-to-video")
|
||||
# Set defaults for image-to-video
|
||||
kwargs.setdefault("duration", 5)
|
||||
kwargs.setdefault("resolution", "720p")
|
||||
else:
|
||||
raise ValueError(f"Invalid operation_type: {operation_type}. Must be 'text-to-video' or 'image-to-video'")
|
||||
|
||||
# PRE-FLIGHT VALIDATION: Validate video generation before API call
|
||||
# MUST happen BEFORE any API calls - return immediately if validation fails
|
||||
from services.database import get_db
|
||||
@@ -259,32 +500,141 @@ def ai_video_generate(
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
logger.info(f"[Video Generation] ✅ Pre-flight validation passed - proceeding with video generation")
|
||||
logger.info(f"[Video Generation] ✅ Pre-flight validation passed - proceeding with {operation_type}")
|
||||
|
||||
# Generate video
|
||||
model_name = kwargs.get("model", "tencent/HunyuanVideo")
|
||||
# Progress callback: Initial submission
|
||||
if progress_callback:
|
||||
progress_callback(10.0, f"Submitting {operation_type} request to {provider}...")
|
||||
|
||||
# Generate video based on operation type
|
||||
model_name = kwargs.get("model", _get_default_model(operation_type, provider))
|
||||
try:
|
||||
if provider == "huggingface":
|
||||
video_bytes = _generate_with_huggingface(
|
||||
prompt=prompt,
|
||||
**kwargs,
|
||||
)
|
||||
elif provider == "gemini":
|
||||
video_bytes = _generate_with_gemini(prompt=prompt, **kwargs)
|
||||
elif provider == "openai":
|
||||
video_bytes = _generate_with_openai(prompt=prompt, **kwargs)
|
||||
else:
|
||||
raise RuntimeError(f"Unknown video provider: {provider}")
|
||||
if operation_type == "text-to-video":
|
||||
if provider == "huggingface":
|
||||
video_bytes = _generate_with_huggingface(
|
||||
prompt=prompt,
|
||||
**kwargs,
|
||||
)
|
||||
# For text-to-video, create metadata dict (HuggingFace doesn't return metadata)
|
||||
result_dict = {
|
||||
"video_bytes": video_bytes,
|
||||
"prompt": prompt,
|
||||
"duration": kwargs.get("duration", 5.0),
|
||||
"model_name": model_name,
|
||||
"cost": 0.10, # Default cost, will be calculated in track_video_usage
|
||||
"provider": provider,
|
||||
"resolution": kwargs.get("resolution", "720p"),
|
||||
"width": 1280, # Default, actual may vary
|
||||
"height": 720, # Default, actual may vary
|
||||
"metadata": {},
|
||||
}
|
||||
elif provider == "wavespeed":
|
||||
# WaveSpeed text-to-video - use unified service
|
||||
result_dict = await _generate_text_to_video_wavespeed(
|
||||
prompt=prompt,
|
||||
progress_callback=progress_callback,
|
||||
**kwargs,
|
||||
)
|
||||
elif provider == "gemini":
|
||||
video_bytes = _generate_with_gemini(prompt=prompt, **kwargs)
|
||||
result_dict = {
|
||||
"video_bytes": video_bytes,
|
||||
"prompt": prompt,
|
||||
"duration": kwargs.get("duration", 5.0),
|
||||
"model_name": model_name,
|
||||
"cost": 0.10,
|
||||
"provider": provider,
|
||||
"resolution": kwargs.get("resolution", "720p"),
|
||||
"width": 1280,
|
||||
"height": 720,
|
||||
"metadata": {},
|
||||
}
|
||||
elif provider == "openai":
|
||||
video_bytes = _generate_with_openai(prompt=prompt, **kwargs)
|
||||
result_dict = {
|
||||
"video_bytes": video_bytes,
|
||||
"prompt": prompt,
|
||||
"duration": kwargs.get("duration", 5.0),
|
||||
"model_name": model_name,
|
||||
"cost": 0.10,
|
||||
"provider": provider,
|
||||
"resolution": kwargs.get("resolution", "720p"),
|
||||
"width": 1280,
|
||||
"height": 720,
|
||||
"metadata": {},
|
||||
}
|
||||
else:
|
||||
raise RuntimeError(f"Unknown provider for text-to-video: {provider}")
|
||||
|
||||
elif operation_type == "image-to-video":
|
||||
if provider == "wavespeed":
|
||||
# Progress callback: Starting generation
|
||||
if progress_callback:
|
||||
progress_callback(20.0, "Video generation in progress...")
|
||||
|
||||
# Handle async call from sync context
|
||||
# Since ai_video_generate is sync, we need to run async function
|
||||
try:
|
||||
loop = asyncio.get_event_loop()
|
||||
if loop.is_running():
|
||||
# We're in an async context - use ThreadPoolExecutor to run in new event loop
|
||||
import concurrent.futures
|
||||
with concurrent.futures.ThreadPoolExecutor() as executor:
|
||||
future = executor.submit(
|
||||
asyncio.run,
|
||||
_generate_image_to_video_wavespeed(
|
||||
image_data=image_data,
|
||||
image_base64=image_base64,
|
||||
prompt=prompt or kwargs.get("prompt", ""),
|
||||
progress_callback=progress_callback,
|
||||
**kwargs
|
||||
)
|
||||
)
|
||||
result_dict = future.result()
|
||||
else:
|
||||
# Event loop exists but not running - use it
|
||||
result_dict = loop.run_until_complete(_generate_image_to_video_wavespeed(
|
||||
image_data=image_data,
|
||||
image_base64=image_base64,
|
||||
prompt=prompt or kwargs.get("prompt", ""),
|
||||
progress_callback=progress_callback,
|
||||
**kwargs
|
||||
))
|
||||
except RuntimeError:
|
||||
# No event loop exists, create a new one
|
||||
result_dict = asyncio.run(_generate_image_to_video_wavespeed(
|
||||
image_data=image_data,
|
||||
image_base64=image_base64,
|
||||
prompt=prompt or kwargs.get("prompt", ""),
|
||||
progress_callback=progress_callback,
|
||||
**kwargs
|
||||
))
|
||||
video_bytes = result_dict["video_bytes"]
|
||||
model_name = result_dict.get("model_name", model_name)
|
||||
|
||||
# Progress callback: Processing result
|
||||
if progress_callback:
|
||||
progress_callback(90.0, "Processing video result...")
|
||||
else:
|
||||
raise RuntimeError(f"Unknown provider for image-to-video: {provider}. Only 'wavespeed' is supported.")
|
||||
|
||||
# Track usage (same pattern as text generation)
|
||||
# Use cost from result_dict if available, otherwise calculate
|
||||
cost_override = result_dict.get("cost") if operation_type == "image-to-video" else kwargs.get("cost_override")
|
||||
track_video_usage(
|
||||
user_id=user_id,
|
||||
provider=provider,
|
||||
model_name=model_name,
|
||||
prompt=prompt,
|
||||
prompt=result_dict.get("prompt", prompt or ""),
|
||||
video_bytes=video_bytes,
|
||||
cost_override=cost_override,
|
||||
)
|
||||
|
||||
return video_bytes
|
||||
# Progress callback: Complete
|
||||
if progress_callback:
|
||||
progress_callback(100.0, "Video generation complete!")
|
||||
|
||||
return result_dict
|
||||
|
||||
except HTTPException:
|
||||
# Re-raise HTTPExceptions (e.g., from validation or API errors)
|
||||
@@ -294,6 +644,16 @@ def ai_video_generate(
|
||||
raise HTTPException(status_code=500, detail={"error": str(e)})
|
||||
|
||||
|
||||
def _get_default_model(operation_type: str, provider: str) -> str:
|
||||
"""Get default model for operation type and provider."""
|
||||
defaults = {
|
||||
("text-to-video", "huggingface"): "tencent/HunyuanVideo",
|
||||
("text-to-video", "wavespeed"): "hunyuan-video-1.5",
|
||||
("image-to-video", "wavespeed"): "alibaba/wan-2.5/image-to-video",
|
||||
}
|
||||
return defaults.get((operation_type, provider), "hunyuan-video-1.5")
|
||||
|
||||
|
||||
def track_video_usage(
|
||||
*,
|
||||
user_id: str,
|
||||
@@ -386,7 +746,7 @@ def track_video_usage(
|
||||
cost_total=cost_per_video,
|
||||
response_time=0.0,
|
||||
status_code=200,
|
||||
request_size=len(prompt.encode("utf-8")),
|
||||
request_size=len((prompt or "").encode("utf-8")),
|
||||
response_size=len(video_bytes),
|
||||
billing_period=current_period,
|
||||
)
|
||||
|
||||
10
backend/services/llm_providers/video_generation/__init__.py
Normal file
10
backend/services/llm_providers/video_generation/__init__.py
Normal file
@@ -0,0 +1,10 @@
|
||||
"""
|
||||
Video Generation Services
|
||||
|
||||
Modular services for text-to-video and image-to-video generation.
|
||||
Each provider/model has its own service class for separation of concerns.
|
||||
"""
|
||||
|
||||
from typing import Optional, Dict, Any
|
||||
|
||||
__all__ = []
|
||||
53
backend/services/llm_providers/video_generation/base.py
Normal file
53
backend/services/llm_providers/video_generation/base.py
Normal file
@@ -0,0 +1,53 @@
|
||||
"""
|
||||
Base classes and interfaces for video generation services.
|
||||
|
||||
Provides common interfaces and data structures for video generation providers.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional, Dict, Any, Protocol, Callable
|
||||
|
||||
|
||||
@dataclass
|
||||
class VideoGenerationOptions:
|
||||
"""Options for video generation."""
|
||||
prompt: str
|
||||
duration: int = 5
|
||||
resolution: str = "720p"
|
||||
negative_prompt: Optional[str] = None
|
||||
seed: Optional[int] = None
|
||||
audio_base64: Optional[str] = None
|
||||
enable_prompt_expansion: bool = True
|
||||
model: Optional[str] = None
|
||||
extra: Optional[Dict[str, Any]] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class VideoGenerationResult:
|
||||
"""Result from video generation."""
|
||||
video_bytes: bytes
|
||||
prompt: str
|
||||
duration: float
|
||||
model_name: str
|
||||
cost: float
|
||||
provider: str
|
||||
resolution: str
|
||||
width: int
|
||||
height: int
|
||||
metadata: Dict[str, Any]
|
||||
source_video_url: Optional[str] = None
|
||||
prediction_id: Optional[str] = None
|
||||
|
||||
|
||||
class VideoGenerationProvider(Protocol):
|
||||
"""Protocol for video generation providers."""
|
||||
|
||||
async def generate_video(
|
||||
self,
|
||||
options: VideoGenerationOptions,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
) -> VideoGenerationResult:
|
||||
"""Generate video with given options."""
|
||||
...
|
||||
File diff suppressed because it is too large
Load Diff
@@ -7,20 +7,49 @@ replacing mock research with real-time industry information.
|
||||
Available Services:
|
||||
- GoogleSearchService: Real-time industry research using Google Custom Search API
|
||||
- ExaService: Competitor discovery and analysis using Exa API
|
||||
- TavilyService: AI-powered web search with real-time information
|
||||
- Source ranking and credibility assessment
|
||||
- Content extraction and insight generation
|
||||
|
||||
Core Module (v2.0):
|
||||
- ResearchEngine: Standalone AI research engine for any content tool
|
||||
- ResearchContext: Unified input schema for research requests
|
||||
- ParameterOptimizer: AI-driven parameter optimization
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 1.0
|
||||
Last Updated: January 2025
|
||||
Version: 2.0
|
||||
Last Updated: December 2025
|
||||
"""
|
||||
|
||||
from .google_search_service import GoogleSearchService
|
||||
from .exa_service import ExaService
|
||||
from .tavily_service import TavilyService
|
||||
|
||||
# Core Research Engine (v2.0)
|
||||
from .core import (
|
||||
ResearchEngine,
|
||||
ResearchContext,
|
||||
ResearchPersonalizationContext,
|
||||
ContentType,
|
||||
ResearchGoal,
|
||||
ResearchDepth,
|
||||
ProviderPreference,
|
||||
ParameterOptimizer,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# Legacy services (still used by blog writer)
|
||||
"GoogleSearchService",
|
||||
"ExaService",
|
||||
"TavilyService"
|
||||
"TavilyService",
|
||||
|
||||
# Core Research Engine (v2.0)
|
||||
"ResearchEngine",
|
||||
"ResearchContext",
|
||||
"ResearchPersonalizationContext",
|
||||
"ContentType",
|
||||
"ResearchGoal",
|
||||
"ResearchDepth",
|
||||
"ProviderPreference",
|
||||
"ParameterOptimizer",
|
||||
]
|
||||
|
||||
51
backend/services/research/core/__init__.py
Normal file
51
backend/services/research/core/__init__.py
Normal file
@@ -0,0 +1,51 @@
|
||||
"""
|
||||
Research Engine Core Module
|
||||
|
||||
This is the standalone AI Research Engine that can be imported by
|
||||
Blog Writer, Podcast Maker, YouTube Creator, and other ALwrity tools.
|
||||
|
||||
Design Goals:
|
||||
- Tool-agnostic: Any content tool can import and use this
|
||||
- AI-driven parameter optimization: Users don't need to understand Exa/Tavily internals
|
||||
- Provider priority: Exa → Tavily → Google (fallback)
|
||||
- Personalization-aware: Accepts context from calling tools
|
||||
- Advanced by default: Prioritizes quality over speed
|
||||
|
||||
Usage:
|
||||
from services.research.core import ResearchEngine, ResearchContext
|
||||
|
||||
engine = ResearchEngine()
|
||||
result = await engine.research(ResearchContext(
|
||||
query="AI trends in healthcare 2025",
|
||||
content_type=ContentType.BLOG,
|
||||
persona_context={"industry": "Healthcare", "audience": "Medical professionals"}
|
||||
))
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 2.0
|
||||
Last Updated: December 2025
|
||||
"""
|
||||
|
||||
from .research_context import (
|
||||
ResearchContext,
|
||||
ResearchPersonalizationContext,
|
||||
ContentType,
|
||||
ResearchGoal,
|
||||
ResearchDepth,
|
||||
ProviderPreference,
|
||||
)
|
||||
from .parameter_optimizer import ParameterOptimizer
|
||||
from .research_engine import ResearchEngine
|
||||
|
||||
__all__ = [
|
||||
# Context schemas
|
||||
"ResearchContext",
|
||||
"ResearchPersonalizationContext",
|
||||
"ContentType",
|
||||
"ResearchGoal",
|
||||
"ResearchDepth",
|
||||
"ProviderPreference",
|
||||
# Core classes
|
||||
"ParameterOptimizer",
|
||||
"ResearchEngine",
|
||||
]
|
||||
384
backend/services/research/core/parameter_optimizer.py
Normal file
384
backend/services/research/core/parameter_optimizer.py
Normal file
@@ -0,0 +1,384 @@
|
||||
"""
|
||||
AI Parameter Optimizer for Research Engine
|
||||
|
||||
Uses AI to analyze the research query and context to select optimal
|
||||
parameters for Exa and Tavily APIs. This abstracts the complexity
|
||||
from non-technical users.
|
||||
|
||||
Key Decisions:
|
||||
- Provider selection (Exa vs Tavily vs Google)
|
||||
- Search type (neural vs keyword)
|
||||
- Category/topic selection
|
||||
- Depth and result limits
|
||||
- Domain filtering
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 2.0
|
||||
"""
|
||||
|
||||
import os
|
||||
import re
|
||||
from typing import Dict, Any, Optional, Tuple
|
||||
from loguru import logger
|
||||
|
||||
from .research_context import (
|
||||
ResearchContext,
|
||||
ResearchGoal,
|
||||
ResearchDepth,
|
||||
ProviderPreference,
|
||||
ContentType,
|
||||
)
|
||||
from models.blog_models import ResearchConfig, ResearchProvider, ResearchMode
|
||||
|
||||
|
||||
class ParameterOptimizer:
|
||||
"""
|
||||
AI-driven parameter optimization for research providers.
|
||||
|
||||
Analyzes the research context and selects optimal parameters
|
||||
for Exa, Tavily, or Google without requiring user expertise.
|
||||
"""
|
||||
|
||||
# Query patterns for intelligent routing
|
||||
TRENDING_PATTERNS = [
|
||||
r'\b(latest|recent|new|2024|2025|current|trending|news)\b',
|
||||
r'\b(update|announcement|launch|release)\b',
|
||||
]
|
||||
|
||||
TECHNICAL_PATTERNS = [
|
||||
r'\b(api|sdk|framework|library|implementation|architecture)\b',
|
||||
r'\b(code|programming|developer|technical|engineering)\b',
|
||||
]
|
||||
|
||||
COMPETITIVE_PATTERNS = [
|
||||
r'\b(competitor|alternative|vs|versus|compare|comparison)\b',
|
||||
r'\b(market|industry|landscape|players)\b',
|
||||
]
|
||||
|
||||
FACTUAL_PATTERNS = [
|
||||
r'\b(statistics|data|research|study|report|survey)\b',
|
||||
r'\b(percent|percentage|number|figure|metric)\b',
|
||||
]
|
||||
|
||||
# Exa category mapping based on query analysis
|
||||
EXA_CATEGORY_MAP = {
|
||||
'research': 'research paper',
|
||||
'news': 'news',
|
||||
'company': 'company',
|
||||
'personal': 'personal site',
|
||||
'github': 'github',
|
||||
'linkedin': 'linkedin profile',
|
||||
'finance': 'financial report',
|
||||
}
|
||||
|
||||
# Tavily topic mapping
|
||||
TAVILY_TOPIC_MAP = {
|
||||
ResearchGoal.TRENDING: 'news',
|
||||
ResearchGoal.FACTUAL: 'general',
|
||||
ResearchGoal.COMPETITIVE: 'general',
|
||||
ResearchGoal.TECHNICAL: 'general',
|
||||
ResearchGoal.EDUCATIONAL: 'general',
|
||||
ResearchGoal.INSPIRATIONAL: 'general',
|
||||
}
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the optimizer."""
|
||||
self.exa_available = bool(os.getenv("EXA_API_KEY"))
|
||||
self.tavily_available = bool(os.getenv("TAVILY_API_KEY"))
|
||||
logger.info(f"ParameterOptimizer initialized: exa={self.exa_available}, tavily={self.tavily_available}")
|
||||
|
||||
def optimize(self, context: ResearchContext) -> Tuple[ResearchProvider, ResearchConfig]:
|
||||
"""
|
||||
Analyze research context and return optimized provider and config.
|
||||
|
||||
Args:
|
||||
context: The research context from the calling tool
|
||||
|
||||
Returns:
|
||||
Tuple of (selected_provider, optimized_config)
|
||||
"""
|
||||
# If advanced mode, use raw parameters
|
||||
if context.advanced_mode:
|
||||
return self._build_advanced_config(context)
|
||||
|
||||
# Analyze query to determine optimal approach
|
||||
query_analysis = self._analyze_query(context.query)
|
||||
|
||||
# Select provider based on analysis and preferences
|
||||
provider = self._select_provider(context, query_analysis)
|
||||
|
||||
# Build optimized config for selected provider
|
||||
config = self._build_config(context, provider, query_analysis)
|
||||
|
||||
logger.info(f"Optimized research: provider={provider.value}, mode={config.mode.value}")
|
||||
|
||||
return provider, config
|
||||
|
||||
def _analyze_query(self, query: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Analyze the query to understand intent and optimal approach.
|
||||
|
||||
Returns dict with:
|
||||
- is_trending: Query is about recent/current events
|
||||
- is_technical: Query is technical in nature
|
||||
- is_competitive: Query is about competition/comparison
|
||||
- is_factual: Query needs data/statistics
|
||||
- suggested_category: Exa category if applicable
|
||||
- suggested_topic: Tavily topic
|
||||
"""
|
||||
query_lower = query.lower()
|
||||
|
||||
analysis = {
|
||||
'is_trending': self._matches_patterns(query_lower, self.TRENDING_PATTERNS),
|
||||
'is_technical': self._matches_patterns(query_lower, self.TECHNICAL_PATTERNS),
|
||||
'is_competitive': self._matches_patterns(query_lower, self.COMPETITIVE_PATTERNS),
|
||||
'is_factual': self._matches_patterns(query_lower, self.FACTUAL_PATTERNS),
|
||||
'suggested_category': None,
|
||||
'suggested_topic': 'general',
|
||||
'suggested_search_type': 'auto',
|
||||
}
|
||||
|
||||
# Determine Exa category
|
||||
if 'research' in query_lower or 'study' in query_lower or 'paper' in query_lower:
|
||||
analysis['suggested_category'] = 'research paper'
|
||||
elif 'github' in query_lower or 'repository' in query_lower:
|
||||
analysis['suggested_category'] = 'github'
|
||||
elif 'linkedin' in query_lower or 'professional' in query_lower:
|
||||
analysis['suggested_category'] = 'linkedin profile'
|
||||
elif analysis['is_trending']:
|
||||
analysis['suggested_category'] = 'news'
|
||||
elif 'company' in query_lower or 'startup' in query_lower:
|
||||
analysis['suggested_category'] = 'company'
|
||||
|
||||
# Determine Tavily topic
|
||||
if analysis['is_trending']:
|
||||
analysis['suggested_topic'] = 'news'
|
||||
elif 'finance' in query_lower or 'stock' in query_lower or 'investment' in query_lower:
|
||||
analysis['suggested_topic'] = 'finance'
|
||||
else:
|
||||
analysis['suggested_topic'] = 'general'
|
||||
|
||||
# Determine search type
|
||||
if analysis['is_technical'] or analysis['is_factual']:
|
||||
analysis['suggested_search_type'] = 'neural' # Better for semantic understanding
|
||||
elif analysis['is_trending']:
|
||||
analysis['suggested_search_type'] = 'keyword' # Better for current events
|
||||
|
||||
return analysis
|
||||
|
||||
def _matches_patterns(self, text: str, patterns: list) -> bool:
|
||||
"""Check if text matches any of the patterns."""
|
||||
for pattern in patterns:
|
||||
if re.search(pattern, text, re.IGNORECASE):
|
||||
return True
|
||||
return False
|
||||
|
||||
def _select_provider(self, context: ResearchContext, analysis: Dict[str, Any]) -> ResearchProvider:
|
||||
"""
|
||||
Select the optimal provider based on context and query analysis.
|
||||
|
||||
Priority: Exa → Tavily → Google for ALL modes (including basic).
|
||||
This provides better semantic search results for content creators.
|
||||
|
||||
Exa's neural search excels at understanding context and meaning,
|
||||
which is valuable for all research types, not just technical queries.
|
||||
"""
|
||||
preference = context.provider_preference
|
||||
|
||||
# If user explicitly requested a provider, respect that
|
||||
if preference == ProviderPreference.EXA:
|
||||
if self.exa_available:
|
||||
return ResearchProvider.EXA
|
||||
logger.warning("Exa requested but not available, falling back")
|
||||
|
||||
if preference == ProviderPreference.TAVILY:
|
||||
if self.tavily_available:
|
||||
return ResearchProvider.TAVILY
|
||||
logger.warning("Tavily requested but not available, falling back")
|
||||
|
||||
if preference == ProviderPreference.GOOGLE:
|
||||
return ResearchProvider.GOOGLE
|
||||
|
||||
# AUTO mode: Always prefer Exa → Tavily → Google
|
||||
# Exa provides superior semantic search for all content types
|
||||
if self.exa_available:
|
||||
logger.info(f"Selected Exa (primary provider): query analysis shows " +
|
||||
f"technical={analysis.get('is_technical', False)}, " +
|
||||
f"trending={analysis.get('is_trending', False)}")
|
||||
return ResearchProvider.EXA
|
||||
|
||||
# Tavily as secondary option - good for real-time and news
|
||||
if self.tavily_available:
|
||||
logger.info(f"Selected Tavily (secondary): Exa unavailable, " +
|
||||
f"trending={analysis.get('is_trending', False)}")
|
||||
return ResearchProvider.TAVILY
|
||||
|
||||
# Google grounding as fallback
|
||||
logger.info("Selected Google (fallback): Exa and Tavily unavailable")
|
||||
return ResearchProvider.GOOGLE
|
||||
|
||||
def _build_config(
|
||||
self,
|
||||
context: ResearchContext,
|
||||
provider: ResearchProvider,
|
||||
analysis: Dict[str, Any]
|
||||
) -> ResearchConfig:
|
||||
"""Build optimized ResearchConfig for the selected provider."""
|
||||
|
||||
# Map ResearchDepth to ResearchMode
|
||||
mode_map = {
|
||||
ResearchDepth.QUICK: ResearchMode.BASIC,
|
||||
ResearchDepth.STANDARD: ResearchMode.BASIC,
|
||||
ResearchDepth.COMPREHENSIVE: ResearchMode.COMPREHENSIVE,
|
||||
ResearchDepth.EXPERT: ResearchMode.COMPREHENSIVE,
|
||||
}
|
||||
mode = mode_map.get(context.depth, ResearchMode.BASIC)
|
||||
|
||||
# Base config
|
||||
config = ResearchConfig(
|
||||
mode=mode,
|
||||
provider=provider,
|
||||
max_sources=context.max_sources,
|
||||
include_statistics=context.personalization.include_statistics if context.personalization else True,
|
||||
include_expert_quotes=context.personalization.include_expert_quotes if context.personalization else True,
|
||||
include_competitors=analysis['is_competitive'],
|
||||
include_trends=analysis['is_trending'],
|
||||
)
|
||||
|
||||
# Provider-specific optimizations
|
||||
if provider == ResearchProvider.EXA:
|
||||
config = self._optimize_exa_config(config, context, analysis)
|
||||
elif provider == ResearchProvider.TAVILY:
|
||||
config = self._optimize_tavily_config(config, context, analysis)
|
||||
|
||||
# Apply domain filters
|
||||
if context.include_domains:
|
||||
if provider == ResearchProvider.EXA:
|
||||
config.exa_include_domains = context.include_domains
|
||||
elif provider == ResearchProvider.TAVILY:
|
||||
config.tavily_include_domains = context.include_domains[:300] # Tavily limit
|
||||
|
||||
if context.exclude_domains:
|
||||
if provider == ResearchProvider.EXA:
|
||||
config.exa_exclude_domains = context.exclude_domains
|
||||
elif provider == ResearchProvider.TAVILY:
|
||||
config.tavily_exclude_domains = context.exclude_domains[:150] # Tavily limit
|
||||
|
||||
return config
|
||||
|
||||
def _optimize_exa_config(
|
||||
self,
|
||||
config: ResearchConfig,
|
||||
context: ResearchContext,
|
||||
analysis: Dict[str, Any]
|
||||
) -> ResearchConfig:
|
||||
"""Add Exa-specific optimizations."""
|
||||
|
||||
# Set category based on analysis
|
||||
if analysis['suggested_category']:
|
||||
config.exa_category = analysis['suggested_category']
|
||||
|
||||
# Set search type
|
||||
config.exa_search_type = analysis.get('suggested_search_type', 'auto')
|
||||
|
||||
# For comprehensive research, use neural search
|
||||
if context.depth in [ResearchDepth.COMPREHENSIVE, ResearchDepth.EXPERT]:
|
||||
config.exa_search_type = 'neural'
|
||||
|
||||
return config
|
||||
|
||||
def _optimize_tavily_config(
|
||||
self,
|
||||
config: ResearchConfig,
|
||||
context: ResearchContext,
|
||||
analysis: Dict[str, Any]
|
||||
) -> ResearchConfig:
|
||||
"""Add Tavily-specific optimizations."""
|
||||
|
||||
# Set topic based on analysis
|
||||
config.tavily_topic = analysis.get('suggested_topic', 'general')
|
||||
|
||||
# Set search depth based on research depth
|
||||
if context.depth in [ResearchDepth.COMPREHENSIVE, ResearchDepth.EXPERT]:
|
||||
config.tavily_search_depth = 'advanced' # 2 credits, but better results
|
||||
config.tavily_chunks_per_source = 3
|
||||
else:
|
||||
config.tavily_search_depth = 'basic' # 1 credit
|
||||
|
||||
# Set time range based on recency
|
||||
if context.recency:
|
||||
recency_map = {
|
||||
'day': 'd',
|
||||
'week': 'w',
|
||||
'month': 'm',
|
||||
'year': 'y',
|
||||
}
|
||||
config.tavily_time_range = recency_map.get(context.recency, context.recency)
|
||||
elif analysis['is_trending']:
|
||||
config.tavily_time_range = 'w' # Last week for trending topics
|
||||
|
||||
# Include answer for comprehensive research
|
||||
if context.depth in [ResearchDepth.COMPREHENSIVE, ResearchDepth.EXPERT]:
|
||||
config.tavily_include_answer = 'advanced'
|
||||
|
||||
# Include raw content for expert depth
|
||||
if context.depth == ResearchDepth.EXPERT:
|
||||
config.tavily_include_raw_content = 'markdown'
|
||||
|
||||
return config
|
||||
|
||||
def _build_advanced_config(self, context: ResearchContext) -> Tuple[ResearchProvider, ResearchConfig]:
|
||||
"""
|
||||
Build config from raw advanced parameters.
|
||||
Used when advanced_mode=True and user wants full control.
|
||||
"""
|
||||
# Determine provider from explicit parameters
|
||||
provider = ResearchProvider.GOOGLE
|
||||
|
||||
if context.exa_category or context.exa_search_type:
|
||||
provider = ResearchProvider.EXA if self.exa_available else ResearchProvider.GOOGLE
|
||||
elif context.tavily_topic or context.tavily_search_depth:
|
||||
provider = ResearchProvider.TAVILY if self.tavily_available else ResearchProvider.GOOGLE
|
||||
|
||||
# Check preference override
|
||||
if context.provider_preference == ProviderPreference.EXA and self.exa_available:
|
||||
provider = ResearchProvider.EXA
|
||||
elif context.provider_preference == ProviderPreference.TAVILY and self.tavily_available:
|
||||
provider = ResearchProvider.TAVILY
|
||||
elif context.provider_preference == ProviderPreference.GOOGLE:
|
||||
provider = ResearchProvider.GOOGLE
|
||||
|
||||
# Map depth to mode
|
||||
mode_map = {
|
||||
ResearchDepth.QUICK: ResearchMode.BASIC,
|
||||
ResearchDepth.STANDARD: ResearchMode.BASIC,
|
||||
ResearchDepth.COMPREHENSIVE: ResearchMode.COMPREHENSIVE,
|
||||
ResearchDepth.EXPERT: ResearchMode.COMPREHENSIVE,
|
||||
}
|
||||
mode = mode_map.get(context.depth, ResearchMode.BASIC)
|
||||
|
||||
# Build config with raw parameters
|
||||
config = ResearchConfig(
|
||||
mode=mode,
|
||||
provider=provider,
|
||||
max_sources=context.max_sources,
|
||||
# Exa
|
||||
exa_category=context.exa_category,
|
||||
exa_search_type=context.exa_search_type,
|
||||
exa_include_domains=context.include_domains,
|
||||
exa_exclude_domains=context.exclude_domains,
|
||||
# Tavily
|
||||
tavily_topic=context.tavily_topic,
|
||||
tavily_search_depth=context.tavily_search_depth,
|
||||
tavily_include_domains=context.include_domains[:300] if context.include_domains else [],
|
||||
tavily_exclude_domains=context.exclude_domains[:150] if context.exclude_domains else [],
|
||||
tavily_include_answer=context.tavily_include_answer,
|
||||
tavily_include_raw_content=context.tavily_include_raw_content,
|
||||
tavily_time_range=context.tavily_time_range,
|
||||
tavily_country=context.tavily_country,
|
||||
)
|
||||
|
||||
logger.info(f"Advanced config: provider={provider.value}, mode={mode.value}")
|
||||
|
||||
return provider, config
|
||||
|
||||
198
backend/services/research/core/research_context.py
Normal file
198
backend/services/research/core/research_context.py
Normal file
@@ -0,0 +1,198 @@
|
||||
"""
|
||||
Research Context Schema
|
||||
|
||||
Defines the unified input schema for the Research Engine.
|
||||
Any tool (Blog Writer, Podcast Maker, YouTube Creator) can create a ResearchContext
|
||||
and pass it to the Research Engine.
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 2.0
|
||||
"""
|
||||
|
||||
from enum import Enum
|
||||
from typing import Optional, List, Dict, Any
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class ContentType(str, Enum):
|
||||
"""Type of content being created - affects research focus."""
|
||||
BLOG = "blog"
|
||||
PODCAST = "podcast"
|
||||
VIDEO = "video"
|
||||
SOCIAL = "social"
|
||||
EMAIL = "email"
|
||||
NEWSLETTER = "newsletter"
|
||||
WHITEPAPER = "whitepaper"
|
||||
GENERAL = "general"
|
||||
|
||||
|
||||
class ResearchGoal(str, Enum):
|
||||
"""Primary goal of the research - affects provider selection and depth."""
|
||||
FACTUAL = "factual" # Stats, data, citations
|
||||
TRENDING = "trending" # Current trends, news
|
||||
COMPETITIVE = "competitive" # Competitor analysis
|
||||
EDUCATIONAL = "educational" # How-to, explanations
|
||||
INSPIRATIONAL = "inspirational" # Stories, quotes
|
||||
TECHNICAL = "technical" # Deep technical content
|
||||
|
||||
|
||||
class ResearchDepth(str, Enum):
|
||||
"""Depth of research - maps to existing ResearchMode."""
|
||||
QUICK = "quick" # Fast, surface-level (maps to BASIC)
|
||||
STANDARD = "standard" # Balanced depth (maps to BASIC with more sources)
|
||||
COMPREHENSIVE = "comprehensive" # Deep research (maps to COMPREHENSIVE)
|
||||
EXPERT = "expert" # Maximum depth with expert sources
|
||||
|
||||
|
||||
class ProviderPreference(str, Enum):
|
||||
"""Provider preference - AUTO lets the engine decide."""
|
||||
AUTO = "auto" # AI decides based on query (default)
|
||||
EXA = "exa" # Force Exa neural search
|
||||
TAVILY = "tavily" # Force Tavily AI search
|
||||
GOOGLE = "google" # Force Google grounding
|
||||
HYBRID = "hybrid" # Use multiple providers
|
||||
|
||||
|
||||
class ResearchPersonalizationContext(BaseModel):
|
||||
"""
|
||||
Context from the calling tool (Blog Writer, Podcast Maker, etc.)
|
||||
This personalizes the research without the Research Engine knowing
|
||||
the specific tool implementation.
|
||||
"""
|
||||
# Who is creating the content
|
||||
creator_id: Optional[str] = None # Clerk user ID
|
||||
|
||||
# Content context
|
||||
content_type: ContentType = ContentType.GENERAL
|
||||
industry: Optional[str] = None
|
||||
target_audience: Optional[str] = None
|
||||
tone: Optional[str] = None # professional, casual, technical, etc.
|
||||
|
||||
# Persona data (from onboarding)
|
||||
persona_id: Optional[str] = None
|
||||
brand_voice: Optional[str] = None
|
||||
competitor_urls: List[str] = Field(default_factory=list)
|
||||
|
||||
# Content requirements
|
||||
word_count_target: Optional[int] = None
|
||||
include_statistics: bool = True
|
||||
include_expert_quotes: bool = True
|
||||
include_case_studies: bool = False
|
||||
include_visuals: bool = False
|
||||
|
||||
# Platform-specific hints
|
||||
platform: Optional[str] = None # medium, wordpress, youtube, spotify, etc.
|
||||
|
||||
class Config:
|
||||
use_enum_values = True
|
||||
|
||||
|
||||
class ResearchContext(BaseModel):
|
||||
"""
|
||||
Main input schema for the Research Engine.
|
||||
|
||||
This is what any tool passes to the Research Engine to get research results.
|
||||
The engine uses AI to optimize parameters based on this context.
|
||||
"""
|
||||
# Primary research input
|
||||
query: str = Field(..., description="Main research query or topic")
|
||||
keywords: List[str] = Field(default_factory=list, description="Additional keywords")
|
||||
|
||||
# Research configuration
|
||||
goal: ResearchGoal = ResearchGoal.FACTUAL
|
||||
depth: ResearchDepth = ResearchDepth.STANDARD
|
||||
provider_preference: ProviderPreference = ProviderPreference.AUTO
|
||||
|
||||
# Personalization from calling tool
|
||||
personalization: Optional[ResearchPersonalizationContext] = None
|
||||
|
||||
# Constraints
|
||||
max_sources: int = Field(default=10, ge=1, le=25)
|
||||
recency: Optional[str] = None # "day", "week", "month", "year", None for all-time
|
||||
|
||||
# Domain filtering
|
||||
include_domains: List[str] = Field(default_factory=list)
|
||||
exclude_domains: List[str] = Field(default_factory=list)
|
||||
|
||||
# Advanced mode (exposes raw provider parameters)
|
||||
advanced_mode: bool = False
|
||||
|
||||
# Raw provider parameters (only used if advanced_mode=True)
|
||||
# Exa-specific
|
||||
exa_category: Optional[str] = None
|
||||
exa_search_type: Optional[str] = None # auto, keyword, neural
|
||||
|
||||
# Tavily-specific
|
||||
tavily_topic: Optional[str] = None # general, news, finance
|
||||
tavily_search_depth: Optional[str] = None # basic, advanced
|
||||
tavily_include_answer: bool = False
|
||||
tavily_include_raw_content: bool = False
|
||||
tavily_time_range: Optional[str] = None
|
||||
tavily_country: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
use_enum_values = True
|
||||
|
||||
def get_effective_query(self) -> str:
|
||||
"""Build effective query combining query and keywords."""
|
||||
if self.keywords:
|
||||
return f"{self.query} {' '.join(self.keywords)}"
|
||||
return self.query
|
||||
|
||||
def get_industry(self) -> str:
|
||||
"""Get industry from personalization or default."""
|
||||
if self.personalization and self.personalization.industry:
|
||||
return self.personalization.industry
|
||||
return "General"
|
||||
|
||||
def get_audience(self) -> str:
|
||||
"""Get target audience from personalization or default."""
|
||||
if self.personalization and self.personalization.target_audience:
|
||||
return self.personalization.target_audience
|
||||
return "General"
|
||||
|
||||
def get_user_id(self) -> Optional[str]:
|
||||
"""Get user ID from personalization."""
|
||||
if self.personalization:
|
||||
return self.personalization.creator_id
|
||||
return None
|
||||
|
||||
|
||||
class ResearchResult(BaseModel):
|
||||
"""
|
||||
Output schema from the Research Engine.
|
||||
Standardized format that any tool can consume.
|
||||
"""
|
||||
success: bool = True
|
||||
|
||||
# Content
|
||||
summary: Optional[str] = None # AI-generated summary of findings
|
||||
raw_content: Optional[str] = None # Raw aggregated content for LLM processing
|
||||
|
||||
# Sources
|
||||
sources: List[Dict[str, Any]] = Field(default_factory=list)
|
||||
|
||||
# Analysis (reuses existing blog writer analysis)
|
||||
keyword_analysis: Dict[str, Any] = Field(default_factory=dict)
|
||||
competitor_analysis: Dict[str, Any] = Field(default_factory=dict)
|
||||
suggested_angles: List[str] = Field(default_factory=list)
|
||||
|
||||
# Metadata
|
||||
provider_used: str = "google" # Which provider was actually used
|
||||
search_queries: List[str] = Field(default_factory=list)
|
||||
grounding_metadata: Optional[Dict[str, Any]] = None
|
||||
|
||||
# Cost tracking
|
||||
estimated_cost: float = 0.0
|
||||
|
||||
# Error handling
|
||||
error_message: Optional[str] = None
|
||||
error_code: Optional[str] = None
|
||||
retry_suggested: bool = False
|
||||
|
||||
# Original context for reference
|
||||
original_query: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
use_enum_values = True
|
||||
|
||||
558
backend/services/research/core/research_engine.py
Normal file
558
backend/services/research/core/research_engine.py
Normal file
@@ -0,0 +1,558 @@
|
||||
"""
|
||||
Research Engine - Core Orchestrator
|
||||
|
||||
The main entry point for AI research across all ALwrity tools.
|
||||
This engine wraps existing providers (Exa, Tavily, Google) and provides
|
||||
a unified interface for any content generation tool.
|
||||
|
||||
Usage:
|
||||
from services.research.core import ResearchEngine, ResearchContext, ContentType
|
||||
|
||||
engine = ResearchEngine()
|
||||
result = await engine.research(ResearchContext(
|
||||
query="AI trends in healthcare 2025",
|
||||
content_type=ContentType.PODCAST,
|
||||
personalization=ResearchPersonalizationContext(
|
||||
industry="Healthcare",
|
||||
target_audience="Medical professionals"
|
||||
)
|
||||
))
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 2.0
|
||||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
from typing import Dict, Any, Optional, Callable
|
||||
from loguru import logger
|
||||
|
||||
from .research_context import (
|
||||
ResearchContext,
|
||||
ResearchResult,
|
||||
ResearchDepth,
|
||||
ContentType,
|
||||
ResearchPersonalizationContext,
|
||||
)
|
||||
from .parameter_optimizer import ParameterOptimizer
|
||||
|
||||
# Reuse existing blog writer models and services
|
||||
from models.blog_models import (
|
||||
BlogResearchRequest,
|
||||
BlogResearchResponse,
|
||||
ResearchConfig,
|
||||
ResearchProvider,
|
||||
ResearchMode,
|
||||
PersonaInfo,
|
||||
ResearchSource,
|
||||
)
|
||||
|
||||
# Research persona for personalization
|
||||
from models.research_persona_models import ResearchPersona
|
||||
|
||||
|
||||
class ResearchEngine:
|
||||
"""
|
||||
AI Research Engine - Standalone module for content research.
|
||||
|
||||
This engine:
|
||||
1. Accepts a ResearchContext from any tool
|
||||
2. Uses AI to optimize parameters for Exa/Tavily
|
||||
3. Integrates research persona for personalization
|
||||
4. Executes research using existing providers
|
||||
5. Returns standardized ResearchResult
|
||||
|
||||
Can be imported by Blog Writer, Podcast Maker, YouTube Creator, etc.
|
||||
"""
|
||||
|
||||
def __init__(self, db_session=None):
|
||||
"""Initialize the Research Engine."""
|
||||
self.optimizer = ParameterOptimizer()
|
||||
self._providers_initialized = False
|
||||
self._exa_provider = None
|
||||
self._tavily_provider = None
|
||||
self._google_provider = None
|
||||
self._db_session = db_session
|
||||
|
||||
# Check provider availability
|
||||
self.exa_available = bool(os.getenv("EXA_API_KEY"))
|
||||
self.tavily_available = bool(os.getenv("TAVILY_API_KEY"))
|
||||
|
||||
logger.info(f"ResearchEngine initialized: exa={self.exa_available}, tavily={self.tavily_available}")
|
||||
|
||||
def _get_research_persona(self, user_id: str, generate_if_missing: bool = True) -> Optional[ResearchPersona]:
|
||||
"""
|
||||
Fetch research persona for user, generating if missing.
|
||||
|
||||
Phase 2: Since onboarding is mandatory and always completes before accessing
|
||||
any tool, we can safely generate research persona on first use. This ensures
|
||||
hyper-personalization without requiring "General" fallbacks.
|
||||
|
||||
Args:
|
||||
user_id: User ID (Clerk string)
|
||||
generate_if_missing: If True, generate persona if not cached (default: True)
|
||||
|
||||
Returns:
|
||||
ResearchPersona if successful, None only if user has no core persona
|
||||
"""
|
||||
if not user_id:
|
||||
return None
|
||||
|
||||
try:
|
||||
from services.research.research_persona_service import ResearchPersonaService
|
||||
|
||||
db = self._db_session
|
||||
if not db:
|
||||
from services.database import get_db_session
|
||||
db = get_db_session()
|
||||
|
||||
persona_service = ResearchPersonaService(db_session=db)
|
||||
|
||||
if generate_if_missing:
|
||||
# Phase 2: Use get_or_generate() to create persona on first visit
|
||||
# This triggers LLM call if not cached, but onboarding guarantees
|
||||
# core persona exists, so generation will succeed
|
||||
logger.info(f"🔄 Getting/generating research persona for user {user_id}...")
|
||||
persona = persona_service.get_or_generate(user_id, force_refresh=False)
|
||||
|
||||
if persona:
|
||||
logger.info(f"✅ Research persona ready for user {user_id}: industry={persona.default_industry}")
|
||||
else:
|
||||
logger.warning(f"⚠️ Could not get/generate research persona for user {user_id} - using core persona fallback")
|
||||
else:
|
||||
# Fast path: only return cached (for config endpoints)
|
||||
persona = persona_service.get_cached_only(user_id)
|
||||
if persona:
|
||||
logger.debug(f"Research persona loaded from cache for user {user_id}")
|
||||
|
||||
return persona
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to load research persona for user {user_id}: {e}")
|
||||
return None
|
||||
|
||||
def _enrich_context_with_persona(
|
||||
self,
|
||||
context: ResearchContext,
|
||||
persona: ResearchPersona
|
||||
) -> ResearchContext:
|
||||
"""
|
||||
Enrich the research context with persona data.
|
||||
|
||||
Only applies persona defaults if the context doesn't already have values.
|
||||
User-provided values always take precedence.
|
||||
"""
|
||||
# Create personalization context if not exists
|
||||
if not context.personalization:
|
||||
context.personalization = ResearchPersonalizationContext()
|
||||
|
||||
# Apply persona defaults only if not already set
|
||||
if not context.personalization.industry or context.personalization.industry == "General":
|
||||
if persona.default_industry:
|
||||
context.personalization.industry = persona.default_industry
|
||||
logger.debug(f"Applied persona industry: {persona.default_industry}")
|
||||
|
||||
if not context.personalization.target_audience or context.personalization.target_audience == "General":
|
||||
if persona.default_target_audience:
|
||||
context.personalization.target_audience = persona.default_target_audience
|
||||
logger.debug(f"Applied persona target_audience: {persona.default_target_audience}")
|
||||
|
||||
# Apply suggested Exa domains if not already set
|
||||
if not context.include_domains and persona.suggested_exa_domains:
|
||||
context.include_domains = persona.suggested_exa_domains[:6] # Limit to 6 domains
|
||||
logger.debug(f"Applied persona domains: {context.include_domains}")
|
||||
|
||||
# Apply suggested Exa category if not already set
|
||||
if not context.exa_category and persona.suggested_exa_category:
|
||||
context.exa_category = persona.suggested_exa_category
|
||||
logger.debug(f"Applied persona exa_category: {persona.suggested_exa_category}")
|
||||
|
||||
return context
|
||||
|
||||
async def research(
|
||||
self,
|
||||
context: ResearchContext,
|
||||
progress_callback: Optional[Callable[[str], None]] = None
|
||||
) -> ResearchResult:
|
||||
"""
|
||||
Execute research based on the given context.
|
||||
|
||||
Args:
|
||||
context: Research context with query, goals, and personalization
|
||||
progress_callback: Optional callback for progress updates
|
||||
|
||||
Returns:
|
||||
ResearchResult with sources, analysis, and content
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# Progress update
|
||||
self._progress(progress_callback, "🔍 Analyzing research query...")
|
||||
|
||||
# Enrich context with research persona (Phase 2: generate if missing)
|
||||
user_id = context.get_user_id()
|
||||
if user_id:
|
||||
self._progress(progress_callback, "👤 Loading personalized research profile...")
|
||||
persona = self._get_research_persona(user_id, generate_if_missing=True)
|
||||
if persona:
|
||||
self._progress(progress_callback, "✨ Applying hyper-personalized settings...")
|
||||
context = self._enrich_context_with_persona(context, persona)
|
||||
else:
|
||||
logger.warning(f"No research persona available for user {user_id} - proceeding with provided context")
|
||||
|
||||
# Optimize parameters based on enriched context
|
||||
provider, config = self.optimizer.optimize(context)
|
||||
|
||||
self._progress(progress_callback, f"🤖 Selected {provider.value.upper()} for research")
|
||||
|
||||
# Build the request using existing blog models
|
||||
request = self._build_request(context, config)
|
||||
user_id = context.get_user_id() or ""
|
||||
|
||||
# Execute research using appropriate provider
|
||||
self._progress(progress_callback, f"🌐 Connecting to {provider.value} search...")
|
||||
|
||||
if provider == ResearchProvider.EXA:
|
||||
response = await self._execute_exa_research(request, config, user_id, progress_callback)
|
||||
elif provider == ResearchProvider.TAVILY:
|
||||
response = await self._execute_tavily_research(request, config, user_id, progress_callback)
|
||||
else:
|
||||
response = await self._execute_google_research(request, config, user_id, progress_callback)
|
||||
|
||||
# Transform response to ResearchResult
|
||||
self._progress(progress_callback, "📊 Processing results...")
|
||||
|
||||
result = self._transform_response(response, provider, context)
|
||||
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
logger.info(f"Research completed in {duration_ms:.0f}ms: {len(result.sources)} sources")
|
||||
|
||||
self._progress(progress_callback, f"✅ Research complete: {len(result.sources)} sources found")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Research failed: {e}")
|
||||
return ResearchResult(
|
||||
success=False,
|
||||
error_message=str(e),
|
||||
error_code="RESEARCH_FAILED",
|
||||
retry_suggested=True,
|
||||
original_query=context.query
|
||||
)
|
||||
|
||||
def _progress(self, callback: Optional[Callable[[str], None]], message: str):
|
||||
"""Send progress update if callback provided."""
|
||||
if callback:
|
||||
callback(message)
|
||||
logger.info(f"[Research] {message}")
|
||||
|
||||
def _build_request(self, context: ResearchContext, config: ResearchConfig) -> BlogResearchRequest:
|
||||
"""Build BlogResearchRequest from ResearchContext."""
|
||||
|
||||
# Extract keywords from query
|
||||
keywords = context.keywords if context.keywords else [context.query]
|
||||
|
||||
# Build persona info from personalization
|
||||
persona = None
|
||||
if context.personalization:
|
||||
persona = PersonaInfo(
|
||||
persona_id=context.personalization.persona_id,
|
||||
tone=context.personalization.tone,
|
||||
audience=context.personalization.target_audience,
|
||||
industry=context.personalization.industry,
|
||||
)
|
||||
|
||||
return BlogResearchRequest(
|
||||
keywords=keywords,
|
||||
topic=context.query,
|
||||
industry=context.get_industry(),
|
||||
target_audience=context.get_audience(),
|
||||
tone=context.personalization.tone if context.personalization else None,
|
||||
word_count_target=context.personalization.word_count_target if context.personalization else 1500,
|
||||
persona=persona,
|
||||
research_mode=config.mode,
|
||||
config=config,
|
||||
)
|
||||
|
||||
async def _execute_exa_research(
|
||||
self,
|
||||
request: BlogResearchRequest,
|
||||
config: ResearchConfig,
|
||||
user_id: str,
|
||||
progress_callback: Optional[Callable[[str], None]] = None
|
||||
) -> BlogResearchResponse:
|
||||
"""Execute research using Exa provider."""
|
||||
from services.blog_writer.research.exa_provider import ExaResearchProvider
|
||||
from services.blog_writer.research.research_strategies import get_strategy_for_mode
|
||||
|
||||
self._progress(progress_callback, "🔍 Executing Exa neural search...")
|
||||
|
||||
# Get strategy for building prompt
|
||||
strategy = get_strategy_for_mode(config.mode)
|
||||
topic = request.topic or ", ".join(request.keywords)
|
||||
industry = request.industry or "General"
|
||||
target_audience = request.target_audience or "General"
|
||||
|
||||
research_prompt = strategy.build_research_prompt(topic, industry, target_audience, config)
|
||||
|
||||
# Execute Exa search
|
||||
try:
|
||||
exa_provider = ExaResearchProvider()
|
||||
raw_result = await exa_provider.search(
|
||||
research_prompt, topic, industry, target_audience, config, user_id
|
||||
)
|
||||
|
||||
# Track usage
|
||||
cost = raw_result.get('cost', {}).get('total', 0.005) if isinstance(raw_result.get('cost'), dict) else 0.005
|
||||
exa_provider.track_exa_usage(user_id, cost)
|
||||
|
||||
self._progress(progress_callback, f"📝 Found {len(raw_result.get('sources', []))} sources")
|
||||
|
||||
# Run common analysis
|
||||
return await self._run_analysis(request, raw_result, config, user_id, progress_callback)
|
||||
|
||||
except RuntimeError as e:
|
||||
if "EXA_API_KEY not configured" in str(e):
|
||||
logger.warning("Exa not configured, falling back to Tavily")
|
||||
self._progress(progress_callback, "⚠️ Exa unavailable, trying Tavily...")
|
||||
config.provider = ResearchProvider.TAVILY
|
||||
return await self._execute_tavily_research(request, config, user_id, progress_callback)
|
||||
raise
|
||||
|
||||
async def _execute_tavily_research(
|
||||
self,
|
||||
request: BlogResearchRequest,
|
||||
config: ResearchConfig,
|
||||
user_id: str,
|
||||
progress_callback: Optional[Callable[[str], None]] = None
|
||||
) -> BlogResearchResponse:
|
||||
"""Execute research using Tavily provider."""
|
||||
from services.blog_writer.research.tavily_provider import TavilyResearchProvider
|
||||
from services.blog_writer.research.research_strategies import get_strategy_for_mode
|
||||
|
||||
self._progress(progress_callback, "🔍 Executing Tavily AI search...")
|
||||
|
||||
# Get strategy for building prompt
|
||||
strategy = get_strategy_for_mode(config.mode)
|
||||
topic = request.topic or ", ".join(request.keywords)
|
||||
industry = request.industry or "General"
|
||||
target_audience = request.target_audience or "General"
|
||||
|
||||
research_prompt = strategy.build_research_prompt(topic, industry, target_audience, config)
|
||||
|
||||
# Execute Tavily search
|
||||
try:
|
||||
tavily_provider = TavilyResearchProvider()
|
||||
raw_result = await tavily_provider.search(
|
||||
research_prompt, topic, industry, target_audience, config, user_id
|
||||
)
|
||||
|
||||
# Track usage
|
||||
cost = raw_result.get('cost', {}).get('total', 0.001) if isinstance(raw_result.get('cost'), dict) else 0.001
|
||||
search_depth = config.tavily_search_depth or "basic"
|
||||
tavily_provider.track_tavily_usage(user_id, cost, search_depth)
|
||||
|
||||
self._progress(progress_callback, f"📝 Found {len(raw_result.get('sources', []))} sources")
|
||||
|
||||
# Run common analysis
|
||||
return await self._run_analysis(request, raw_result, config, user_id, progress_callback)
|
||||
|
||||
except RuntimeError as e:
|
||||
if "TAVILY_API_KEY not configured" in str(e):
|
||||
logger.warning("Tavily not configured, falling back to Google")
|
||||
self._progress(progress_callback, "⚠️ Tavily unavailable, using Google Search...")
|
||||
config.provider = ResearchProvider.GOOGLE
|
||||
return await self._execute_google_research(request, config, user_id, progress_callback)
|
||||
raise
|
||||
|
||||
async def _execute_google_research(
|
||||
self,
|
||||
request: BlogResearchRequest,
|
||||
config: ResearchConfig,
|
||||
user_id: str,
|
||||
progress_callback: Optional[Callable[[str], None]] = None
|
||||
) -> BlogResearchResponse:
|
||||
"""Execute research using Google/Gemini grounding."""
|
||||
from services.blog_writer.research.google_provider import GoogleResearchProvider
|
||||
from services.blog_writer.research.research_strategies import get_strategy_for_mode
|
||||
|
||||
self._progress(progress_callback, "🔍 Executing Google Search grounding...")
|
||||
|
||||
# Get strategy for building prompt
|
||||
strategy = get_strategy_for_mode(config.mode)
|
||||
topic = request.topic or ", ".join(request.keywords)
|
||||
industry = request.industry or "General"
|
||||
target_audience = request.target_audience or "General"
|
||||
|
||||
research_prompt = strategy.build_research_prompt(topic, industry, target_audience, config)
|
||||
|
||||
# Execute Google search
|
||||
google_provider = GoogleResearchProvider()
|
||||
raw_result = await google_provider.search(
|
||||
research_prompt, topic, industry, target_audience, config, user_id
|
||||
)
|
||||
|
||||
self._progress(progress_callback, "📝 Processing grounded results...")
|
||||
|
||||
# Run common analysis
|
||||
return await self._run_analysis(request, raw_result, config, user_id, progress_callback, is_google=True)
|
||||
|
||||
async def _run_analysis(
|
||||
self,
|
||||
request: BlogResearchRequest,
|
||||
raw_result: Dict[str, Any],
|
||||
config: ResearchConfig,
|
||||
user_id: str,
|
||||
progress_callback: Optional[Callable[[str], None]] = None,
|
||||
is_google: bool = False
|
||||
) -> BlogResearchResponse:
|
||||
"""Run common analysis on raw results."""
|
||||
from services.blog_writer.research.keyword_analyzer import KeywordAnalyzer
|
||||
from services.blog_writer.research.competitor_analyzer import CompetitorAnalyzer
|
||||
from services.blog_writer.research.content_angle_generator import ContentAngleGenerator
|
||||
from services.blog_writer.research.data_filter import ResearchDataFilter
|
||||
|
||||
self._progress(progress_callback, "🔍 Analyzing keywords and content angles...")
|
||||
|
||||
# Extract content for analysis
|
||||
if is_google:
|
||||
content = raw_result.get("content", "")
|
||||
sources = self._extract_sources_from_grounding(raw_result)
|
||||
search_queries = raw_result.get("search_queries", []) or []
|
||||
grounding_metadata = self._extract_grounding_metadata(raw_result)
|
||||
else:
|
||||
content = raw_result.get('content', '')
|
||||
sources = [ResearchSource(**s) if isinstance(s, dict) else s for s in raw_result.get('sources', [])]
|
||||
search_queries = raw_result.get('search_queries', [])
|
||||
grounding_metadata = None
|
||||
|
||||
topic = request.topic or ", ".join(request.keywords)
|
||||
industry = request.industry or "General"
|
||||
|
||||
# Run analyzers
|
||||
keyword_analyzer = KeywordAnalyzer()
|
||||
competitor_analyzer = CompetitorAnalyzer()
|
||||
content_angle_generator = ContentAngleGenerator()
|
||||
data_filter = ResearchDataFilter()
|
||||
|
||||
keyword_analysis = keyword_analyzer.analyze(content, request.keywords, user_id=user_id)
|
||||
competitor_analysis = competitor_analyzer.analyze(content, user_id=user_id)
|
||||
suggested_angles = content_angle_generator.generate(content, topic, industry, user_id=user_id)
|
||||
|
||||
# Build response
|
||||
response = BlogResearchResponse(
|
||||
success=True,
|
||||
sources=sources,
|
||||
keyword_analysis=keyword_analysis,
|
||||
competitor_analysis=competitor_analysis,
|
||||
suggested_angles=suggested_angles,
|
||||
search_widget="",
|
||||
search_queries=search_queries,
|
||||
grounding_metadata=grounding_metadata,
|
||||
original_keywords=request.keywords,
|
||||
)
|
||||
|
||||
# Filter and clean research data
|
||||
self._progress(progress_callback, "✨ Filtering and optimizing results...")
|
||||
filtered_response = data_filter.filter_research_data(response)
|
||||
|
||||
return filtered_response
|
||||
|
||||
def _extract_sources_from_grounding(self, gemini_result: Dict[str, Any]) -> list:
|
||||
"""Extract sources from Gemini grounding metadata."""
|
||||
from models.blog_models import ResearchSource
|
||||
|
||||
sources = []
|
||||
if not gemini_result or not isinstance(gemini_result, dict):
|
||||
return sources
|
||||
|
||||
raw_sources = gemini_result.get("sources", []) or []
|
||||
|
||||
for src in raw_sources:
|
||||
source = ResearchSource(
|
||||
title=src.get("title", "Untitled"),
|
||||
url=src.get("url", ""),
|
||||
excerpt=src.get("content", "")[:500] if src.get("content") else f"Source from {src.get('title', 'web')}",
|
||||
credibility_score=float(src.get("credibility_score", 0.8)),
|
||||
published_at=str(src.get("publication_date", "2024-01-01")),
|
||||
index=src.get("index"),
|
||||
source_type=src.get("type", "web")
|
||||
)
|
||||
sources.append(source)
|
||||
|
||||
return sources
|
||||
|
||||
def _extract_grounding_metadata(self, gemini_result: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
||||
"""Extract grounding metadata from Gemini result."""
|
||||
if not gemini_result or not isinstance(gemini_result, dict):
|
||||
return None
|
||||
|
||||
return gemini_result.get("grounding_metadata")
|
||||
|
||||
def _transform_response(
|
||||
self,
|
||||
response: BlogResearchResponse,
|
||||
provider: ResearchProvider,
|
||||
context: ResearchContext
|
||||
) -> ResearchResult:
|
||||
"""Transform BlogResearchResponse to ResearchResult."""
|
||||
|
||||
# Convert sources to dicts
|
||||
sources = []
|
||||
for s in response.sources:
|
||||
if hasattr(s, 'dict'):
|
||||
sources.append(s.dict())
|
||||
elif isinstance(s, dict):
|
||||
sources.append(s)
|
||||
else:
|
||||
sources.append({
|
||||
'title': getattr(s, 'title', ''),
|
||||
'url': getattr(s, 'url', ''),
|
||||
'excerpt': getattr(s, 'excerpt', ''),
|
||||
})
|
||||
|
||||
# Extract grounding metadata
|
||||
grounding = None
|
||||
if response.grounding_metadata:
|
||||
if hasattr(response.grounding_metadata, 'dict'):
|
||||
grounding = response.grounding_metadata.dict()
|
||||
else:
|
||||
grounding = response.grounding_metadata
|
||||
|
||||
return ResearchResult(
|
||||
success=response.success,
|
||||
sources=sources,
|
||||
keyword_analysis=response.keyword_analysis,
|
||||
competitor_analysis=response.competitor_analysis,
|
||||
suggested_angles=response.suggested_angles,
|
||||
provider_used=provider.value,
|
||||
search_queries=response.search_queries,
|
||||
grounding_metadata=grounding,
|
||||
original_query=context.query,
|
||||
error_message=response.error_message,
|
||||
error_code=response.error_code if hasattr(response, 'error_code') else None,
|
||||
retry_suggested=response.retry_suggested if hasattr(response, 'retry_suggested') else False,
|
||||
)
|
||||
|
||||
def get_provider_status(self) -> Dict[str, Any]:
|
||||
"""Get status of available providers."""
|
||||
return {
|
||||
"exa": {
|
||||
"available": self.exa_available,
|
||||
"priority": 1,
|
||||
"description": "Neural search for semantic understanding"
|
||||
},
|
||||
"tavily": {
|
||||
"available": self.tavily_available,
|
||||
"priority": 2,
|
||||
"description": "AI-powered web search"
|
||||
},
|
||||
"google": {
|
||||
"available": True, # Always available via Gemini
|
||||
"priority": 3,
|
||||
"description": "Google Search grounding"
|
||||
}
|
||||
}
|
||||
|
||||
23
backend/services/research/intent/__init__.py
Normal file
23
backend/services/research/intent/__init__.py
Normal file
@@ -0,0 +1,23 @@
|
||||
"""
|
||||
Research Intent Package
|
||||
|
||||
This package provides intent-driven research capabilities:
|
||||
- Intent inference from user input
|
||||
- Targeted query generation
|
||||
- Intent-aware result analysis
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 1.0
|
||||
"""
|
||||
|
||||
from .research_intent_inference import ResearchIntentInference
|
||||
from .intent_query_generator import IntentQueryGenerator
|
||||
from .intent_aware_analyzer import IntentAwareAnalyzer
|
||||
from .intent_prompt_builder import IntentPromptBuilder
|
||||
|
||||
__all__ = [
|
||||
"ResearchIntentInference",
|
||||
"IntentQueryGenerator",
|
||||
"IntentAwareAnalyzer",
|
||||
"IntentPromptBuilder",
|
||||
]
|
||||
547
backend/services/research/intent/intent_aware_analyzer.py
Normal file
547
backend/services/research/intent/intent_aware_analyzer.py
Normal file
@@ -0,0 +1,547 @@
|
||||
"""
|
||||
Intent-Aware Result Analyzer
|
||||
|
||||
Analyzes research results based on user intent.
|
||||
Extracts exactly what the user needs from raw research data.
|
||||
|
||||
This is the key innovation - instead of generic analysis,
|
||||
we analyze results through the lens of what the user wants to accomplish.
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 1.0
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Dict, Any, List, Optional
|
||||
from loguru import logger
|
||||
|
||||
from models.research_intent_models import (
|
||||
ResearchIntent,
|
||||
IntentDrivenResearchResult,
|
||||
ExpectedDeliverable,
|
||||
StatisticWithCitation,
|
||||
ExpertQuote,
|
||||
CaseStudySummary,
|
||||
TrendAnalysis,
|
||||
ComparisonTable,
|
||||
ComparisonItem,
|
||||
ProsCons,
|
||||
SourceWithRelevance,
|
||||
)
|
||||
from models.research_persona_models import ResearchPersona
|
||||
from .intent_prompt_builder import IntentPromptBuilder
|
||||
|
||||
|
||||
class IntentAwareAnalyzer:
|
||||
"""
|
||||
Analyzes research results based on user intent.
|
||||
|
||||
Instead of generic summaries, this extracts exactly what the user
|
||||
needs: statistics, quotes, case studies, trends, etc.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the analyzer."""
|
||||
self.prompt_builder = IntentPromptBuilder()
|
||||
logger.info("IntentAwareAnalyzer initialized")
|
||||
|
||||
async def analyze(
|
||||
self,
|
||||
raw_results: Dict[str, Any],
|
||||
intent: ResearchIntent,
|
||||
research_persona: Optional[ResearchPersona] = None,
|
||||
) -> IntentDrivenResearchResult:
|
||||
"""
|
||||
Analyze raw research results based on user intent.
|
||||
|
||||
Args:
|
||||
raw_results: Raw results from Exa/Tavily/Google
|
||||
intent: The user's research intent
|
||||
research_persona: Optional persona for context
|
||||
|
||||
Returns:
|
||||
IntentDrivenResearchResult with extracted deliverables
|
||||
"""
|
||||
try:
|
||||
logger.info(f"Analyzing results for intent: {intent.primary_question[:50]}...")
|
||||
|
||||
# Format raw results for analysis
|
||||
formatted_results = self._format_raw_results(raw_results)
|
||||
|
||||
# Build the analysis prompt
|
||||
prompt = self.prompt_builder.build_intent_aware_analysis_prompt(
|
||||
raw_results=formatted_results,
|
||||
intent=intent,
|
||||
research_persona=research_persona,
|
||||
)
|
||||
|
||||
# Define the expected JSON schema
|
||||
analysis_schema = self._build_analysis_schema(intent.expected_deliverables)
|
||||
|
||||
# Call LLM for analysis
|
||||
from services.llm_providers.main_text_generation import llm_text_gen
|
||||
|
||||
result = llm_text_gen(
|
||||
prompt=prompt,
|
||||
json_struct=analysis_schema,
|
||||
user_id=None
|
||||
)
|
||||
|
||||
if isinstance(result, dict) and "error" in result:
|
||||
logger.error(f"Intent-aware analysis failed: {result.get('error')}")
|
||||
return self._create_fallback_result(raw_results, intent)
|
||||
|
||||
# Parse and validate the result
|
||||
analyzed_result = self._parse_analysis_result(result, intent, raw_results)
|
||||
|
||||
logger.info(
|
||||
f"Analysis complete: {len(analyzed_result.key_takeaways)} takeaways, "
|
||||
f"{len(analyzed_result.statistics)} stats, "
|
||||
f"{len(analyzed_result.sources)} sources"
|
||||
)
|
||||
|
||||
return analyzed_result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in intent-aware analysis: {e}")
|
||||
return self._create_fallback_result(raw_results, intent)
|
||||
|
||||
def _format_raw_results(self, raw_results: Dict[str, Any]) -> str:
|
||||
"""Format raw research results for LLM analysis."""
|
||||
|
||||
formatted_parts = []
|
||||
|
||||
# Extract content
|
||||
content = raw_results.get("content", "")
|
||||
if content:
|
||||
formatted_parts.append(f"=== MAIN CONTENT ===\n{content[:8000]}")
|
||||
|
||||
# Extract sources with their content
|
||||
sources = raw_results.get("sources", [])
|
||||
if sources:
|
||||
formatted_parts.append("\n=== SOURCES ===")
|
||||
for i, source in enumerate(sources[:15], 1): # Limit to 15 sources
|
||||
title = source.get("title", "Untitled")
|
||||
url = source.get("url", "")
|
||||
excerpt = source.get("excerpt", source.get("text", source.get("content", "")))
|
||||
|
||||
formatted_parts.append(f"\nSource {i}: {title}")
|
||||
formatted_parts.append(f"URL: {url}")
|
||||
if excerpt:
|
||||
formatted_parts.append(f"Content: {excerpt[:500]}")
|
||||
|
||||
# Extract grounding metadata if available (from Google)
|
||||
grounding = raw_results.get("grounding_metadata", {})
|
||||
if grounding:
|
||||
formatted_parts.append("\n=== GROUNDING DATA ===")
|
||||
formatted_parts.append(json.dumps(grounding, indent=2)[:2000])
|
||||
|
||||
# Extract any AI answers (from Tavily)
|
||||
answer = raw_results.get("answer", "")
|
||||
if answer:
|
||||
formatted_parts.append(f"\n=== AI-GENERATED ANSWER ===\n{answer}")
|
||||
|
||||
return "\n".join(formatted_parts)
|
||||
|
||||
def _build_analysis_schema(self, expected_deliverables: List[str]) -> Dict[str, Any]:
|
||||
"""Build JSON schema based on expected deliverables."""
|
||||
|
||||
# Base schema
|
||||
schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"primary_answer": {"type": "string"},
|
||||
"secondary_answers": {
|
||||
"type": "object",
|
||||
"additionalProperties": {"type": "string"}
|
||||
},
|
||||
"executive_summary": {"type": "string"},
|
||||
"key_takeaways": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"},
|
||||
"maxItems": 7
|
||||
},
|
||||
"confidence": {"type": "number"},
|
||||
"gaps_identified": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"}
|
||||
},
|
||||
"follow_up_queries": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"}
|
||||
},
|
||||
},
|
||||
"required": ["primary_answer", "executive_summary", "key_takeaways", "confidence"]
|
||||
}
|
||||
|
||||
# Add deliverable-specific properties
|
||||
if ExpectedDeliverable.KEY_STATISTICS.value in expected_deliverables:
|
||||
schema["properties"]["statistics"] = {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"statistic": {"type": "string"},
|
||||
"value": {"type": "string"},
|
||||
"context": {"type": "string"},
|
||||
"source": {"type": "string"},
|
||||
"url": {"type": "string"},
|
||||
"credibility": {"type": "number"},
|
||||
"recency": {"type": "string"}
|
||||
},
|
||||
"required": ["statistic", "context", "source", "url"]
|
||||
}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.EXPERT_QUOTES.value in expected_deliverables:
|
||||
schema["properties"]["expert_quotes"] = {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"quote": {"type": "string"},
|
||||
"speaker": {"type": "string"},
|
||||
"title": {"type": "string"},
|
||||
"organization": {"type": "string"},
|
||||
"source": {"type": "string"},
|
||||
"url": {"type": "string"}
|
||||
},
|
||||
"required": ["quote", "speaker", "source", "url"]
|
||||
}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.CASE_STUDIES.value in expected_deliverables:
|
||||
schema["properties"]["case_studies"] = {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"title": {"type": "string"},
|
||||
"organization": {"type": "string"},
|
||||
"challenge": {"type": "string"},
|
||||
"solution": {"type": "string"},
|
||||
"outcome": {"type": "string"},
|
||||
"key_metrics": {"type": "array", "items": {"type": "string"}},
|
||||
"source": {"type": "string"},
|
||||
"url": {"type": "string"}
|
||||
},
|
||||
"required": ["title", "organization", "challenge", "solution", "outcome"]
|
||||
}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.TRENDS.value in expected_deliverables:
|
||||
schema["properties"]["trends"] = {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"trend": {"type": "string"},
|
||||
"direction": {"type": "string"},
|
||||
"evidence": {"type": "array", "items": {"type": "string"}},
|
||||
"impact": {"type": "string"},
|
||||
"timeline": {"type": "string"},
|
||||
"sources": {"type": "array", "items": {"type": "string"}}
|
||||
},
|
||||
"required": ["trend", "direction", "evidence"]
|
||||
}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.COMPARISONS.value in expected_deliverables:
|
||||
schema["properties"]["comparisons"] = {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"title": {"type": "string"},
|
||||
"criteria": {"type": "array", "items": {"type": "string"}},
|
||||
"items": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {"type": "string"},
|
||||
"pros": {"type": "array", "items": {"type": "string"}},
|
||||
"cons": {"type": "array", "items": {"type": "string"}},
|
||||
"features": {"type": "object"}
|
||||
}
|
||||
}
|
||||
},
|
||||
"verdict": {"type": "string"}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.PROS_CONS.value in expected_deliverables:
|
||||
schema["properties"]["pros_cons"] = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"subject": {"type": "string"},
|
||||
"pros": {"type": "array", "items": {"type": "string"}},
|
||||
"cons": {"type": "array", "items": {"type": "string"}},
|
||||
"balanced_verdict": {"type": "string"}
|
||||
}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.BEST_PRACTICES.value in expected_deliverables:
|
||||
schema["properties"]["best_practices"] = {
|
||||
"type": "array",
|
||||
"items": {"type": "string"}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.STEP_BY_STEP.value in expected_deliverables:
|
||||
schema["properties"]["step_by_step"] = {
|
||||
"type": "array",
|
||||
"items": {"type": "string"}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.DEFINITIONS.value in expected_deliverables:
|
||||
schema["properties"]["definitions"] = {
|
||||
"type": "object",
|
||||
"additionalProperties": {"type": "string"}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.EXAMPLES.value in expected_deliverables:
|
||||
schema["properties"]["examples"] = {
|
||||
"type": "array",
|
||||
"items": {"type": "string"}
|
||||
}
|
||||
|
||||
if ExpectedDeliverable.PREDICTIONS.value in expected_deliverables:
|
||||
schema["properties"]["predictions"] = {
|
||||
"type": "array",
|
||||
"items": {"type": "string"}
|
||||
}
|
||||
|
||||
# Always include sources and suggested outline
|
||||
schema["properties"]["sources"] = {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"title": {"type": "string"},
|
||||
"url": {"type": "string"},
|
||||
"relevance_score": {"type": "number"},
|
||||
"relevance_reason": {"type": "string"},
|
||||
"content_type": {"type": "string"},
|
||||
"credibility_score": {"type": "number"}
|
||||
},
|
||||
"required": ["title", "url"]
|
||||
}
|
||||
}
|
||||
|
||||
schema["properties"]["suggested_outline"] = {
|
||||
"type": "array",
|
||||
"items": {"type": "string"}
|
||||
}
|
||||
|
||||
return schema
|
||||
|
||||
def _parse_analysis_result(
|
||||
self,
|
||||
result: Dict[str, Any],
|
||||
intent: ResearchIntent,
|
||||
raw_results: Dict[str, Any],
|
||||
) -> IntentDrivenResearchResult:
|
||||
"""Parse LLM analysis result into structured format."""
|
||||
|
||||
# Parse statistics
|
||||
statistics = []
|
||||
for stat in result.get("statistics", []):
|
||||
try:
|
||||
statistics.append(StatisticWithCitation(
|
||||
statistic=stat.get("statistic", ""),
|
||||
value=stat.get("value"),
|
||||
context=stat.get("context", ""),
|
||||
source=stat.get("source", ""),
|
||||
url=stat.get("url", ""),
|
||||
credibility=float(stat.get("credibility", 0.8)),
|
||||
recency=stat.get("recency"),
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse statistic: {e}")
|
||||
|
||||
# Parse expert quotes
|
||||
expert_quotes = []
|
||||
for quote in result.get("expert_quotes", []):
|
||||
try:
|
||||
expert_quotes.append(ExpertQuote(
|
||||
quote=quote.get("quote", ""),
|
||||
speaker=quote.get("speaker", ""),
|
||||
title=quote.get("title"),
|
||||
organization=quote.get("organization"),
|
||||
context=quote.get("context"),
|
||||
source=quote.get("source", ""),
|
||||
url=quote.get("url", ""),
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse expert quote: {e}")
|
||||
|
||||
# Parse case studies
|
||||
case_studies = []
|
||||
for cs in result.get("case_studies", []):
|
||||
try:
|
||||
case_studies.append(CaseStudySummary(
|
||||
title=cs.get("title", ""),
|
||||
organization=cs.get("organization", ""),
|
||||
challenge=cs.get("challenge", ""),
|
||||
solution=cs.get("solution", ""),
|
||||
outcome=cs.get("outcome", ""),
|
||||
key_metrics=cs.get("key_metrics", []),
|
||||
source=cs.get("source", ""),
|
||||
url=cs.get("url", ""),
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse case study: {e}")
|
||||
|
||||
# Parse trends
|
||||
trends = []
|
||||
for trend in result.get("trends", []):
|
||||
try:
|
||||
trends.append(TrendAnalysis(
|
||||
trend=trend.get("trend", ""),
|
||||
direction=trend.get("direction", "growing"),
|
||||
evidence=trend.get("evidence", []),
|
||||
impact=trend.get("impact"),
|
||||
timeline=trend.get("timeline"),
|
||||
sources=trend.get("sources", []),
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse trend: {e}")
|
||||
|
||||
# Parse comparisons
|
||||
comparisons = []
|
||||
for comp in result.get("comparisons", []):
|
||||
try:
|
||||
items = []
|
||||
for item in comp.get("items", []):
|
||||
items.append(ComparisonItem(
|
||||
name=item.get("name", ""),
|
||||
description=item.get("description"),
|
||||
pros=item.get("pros", []),
|
||||
cons=item.get("cons", []),
|
||||
features=item.get("features", {}),
|
||||
rating=item.get("rating"),
|
||||
source=item.get("source"),
|
||||
))
|
||||
comparisons.append(ComparisonTable(
|
||||
title=comp.get("title", ""),
|
||||
criteria=comp.get("criteria", []),
|
||||
items=items,
|
||||
winner=comp.get("winner"),
|
||||
verdict=comp.get("verdict"),
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse comparison: {e}")
|
||||
|
||||
# Parse pros/cons
|
||||
pros_cons = None
|
||||
pc_data = result.get("pros_cons")
|
||||
if pc_data:
|
||||
try:
|
||||
pros_cons = ProsCons(
|
||||
subject=pc_data.get("subject", intent.original_input),
|
||||
pros=pc_data.get("pros", []),
|
||||
cons=pc_data.get("cons", []),
|
||||
balanced_verdict=pc_data.get("balanced_verdict", ""),
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse pros/cons: {e}")
|
||||
|
||||
# Parse sources
|
||||
sources = []
|
||||
for src in result.get("sources", []):
|
||||
try:
|
||||
sources.append(SourceWithRelevance(
|
||||
title=src.get("title", ""),
|
||||
url=src.get("url", ""),
|
||||
excerpt=src.get("excerpt"),
|
||||
relevance_score=float(src.get("relevance_score", 0.8)),
|
||||
relevance_reason=src.get("relevance_reason"),
|
||||
content_type=src.get("content_type"),
|
||||
published_date=src.get("published_date"),
|
||||
credibility_score=float(src.get("credibility_score", 0.8)),
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse source: {e}")
|
||||
|
||||
# If no sources from analysis, extract from raw results
|
||||
if not sources:
|
||||
sources = self._extract_sources_from_raw(raw_results)
|
||||
|
||||
return IntentDrivenResearchResult(
|
||||
success=True,
|
||||
primary_answer=result.get("primary_answer", ""),
|
||||
secondary_answers=result.get("secondary_answers", {}),
|
||||
statistics=statistics,
|
||||
expert_quotes=expert_quotes,
|
||||
case_studies=case_studies,
|
||||
comparisons=comparisons,
|
||||
trends=trends,
|
||||
best_practices=result.get("best_practices", []),
|
||||
step_by_step=result.get("step_by_step", []),
|
||||
pros_cons=pros_cons,
|
||||
definitions=result.get("definitions", {}),
|
||||
examples=result.get("examples", []),
|
||||
predictions=result.get("predictions", []),
|
||||
executive_summary=result.get("executive_summary", ""),
|
||||
key_takeaways=result.get("key_takeaways", []),
|
||||
suggested_outline=result.get("suggested_outline", []),
|
||||
sources=sources,
|
||||
raw_content=self._format_raw_results(raw_results)[:5000],
|
||||
confidence=float(result.get("confidence", 0.7)),
|
||||
gaps_identified=result.get("gaps_identified", []),
|
||||
follow_up_queries=result.get("follow_up_queries", []),
|
||||
original_intent=intent,
|
||||
)
|
||||
|
||||
def _extract_sources_from_raw(self, raw_results: Dict[str, Any]) -> List[SourceWithRelevance]:
|
||||
"""Extract sources from raw results when analysis doesn't provide them."""
|
||||
|
||||
sources = []
|
||||
for src in raw_results.get("sources", [])[:10]:
|
||||
try:
|
||||
sources.append(SourceWithRelevance(
|
||||
title=src.get("title", "Untitled"),
|
||||
url=src.get("url", ""),
|
||||
excerpt=src.get("excerpt", src.get("text", ""))[:200],
|
||||
relevance_score=0.8,
|
||||
credibility_score=float(src.get("credibility_score", 0.8)),
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to extract source: {e}")
|
||||
|
||||
return sources
|
||||
|
||||
def _create_fallback_result(
|
||||
self,
|
||||
raw_results: Dict[str, Any],
|
||||
intent: ResearchIntent,
|
||||
) -> IntentDrivenResearchResult:
|
||||
"""Create a fallback result when AI analysis fails."""
|
||||
|
||||
# Extract basic information from raw results
|
||||
content = raw_results.get("content", "")
|
||||
sources = self._extract_sources_from_raw(raw_results)
|
||||
|
||||
# Create basic takeaways from content
|
||||
key_takeaways = []
|
||||
if content:
|
||||
sentences = content.split(". ")[:5]
|
||||
key_takeaways = [s.strip() + "." for s in sentences if len(s) > 20]
|
||||
|
||||
return IntentDrivenResearchResult(
|
||||
success=True,
|
||||
primary_answer=f"Research findings for: {intent.primary_question}",
|
||||
secondary_answers={},
|
||||
executive_summary=content[:300] if content else "Research completed",
|
||||
key_takeaways=key_takeaways,
|
||||
sources=sources,
|
||||
raw_content=self._format_raw_results(raw_results)[:5000],
|
||||
confidence=0.5,
|
||||
gaps_identified=[
|
||||
"AI analysis failed - showing raw results",
|
||||
"Manual review recommended"
|
||||
],
|
||||
follow_up_queries=[],
|
||||
original_intent=intent,
|
||||
)
|
||||
627
backend/services/research/intent/intent_prompt_builder.py
Normal file
627
backend/services/research/intent/intent_prompt_builder.py
Normal file
@@ -0,0 +1,627 @@
|
||||
"""
|
||||
Intent Prompt Builder
|
||||
|
||||
Builds comprehensive AI prompts for:
|
||||
1. Intent inference from user input
|
||||
2. Targeted query generation
|
||||
3. Intent-aware result analysis
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 1.0
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Dict, Any, List, Optional
|
||||
from loguru import logger
|
||||
|
||||
from models.research_intent_models import (
|
||||
ResearchIntent,
|
||||
ResearchPurpose,
|
||||
ContentOutput,
|
||||
ExpectedDeliverable,
|
||||
ResearchDepthLevel,
|
||||
)
|
||||
from models.research_persona_models import ResearchPersona
|
||||
|
||||
|
||||
class IntentPromptBuilder:
|
||||
"""Builds prompts for intent-driven research."""
|
||||
|
||||
# Purpose explanations for the AI
|
||||
PURPOSE_EXPLANATIONS = {
|
||||
ResearchPurpose.LEARN: "User wants to understand a topic for personal knowledge",
|
||||
ResearchPurpose.CREATE_CONTENT: "User will create content (blog, video, podcast) from this research",
|
||||
ResearchPurpose.MAKE_DECISION: "User needs to make a choice/decision based on research",
|
||||
ResearchPurpose.COMPARE: "User wants to compare alternatives or competitors",
|
||||
ResearchPurpose.SOLVE_PROBLEM: "User is looking for a solution to a specific problem",
|
||||
ResearchPurpose.FIND_DATA: "User needs specific statistics, facts, or citations",
|
||||
ResearchPurpose.EXPLORE_TRENDS: "User wants to understand current/future trends",
|
||||
ResearchPurpose.VALIDATE: "User wants to verify or fact-check information",
|
||||
ResearchPurpose.GENERATE_IDEAS: "User wants to brainstorm content ideas",
|
||||
}
|
||||
|
||||
# Deliverable descriptions
|
||||
DELIVERABLE_DESCRIPTIONS = {
|
||||
ExpectedDeliverable.KEY_STATISTICS: "Numbers, percentages, data points with citations",
|
||||
ExpectedDeliverable.EXPERT_QUOTES: "Authoritative quotes from industry experts",
|
||||
ExpectedDeliverable.CASE_STUDIES: "Real examples and success stories",
|
||||
ExpectedDeliverable.COMPARISONS: "Side-by-side analysis tables",
|
||||
ExpectedDeliverable.TRENDS: "Current and emerging industry trends",
|
||||
ExpectedDeliverable.BEST_PRACTICES: "Recommended approaches and guidelines",
|
||||
ExpectedDeliverable.STEP_BY_STEP: "Process guides and how-to instructions",
|
||||
ExpectedDeliverable.PROS_CONS: "Advantages and disadvantages analysis",
|
||||
ExpectedDeliverable.DEFINITIONS: "Clear explanations of concepts and terms",
|
||||
ExpectedDeliverable.CITATIONS: "Authoritative sources for reference",
|
||||
ExpectedDeliverable.EXAMPLES: "Concrete examples to illustrate points",
|
||||
ExpectedDeliverable.PREDICTIONS: "Future outlook and predictions",
|
||||
}
|
||||
|
||||
def build_intent_inference_prompt(
|
||||
self,
|
||||
user_input: str,
|
||||
keywords: List[str],
|
||||
research_persona: Optional[ResearchPersona] = None,
|
||||
competitor_data: Optional[List[Dict]] = None,
|
||||
industry: Optional[str] = None,
|
||||
target_audience: Optional[str] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Build prompt for inferring user's research intent.
|
||||
|
||||
This prompt analyzes the user's input and determines:
|
||||
- What they want to accomplish
|
||||
- What questions they need answered
|
||||
- What specific deliverables they need
|
||||
"""
|
||||
|
||||
# Build persona context
|
||||
persona_context = self._build_persona_context(research_persona, industry, target_audience)
|
||||
|
||||
# Build competitor context
|
||||
competitor_context = self._build_competitor_context(competitor_data)
|
||||
|
||||
prompt = f"""You are an expert research intent analyzer. Your job is to understand what a content creator REALLY needs from their research.
|
||||
|
||||
## USER INPUT
|
||||
"{user_input}"
|
||||
|
||||
{f"KEYWORDS: {', '.join(keywords)}" if keywords else ""}
|
||||
|
||||
## USER CONTEXT
|
||||
{persona_context}
|
||||
|
||||
{competitor_context}
|
||||
|
||||
## YOUR TASK
|
||||
|
||||
Analyze the user's input and infer their research intent. Determine:
|
||||
|
||||
1. **INPUT TYPE**: Is this:
|
||||
- "keywords": Simple topic keywords (e.g., "AI healthcare 2025")
|
||||
- "question": A specific question (e.g., "What are the best AI tools for healthcare?")
|
||||
- "goal": A goal statement (e.g., "I need to write a blog about AI in healthcare")
|
||||
- "mixed": Combination of above
|
||||
|
||||
2. **PRIMARY QUESTION**: What is the main question to answer? Convert their input into a clear question.
|
||||
|
||||
3. **SECONDARY QUESTIONS**: What related questions should also be answered? (3-5 questions)
|
||||
|
||||
4. **PURPOSE**: Why are they researching? Choose ONE:
|
||||
- "learn": Understand a topic for personal knowledge
|
||||
- "create_content": Create content (blog, video, podcast)
|
||||
- "make_decision": Make a choice between options
|
||||
- "compare": Compare alternatives/competitors
|
||||
- "solve_problem": Find a solution
|
||||
- "find_data": Get specific statistics/facts
|
||||
- "explore_trends": Understand industry trends
|
||||
- "validate": Verify claims/information
|
||||
- "generate_ideas": Brainstorm ideas
|
||||
|
||||
5. **CONTENT OUTPUT**: What will they create? Choose ONE:
|
||||
- "blog", "podcast", "video", "social_post", "newsletter", "presentation", "report", "whitepaper", "email", "general"
|
||||
|
||||
6. **EXPECTED DELIVERABLES**: What specific outputs do they need? Choose ALL that apply:
|
||||
- "key_statistics": Numbers, data points
|
||||
- "expert_quotes": Authoritative quotes
|
||||
- "case_studies": Real examples
|
||||
- "comparisons": Side-by-side analysis
|
||||
- "trends": Industry trends
|
||||
- "best_practices": Recommendations
|
||||
- "step_by_step": How-to guides
|
||||
- "pros_cons": Advantages/disadvantages
|
||||
- "definitions": Concept explanations
|
||||
- "citations": Source references
|
||||
- "examples": Concrete examples
|
||||
- "predictions": Future outlook
|
||||
|
||||
7. **DEPTH**: How deep should the research go?
|
||||
- "overview": Quick summary
|
||||
- "detailed": In-depth analysis
|
||||
- "expert": Comprehensive expert-level
|
||||
|
||||
8. **FOCUS AREAS**: What specific aspects should be researched? (2-4 areas)
|
||||
|
||||
9. **PERSPECTIVE**: From whose viewpoint? (e.g., "marketing manager", "small business owner")
|
||||
|
||||
10. **TIME SENSITIVITY**: Is recency important?
|
||||
- "real_time": Latest only (past 24-48 hours)
|
||||
- "recent": Past week/month
|
||||
- "historical": Include older content
|
||||
- "evergreen": Timeless content
|
||||
|
||||
11. **CONFIDENCE**: How confident are you in this inference? (0.0-1.0)
|
||||
- If < 0.7, set needs_clarification to true and provide clarifying_questions
|
||||
|
||||
## OUTPUT FORMAT
|
||||
|
||||
Return a JSON object:
|
||||
```json
|
||||
{{
|
||||
"input_type": "keywords|question|goal|mixed",
|
||||
"primary_question": "The main question to answer",
|
||||
"secondary_questions": ["question 1", "question 2", "question 3"],
|
||||
"purpose": "one of the purpose options",
|
||||
"content_output": "one of the content options",
|
||||
"expected_deliverables": ["deliverable1", "deliverable2"],
|
||||
"depth": "overview|detailed|expert",
|
||||
"focus_areas": ["area1", "area2"],
|
||||
"perspective": "target perspective or null",
|
||||
"time_sensitivity": "real_time|recent|historical|evergreen",
|
||||
"confidence": 0.85,
|
||||
"needs_clarification": false,
|
||||
"clarifying_questions": [],
|
||||
"analysis_summary": "Brief summary of what the user wants"
|
||||
}}
|
||||
```
|
||||
|
||||
## IMPORTANT RULES
|
||||
|
||||
1. Always convert vague input into a specific primary question
|
||||
2. Infer deliverables based on purpose (e.g., create_content → statistics + examples)
|
||||
3. Use persona context to refine perspective and focus areas
|
||||
4. If input is ambiguous, provide clarifying questions
|
||||
5. Default to "detailed" depth unless input suggests otherwise
|
||||
6. For content creation, include relevant deliverables automatically
|
||||
"""
|
||||
|
||||
return prompt
|
||||
|
||||
def build_query_generation_prompt(
|
||||
self,
|
||||
intent: ResearchIntent,
|
||||
research_persona: Optional[ResearchPersona] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Build prompt for generating targeted research queries.
|
||||
|
||||
Generates multiple queries, each targeting a specific deliverable.
|
||||
"""
|
||||
|
||||
deliverables_list = "\n".join([
|
||||
f"- {d}: {self.DELIVERABLE_DESCRIPTIONS.get(ExpectedDeliverable(d), d)}"
|
||||
for d in intent.expected_deliverables
|
||||
])
|
||||
|
||||
persona_keywords = ""
|
||||
if research_persona and research_persona.suggested_keywords:
|
||||
persona_keywords = f"\nSUGGESTED KEYWORDS FROM PERSONA: {', '.join(research_persona.suggested_keywords[:10])}"
|
||||
|
||||
prompt = f"""You are a research query optimizer. Generate multiple targeted search queries based on the user's research intent.
|
||||
|
||||
## RESEARCH INTENT
|
||||
|
||||
PRIMARY QUESTION: {intent.primary_question}
|
||||
|
||||
SECONDARY QUESTIONS:
|
||||
{chr(10).join(f'- {q}' for q in intent.secondary_questions) if intent.secondary_questions else 'None'}
|
||||
|
||||
PURPOSE: {intent.purpose} - {self.PURPOSE_EXPLANATIONS.get(ResearchPurpose(intent.purpose), intent.purpose)}
|
||||
|
||||
CONTENT OUTPUT: {intent.content_output}
|
||||
|
||||
EXPECTED DELIVERABLES:
|
||||
{deliverables_list}
|
||||
|
||||
DEPTH: {intent.depth}
|
||||
|
||||
FOCUS AREAS: {', '.join(intent.focus_areas) if intent.focus_areas else 'General'}
|
||||
|
||||
PERSPECTIVE: {intent.perspective or 'General audience'}
|
||||
|
||||
TIME SENSITIVITY: {intent.time_sensitivity or 'No specific requirement'}
|
||||
{persona_keywords}
|
||||
|
||||
## YOUR TASK
|
||||
|
||||
Generate 4-8 targeted research queries. Each query should:
|
||||
1. Target a specific deliverable or question
|
||||
2. Be optimized for semantic search (Exa/Tavily)
|
||||
3. Include relevant context for better results
|
||||
|
||||
For each query, specify:
|
||||
- The query string
|
||||
- What deliverable it targets
|
||||
- Best provider (exa for semantic/deep, tavily for news/real-time, google for factual)
|
||||
- Priority (1-5, higher = more important)
|
||||
- What we expect to find
|
||||
|
||||
## OUTPUT FORMAT
|
||||
|
||||
Return a JSON object:
|
||||
```json
|
||||
{{
|
||||
"queries": [
|
||||
{{
|
||||
"query": "Healthcare AI adoption statistics 2025 hospitals implementation data",
|
||||
"purpose": "key_statistics",
|
||||
"provider": "exa",
|
||||
"priority": 5,
|
||||
"expected_results": "Statistics on hospital AI adoption rates"
|
||||
}},
|
||||
{{
|
||||
"query": "AI healthcare trends predictions future outlook 2025 2026",
|
||||
"purpose": "trends",
|
||||
"provider": "tavily",
|
||||
"priority": 4,
|
||||
"expected_results": "Current trends and future predictions in healthcare AI"
|
||||
}}
|
||||
],
|
||||
"enhanced_keywords": ["keyword1", "keyword2", "keyword3"],
|
||||
"research_angles": [
|
||||
"Angle 1: Focus on adoption challenges",
|
||||
"Angle 2: Focus on ROI and outcomes"
|
||||
]
|
||||
}}
|
||||
```
|
||||
|
||||
## QUERY OPTIMIZATION RULES
|
||||
|
||||
1. For STATISTICS: Include words like "statistics", "data", "percentage", "report", "study"
|
||||
2. For CASE STUDIES: Include "case study", "success story", "implementation", "example"
|
||||
3. For TRENDS: Include "trends", "future", "predictions", "emerging", year numbers
|
||||
4. For EXPERT QUOTES: Include expert names if known, or "expert opinion", "interview"
|
||||
5. For COMPARISONS: Include "vs", "compare", "comparison", "alternative"
|
||||
6. For NEWS/REAL-TIME: Use Tavily, include recent year/month
|
||||
7. For ACADEMIC/DEEP: Use Exa with neural search
|
||||
"""
|
||||
|
||||
return prompt
|
||||
|
||||
def build_intent_aware_analysis_prompt(
|
||||
self,
|
||||
raw_results: str,
|
||||
intent: ResearchIntent,
|
||||
research_persona: Optional[ResearchPersona] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Build prompt for analyzing research results based on user intent.
|
||||
|
||||
This is the key prompt that extracts exactly what the user needs.
|
||||
"""
|
||||
|
||||
purpose_explanation = self.PURPOSE_EXPLANATIONS.get(
|
||||
ResearchPurpose(intent.purpose),
|
||||
intent.purpose
|
||||
)
|
||||
|
||||
deliverables_instructions = self._build_deliverables_instructions(intent.expected_deliverables)
|
||||
|
||||
perspective_instruction = ""
|
||||
if intent.perspective:
|
||||
perspective_instruction = f"\n**PERSPECTIVE**: Analyze results from the viewpoint of: {intent.perspective}"
|
||||
|
||||
prompt = f"""You are a research analyst helping a content creator find exactly what they need. Your job is to analyze raw research results and extract precisely what the user is looking for.
|
||||
|
||||
## USER'S RESEARCH INTENT
|
||||
|
||||
PRIMARY QUESTION: {intent.primary_question}
|
||||
|
||||
SECONDARY QUESTIONS:
|
||||
{chr(10).join(f'- {q}' for q in intent.secondary_questions) if intent.secondary_questions else 'None specified'}
|
||||
|
||||
PURPOSE: {intent.purpose}
|
||||
→ {purpose_explanation}
|
||||
|
||||
CONTENT OUTPUT: {intent.content_output}
|
||||
|
||||
EXPECTED DELIVERABLES: {', '.join(intent.expected_deliverables)}
|
||||
|
||||
FOCUS AREAS: {', '.join(intent.focus_areas) if intent.focus_areas else 'General'}
|
||||
{perspective_instruction}
|
||||
|
||||
## RAW RESEARCH RESULTS
|
||||
|
||||
{raw_results[:15000]} # Truncated for token limits
|
||||
|
||||
## YOUR TASK
|
||||
|
||||
Analyze the raw research results and extract EXACTLY what the user needs.
|
||||
|
||||
{deliverables_instructions}
|
||||
|
||||
## OUTPUT REQUIREMENTS
|
||||
|
||||
Provide results in this JSON structure:
|
||||
|
||||
```json
|
||||
{{
|
||||
"primary_answer": "Direct 2-3 sentence answer to the primary question",
|
||||
"secondary_answers": {{
|
||||
"Question 1?": "Answer to question 1",
|
||||
"Question 2?": "Answer to question 2"
|
||||
}},
|
||||
"executive_summary": "2-3 sentence executive summary of all findings",
|
||||
"key_takeaways": [
|
||||
"Key takeaway 1 - most important finding",
|
||||
"Key takeaway 2",
|
||||
"Key takeaway 3",
|
||||
"Key takeaway 4",
|
||||
"Key takeaway 5"
|
||||
],
|
||||
"statistics": [
|
||||
{{
|
||||
"statistic": "72% of hospitals plan to adopt AI by 2025",
|
||||
"value": "72%",
|
||||
"context": "Survey of 500 US hospitals in 2024",
|
||||
"source": "Healthcare AI Report 2024",
|
||||
"url": "https://example.com/report",
|
||||
"credibility": 0.9,
|
||||
"recency": "2024"
|
||||
}}
|
||||
],
|
||||
"expert_quotes": [
|
||||
{{
|
||||
"quote": "AI will revolutionize patient care within 5 years",
|
||||
"speaker": "Dr. Jane Smith",
|
||||
"title": "Chief Medical Officer",
|
||||
"organization": "HealthTech Inc",
|
||||
"source": "TechCrunch",
|
||||
"url": "https://example.com/article"
|
||||
}}
|
||||
],
|
||||
"case_studies": [
|
||||
{{
|
||||
"title": "Mayo Clinic AI Implementation",
|
||||
"organization": "Mayo Clinic",
|
||||
"challenge": "High patient wait times",
|
||||
"solution": "AI-powered triage system",
|
||||
"outcome": "40% reduction in wait times",
|
||||
"key_metrics": ["40% faster triage", "95% patient satisfaction"],
|
||||
"source": "Healthcare IT News",
|
||||
"url": "https://example.com"
|
||||
}}
|
||||
],
|
||||
"trends": [
|
||||
{{
|
||||
"trend": "AI-assisted diagnostics adoption",
|
||||
"direction": "growing",
|
||||
"evidence": ["25% YoY growth", "Major hospital chains investing"],
|
||||
"impact": "Could reduce misdiagnosis by 30%",
|
||||
"timeline": "Expected mainstream by 2027",
|
||||
"sources": ["url1", "url2"]
|
||||
}}
|
||||
],
|
||||
"comparisons": [
|
||||
{{
|
||||
"title": "Top AI Healthcare Platforms",
|
||||
"criteria": ["Cost", "Features", "Support"],
|
||||
"items": [
|
||||
{{
|
||||
"name": "Platform A",
|
||||
"pros": ["Easy integration", "Good support"],
|
||||
"cons": ["Higher cost"],
|
||||
"features": {{"Cost": "$500/month", "Support": "24/7"}}
|
||||
}}
|
||||
],
|
||||
"verdict": "Platform A best for large hospitals"
|
||||
}}
|
||||
],
|
||||
"best_practices": [
|
||||
"Start with a pilot program before full deployment",
|
||||
"Ensure staff training is comprehensive"
|
||||
],
|
||||
"step_by_step": [
|
||||
"Step 1: Assess current infrastructure",
|
||||
"Step 2: Define use cases",
|
||||
"Step 3: Select vendor"
|
||||
],
|
||||
"pros_cons": {{
|
||||
"subject": "AI in Healthcare",
|
||||
"pros": ["Improved accuracy", "Cost savings"],
|
||||
"cons": ["Initial investment", "Training required"],
|
||||
"balanced_verdict": "Benefits outweigh costs for most hospitals"
|
||||
}},
|
||||
"definitions": {{
|
||||
"Clinical AI": "AI systems designed for medical diagnosis and treatment recommendations"
|
||||
}},
|
||||
"examples": [
|
||||
"Example: Hospital X reduced readmissions by 25% using predictive AI"
|
||||
],
|
||||
"predictions": [
|
||||
"By 2030, AI will assist in 80% of initial diagnoses"
|
||||
],
|
||||
"suggested_outline": [
|
||||
"1. Introduction: The AI Healthcare Revolution",
|
||||
"2. Current State: Where We Are Today",
|
||||
"3. Key Statistics and Trends",
|
||||
"4. Case Studies: Success Stories",
|
||||
"5. Implementation Guide",
|
||||
"6. Future Outlook"
|
||||
],
|
||||
"sources": [
|
||||
{{
|
||||
"title": "Healthcare AI Report 2024",
|
||||
"url": "https://example.com",
|
||||
"relevance_score": 0.95,
|
||||
"relevance_reason": "Directly addresses adoption statistics",
|
||||
"content_type": "research report",
|
||||
"credibility_score": 0.9
|
||||
}}
|
||||
],
|
||||
"confidence": 0.85,
|
||||
"gaps_identified": [
|
||||
"Specific cost data for small clinics not found",
|
||||
"Limited information on regulatory challenges"
|
||||
],
|
||||
"follow_up_queries": [
|
||||
"AI healthcare regulations FDA 2025",
|
||||
"Small clinic AI implementation costs"
|
||||
]
|
||||
}}
|
||||
```
|
||||
|
||||
## CRITICAL RULES
|
||||
|
||||
1. **ONLY include information directly from the raw results** - do not make up data
|
||||
2. **ALWAYS include source URLs** for every statistic, quote, and case study
|
||||
3. **If a deliverable type has no relevant data**, return an empty array for it
|
||||
4. **Prioritize recency and credibility** when multiple sources conflict
|
||||
5. **Answer the PRIMARY QUESTION directly** in 2-3 clear sentences
|
||||
6. **Keep KEY TAKEAWAYS to 5-7 points** - the most important findings
|
||||
7. **Add to gaps_identified** if expected information is missing
|
||||
8. **Suggest follow_up_queries** for gaps or incomplete areas
|
||||
9. **Rate confidence** based on how well results match the user's intent
|
||||
10. **Include deliverables ONLY if they are in expected_deliverables** or critical to the question
|
||||
"""
|
||||
|
||||
return prompt
|
||||
|
||||
def _build_persona_context(
|
||||
self,
|
||||
research_persona: Optional[ResearchPersona],
|
||||
industry: Optional[str],
|
||||
target_audience: Optional[str],
|
||||
) -> str:
|
||||
"""Build persona context section for prompts."""
|
||||
|
||||
if not research_persona and not industry:
|
||||
return "No specific persona context available."
|
||||
|
||||
context_parts = []
|
||||
|
||||
if research_persona:
|
||||
context_parts.append(f"INDUSTRY: {research_persona.default_industry}")
|
||||
context_parts.append(f"TARGET AUDIENCE: {research_persona.default_target_audience}")
|
||||
if research_persona.suggested_keywords:
|
||||
context_parts.append(f"TYPICAL TOPICS: {', '.join(research_persona.suggested_keywords[:5])}")
|
||||
if research_persona.research_angles:
|
||||
context_parts.append(f"RESEARCH ANGLES: {', '.join(research_persona.research_angles[:3])}")
|
||||
else:
|
||||
if industry:
|
||||
context_parts.append(f"INDUSTRY: {industry}")
|
||||
if target_audience:
|
||||
context_parts.append(f"TARGET AUDIENCE: {target_audience}")
|
||||
|
||||
return "\n".join(context_parts)
|
||||
|
||||
def _build_competitor_context(self, competitor_data: Optional[List[Dict]]) -> str:
|
||||
"""Build competitor context section for prompts."""
|
||||
|
||||
if not competitor_data:
|
||||
return ""
|
||||
|
||||
competitor_names = []
|
||||
for comp in competitor_data[:5]: # Limit to 5
|
||||
name = comp.get("name") or comp.get("domain") or comp.get("url", "Unknown")
|
||||
competitor_names.append(name)
|
||||
|
||||
if competitor_names:
|
||||
return f"\nKNOWN COMPETITORS: {', '.join(competitor_names)}"
|
||||
|
||||
return ""
|
||||
|
||||
def _build_deliverables_instructions(self, expected_deliverables: List[str]) -> str:
|
||||
"""Build specific extraction instructions for each expected deliverable."""
|
||||
|
||||
instructions = ["### EXTRACTION INSTRUCTIONS\n"]
|
||||
instructions.append("For each requested deliverable, extract the following:\n")
|
||||
|
||||
deliverable_instructions = {
|
||||
ExpectedDeliverable.KEY_STATISTICS: """
|
||||
**STATISTICS**:
|
||||
- Extract ALL relevant statistics with exact numbers
|
||||
- Include source attribution (publication name, URL)
|
||||
- Note the recency of the data
|
||||
- Rate credibility based on source authority
|
||||
- Format: statistic statement, value, context, source, URL, credibility score
|
||||
""",
|
||||
ExpectedDeliverable.EXPERT_QUOTES: """
|
||||
**EXPERT QUOTES**:
|
||||
- Extract authoritative quotes from named experts
|
||||
- Include speaker name, title, and organization
|
||||
- Provide context for the quote
|
||||
- Include source URL
|
||||
""",
|
||||
ExpectedDeliverable.CASE_STUDIES: """
|
||||
**CASE STUDIES**:
|
||||
- Summarize each case study: challenge → solution → outcome
|
||||
- Include key metrics and results
|
||||
- Name the organization involved
|
||||
- Provide source URL
|
||||
""",
|
||||
ExpectedDeliverable.TRENDS: """
|
||||
**TRENDS**:
|
||||
- Identify current and emerging trends
|
||||
- Note direction: growing, declining, emerging, or stable
|
||||
- List supporting evidence
|
||||
- Include timeline predictions if available
|
||||
- Cite sources
|
||||
""",
|
||||
ExpectedDeliverable.COMPARISONS: """
|
||||
**COMPARISONS**:
|
||||
- Build comparison tables where applicable
|
||||
- Define clear comparison criteria
|
||||
- List pros and cons for each option
|
||||
- Provide a verdict/recommendation if data supports it
|
||||
""",
|
||||
ExpectedDeliverable.BEST_PRACTICES: """
|
||||
**BEST PRACTICES**:
|
||||
- Extract recommended approaches
|
||||
- Provide actionable guidelines
|
||||
- Order by importance or sequence
|
||||
""",
|
||||
ExpectedDeliverable.STEP_BY_STEP: """
|
||||
**STEP BY STEP**:
|
||||
- Extract process/how-to instructions
|
||||
- Number steps clearly
|
||||
- Include any prerequisites or requirements
|
||||
""",
|
||||
ExpectedDeliverable.PROS_CONS: """
|
||||
**PROS AND CONS**:
|
||||
- List advantages (pros)
|
||||
- List disadvantages (cons)
|
||||
- Provide a balanced verdict
|
||||
""",
|
||||
ExpectedDeliverable.DEFINITIONS: """
|
||||
**DEFINITIONS**:
|
||||
- Extract clear explanations of key terms and concepts
|
||||
- Keep definitions concise but comprehensive
|
||||
""",
|
||||
ExpectedDeliverable.EXAMPLES: """
|
||||
**EXAMPLES**:
|
||||
- Extract concrete examples that illustrate key points
|
||||
- Include real-world applications
|
||||
""",
|
||||
ExpectedDeliverable.PREDICTIONS: """
|
||||
**PREDICTIONS**:
|
||||
- Extract future outlook and predictions
|
||||
- Note the source and their track record if known
|
||||
- Include timeframes where mentioned
|
||||
""",
|
||||
ExpectedDeliverable.CITATIONS: """
|
||||
**CITATIONS**:
|
||||
- List all authoritative sources with URLs
|
||||
- Rate credibility and relevance
|
||||
- Note content type (research, news, opinion, etc.)
|
||||
""",
|
||||
}
|
||||
|
||||
for deliverable in expected_deliverables:
|
||||
try:
|
||||
d_enum = ExpectedDeliverable(deliverable)
|
||||
if d_enum in deliverable_instructions:
|
||||
instructions.append(deliverable_instructions[d_enum])
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
return "\n".join(instructions)
|
||||
387
backend/services/research/intent/intent_query_generator.py
Normal file
387
backend/services/research/intent/intent_query_generator.py
Normal file
@@ -0,0 +1,387 @@
|
||||
"""
|
||||
Intent Query Generator
|
||||
|
||||
Generates multiple targeted research queries based on user intent.
|
||||
Each query targets a specific deliverable or question.
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 1.0
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Dict, Any, List, Optional
|
||||
from loguru import logger
|
||||
|
||||
from models.research_intent_models import (
|
||||
ResearchIntent,
|
||||
ResearchQuery,
|
||||
ExpectedDeliverable,
|
||||
ResearchPurpose,
|
||||
)
|
||||
from models.research_persona_models import ResearchPersona
|
||||
from .intent_prompt_builder import IntentPromptBuilder
|
||||
|
||||
|
||||
class IntentQueryGenerator:
|
||||
"""
|
||||
Generates targeted research queries based on user intent.
|
||||
|
||||
Instead of a single generic search, generates multiple queries
|
||||
each targeting a specific deliverable or question.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the query generator."""
|
||||
self.prompt_builder = IntentPromptBuilder()
|
||||
logger.info("IntentQueryGenerator initialized")
|
||||
|
||||
async def generate_queries(
|
||||
self,
|
||||
intent: ResearchIntent,
|
||||
research_persona: Optional[ResearchPersona] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate targeted research queries based on intent.
|
||||
|
||||
Args:
|
||||
intent: The inferred research intent
|
||||
research_persona: Optional persona for context
|
||||
|
||||
Returns:
|
||||
Dict with queries, enhanced_keywords, and research_angles
|
||||
"""
|
||||
try:
|
||||
logger.info(f"Generating queries for: {intent.primary_question[:50]}...")
|
||||
|
||||
# Build the query generation prompt
|
||||
prompt = self.prompt_builder.build_query_generation_prompt(
|
||||
intent=intent,
|
||||
research_persona=research_persona,
|
||||
)
|
||||
|
||||
# Define the expected JSON schema
|
||||
query_schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"queries": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {"type": "string"},
|
||||
"purpose": {"type": "string"},
|
||||
"provider": {"type": "string"},
|
||||
"priority": {"type": "integer"},
|
||||
"expected_results": {"type": "string"}
|
||||
},
|
||||
"required": ["query", "purpose", "provider", "priority", "expected_results"]
|
||||
}
|
||||
},
|
||||
"enhanced_keywords": {"type": "array", "items": {"type": "string"}},
|
||||
"research_angles": {"type": "array", "items": {"type": "string"}}
|
||||
},
|
||||
"required": ["queries", "enhanced_keywords", "research_angles"]
|
||||
}
|
||||
|
||||
# Call LLM for query generation
|
||||
from services.llm_providers.main_text_generation import llm_text_gen
|
||||
|
||||
result = llm_text_gen(
|
||||
prompt=prompt,
|
||||
json_struct=query_schema,
|
||||
user_id=None
|
||||
)
|
||||
|
||||
if isinstance(result, dict) and "error" in result:
|
||||
logger.error(f"Query generation failed: {result.get('error')}")
|
||||
return self._create_fallback_queries(intent)
|
||||
|
||||
# Parse queries
|
||||
queries = self._parse_queries(result.get("queries", []))
|
||||
|
||||
# Ensure we have queries for all expected deliverables
|
||||
queries = self._ensure_deliverable_coverage(queries, intent)
|
||||
|
||||
# Sort by priority
|
||||
queries.sort(key=lambda q: q.priority, reverse=True)
|
||||
|
||||
logger.info(f"Generated {len(queries)} targeted queries")
|
||||
|
||||
return {
|
||||
"queries": queries,
|
||||
"enhanced_keywords": result.get("enhanced_keywords", []),
|
||||
"research_angles": result.get("research_angles", []),
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating queries: {e}")
|
||||
return self._create_fallback_queries(intent)
|
||||
|
||||
def _parse_queries(self, raw_queries: List[Dict]) -> List[ResearchQuery]:
|
||||
"""Parse raw query data into ResearchQuery objects."""
|
||||
|
||||
queries = []
|
||||
for q in raw_queries:
|
||||
try:
|
||||
# Validate purpose
|
||||
purpose_str = q.get("purpose", "key_statistics")
|
||||
try:
|
||||
purpose = ExpectedDeliverable(purpose_str)
|
||||
except ValueError:
|
||||
purpose = ExpectedDeliverable.KEY_STATISTICS
|
||||
|
||||
query = ResearchQuery(
|
||||
query=q.get("query", ""),
|
||||
purpose=purpose,
|
||||
provider=q.get("provider", "exa"),
|
||||
priority=min(max(int(q.get("priority", 3)), 1), 5), # Clamp 1-5
|
||||
expected_results=q.get("expected_results", ""),
|
||||
)
|
||||
queries.append(query)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse query: {e}")
|
||||
continue
|
||||
|
||||
return queries
|
||||
|
||||
def _ensure_deliverable_coverage(
|
||||
self,
|
||||
queries: List[ResearchQuery],
|
||||
intent: ResearchIntent,
|
||||
) -> List[ResearchQuery]:
|
||||
"""Ensure we have queries for all expected deliverables."""
|
||||
|
||||
# Get deliverables already covered
|
||||
covered = set(q.purpose.value for q in queries)
|
||||
|
||||
# Check for missing deliverables
|
||||
for deliverable in intent.expected_deliverables:
|
||||
if deliverable not in covered:
|
||||
# Generate a query for this deliverable
|
||||
query = self._generate_query_for_deliverable(
|
||||
deliverable=deliverable,
|
||||
intent=intent,
|
||||
)
|
||||
queries.append(query)
|
||||
|
||||
return queries
|
||||
|
||||
def _generate_query_for_deliverable(
|
||||
self,
|
||||
deliverable: str,
|
||||
intent: ResearchIntent,
|
||||
) -> ResearchQuery:
|
||||
"""Generate a query targeting a specific deliverable."""
|
||||
|
||||
# Extract topic from primary question
|
||||
topic = intent.original_input
|
||||
|
||||
# Query templates by deliverable type
|
||||
templates = {
|
||||
ExpectedDeliverable.KEY_STATISTICS.value: {
|
||||
"query": f"{topic} statistics data report study",
|
||||
"provider": "exa",
|
||||
"priority": 5,
|
||||
"expected": "Statistical data and research findings",
|
||||
},
|
||||
ExpectedDeliverable.EXPERT_QUOTES.value: {
|
||||
"query": f"{topic} expert opinion interview insights",
|
||||
"provider": "exa",
|
||||
"priority": 4,
|
||||
"expected": "Expert opinions and authoritative quotes",
|
||||
},
|
||||
ExpectedDeliverable.CASE_STUDIES.value: {
|
||||
"query": f"{topic} case study success story implementation example",
|
||||
"provider": "exa",
|
||||
"priority": 4,
|
||||
"expected": "Real-world case studies and examples",
|
||||
},
|
||||
ExpectedDeliverable.TRENDS.value: {
|
||||
"query": f"{topic} trends 2025 future predictions emerging",
|
||||
"provider": "tavily",
|
||||
"priority": 4,
|
||||
"expected": "Current trends and future predictions",
|
||||
},
|
||||
ExpectedDeliverable.COMPARISONS.value: {
|
||||
"query": f"{topic} comparison vs versus alternatives",
|
||||
"provider": "exa",
|
||||
"priority": 4,
|
||||
"expected": "Comparison and alternative options",
|
||||
},
|
||||
ExpectedDeliverable.BEST_PRACTICES.value: {
|
||||
"query": f"{topic} best practices recommendations guidelines",
|
||||
"provider": "exa",
|
||||
"priority": 3,
|
||||
"expected": "Best practices and recommendations",
|
||||
},
|
||||
ExpectedDeliverable.STEP_BY_STEP.value: {
|
||||
"query": f"{topic} how to guide tutorial steps",
|
||||
"provider": "exa",
|
||||
"priority": 3,
|
||||
"expected": "Step-by-step guides and tutorials",
|
||||
},
|
||||
ExpectedDeliverable.PROS_CONS.value: {
|
||||
"query": f"{topic} advantages disadvantages pros cons benefits",
|
||||
"provider": "exa",
|
||||
"priority": 3,
|
||||
"expected": "Pros, cons, and trade-offs",
|
||||
},
|
||||
ExpectedDeliverable.DEFINITIONS.value: {
|
||||
"query": f"what is {topic} definition explained",
|
||||
"provider": "exa",
|
||||
"priority": 3,
|
||||
"expected": "Clear definitions and explanations",
|
||||
},
|
||||
ExpectedDeliverable.EXAMPLES.value: {
|
||||
"query": f"{topic} examples real world applications",
|
||||
"provider": "exa",
|
||||
"priority": 3,
|
||||
"expected": "Real-world examples and applications",
|
||||
},
|
||||
ExpectedDeliverable.PREDICTIONS.value: {
|
||||
"query": f"{topic} future outlook predictions 2025 2030",
|
||||
"provider": "tavily",
|
||||
"priority": 4,
|
||||
"expected": "Future predictions and outlook",
|
||||
},
|
||||
ExpectedDeliverable.CITATIONS.value: {
|
||||
"query": f"{topic} research paper study academic",
|
||||
"provider": "exa",
|
||||
"priority": 4,
|
||||
"expected": "Authoritative academic sources",
|
||||
},
|
||||
}
|
||||
|
||||
template = templates.get(deliverable, {
|
||||
"query": f"{topic}",
|
||||
"provider": "exa",
|
||||
"priority": 3,
|
||||
"expected": "General information",
|
||||
})
|
||||
|
||||
return ResearchQuery(
|
||||
query=template["query"],
|
||||
purpose=ExpectedDeliverable(deliverable) if deliverable in [e.value for e in ExpectedDeliverable] else ExpectedDeliverable.KEY_STATISTICS,
|
||||
provider=template["provider"],
|
||||
priority=template["priority"],
|
||||
expected_results=template["expected"],
|
||||
)
|
||||
|
||||
def _create_fallback_queries(self, intent: ResearchIntent) -> Dict[str, Any]:
|
||||
"""Create fallback queries when AI generation fails."""
|
||||
|
||||
topic = intent.original_input
|
||||
|
||||
# Generate basic queries for each expected deliverable
|
||||
queries = []
|
||||
for deliverable in intent.expected_deliverables[:5]: # Limit to 5
|
||||
query = self._generate_query_for_deliverable(deliverable, intent)
|
||||
queries.append(query)
|
||||
|
||||
# Add a general query if we have none
|
||||
if not queries:
|
||||
queries.append(ResearchQuery(
|
||||
query=topic,
|
||||
purpose=ExpectedDeliverable.KEY_STATISTICS,
|
||||
provider="exa",
|
||||
priority=5,
|
||||
expected_results="General information and insights",
|
||||
))
|
||||
|
||||
return {
|
||||
"queries": queries,
|
||||
"enhanced_keywords": topic.split()[:10],
|
||||
"research_angles": [
|
||||
f"Overview of {topic}",
|
||||
f"Latest trends in {topic}",
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
class QueryOptimizer:
|
||||
"""
|
||||
Optimizes queries for different research providers.
|
||||
|
||||
Different providers have different strengths:
|
||||
- Exa: Semantic search, good for deep research
|
||||
- Tavily: Real-time search, good for news/trends
|
||||
- Google: Factual search, good for basic info
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def optimize_for_exa(query: str, intent: ResearchIntent) -> Dict[str, Any]:
|
||||
"""Optimize query and parameters for Exa."""
|
||||
|
||||
# Determine best Exa settings based on deliverable
|
||||
deliverables = intent.expected_deliverables
|
||||
|
||||
# Determine category
|
||||
category = None
|
||||
if ExpectedDeliverable.CITATIONS.value in deliverables:
|
||||
category = "research paper"
|
||||
elif ExpectedDeliverable.TRENDS.value in deliverables:
|
||||
category = "news"
|
||||
elif intent.purpose == ResearchPurpose.COMPARE.value:
|
||||
category = "company"
|
||||
|
||||
# Determine search type
|
||||
search_type = "neural" # Default to neural for semantic understanding
|
||||
if ExpectedDeliverable.TRENDS.value in deliverables:
|
||||
search_type = "auto" # Auto is better for time-sensitive queries
|
||||
|
||||
# Number of results
|
||||
num_results = 10
|
||||
if intent.depth == "expert":
|
||||
num_results = 20
|
||||
elif intent.depth == "overview":
|
||||
num_results = 5
|
||||
|
||||
return {
|
||||
"query": query,
|
||||
"type": search_type,
|
||||
"category": category,
|
||||
"num_results": num_results,
|
||||
"text": True,
|
||||
"highlights": True,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def optimize_for_tavily(query: str, intent: ResearchIntent) -> Dict[str, Any]:
|
||||
"""Optimize query and parameters for Tavily."""
|
||||
|
||||
deliverables = intent.expected_deliverables
|
||||
|
||||
# Determine topic
|
||||
topic = "general"
|
||||
if ExpectedDeliverable.TRENDS.value in deliverables:
|
||||
topic = "news"
|
||||
|
||||
# Determine search depth
|
||||
search_depth = "basic"
|
||||
if intent.depth in ["detailed", "expert"]:
|
||||
search_depth = "advanced"
|
||||
|
||||
# Include answer for factual queries
|
||||
include_answer = False
|
||||
if ExpectedDeliverable.DEFINITIONS.value in deliverables:
|
||||
include_answer = "advanced"
|
||||
elif ExpectedDeliverable.KEY_STATISTICS.value in deliverables:
|
||||
include_answer = "basic"
|
||||
|
||||
# Time range for trends
|
||||
time_range = None
|
||||
if intent.time_sensitivity == "real_time":
|
||||
time_range = "day"
|
||||
elif intent.time_sensitivity == "recent":
|
||||
time_range = "week"
|
||||
elif ExpectedDeliverable.TRENDS.value in deliverables:
|
||||
time_range = "month"
|
||||
|
||||
return {
|
||||
"query": query,
|
||||
"topic": topic,
|
||||
"search_depth": search_depth,
|
||||
"include_answer": include_answer,
|
||||
"time_range": time_range,
|
||||
"max_results": 10,
|
||||
}
|
||||
378
backend/services/research/intent/research_intent_inference.py
Normal file
378
backend/services/research/intent/research_intent_inference.py
Normal file
@@ -0,0 +1,378 @@
|
||||
"""
|
||||
Research Intent Inference Service
|
||||
|
||||
Analyzes user input to understand their research intent.
|
||||
Uses AI to infer:
|
||||
- What the user wants to accomplish
|
||||
- What questions need answering
|
||||
- What deliverables they expect
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 1.0
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Dict, Any, List, Optional
|
||||
from loguru import logger
|
||||
|
||||
from models.research_intent_models import (
|
||||
ResearchIntent,
|
||||
ResearchPurpose,
|
||||
ContentOutput,
|
||||
ExpectedDeliverable,
|
||||
ResearchDepthLevel,
|
||||
InputType,
|
||||
IntentInferenceRequest,
|
||||
IntentInferenceResponse,
|
||||
ResearchQuery,
|
||||
)
|
||||
from models.research_persona_models import ResearchPersona
|
||||
from .intent_prompt_builder import IntentPromptBuilder
|
||||
|
||||
|
||||
class ResearchIntentInference:
|
||||
"""
|
||||
Infers user research intent from minimal input.
|
||||
|
||||
Instead of asking a formal questionnaire, this service
|
||||
uses AI to understand what the user really wants.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the intent inference service."""
|
||||
self.prompt_builder = IntentPromptBuilder()
|
||||
logger.info("ResearchIntentInference initialized")
|
||||
|
||||
async def infer_intent(
|
||||
self,
|
||||
user_input: str,
|
||||
keywords: Optional[List[str]] = None,
|
||||
research_persona: Optional[ResearchPersona] = None,
|
||||
competitor_data: Optional[List[Dict]] = None,
|
||||
industry: Optional[str] = None,
|
||||
target_audience: Optional[str] = None,
|
||||
) -> IntentInferenceResponse:
|
||||
"""
|
||||
Analyze user input and infer their research intent.
|
||||
|
||||
Args:
|
||||
user_input: User's keywords, question, or goal
|
||||
keywords: Extracted keywords (optional)
|
||||
research_persona: User's research persona (optional)
|
||||
competitor_data: Competitor analysis data (optional)
|
||||
industry: Industry context (optional)
|
||||
target_audience: Target audience context (optional)
|
||||
|
||||
Returns:
|
||||
IntentInferenceResponse with inferred intent and suggested queries
|
||||
"""
|
||||
try:
|
||||
logger.info(f"Inferring intent for: {user_input[:100]}...")
|
||||
|
||||
keywords = keywords or []
|
||||
|
||||
# Build the inference prompt
|
||||
prompt = self.prompt_builder.build_intent_inference_prompt(
|
||||
user_input=user_input,
|
||||
keywords=keywords,
|
||||
research_persona=research_persona,
|
||||
competitor_data=competitor_data,
|
||||
industry=industry,
|
||||
target_audience=target_audience,
|
||||
)
|
||||
|
||||
# Define the expected JSON schema
|
||||
intent_schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"input_type": {"type": "string", "enum": ["keywords", "question", "goal", "mixed"]},
|
||||
"primary_question": {"type": "string"},
|
||||
"secondary_questions": {"type": "array", "items": {"type": "string"}},
|
||||
"purpose": {"type": "string"},
|
||||
"content_output": {"type": "string"},
|
||||
"expected_deliverables": {"type": "array", "items": {"type": "string"}},
|
||||
"depth": {"type": "string", "enum": ["overview", "detailed", "expert"]},
|
||||
"focus_areas": {"type": "array", "items": {"type": "string"}},
|
||||
"perspective": {"type": "string"},
|
||||
"time_sensitivity": {"type": "string"},
|
||||
"confidence": {"type": "number"},
|
||||
"needs_clarification": {"type": "boolean"},
|
||||
"clarifying_questions": {"type": "array", "items": {"type": "string"}},
|
||||
"analysis_summary": {"type": "string"}
|
||||
},
|
||||
"required": [
|
||||
"input_type", "primary_question", "purpose", "content_output",
|
||||
"expected_deliverables", "depth", "confidence", "analysis_summary"
|
||||
]
|
||||
}
|
||||
|
||||
# Call LLM for intent inference
|
||||
from services.llm_providers.main_text_generation import llm_text_gen
|
||||
|
||||
result = llm_text_gen(
|
||||
prompt=prompt,
|
||||
json_struct=intent_schema,
|
||||
user_id=None
|
||||
)
|
||||
|
||||
if isinstance(result, dict) and "error" in result:
|
||||
logger.error(f"Intent inference failed: {result.get('error')}")
|
||||
return self._create_fallback_response(user_input, keywords)
|
||||
|
||||
# Parse and validate the result
|
||||
intent = self._parse_intent_result(result, user_input)
|
||||
|
||||
# Generate quick options for UI
|
||||
quick_options = self._generate_quick_options(intent, result)
|
||||
|
||||
# Create response
|
||||
response = IntentInferenceResponse(
|
||||
success=True,
|
||||
intent=intent,
|
||||
analysis_summary=result.get("analysis_summary", "Research intent analyzed"),
|
||||
suggested_queries=[], # Will be populated by query generator
|
||||
suggested_keywords=self._extract_keywords_from_input(user_input, keywords),
|
||||
suggested_angles=result.get("focus_areas", []),
|
||||
quick_options=quick_options,
|
||||
)
|
||||
|
||||
logger.info(f"Intent inferred: purpose={intent.purpose}, confidence={intent.confidence}")
|
||||
return response
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error inferring intent: {e}")
|
||||
return self._create_fallback_response(user_input, keywords or [])
|
||||
|
||||
def _parse_intent_result(self, result: Dict[str, Any], user_input: str) -> ResearchIntent:
|
||||
"""Parse LLM result into ResearchIntent model."""
|
||||
|
||||
# Map string values to enums safely
|
||||
input_type = self._safe_enum(InputType, result.get("input_type", "keywords"), InputType.KEYWORDS)
|
||||
purpose = self._safe_enum(ResearchPurpose, result.get("purpose", "learn"), ResearchPurpose.LEARN)
|
||||
content_output = self._safe_enum(ContentOutput, result.get("content_output", "general"), ContentOutput.GENERAL)
|
||||
depth = self._safe_enum(ResearchDepthLevel, result.get("depth", "detailed"), ResearchDepthLevel.DETAILED)
|
||||
|
||||
# Parse expected deliverables
|
||||
raw_deliverables = result.get("expected_deliverables", [])
|
||||
expected_deliverables = []
|
||||
for d in raw_deliverables:
|
||||
try:
|
||||
expected_deliverables.append(ExpectedDeliverable(d).value)
|
||||
except ValueError:
|
||||
# Skip invalid deliverables
|
||||
pass
|
||||
|
||||
# Ensure we have at least some deliverables
|
||||
if not expected_deliverables:
|
||||
expected_deliverables = self._infer_deliverables_from_purpose(purpose)
|
||||
|
||||
return ResearchIntent(
|
||||
primary_question=result.get("primary_question", user_input),
|
||||
secondary_questions=result.get("secondary_questions", []),
|
||||
purpose=purpose.value,
|
||||
content_output=content_output.value,
|
||||
expected_deliverables=expected_deliverables,
|
||||
depth=depth.value,
|
||||
focus_areas=result.get("focus_areas", []),
|
||||
perspective=result.get("perspective"),
|
||||
time_sensitivity=result.get("time_sensitivity"),
|
||||
input_type=input_type.value,
|
||||
original_input=user_input,
|
||||
confidence=float(result.get("confidence", 0.7)),
|
||||
needs_clarification=result.get("needs_clarification", False),
|
||||
clarifying_questions=result.get("clarifying_questions", []),
|
||||
)
|
||||
|
||||
def _safe_enum(self, enum_class, value: str, default):
|
||||
"""Safely convert string to enum, returning default if invalid."""
|
||||
try:
|
||||
return enum_class(value)
|
||||
except ValueError:
|
||||
return default
|
||||
|
||||
def _infer_deliverables_from_purpose(self, purpose: ResearchPurpose) -> List[str]:
|
||||
"""Infer expected deliverables based on research purpose."""
|
||||
|
||||
purpose_deliverables = {
|
||||
ResearchPurpose.LEARN: [
|
||||
ExpectedDeliverable.DEFINITIONS.value,
|
||||
ExpectedDeliverable.EXAMPLES.value,
|
||||
ExpectedDeliverable.KEY_STATISTICS.value,
|
||||
],
|
||||
ResearchPurpose.CREATE_CONTENT: [
|
||||
ExpectedDeliverable.KEY_STATISTICS.value,
|
||||
ExpectedDeliverable.EXPERT_QUOTES.value,
|
||||
ExpectedDeliverable.EXAMPLES.value,
|
||||
ExpectedDeliverable.CASE_STUDIES.value,
|
||||
],
|
||||
ResearchPurpose.MAKE_DECISION: [
|
||||
ExpectedDeliverable.PROS_CONS.value,
|
||||
ExpectedDeliverable.COMPARISONS.value,
|
||||
ExpectedDeliverable.BEST_PRACTICES.value,
|
||||
],
|
||||
ResearchPurpose.COMPARE: [
|
||||
ExpectedDeliverable.COMPARISONS.value,
|
||||
ExpectedDeliverable.PROS_CONS.value,
|
||||
ExpectedDeliverable.KEY_STATISTICS.value,
|
||||
],
|
||||
ResearchPurpose.SOLVE_PROBLEM: [
|
||||
ExpectedDeliverable.STEP_BY_STEP.value,
|
||||
ExpectedDeliverable.BEST_PRACTICES.value,
|
||||
ExpectedDeliverable.CASE_STUDIES.value,
|
||||
],
|
||||
ResearchPurpose.FIND_DATA: [
|
||||
ExpectedDeliverable.KEY_STATISTICS.value,
|
||||
ExpectedDeliverable.CITATIONS.value,
|
||||
],
|
||||
ResearchPurpose.EXPLORE_TRENDS: [
|
||||
ExpectedDeliverable.TRENDS.value,
|
||||
ExpectedDeliverable.PREDICTIONS.value,
|
||||
ExpectedDeliverable.KEY_STATISTICS.value,
|
||||
],
|
||||
ResearchPurpose.VALIDATE: [
|
||||
ExpectedDeliverable.CITATIONS.value,
|
||||
ExpectedDeliverable.KEY_STATISTICS.value,
|
||||
ExpectedDeliverable.EXPERT_QUOTES.value,
|
||||
],
|
||||
ResearchPurpose.GENERATE_IDEAS: [
|
||||
ExpectedDeliverable.EXAMPLES.value,
|
||||
ExpectedDeliverable.TRENDS.value,
|
||||
ExpectedDeliverable.CASE_STUDIES.value,
|
||||
],
|
||||
}
|
||||
|
||||
return purpose_deliverables.get(purpose, [ExpectedDeliverable.KEY_STATISTICS.value])
|
||||
|
||||
def _generate_quick_options(self, intent: ResearchIntent, result: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Generate quick options for UI confirmation."""
|
||||
|
||||
options = []
|
||||
|
||||
# Purpose option
|
||||
options.append({
|
||||
"id": "purpose",
|
||||
"label": "Research Purpose",
|
||||
"value": intent.purpose,
|
||||
"display": self._purpose_display(intent.purpose),
|
||||
"alternatives": [p.value for p in ResearchPurpose],
|
||||
"confidence": result.get("confidence", 0.7),
|
||||
})
|
||||
|
||||
# Content output option
|
||||
if intent.content_output != ContentOutput.GENERAL.value:
|
||||
options.append({
|
||||
"id": "content_output",
|
||||
"label": "Content Type",
|
||||
"value": intent.content_output,
|
||||
"display": intent.content_output.replace("_", " ").title(),
|
||||
"alternatives": [c.value for c in ContentOutput],
|
||||
"confidence": result.get("confidence", 0.7),
|
||||
})
|
||||
|
||||
# Deliverables option
|
||||
options.append({
|
||||
"id": "deliverables",
|
||||
"label": "What I'll Find",
|
||||
"value": intent.expected_deliverables,
|
||||
"display": [d.replace("_", " ").title() for d in intent.expected_deliverables[:4]],
|
||||
"alternatives": [d.value for d in ExpectedDeliverable],
|
||||
"confidence": result.get("confidence", 0.7),
|
||||
"multi_select": True,
|
||||
})
|
||||
|
||||
# Depth option
|
||||
options.append({
|
||||
"id": "depth",
|
||||
"label": "Research Depth",
|
||||
"value": intent.depth,
|
||||
"display": intent.depth.title(),
|
||||
"alternatives": [d.value for d in ResearchDepthLevel],
|
||||
"confidence": result.get("confidence", 0.7),
|
||||
})
|
||||
|
||||
return options
|
||||
|
||||
def _purpose_display(self, purpose: str) -> str:
|
||||
"""Get display-friendly purpose text."""
|
||||
display_map = {
|
||||
"learn": "Understand this topic",
|
||||
"create_content": "Create content about this",
|
||||
"make_decision": "Make a decision",
|
||||
"compare": "Compare options",
|
||||
"solve_problem": "Solve a problem",
|
||||
"find_data": "Find specific data",
|
||||
"explore_trends": "Explore trends",
|
||||
"validate": "Validate information",
|
||||
"generate_ideas": "Generate ideas",
|
||||
}
|
||||
return display_map.get(purpose, purpose.replace("_", " ").title())
|
||||
|
||||
def _extract_keywords_from_input(self, user_input: str, keywords: List[str]) -> List[str]:
|
||||
"""Extract and enhance keywords from user input."""
|
||||
|
||||
# Start with provided keywords
|
||||
extracted = list(keywords) if keywords else []
|
||||
|
||||
# Simple extraction from input (split on common delimiters)
|
||||
words = user_input.lower().replace(",", " ").replace(";", " ").split()
|
||||
|
||||
# Filter out common words
|
||||
stop_words = {
|
||||
"the", "a", "an", "is", "are", "was", "were", "be", "been", "being",
|
||||
"have", "has", "had", "do", "does", "did", "will", "would", "could",
|
||||
"should", "may", "might", "must", "shall", "can", "need", "dare",
|
||||
"to", "of", "in", "for", "on", "with", "at", "by", "from", "up",
|
||||
"about", "into", "through", "during", "before", "after", "above",
|
||||
"below", "between", "under", "again", "further", "then", "once",
|
||||
"here", "there", "when", "where", "why", "how", "all", "each",
|
||||
"few", "more", "most", "other", "some", "such", "no", "nor", "not",
|
||||
"only", "own", "same", "so", "than", "too", "very", "just", "and",
|
||||
"but", "if", "or", "because", "as", "until", "while", "i", "we",
|
||||
"you", "they", "what", "which", "who", "whom", "this", "that",
|
||||
"these", "those", "am", "want", "write", "blog", "post", "article",
|
||||
}
|
||||
|
||||
for word in words:
|
||||
if word not in stop_words and len(word) > 2 and word not in extracted:
|
||||
extracted.append(word)
|
||||
|
||||
return extracted[:15] # Limit to 15 keywords
|
||||
|
||||
def _create_fallback_response(self, user_input: str, keywords: List[str]) -> IntentInferenceResponse:
|
||||
"""Create a fallback response when AI inference fails."""
|
||||
|
||||
# Create a basic intent from the input
|
||||
fallback_intent = ResearchIntent(
|
||||
primary_question=f"What are the key insights about: {user_input}?",
|
||||
secondary_questions=[
|
||||
f"What are the latest trends in {user_input}?",
|
||||
f"What are best practices for {user_input}?",
|
||||
],
|
||||
purpose=ResearchPurpose.LEARN.value,
|
||||
content_output=ContentOutput.GENERAL.value,
|
||||
expected_deliverables=[
|
||||
ExpectedDeliverable.KEY_STATISTICS.value,
|
||||
ExpectedDeliverable.EXAMPLES.value,
|
||||
ExpectedDeliverable.BEST_PRACTICES.value,
|
||||
],
|
||||
depth=ResearchDepthLevel.DETAILED.value,
|
||||
focus_areas=[],
|
||||
input_type=InputType.KEYWORDS.value,
|
||||
original_input=user_input,
|
||||
confidence=0.5,
|
||||
needs_clarification=True,
|
||||
clarifying_questions=[
|
||||
"What type of content are you creating?",
|
||||
"What specific aspects are you most interested in?",
|
||||
],
|
||||
)
|
||||
|
||||
return IntentInferenceResponse(
|
||||
success=True, # Still return success, just with lower confidence
|
||||
intent=fallback_intent,
|
||||
analysis_summary=f"Basic research analysis for: {user_input}",
|
||||
suggested_queries=[],
|
||||
suggested_keywords=keywords,
|
||||
suggested_angles=[],
|
||||
quick_options=[],
|
||||
)
|
||||
@@ -5,7 +5,7 @@ Handles building comprehensive prompts for research persona generation.
|
||||
Generates personalized research defaults, suggestions, and configurations.
|
||||
"""
|
||||
|
||||
from typing import Dict, Any
|
||||
from typing import Dict, Any, List
|
||||
import json
|
||||
from loguru import logger
|
||||
|
||||
@@ -21,9 +21,34 @@ class ResearchPersonaPromptBuilder:
|
||||
persona_data = onboarding_data.get("persona_data", {}) or {}
|
||||
research_prefs = onboarding_data.get("research_preferences", {}) or {}
|
||||
business_info = onboarding_data.get("business_info", {}) or {}
|
||||
competitor_analysis = onboarding_data.get("competitor_analysis", []) or []
|
||||
|
||||
# Extract core persona
|
||||
core_persona = persona_data.get("core_persona", {}) or {}
|
||||
# Extract core persona - handle both camelCase and snake_case
|
||||
core_persona = persona_data.get("corePersona") or persona_data.get("core_persona") or {}
|
||||
|
||||
# Phase 1: Extract key website analysis fields for enhanced personalization
|
||||
writing_style = website_analysis.get("writing_style", {}) or {}
|
||||
content_type = website_analysis.get("content_type", {}) or {}
|
||||
crawl_result = website_analysis.get("crawl_result", {}) or {}
|
||||
|
||||
# Phase 2: Extract additional fields for pattern-based personalization
|
||||
style_patterns = website_analysis.get("style_patterns", {}) or {}
|
||||
content_characteristics = website_analysis.get("content_characteristics", {}) or {}
|
||||
style_guidelines = website_analysis.get("style_guidelines", {}) or {}
|
||||
|
||||
# Extract topics/keywords from crawl_result (if available)
|
||||
extracted_topics = self._extract_topics_from_crawl(crawl_result)
|
||||
extracted_keywords = self._extract_keywords_from_crawl(crawl_result)
|
||||
|
||||
# Phase 2: Extract patterns and vocabulary level
|
||||
extracted_patterns = self._extract_writing_patterns(style_patterns)
|
||||
vocabulary_level = content_characteristics.get("vocabulary_level", "medium") if content_characteristics else "medium"
|
||||
extracted_guidelines = self._extract_style_guidelines(style_guidelines)
|
||||
|
||||
# Phase 3: Full crawl analysis and comprehensive mapping
|
||||
crawl_analysis = self._analyze_crawl_result_comprehensive(crawl_result)
|
||||
writing_style_mapping = self._map_writing_style_comprehensive(writing_style, content_characteristics)
|
||||
content_themes = self._extract_content_themes(crawl_result, extracted_topics)
|
||||
|
||||
prompt = f"""
|
||||
COMPREHENSIVE RESEARCH PERSONA GENERATION TASK: Create a highly detailed, personalized research persona based on the user's business, writing style, and content strategy. This persona will provide intelligent defaults and suggestions for research inputs.
|
||||
@@ -42,53 +67,233 @@ CORE PERSONA:
|
||||
RESEARCH PREFERENCES:
|
||||
{json.dumps(research_prefs, indent=2)}
|
||||
|
||||
COMPETITOR ANALYSIS:
|
||||
{json.dumps(competitor_analysis, indent=2) if competitor_analysis else "No competitor data available"}
|
||||
|
||||
=== PHASE 1: WEBSITE ANALYSIS INTELLIGENCE ===
|
||||
|
||||
WRITING STYLE (for research depth mapping):
|
||||
{json.dumps(writing_style, indent=2) if writing_style else "Not available"}
|
||||
|
||||
CONTENT TYPE (for preset generation):
|
||||
{json.dumps(content_type, indent=2) if content_type else "Not available"}
|
||||
|
||||
EXTRACTED TOPICS FROM WEBSITE CONTENT:
|
||||
{json.dumps(extracted_topics, indent=2) if extracted_topics else "No topics extracted"}
|
||||
|
||||
EXTRACTED KEYWORDS FROM WEBSITE CONTENT:
|
||||
{json.dumps(extracted_keywords[:20], indent=2) if extracted_keywords else "No keywords extracted"}
|
||||
|
||||
=== PHASE 2: WRITING PATTERNS & STYLE INTELLIGENCE ===
|
||||
|
||||
STYLE PATTERNS (for research angles):
|
||||
{json.dumps(style_patterns, indent=2) if style_patterns else "Not available"}
|
||||
|
||||
EXTRACTED WRITING PATTERNS:
|
||||
{json.dumps(extracted_patterns, indent=2) if extracted_patterns else "No patterns extracted"}
|
||||
|
||||
CONTENT CHARACTERISTICS (for keyword sophistication):
|
||||
{json.dumps(content_characteristics, indent=2) if content_characteristics else "Not available"}
|
||||
|
||||
VOCABULARY LEVEL:
|
||||
{vocabulary_level}
|
||||
|
||||
STYLE GUIDELINES (for query enhancement):
|
||||
{json.dumps(style_guidelines, indent=2) if style_guidelines else "Not available"}
|
||||
|
||||
EXTRACTED GUIDELINES:
|
||||
{json.dumps(extracted_guidelines, indent=2) if extracted_guidelines else "No guidelines extracted"}
|
||||
|
||||
=== PHASE 3: COMPREHENSIVE ANALYSIS & MAPPING ===
|
||||
|
||||
CRAWL ANALYSIS (Full Content Intelligence):
|
||||
{json.dumps(crawl_analysis, indent=2) if crawl_analysis else "No crawl analysis available"}
|
||||
|
||||
WRITING STYLE COMPREHENSIVE MAPPING:
|
||||
{json.dumps(writing_style_mapping, indent=2) if writing_style_mapping else "No style mapping available"}
|
||||
|
||||
CONTENT THEMES (Extracted from Website):
|
||||
{json.dumps(content_themes, indent=2) if content_themes else "No themes extracted"}
|
||||
|
||||
=== RESEARCH PERSONA GENERATION REQUIREMENTS ===
|
||||
|
||||
Generate a comprehensive research persona in JSON format with the following structure:
|
||||
|
||||
1. DEFAULT VALUES:
|
||||
- "default_industry": Extract from core_persona.industry, business_info.industry, or website_analysis target_audience. Use "General" only if none available.
|
||||
- "default_industry": Extract from core_persona.industry, business_info.industry, or website_analysis target_audience. If none available, infer from content patterns in website_analysis or research_preferences. Never use "General" - always provide a specific industry based on context.
|
||||
- "default_target_audience": Extract from core_persona.target_audience, website_analysis.target_audience, or business_info.target_audience. Be specific and descriptive.
|
||||
- "default_research_mode": Suggest "basic", "comprehensive", or "targeted" based on research_preferences.research_depth and content_type preferences.
|
||||
- "default_provider": Suggest "google" for news/trends, "exa" for academic/technical deep-dives, or "google" as default.
|
||||
- "default_research_mode": **PHASE 3 ENHANCEMENT** - Use comprehensive writing_style_mapping:
|
||||
* **PRIMARY**: Use writing_style_mapping.research_depth_preference (from comprehensive analysis)
|
||||
* **SECONDARY**: Map from writing_style.complexity:
|
||||
- If writing_style.complexity == "high": Use "comprehensive" (deep research needed)
|
||||
- If writing_style.complexity == "medium": Use "targeted" (balanced research)
|
||||
- If writing_style.complexity == "low": Use "basic" (quick research)
|
||||
* **FALLBACK**: Use research_preferences.research_depth if complexity not available
|
||||
* This ensures research depth matches the user's writing sophistication level and comprehensive style analysis
|
||||
- "default_provider": **PHASE 3 ENHANCEMENT** - Use writing_style_mapping.provider_preference:
|
||||
* **PRIMARY**: Use writing_style_mapping.provider_preference (from comprehensive style analysis)
|
||||
* **SECONDARY**: Suggest based on user's typical research needs:
|
||||
- Academic/research users: "exa" (semantic search, papers)
|
||||
- News/current events users: "tavily" (real-time, AI answers)
|
||||
- General business users: "exa" (better for content creation)
|
||||
* **DEFAULT**: "exa" (generally better for content creators)
|
||||
|
||||
2. KEYWORD INTELLIGENCE:
|
||||
- "suggested_keywords": Generate 8-12 keywords relevant to the user's industry, interests (from core_persona), and content goals.
|
||||
- "keyword_expansion_patterns": Create a dictionary mapping common keywords to expanded, industry-specific terms. Include 10-15 patterns like:
|
||||
{{"AI": ["healthcare AI", "medical AI", "clinical AI", "diagnostic AI"], "tools": ["medical devices", "clinical tools"], ...}}
|
||||
Focus on industry-specific terminology from the user's domain.
|
||||
- "suggested_keywords": **PHASE 1 ENHANCEMENT** - Prioritize extracted keywords from crawl_result:
|
||||
* First, use extracted_keywords from website content (top 8-10 most relevant)
|
||||
* Then, supplement with keywords from user's industry, interests (from core_persona), and content goals
|
||||
* Total: 8-12 keywords, with at least 50% from extracted_keywords if available
|
||||
* This ensures keywords reflect the user's actual content topics
|
||||
- "keyword_expansion_patterns": **PHASE 2 ENHANCEMENT** - Create a dictionary mapping common keywords to expanded, industry-specific terms based on vocabulary_level:
|
||||
* If vocabulary_level == "advanced": Use sophisticated, technical, industry-specific terminology
|
||||
Example: {{"AI": ["machine learning algorithms", "neural network architectures", "deep learning frameworks", "algorithmic intelligence systems"], "tools": ["enterprise software platforms", "integrated development environments", "cloud-native solutions"]}}
|
||||
* If vocabulary_level == "medium": Use balanced, professional terminology
|
||||
Example: {{"AI": ["artificial intelligence", "automated systems", "smart technology", "intelligent automation"], "tools": ["software solutions", "digital platforms", "business applications"]}}
|
||||
* If vocabulary_level == "simple": Use accessible, beginner-friendly terminology
|
||||
Example: {{"AI": ["smart technology", "automated tools", "helpful software", "intelligent helpers"], "tools": ["apps", "software", "platforms", "online services"]}}
|
||||
* Include 10-15 patterns, matching the user's vocabulary sophistication level
|
||||
* Focus on industry-specific terminology from the user's domain, but at the appropriate complexity level
|
||||
|
||||
3. DOMAIN EXPERTISE:
|
||||
3. PROVIDER-SPECIFIC OPTIMIZATION:
|
||||
- "suggested_exa_domains": List 4-6 authoritative domains for the user's industry (e.g., Healthcare: ["pubmed.gov", "nejm.org", "thelancet.com"]).
|
||||
- "suggested_exa_category": Suggest appropriate Exa category based on industry:
|
||||
- Healthcare/Science: "research paper"
|
||||
- Finance: "financial report"
|
||||
- Technology/Business: "company" or "news"
|
||||
- Social Media/Marketing: "tweet" or "linkedin profile"
|
||||
- Default: null (empty string for all categories)
|
||||
- "suggested_exa_search_type": Suggest Exa search algorithm:
|
||||
- Academic/research content: "neural" (semantic understanding)
|
||||
- Current news/trends: "fast" (speed optimized)
|
||||
- General research: "auto" (balanced)
|
||||
- Code/technical: "neural"
|
||||
- "suggested_tavily_topic": Choose based on content type:
|
||||
- Financial content: "finance"
|
||||
- News/current events: "news"
|
||||
- General research: "general"
|
||||
- "suggested_tavily_search_depth": Choose based on research needs:
|
||||
- Quick overview: "basic" (1 credit, faster)
|
||||
- In-depth analysis: "advanced" (2 credits, more comprehensive)
|
||||
- Breaking news: "fast" (speed optimized)
|
||||
- "suggested_tavily_include_answer": AI-generated answers:
|
||||
- For factual queries needing quick answers: "advanced"
|
||||
- For research summaries: "basic"
|
||||
- When building custom content: "false" (use raw results)
|
||||
- "suggested_tavily_time_range": Time filtering:
|
||||
- Breaking news: "day"
|
||||
- Recent developments: "week"
|
||||
- Industry analysis: "month"
|
||||
- Historical research: null (no time limit)
|
||||
- "suggested_tavily_raw_content_format": Raw content for LLM processing:
|
||||
- For blog content creation: "markdown" (structured)
|
||||
- For simple text extraction: "text"
|
||||
- No raw content needed: "false"
|
||||
- "provider_recommendations": Map use cases to best providers:
|
||||
{{"trends": "tavily", "deep_research": "exa", "factual": "google", "news": "tavily", "academic": "exa"}}
|
||||
|
||||
4. RESEARCH ANGLES:
|
||||
- "research_angles": Generate 5-8 alternative research angles/focuses based on:
|
||||
- User's pain points and challenges (from core_persona)
|
||||
- Industry trends and opportunities
|
||||
- Content goals (from research_preferences)
|
||||
- Audience interests (from core_persona.interests)
|
||||
Examples: "Compare {{topic}} tools", "{{topic}} ROI analysis", "Latest {{topic}} trends", etc.
|
||||
- "research_angles": **PHASE 2 ENHANCEMENT** - Generate 5-8 alternative research angles/focuses based on:
|
||||
* **PRIMARY SOURCE**: Extract from extracted_patterns (writing patterns from style_patterns):
|
||||
- If "comparison" in patterns: "Compare {{topic}} solutions and alternatives"
|
||||
- If "how-to" or "tutorial" in patterns: "Step-by-step guide to {{topic}} implementation"
|
||||
- If "case-study" or "case_study" in patterns: "Real-world {{topic}} case studies and success stories"
|
||||
- If "trend-analysis" or "trends" in patterns: "Latest {{topic}} trends and future predictions"
|
||||
- If "best-practices" or "best_practices" in patterns: "{{topic}} best practices and industry standards"
|
||||
- If "review" or "evaluation" in patterns: "{{topic}} review and evaluation criteria"
|
||||
- If "problem-solving" in patterns: "{{topic}} problem-solving strategies and solutions"
|
||||
* **SECONDARY SOURCES** (if patterns not available):
|
||||
- User's pain points and challenges (from core_persona.identity or core_persona)
|
||||
- Industry trends and opportunities (from website_analysis or business_info)
|
||||
- Content goals (from research_preferences.content_types)
|
||||
- Audience interests (from core_persona or website_analysis.target_audience)
|
||||
- Competitive landscape (if competitor_analysis exists, include competitive angles)
|
||||
* Make angles specific to the user's industry and actionable for content creation
|
||||
* Use the same language style and structure as the user's writing patterns
|
||||
|
||||
5. QUERY ENHANCEMENT:
|
||||
- "query_enhancement_rules": Create templates for improving vague user queries:
|
||||
{{"vague_ai": "Research: AI applications in {{industry}} for {{audience}}", "vague_tools": "Compare top {{industry}} tools", ...}}
|
||||
Include 5-8 enhancement patterns.
|
||||
- "query_enhancement_rules": **PHASE 2 ENHANCEMENT** - Create templates for improving vague user queries based on extracted_guidelines:
|
||||
* **PRIMARY SOURCE**: Use extracted_guidelines (from style_guidelines) to create enhancement rules:
|
||||
- If guidelines include "Use specific examples": {{"vague_query": "Research: {{query}} with specific examples and case studies"}}
|
||||
- If guidelines include "Include data points" or "statistics": {{"general_query": "Research: {{query}} including statistics, metrics, and data analysis"}}
|
||||
- If guidelines include "Reference industry standards": {{"basic_query": "Research: {{query}} with industry benchmarks and best practices"}}
|
||||
- If guidelines include "Cite authoritative sources": {{"factual_query": "Research: {{query}} from authoritative sources and expert opinions"}}
|
||||
- If guidelines include "Provide actionable insights": {{"theoretical_query": "Research: {{query}} with actionable strategies and implementation steps"}}
|
||||
- If guidelines include "Compare alternatives": {{"single_item_query": "Research: Compare {{query}} alternatives and evaluate options"}}
|
||||
* **FALLBACK PATTERNS** (if guidelines not available):
|
||||
{{"vague_ai": "Research: AI applications in {{industry}} for {{audience}}", "vague_tools": "Compare top {{industry}} tools", "vague_trends": "Research latest {{industry}} trends and developments", ...}}
|
||||
* Include 5-8 enhancement patterns
|
||||
* Match the enhancement style to the user's writing guidelines and preferences
|
||||
|
||||
6. RECOMMENDED PRESETS:
|
||||
- "recommended_presets": Generate 3-5 personalized research preset templates. Each preset should include:
|
||||
- name: Descriptive name (e.g., "{{Industry}} Trends", "{{Audience}} Insights")
|
||||
- keywords: Research query string
|
||||
- industry: User's industry
|
||||
- target_audience: User's target audience
|
||||
- research_mode: "basic", "comprehensive", or "targeted"
|
||||
- config: Complete ResearchConfig object with appropriate settings
|
||||
- description: Brief explanation of what this preset researches
|
||||
Make presets relevant to the user's specific industry, audience, and content goals.
|
||||
- "recommended_presets": **PHASE 3 ENHANCEMENT** - Generate 3-5 personalized research preset templates using comprehensive analysis:
|
||||
* **USE CONTENT THEMES**: If content_themes available, create at least one preset per major theme (up to 3 themes)
|
||||
- Example: If themes include ["AI automation", "content marketing", "SEO strategies"], create presets for each
|
||||
- Use theme names in preset keywords: "Research latest {theme} trends and best practices"
|
||||
* **USE CRAWL ANALYSIS**: Leverage crawl_analysis.content_categories and crawl_analysis.main_topics for preset generation
|
||||
- Create presets that match the user's actual website content categories
|
||||
- Use main_topics for preset keywords and descriptions
|
||||
* **CONTENT TYPE BASED**: Generate presets based on content_type (from Phase 1):
|
||||
* **Content-Type-Specific Presets**: Use content_type.primary_type and content_type.secondary_types to create presets:
|
||||
- If primary_type == "blog": Create "Blog Topic Research" preset with trending topics
|
||||
- If primary_type == "article": Create "Article Research" preset with in-depth analysis
|
||||
- If primary_type == "case_study": Create "Case Study Research" preset with real-world examples
|
||||
- If primary_type == "tutorial": Create "Tutorial Research" preset with step-by-step guides
|
||||
- If "tutorial" in secondary_types: Add "How-To Guide Research" preset
|
||||
- If "comparison" in secondary_types or style_patterns: Add "Comparison Research" preset
|
||||
- If content_type.purpose == "thought_leadership": Create "Thought Leadership Research" with expert insights
|
||||
- If content_type.purpose == "education": Create "Educational Content Research" preset
|
||||
* **Use Extracted Topics**: If extracted_topics available, create at least one preset using actual website topics:
|
||||
- "Latest {extracted_topic} Trends" preset
|
||||
- "{extracted_topic} Best Practices" preset
|
||||
* Each preset should include:
|
||||
- name: Descriptive, action-oriented name that clearly indicates what research will be done
|
||||
* Use research_angles as inspiration for preset names (e.g., "Compare {Industry} Tools", "{Industry} ROI Analysis")
|
||||
* If competitor_analysis exists, create at least one competitive analysis preset (e.g., "Competitive Landscape Analysis")
|
||||
* Make names specific and actionable, not generic
|
||||
* **NEW**: Include content type in name when relevant (e.g., "Blog: {Industry} Trends", "Tutorial: {Topic} Guide")
|
||||
- keywords: Research query string that is:
|
||||
* **NEW**: Use extracted_topics and extracted_keywords when available for more relevant queries
|
||||
* Specific and detailed (not vague like "AI tools")
|
||||
* Industry-focused (includes industry context)
|
||||
* Audience-aware (considers target audience needs)
|
||||
* Actionable (user can immediately understand what research will provide)
|
||||
* Examples: "Research latest AI-powered marketing automation platforms for B2B SaaS companies" (GOOD)
|
||||
* Avoid: "AI tools" or "marketing research" (TOO VAGUE)
|
||||
- industry: User's industry (from business_info or inferred)
|
||||
- target_audience: User's target audience (from business_info or inferred)
|
||||
- research_mode: "basic", "comprehensive", or "targeted" based on:
|
||||
* **NEW**: Also consider content_type.purpose:
|
||||
- "thought_leadership" → "comprehensive" (needs deep research)
|
||||
- "education" → "comprehensive" (needs thorough coverage)
|
||||
- "marketing" → "targeted" (needs specific insights)
|
||||
- "entertainment" → "basic" (needs quick facts)
|
||||
* "comprehensive" for deep analysis, trends, competitive research
|
||||
* "targeted" for specific questions, quick insights
|
||||
* "basic" for simple fact-finding
|
||||
- config: Complete ResearchConfig object with:
|
||||
* provider: Use suggested_exa_category to determine if "exa" or "tavily" is better
|
||||
* exa_category: Use suggested_exa_category if available
|
||||
* exa_include_domains: Use suggested_exa_domains if available (limit to 3-5 most relevant)
|
||||
* exa_search_type: Use suggested_exa_search_type if available
|
||||
* max_sources: 15-25 for comprehensive, 10-15 for targeted, 8-12 for basic
|
||||
* include_competitors: true if competitor_analysis exists and preset is about competitive research
|
||||
* include_trends: true for trend-focused presets
|
||||
* include_statistics: true for data-driven research
|
||||
* include_expert_quotes: true for comprehensive research or thought_leadership content
|
||||
- description: Brief (1-2 sentences) explaining what this preset researches and why it's valuable
|
||||
- icon: Optional emoji that represents the preset (e.g., "📊" for trends, "🎯" for targeted, "🔍" for analysis, "📝" for blog, "📚" for tutorial)
|
||||
- gradient: Optional CSS gradient for visual appeal
|
||||
|
||||
PRESET GENERATION GUIDELINES:
|
||||
- **PHASE 1 PRIORITY**: Create presets that match the user's actual content types (from content_type)
|
||||
- Use extracted_topics to create presets based on actual website content
|
||||
- Create presets that the user would actually want to use for their content creation
|
||||
- Use research_angles to inspire preset names and keywords
|
||||
- If competitor_analysis has data, create at least one competitive analysis preset
|
||||
- Make each preset unique with different research focus (trends, tools, best practices, competitive, etc.)
|
||||
- Ensure keywords are detailed enough to generate meaningful research
|
||||
- Vary research_mode across presets to offer different depth levels
|
||||
- Use industry-specific terminology in preset names and keywords
|
||||
|
||||
7. RESEARCH PREFERENCES:
|
||||
- "research_preferences": Extract and structure research preferences from onboarding:
|
||||
@@ -109,8 +314,19 @@ Return a valid JSON object matching this exact structure:
|
||||
"keyword_expansion_patterns": {{
|
||||
"keyword": ["expansion1", "expansion2", ...]
|
||||
}},
|
||||
"suggested_exa_domains": ["domain1.com", "domain2.com", ...],
|
||||
"suggested_exa_category": "string or null",
|
||||
"suggested_exa_domains": ["domain1.com", "domain2.com", ...],
|
||||
"suggested_exa_category": "string or null",
|
||||
"suggested_exa_search_type": "auto | neural | keyword | fast | deep",
|
||||
"suggested_tavily_topic": "general | news | finance",
|
||||
"suggested_tavily_search_depth": "basic | advanced | fast | ultra-fast",
|
||||
"suggested_tavily_include_answer": "false | basic | advanced",
|
||||
"suggested_tavily_time_range": "day | week | month | year or null",
|
||||
"suggested_tavily_raw_content_format": "false | markdown | text",
|
||||
"provider_recommendations": {{
|
||||
"trends": "tavily",
|
||||
"deep_research": "exa",
|
||||
"factual": "google"
|
||||
}},
|
||||
"research_angles": ["angle1", "angle2", ...],
|
||||
"query_enhancement_rules": {{
|
||||
"pattern": "template"
|
||||
@@ -150,18 +366,291 @@ Return a valid JSON object matching this exact structure:
|
||||
=== IMPORTANT INSTRUCTIONS ===
|
||||
|
||||
1. Be highly specific and personalized - use actual data from the user's business, persona, and preferences.
|
||||
2. Avoid generic suggestions - every field should reflect the user's unique context.
|
||||
3. For industries not clearly identified, infer from website_analysis.content_characteristics or writing_style.
|
||||
4. Ensure all suggested keywords, domains, and angles are relevant to the user's industry and audience.
|
||||
5. Generate realistic, actionable presets that the user would actually want to use.
|
||||
6. Confidence score should reflect data richness (0-100): higher if rich onboarding data, lower if minimal data.
|
||||
7. Return ONLY valid JSON - no markdown formatting, no explanatory text.
|
||||
2. NEVER use "General" for industry or target_audience - always infer or create specific categories based on available context.
|
||||
3. For minimal data scenarios:
|
||||
- If industry is unclear, infer from research_preferences.content_types or website_analysis.content_characteristics
|
||||
- If target_audience is unclear, infer from writing_style patterns or content goals
|
||||
- Use business_info to fill gaps when persona_data is incomplete
|
||||
4. Generate industry-specific intelligence even with limited data:
|
||||
- For content creators: assume "Content Marketing" or "Digital Publishing"
|
||||
- For business users: assume "Business Consulting" or "Professional Services"
|
||||
- For technical users: assume "Technology" or "Software Development"
|
||||
5. Ensure all suggested keywords, domains, and angles are relevant to the user's industry and audience.
|
||||
6. Generate realistic, actionable presets that the user would actually want to use.
|
||||
7. Confidence score should reflect data richness (0-100): higher if rich onboarding data, lower if minimal data.
|
||||
8. Return ONLY valid JSON - no markdown formatting, no explanatory text.
|
||||
|
||||
Generate the research persona now:
|
||||
"""
|
||||
|
||||
return prompt
|
||||
|
||||
def _extract_topics_from_crawl(self, crawl_result: Dict[str, Any]) -> List[str]:
|
||||
"""
|
||||
Extract topics from crawl_result JSON data.
|
||||
|
||||
Args:
|
||||
crawl_result: Dictionary containing crawled website data
|
||||
|
||||
Returns:
|
||||
List of extracted topics (max 15)
|
||||
"""
|
||||
topics = []
|
||||
|
||||
if not crawl_result:
|
||||
return topics
|
||||
|
||||
try:
|
||||
# Try to extract from common crawl result structures
|
||||
# Method 1: Direct topics field
|
||||
if isinstance(crawl_result.get('topics'), list):
|
||||
topics.extend(crawl_result['topics'][:10])
|
||||
|
||||
# Method 2: Extract from headings
|
||||
if isinstance(crawl_result.get('headings'), list):
|
||||
headings = crawl_result['headings']
|
||||
# Filter out common non-topic headings
|
||||
filtered_headings = [
|
||||
h for h in headings[:15]
|
||||
if h and len(h.strip()) > 3
|
||||
and h.lower() not in ['home', 'about', 'contact', 'menu', 'navigation', 'footer', 'header']
|
||||
]
|
||||
topics.extend(filtered_headings)
|
||||
|
||||
# Method 3: Extract from page titles
|
||||
if isinstance(crawl_result.get('titles'), list):
|
||||
titles = crawl_result['titles']
|
||||
topics.extend([t for t in titles[:10] if t and len(t.strip()) > 3])
|
||||
|
||||
# Method 4: Extract from content sections
|
||||
if isinstance(crawl_result.get('sections'), list):
|
||||
sections = crawl_result['sections']
|
||||
for section in sections[:10]:
|
||||
if isinstance(section, dict):
|
||||
section_title = section.get('title') or section.get('heading')
|
||||
if section_title and len(section_title.strip()) > 3:
|
||||
topics.append(section_title)
|
||||
|
||||
# Method 5: Extract from metadata
|
||||
if isinstance(crawl_result.get('metadata'), dict):
|
||||
meta = crawl_result['metadata']
|
||||
if meta.get('title'):
|
||||
topics.append(meta['title'])
|
||||
if isinstance(meta.get('keywords'), list):
|
||||
topics.extend(meta['keywords'][:5])
|
||||
|
||||
# Remove duplicates and clean
|
||||
unique_topics = []
|
||||
seen = set()
|
||||
for topic in topics:
|
||||
if topic and isinstance(topic, str):
|
||||
cleaned = topic.strip()
|
||||
if cleaned and cleaned.lower() not in seen:
|
||||
seen.add(cleaned.lower())
|
||||
unique_topics.append(cleaned)
|
||||
|
||||
return unique_topics[:15] # Limit to 15 topics
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error extracting topics from crawl_result: {e}")
|
||||
return []
|
||||
|
||||
def _extract_keywords_from_crawl(self, crawl_result: Dict[str, Any]) -> List[str]:
|
||||
"""
|
||||
Extract keywords from crawl_result JSON data.
|
||||
|
||||
Args:
|
||||
crawl_result: Dictionary containing crawled website data
|
||||
|
||||
Returns:
|
||||
List of extracted keywords (max 20)
|
||||
"""
|
||||
keywords = []
|
||||
|
||||
if not crawl_result:
|
||||
return keywords
|
||||
|
||||
try:
|
||||
# Method 1: Direct keywords field
|
||||
if isinstance(crawl_result.get('keywords'), list):
|
||||
keywords.extend(crawl_result['keywords'][:15])
|
||||
|
||||
# Method 2: Extract from metadata keywords
|
||||
if isinstance(crawl_result.get('metadata'), dict):
|
||||
meta = crawl_result['metadata']
|
||||
if isinstance(meta.get('keywords'), list):
|
||||
keywords.extend(meta['keywords'][:10])
|
||||
if meta.get('description'):
|
||||
# Extract potential keywords from description (simple word extraction)
|
||||
desc = meta['description']
|
||||
words = [w.strip() for w in desc.split() if len(w.strip()) > 4]
|
||||
keywords.extend(words[:5])
|
||||
|
||||
# Method 3: Extract from tags
|
||||
if isinstance(crawl_result.get('tags'), list):
|
||||
keywords.extend(crawl_result['tags'][:10])
|
||||
|
||||
# Method 4: Extract from content (simple frequency-based, if available)
|
||||
if isinstance(crawl_result.get('content'), str):
|
||||
content = crawl_result['content']
|
||||
# Simple extraction: words that appear multiple times and are > 4 chars
|
||||
words = content.lower().split()
|
||||
word_freq = {}
|
||||
for word in words:
|
||||
cleaned = ''.join(c for c in word if c.isalnum())
|
||||
if len(cleaned) > 4:
|
||||
word_freq[cleaned] = word_freq.get(cleaned, 0) + 1
|
||||
|
||||
# Get top keywords by frequency
|
||||
sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)
|
||||
keywords.extend([word for word, freq in sorted_words[:10] if freq > 1])
|
||||
|
||||
# Remove duplicates and clean
|
||||
unique_keywords = []
|
||||
seen = set()
|
||||
for keyword in keywords:
|
||||
if keyword and isinstance(keyword, str):
|
||||
cleaned = keyword.strip().lower()
|
||||
if cleaned and len(cleaned) > 2 and cleaned not in seen:
|
||||
seen.add(cleaned)
|
||||
unique_keywords.append(keyword.strip())
|
||||
|
||||
return unique_keywords[:20] # Limit to 20 keywords
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error extracting keywords from crawl_result: {e}")
|
||||
return []
|
||||
|
||||
def _extract_writing_patterns(self, style_patterns: Dict[str, Any]) -> List[str]:
|
||||
"""
|
||||
Extract writing patterns from style_patterns JSON data.
|
||||
|
||||
Args:
|
||||
style_patterns: Dictionary containing writing patterns analysis
|
||||
|
||||
Returns:
|
||||
List of extracted patterns (max 10)
|
||||
"""
|
||||
patterns = []
|
||||
|
||||
if not style_patterns:
|
||||
return patterns
|
||||
|
||||
try:
|
||||
# Method 1: Direct patterns field
|
||||
if isinstance(style_patterns.get('patterns'), list):
|
||||
patterns.extend(style_patterns['patterns'][:10])
|
||||
|
||||
# Method 2: Common patterns field
|
||||
if isinstance(style_patterns.get('common_patterns'), list):
|
||||
patterns.extend(style_patterns['common_patterns'][:10])
|
||||
|
||||
# Method 3: Writing patterns field
|
||||
if isinstance(style_patterns.get('writing_patterns'), list):
|
||||
patterns.extend(style_patterns['writing_patterns'][:10])
|
||||
|
||||
# Method 4: Content structure patterns
|
||||
if isinstance(style_patterns.get('content_structure'), dict):
|
||||
structure = style_patterns['content_structure']
|
||||
if isinstance(structure.get('patterns'), list):
|
||||
patterns.extend(structure['patterns'][:5])
|
||||
|
||||
# Method 5: Extract from analysis field
|
||||
if isinstance(style_patterns.get('analysis'), dict):
|
||||
analysis = style_patterns['analysis']
|
||||
if isinstance(analysis.get('identified_patterns'), list):
|
||||
patterns.extend(analysis['identified_patterns'][:10])
|
||||
|
||||
# Normalize patterns (lowercase, remove duplicates)
|
||||
normalized_patterns = []
|
||||
seen = set()
|
||||
for pattern in patterns:
|
||||
if pattern and isinstance(pattern, str):
|
||||
cleaned = pattern.strip().lower().replace('_', '-').replace(' ', '-')
|
||||
if cleaned and cleaned not in seen:
|
||||
seen.add(cleaned)
|
||||
normalized_patterns.append(cleaned)
|
||||
|
||||
return normalized_patterns[:10] # Limit to 10 patterns
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error extracting writing patterns: {e}")
|
||||
return []
|
||||
|
||||
def _extract_style_guidelines(self, style_guidelines: Dict[str, Any]) -> List[str]:
|
||||
"""
|
||||
Extract style guidelines from style_guidelines JSON data.
|
||||
|
||||
Args:
|
||||
style_guidelines: Dictionary containing generated style guidelines
|
||||
|
||||
Returns:
|
||||
List of extracted guidelines (max 15)
|
||||
"""
|
||||
guidelines = []
|
||||
|
||||
if not style_guidelines:
|
||||
return guidelines
|
||||
|
||||
try:
|
||||
# Method 1: Direct guidelines field
|
||||
if isinstance(style_guidelines.get('guidelines'), list):
|
||||
guidelines.extend(style_guidelines['guidelines'][:15])
|
||||
|
||||
# Method 2: Recommendations field
|
||||
if isinstance(style_guidelines.get('recommendations'), list):
|
||||
guidelines.extend(style_guidelines['recommendations'][:15])
|
||||
|
||||
# Method 3: Best practices field
|
||||
if isinstance(style_guidelines.get('best_practices'), list):
|
||||
guidelines.extend(style_guidelines['best_practices'][:10])
|
||||
|
||||
# Method 4: Tone recommendations
|
||||
if isinstance(style_guidelines.get('tone_recommendations'), list):
|
||||
guidelines.extend(style_guidelines['tone_recommendations'][:5])
|
||||
|
||||
# Method 5: Structure guidelines
|
||||
if isinstance(style_guidelines.get('structure_guidelines'), list):
|
||||
guidelines.extend(style_guidelines['structure_guidelines'][:5])
|
||||
|
||||
# Method 6: Vocabulary suggestions
|
||||
if isinstance(style_guidelines.get('vocabulary_suggestions'), list):
|
||||
guidelines.extend(style_guidelines['vocabulary_suggestions'][:5])
|
||||
|
||||
# Method 7: Engagement tips
|
||||
if isinstance(style_guidelines.get('engagement_tips'), list):
|
||||
guidelines.extend(style_guidelines['engagement_tips'][:5])
|
||||
|
||||
# Method 8: Audience considerations
|
||||
if isinstance(style_guidelines.get('audience_considerations'), list):
|
||||
guidelines.extend(style_guidelines['audience_considerations'][:5])
|
||||
|
||||
# Method 9: SEO optimization (if available)
|
||||
if isinstance(style_guidelines.get('seo_optimization'), list):
|
||||
guidelines.extend(style_guidelines['seo_optimization'][:3])
|
||||
|
||||
# Method 10: Conversion optimization (if available)
|
||||
if isinstance(style_guidelines.get('conversion_optimization'), list):
|
||||
guidelines.extend(style_guidelines['conversion_optimization'][:3])
|
||||
|
||||
# Remove duplicates and clean
|
||||
unique_guidelines = []
|
||||
seen = set()
|
||||
for guideline in guidelines:
|
||||
if guideline and isinstance(guideline, str):
|
||||
cleaned = guideline.strip()
|
||||
# Normalize for comparison (lowercase, remove extra spaces)
|
||||
normalized = ' '.join(cleaned.lower().split())
|
||||
if cleaned and normalized not in seen and len(cleaned) > 5:
|
||||
seen.add(normalized)
|
||||
unique_guidelines.append(cleaned)
|
||||
|
||||
return unique_guidelines[:15] # Limit to 15 guidelines
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error extracting style guidelines: {e}")
|
||||
return []
|
||||
|
||||
def get_json_schema(self) -> Dict[str, Any]:
|
||||
"""Return JSON schema for structured LLM response."""
|
||||
# This will be used with llm_text_gen(json_struct=...)
|
||||
|
||||
@@ -367,16 +367,53 @@ class ResearchPersonaService:
|
||||
if demographics:
|
||||
business_info['target_audience'] = demographics if isinstance(demographics, str) else str(demographics)
|
||||
|
||||
# Check if we have enough data
|
||||
if not website_analysis and not persona_data_dict:
|
||||
logger.warning(f"Insufficient onboarding data for user {user_id}")
|
||||
# Check if we have enough data - be more lenient since we can infer from minimal data
|
||||
# We need at least some basic information to generate a meaningful persona
|
||||
has_basic_data = bool(
|
||||
website_analysis or
|
||||
persona_data_dict or
|
||||
research_prefs.get('content_types') or
|
||||
business_info.get('industry')
|
||||
)
|
||||
|
||||
if not has_basic_data:
|
||||
logger.warning(f"Insufficient onboarding data for user {user_id} - no basic data found")
|
||||
return None
|
||||
|
||||
# If we have minimal data, add intelligent defaults to help the AI
|
||||
if not business_info.get('industry'):
|
||||
# Try to infer industry from research preferences or content types
|
||||
content_types = research_prefs.get('content_types', [])
|
||||
if 'blog' in content_types or 'article' in content_types:
|
||||
business_info['industry'] = 'Content Marketing'
|
||||
business_info['inferred'] = True
|
||||
elif 'social_media' in content_types:
|
||||
business_info['industry'] = 'Social Media Marketing'
|
||||
business_info['inferred'] = True
|
||||
elif 'video' in content_types:
|
||||
business_info['industry'] = 'Video Content Creation'
|
||||
business_info['inferred'] = True
|
||||
|
||||
if not business_info.get('target_audience'):
|
||||
# Default to professionals for content creators
|
||||
business_info['target_audience'] = 'Professionals and content consumers'
|
||||
business_info['inferred'] = True
|
||||
|
||||
# Get competitor analysis data (if available)
|
||||
competitor_analysis = None
|
||||
try:
|
||||
competitor_analysis = self.onboarding_service.get_competitor_analysis(user_id, self.db)
|
||||
if competitor_analysis:
|
||||
logger.info(f"Found {len(competitor_analysis)} competitors for research persona generation")
|
||||
except Exception as e:
|
||||
logger.debug(f"Could not retrieve competitor analysis for persona generation: {e}")
|
||||
|
||||
return {
|
||||
"website_analysis": website_analysis,
|
||||
"persona_data": persona_data_dict,
|
||||
"research_preferences": research_prefs,
|
||||
"business_info": business_info
|
||||
"business_info": business_info,
|
||||
"competitor_analysis": competitor_analysis # Add competitor data for better preset generation
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
|
||||
15
backend/services/video_studio/__init__.py
Normal file
15
backend/services/video_studio/__init__.py
Normal file
@@ -0,0 +1,15 @@
|
||||
"""
|
||||
Video Studio Services
|
||||
|
||||
Provides AI-powered video generation capabilities including:
|
||||
- Text-to-video generation
|
||||
- Image-to-video transformation
|
||||
- Avatar and face generation
|
||||
- Video enhancement
|
||||
|
||||
Integrates with WaveSpeed AI models for high-quality results.
|
||||
"""
|
||||
|
||||
from .video_studio_service import VideoStudioService
|
||||
|
||||
__all__ = ["VideoStudioService"]
|
||||
142
backend/services/video_studio/add_audio_to_video_service.py
Normal file
142
backend/services/video_studio/add_audio_to_video_service.py
Normal file
@@ -0,0 +1,142 @@
|
||||
"""
|
||||
Add Audio to Video service for Video Studio.
|
||||
|
||||
Supports multiple models for adding audio to videos:
|
||||
1. Hunyuan Video Foley - Generate realistic Foley and ambient audio from video
|
||||
2. Think Sound - (To be added)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
from typing import Dict, Any, Optional, Callable
|
||||
from fastapi import HTTPException
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
from ..wavespeed.client import WaveSpeedClient
|
||||
|
||||
logger = get_service_logger("video_studio.add_audio_to_video")
|
||||
|
||||
|
||||
class AddAudioToVideoService:
|
||||
"""Service for adding audio to video operations."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize Add Audio to Video service."""
|
||||
self.wavespeed_client = WaveSpeedClient()
|
||||
logger.info("[AddAudioToVideo] Service initialized")
|
||||
|
||||
def calculate_cost(self, model: str, duration: float = 10.0) -> float:
|
||||
"""
|
||||
Calculate cost for adding audio to video operation.
|
||||
|
||||
Args:
|
||||
model: Model to use ("hunyuan-video-foley" or "think-sound")
|
||||
duration: Video duration in seconds (for Hunyuan Video Foley)
|
||||
|
||||
Returns:
|
||||
Cost in USD
|
||||
"""
|
||||
if model == "hunyuan-video-foley":
|
||||
# Estimated pricing: $0.02/s (similar to other video processing models)
|
||||
# Minimum charge: 5 seconds
|
||||
# Maximum: 600 seconds (10 minutes)
|
||||
cost_per_second = 0.02
|
||||
billed_duration = max(5.0, min(duration, 600.0))
|
||||
return cost_per_second * billed_duration
|
||||
elif model == "think-sound":
|
||||
# Think Sound pricing: $0.05 per video (flat rate)
|
||||
return 0.05
|
||||
else:
|
||||
# Default fallback
|
||||
cost_per_second = 0.02
|
||||
billed_duration = max(5.0, min(duration, 600.0))
|
||||
return cost_per_second * billed_duration
|
||||
|
||||
async def add_audio(
|
||||
self,
|
||||
video_data: bytes,
|
||||
model: str = "hunyuan-video-foley",
|
||||
prompt: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
user_id: str = None,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Add audio to video using AI models.
|
||||
|
||||
Args:
|
||||
video_data: Source video as bytes
|
||||
model: Model to use ("hunyuan-video-foley" or "think-sound")
|
||||
prompt: Optional text prompt describing desired sounds (Hunyuan Video Foley)
|
||||
seed: Random seed for reproducibility (-1 for random)
|
||||
user_id: User ID for tracking
|
||||
progress_callback: Optional callback for progress updates
|
||||
|
||||
Returns:
|
||||
Dict with processed video_url, cost, and metadata
|
||||
"""
|
||||
try:
|
||||
logger.info(f"[AddAudioToVideo] Audio addition request: user={user_id}, model={model}, has_prompt={prompt is not None}")
|
||||
|
||||
# Convert video to base64 data URI
|
||||
video_b64 = base64.b64encode(video_data).decode('utf-8')
|
||||
video_uri = f"data:video/mp4;base64,{video_b64}"
|
||||
|
||||
# Handle different models
|
||||
if model == "hunyuan-video-foley":
|
||||
# Use Hunyuan Video Foley
|
||||
processed_video_bytes = await asyncio.to_thread(
|
||||
self.wavespeed_client.hunyuan_video_foley,
|
||||
video=video_uri,
|
||||
prompt=prompt,
|
||||
seed=seed if seed is not None else -1,
|
||||
enable_sync_mode=False, # Always use async with polling
|
||||
timeout=600, # 10 minutes max for long videos
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
else:
|
||||
# Think Sound or other models (to be implemented)
|
||||
logger.warning(f"[AddAudioToVideo] Model '{model}' not yet implemented")
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Model '{model}' is not yet supported. Currently only 'hunyuan-video-foley' is available."
|
||||
)
|
||||
|
||||
# Estimate video duration (rough estimate: 1MB ≈ 1 second at 1080p)
|
||||
# Only needed for Hunyuan Video Foley (per-second pricing)
|
||||
estimated_duration = max(5, len(video_data) / (1024 * 1024)) if model == "hunyuan-video-foley" else 10.0
|
||||
cost = self.calculate_cost(model, estimated_duration)
|
||||
|
||||
# Save processed video
|
||||
from .video_studio_service import VideoStudioService
|
||||
video_service = VideoStudioService()
|
||||
save_result = video_service._save_video_file(
|
||||
video_bytes=processed_video_bytes,
|
||||
operation_type="add_audio",
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
logger.info(f"[AddAudioToVideo] Audio addition successful: user={user_id}, model={model}, cost=${cost:.4f}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": save_result["file_url"],
|
||||
"video_bytes": processed_video_bytes,
|
||||
"cost": cost,
|
||||
"model_used": model,
|
||||
"metadata": {
|
||||
"original_size": len(video_data),
|
||||
"processed_size": len(processed_video_bytes),
|
||||
"estimated_duration": estimated_duration,
|
||||
"has_prompt": prompt is not None,
|
||||
},
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[AddAudioToVideo] Audio addition failed: {e}", exc_info=True)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Adding audio to video failed: {str(e)}"
|
||||
)
|
||||
122
backend/services/video_studio/avatar_service.py
Normal file
122
backend/services/video_studio/avatar_service.py
Normal file
@@ -0,0 +1,122 @@
|
||||
"""
|
||||
Avatar Studio Service
|
||||
|
||||
Service for creating talking avatars using InfiniteTalk and Hunyuan Avatar.
|
||||
Supports both models with automatic selection or explicit model choice.
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, Optional
|
||||
from fastapi import HTTPException
|
||||
from loguru import logger
|
||||
|
||||
from services.image_studio.infinitetalk_adapter import InfiniteTalkService
|
||||
from services.video_studio.hunyuan_avatar_adapter import HunyuanAvatarService
|
||||
from utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.avatar")
|
||||
|
||||
|
||||
class AvatarStudioService:
|
||||
"""Service for Avatar Studio operations using InfiniteTalk and Hunyuan Avatar."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize Avatar Studio service."""
|
||||
self.infinitetalk_service = InfiniteTalkService()
|
||||
self.hunyuan_avatar_service = HunyuanAvatarService()
|
||||
logger.info("[AvatarStudio] Service initialized with InfiniteTalk and Hunyuan Avatar")
|
||||
|
||||
async def create_talking_avatar(
|
||||
self,
|
||||
image_base64: str,
|
||||
audio_base64: str,
|
||||
resolution: str = "720p",
|
||||
prompt: Optional[str] = None,
|
||||
mask_image_base64: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
user_id: str = "video_studio",
|
||||
model: str = "infinitetalk",
|
||||
progress_callback: Optional[callable] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Create talking avatar video using InfiniteTalk or Hunyuan Avatar.
|
||||
|
||||
Args:
|
||||
image_base64: Person image in base64 or data URI
|
||||
audio_base64: Audio file in base64 or data URI
|
||||
resolution: Output resolution (480p or 720p)
|
||||
prompt: Optional prompt for expression/style
|
||||
mask_image_base64: Optional mask for animatable regions (InfiniteTalk only)
|
||||
seed: Optional random seed
|
||||
user_id: User ID for tracking
|
||||
model: Model to use - "infinitetalk" (default) or "hunyuan-avatar"
|
||||
progress_callback: Optional progress callback function
|
||||
|
||||
Returns:
|
||||
Dictionary with video_bytes, metadata, cost, and file info
|
||||
"""
|
||||
logger.info(
|
||||
f"[AvatarStudio] Creating talking avatar: user={user_id}, resolution={resolution}, model={model}"
|
||||
)
|
||||
|
||||
try:
|
||||
if model == "hunyuan-avatar":
|
||||
# Use Hunyuan Avatar (doesn't support mask_image)
|
||||
result = await self.hunyuan_avatar_service.create_talking_avatar(
|
||||
image_base64=image_base64,
|
||||
audio_base64=audio_base64,
|
||||
resolution=resolution,
|
||||
prompt=prompt,
|
||||
seed=seed,
|
||||
user_id=user_id,
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
else:
|
||||
# Default to InfiniteTalk
|
||||
result = await self.infinitetalk_service.create_talking_avatar(
|
||||
image_base64=image_base64,
|
||||
audio_base64=audio_base64,
|
||||
resolution=resolution,
|
||||
prompt=prompt,
|
||||
mask_image_base64=mask_image_base64,
|
||||
seed=seed,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"[AvatarStudio] ✅ Talking avatar created: "
|
||||
f"model={model}, resolution={resolution}, duration={result.get('duration', 0)}s, "
|
||||
f"cost=${result.get('cost', 0):.2f}"
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[AvatarStudio] ❌ Error creating talking avatar: {str(e)}", exc_info=True)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to create talking avatar: {str(e)}"
|
||||
)
|
||||
|
||||
def calculate_cost_estimate(
|
||||
self,
|
||||
resolution: str,
|
||||
estimated_duration: float,
|
||||
model: str = "infinitetalk",
|
||||
) -> float:
|
||||
"""
|
||||
Calculate estimated cost for talking avatar generation.
|
||||
|
||||
Args:
|
||||
resolution: Output resolution (480p or 720p)
|
||||
estimated_duration: Estimated video duration in seconds
|
||||
model: Model to use - "infinitetalk" (default) or "hunyuan-avatar"
|
||||
|
||||
Returns:
|
||||
Estimated cost in USD
|
||||
"""
|
||||
if model == "hunyuan-avatar":
|
||||
return self.hunyuan_avatar_service.calculate_cost(resolution, estimated_duration)
|
||||
else:
|
||||
return self.infinitetalk_service.calculate_cost(resolution, estimated_duration)
|
||||
206
backend/services/video_studio/face_swap_service.py
Normal file
206
backend/services/video_studio/face_swap_service.py
Normal file
@@ -0,0 +1,206 @@
|
||||
"""
|
||||
Face Swap service for Video Studio.
|
||||
|
||||
Supports two models:
|
||||
1. MoCha (wavespeed-ai/wan-2.1/mocha) - Character replacement with motion preservation
|
||||
2. Video Face Swap (wavespeed-ai/video-face-swap) - Simple face swap with multi-face support
|
||||
"""
|
||||
|
||||
import base64
|
||||
from typing import Dict, Any, Optional, Callable
|
||||
from fastapi import HTTPException
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
from ..wavespeed.client import WaveSpeedClient
|
||||
|
||||
logger = get_service_logger("video_studio.face_swap")
|
||||
|
||||
|
||||
class FaceSwapService:
|
||||
"""Service for face/character swap operations."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize Face Swap service."""
|
||||
self.wavespeed_client = WaveSpeedClient()
|
||||
logger.info("[FaceSwap] Service initialized")
|
||||
|
||||
def calculate_cost(self, model: str, resolution: Optional[str] = None, duration: float = 10.0) -> float:
|
||||
"""
|
||||
Calculate cost for face swap operation.
|
||||
|
||||
Args:
|
||||
model: Model to use ("mocha" or "video-face-swap")
|
||||
resolution: Output resolution for MoCha ("480p" or "720p"), ignored for video-face-swap
|
||||
duration: Video duration in seconds
|
||||
|
||||
Returns:
|
||||
Cost in USD
|
||||
"""
|
||||
if model == "video-face-swap":
|
||||
# Video Face Swap pricing: $0.01/s
|
||||
# Minimum charge: 5 seconds
|
||||
# Maximum: 600 seconds (10 minutes)
|
||||
cost_per_second = 0.01
|
||||
billed_duration = max(5.0, min(duration, 600.0))
|
||||
return cost_per_second * billed_duration
|
||||
else:
|
||||
# MoCha pricing: $0.04/s (480p), $0.08/s (720p)
|
||||
# Minimum charge: 5 seconds
|
||||
# Maximum billed: 120 seconds
|
||||
pricing = {
|
||||
"480p": 0.04,
|
||||
"720p": 0.08,
|
||||
}
|
||||
cost_per_second = pricing.get(resolution or "480p", pricing["480p"])
|
||||
billed_duration = max(5.0, min(duration, 120.0))
|
||||
return cost_per_second * billed_duration
|
||||
|
||||
async def swap_face(
|
||||
self,
|
||||
image_data: bytes,
|
||||
video_data: bytes,
|
||||
model: str = "mocha",
|
||||
prompt: Optional[str] = None,
|
||||
resolution: str = "480p",
|
||||
seed: Optional[int] = None,
|
||||
target_gender: str = "all",
|
||||
target_index: int = 0,
|
||||
user_id: str = None,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Perform face/character swap using MoCha or Video Face Swap.
|
||||
|
||||
Args:
|
||||
image_data: Reference image as bytes
|
||||
video_data: Source video as bytes
|
||||
model: Model to use ("mocha" or "video-face-swap")
|
||||
prompt: Optional prompt to guide the swap (MoCha only)
|
||||
resolution: Output resolution for MoCha ("480p" or "720p")
|
||||
seed: Random seed for reproducibility (MoCha only)
|
||||
target_gender: Filter which faces to swap (video-face-swap only: "all", "female", "male")
|
||||
target_index: Select which face to swap (video-face-swap only: 0 = largest)
|
||||
user_id: User ID for tracking
|
||||
progress_callback: Optional callback for progress updates
|
||||
|
||||
Returns:
|
||||
Dict with swapped video_url, cost, and metadata
|
||||
"""
|
||||
try:
|
||||
logger.info(
|
||||
f"[FaceSwap] Face swap request: user={user_id}, "
|
||||
f"model={model}, resolution={resolution if model == 'mocha' else 'N/A'}"
|
||||
)
|
||||
|
||||
if not user_id:
|
||||
raise ValueError("user_id is required for face swap")
|
||||
|
||||
# Validate model
|
||||
if model not in ("mocha", "video-face-swap"):
|
||||
raise ValueError("Model must be 'mocha' or 'video-face-swap'")
|
||||
|
||||
# Convert image to base64 data URI
|
||||
image_b64 = base64.b64encode(image_data).decode('utf-8')
|
||||
image_uri = f"data:image/png;base64,{image_b64}"
|
||||
|
||||
# Convert video to base64 data URI
|
||||
video_b64 = base64.b64encode(video_data).decode('utf-8')
|
||||
video_uri = f"data:video/mp4;base64,{video_b64}"
|
||||
|
||||
# Estimate duration (we'll use a default, actual duration would come from video metadata)
|
||||
estimated_duration = 10.0 # Default estimate, should be improved with actual video duration
|
||||
|
||||
# Calculate cost estimate
|
||||
cost = self.calculate_cost(model, resolution if model == "mocha" else None, estimated_duration)
|
||||
|
||||
if progress_callback:
|
||||
model_name = "MoCha" if model == "mocha" else "Video Face Swap"
|
||||
progress_callback(10.0, f"Submitting face swap request to {model_name}...")
|
||||
|
||||
# Perform face swap based on model
|
||||
if model == "mocha":
|
||||
# Validate resolution for MoCha
|
||||
if resolution not in ("480p", "720p"):
|
||||
raise ValueError("Resolution must be '480p' or '720p' for MoCha")
|
||||
|
||||
# face_swap is synchronous (uses sync_mode internally)
|
||||
swapped_video_bytes = self.wavespeed_client.face_swap(
|
||||
image=image_uri,
|
||||
video=video_uri,
|
||||
prompt=prompt,
|
||||
resolution=resolution,
|
||||
seed=seed,
|
||||
enable_sync_mode=True,
|
||||
timeout=600, # 10 minutes timeout
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
else: # video-face-swap
|
||||
# video_face_swap is synchronous (uses sync_mode internally)
|
||||
swapped_video_bytes = self.wavespeed_client.video_face_swap(
|
||||
video=video_uri,
|
||||
face_image=image_uri,
|
||||
target_gender=target_gender,
|
||||
target_index=target_index,
|
||||
enable_sync_mode=True,
|
||||
timeout=600, # 10 minutes timeout
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(90.0, "Face swap complete, saving video...")
|
||||
|
||||
# Save swapped video
|
||||
from . import VideoStudioService
|
||||
video_service = VideoStudioService()
|
||||
save_result = video_service._save_video_file(
|
||||
video_bytes=swapped_video_bytes,
|
||||
operation_type="face_swap",
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
# Recalculate cost with actual duration if available
|
||||
# For now, use estimated cost
|
||||
actual_cost = cost
|
||||
|
||||
logger.info(
|
||||
f"[FaceSwap] Face swap successful: user={user_id}, "
|
||||
f"resolution={resolution}, cost=${actual_cost:.4f}"
|
||||
)
|
||||
|
||||
metadata = {
|
||||
"original_image_size": len(image_data),
|
||||
"original_video_size": len(video_data),
|
||||
"swapped_video_size": len(swapped_video_bytes),
|
||||
"model": model,
|
||||
}
|
||||
|
||||
if model == "mocha":
|
||||
metadata.update({
|
||||
"resolution": resolution,
|
||||
"seed": seed,
|
||||
"prompt": prompt,
|
||||
})
|
||||
else: # video-face-swap
|
||||
metadata.update({
|
||||
"target_gender": target_gender,
|
||||
"target_index": target_index,
|
||||
})
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": save_result["file_url"],
|
||||
"video_bytes": swapped_video_bytes,
|
||||
"cost": actual_cost,
|
||||
"model": model,
|
||||
"resolution": resolution if model == "mocha" else None,
|
||||
"metadata": metadata,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[FaceSwap] Face swap error: {e}", exc_info=True)
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
}
|
||||
148
backend/services/video_studio/hunyuan_avatar_adapter.py
Normal file
148
backend/services/video_studio/hunyuan_avatar_adapter.py
Normal file
@@ -0,0 +1,148 @@
|
||||
"""Hunyuan Avatar adapter for Avatar Studio."""
|
||||
|
||||
import asyncio
|
||||
from typing import Any, Dict, Optional
|
||||
from fastapi import HTTPException
|
||||
from loguru import logger
|
||||
|
||||
from services.wavespeed.hunyuan_avatar import create_hunyuan_avatar, calculate_hunyuan_avatar_cost
|
||||
from services.wavespeed.client import WaveSpeedClient
|
||||
from utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.hunyuan_avatar")
|
||||
|
||||
|
||||
class HunyuanAvatarService:
|
||||
"""Adapter for Hunyuan Avatar in Avatar Studio context."""
|
||||
|
||||
def __init__(self, client: Optional[WaveSpeedClient] = None):
|
||||
"""Initialize Hunyuan Avatar service adapter."""
|
||||
self.client = client or WaveSpeedClient()
|
||||
logger.info("[Hunyuan Avatar Adapter] Service initialized")
|
||||
|
||||
def calculate_cost(self, resolution: str, duration: float) -> float:
|
||||
"""Calculate cost for Hunyuan Avatar video.
|
||||
|
||||
Args:
|
||||
resolution: Output resolution (480p or 720p)
|
||||
duration: Video duration in seconds
|
||||
|
||||
Returns:
|
||||
Cost in USD
|
||||
"""
|
||||
return calculate_hunyuan_avatar_cost(resolution, duration)
|
||||
|
||||
async def create_talking_avatar(
|
||||
self,
|
||||
image_base64: str,
|
||||
audio_base64: str,
|
||||
resolution: str = "480p",
|
||||
prompt: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
user_id: str = "video_studio",
|
||||
progress_callback: Optional[callable] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Create talking avatar video using Hunyuan Avatar.
|
||||
|
||||
Args:
|
||||
image_base64: Person image in base64 or data URI
|
||||
audio_base64: Audio file in base64 or data URI
|
||||
resolution: Output resolution (480p or 720p, default: 480p)
|
||||
prompt: Optional prompt for expression/style
|
||||
seed: Optional random seed
|
||||
user_id: User ID for tracking
|
||||
progress_callback: Optional progress callback function
|
||||
|
||||
Returns:
|
||||
Dictionary with video_bytes, metadata, and cost
|
||||
"""
|
||||
# Validate resolution
|
||||
if resolution not in ["480p", "720p"]:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Resolution must be '480p' or '720p' for Hunyuan Avatar"
|
||||
)
|
||||
|
||||
# Decode image
|
||||
import base64
|
||||
try:
|
||||
if image_base64.startswith("data:"):
|
||||
if "," not in image_base64:
|
||||
raise ValueError("Invalid data URI format: missing comma separator")
|
||||
header, encoded = image_base64.split(",", 1)
|
||||
mime_parts = header.split(":")[1].split(";")[0] if ":" in header else "image/png"
|
||||
image_mime = mime_parts.strip() or "image/png"
|
||||
image_bytes = base64.b64decode(encoded)
|
||||
else:
|
||||
image_bytes = base64.b64decode(image_base64)
|
||||
image_mime = "image/png"
|
||||
except Exception as e:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Failed to decode image: {str(e)}"
|
||||
)
|
||||
|
||||
# Decode audio
|
||||
try:
|
||||
if audio_base64.startswith("data:"):
|
||||
if "," not in audio_base64:
|
||||
raise ValueError("Invalid data URI format: missing comma separator")
|
||||
header, encoded = audio_base64.split(",", 1)
|
||||
mime_parts = header.split(":")[1].split(";")[0] if ":" in header else "audio/mpeg"
|
||||
audio_mime = mime_parts.strip() or "audio/mpeg"
|
||||
audio_bytes = base64.b64decode(encoded)
|
||||
else:
|
||||
audio_bytes = base64.b64decode(audio_base64)
|
||||
audio_mime = "audio/mpeg"
|
||||
except Exception as e:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Failed to decode audio: {str(e)}"
|
||||
)
|
||||
|
||||
# Call Hunyuan Avatar function (run in thread since it's synchronous)
|
||||
try:
|
||||
result = await asyncio.to_thread(
|
||||
create_hunyuan_avatar,
|
||||
image_bytes=image_bytes,
|
||||
audio_bytes=audio_bytes,
|
||||
resolution=resolution,
|
||||
prompt=prompt,
|
||||
seed=seed,
|
||||
user_id=user_id,
|
||||
image_mime=image_mime,
|
||||
audio_mime=audio_mime,
|
||||
client=self.client,
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[Hunyuan Avatar Adapter] Error: {str(e)}", exc_info=True)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Hunyuan Avatar generation failed: {str(e)}"
|
||||
)
|
||||
|
||||
# Calculate actual cost based on duration
|
||||
actual_cost = self.calculate_cost(resolution, result.get("duration", 5.0))
|
||||
|
||||
# Update result with actual cost and additional metadata
|
||||
result["cost"] = actual_cost
|
||||
result["resolution"] = resolution
|
||||
|
||||
# Get video dimensions from resolution
|
||||
resolution_dims = {
|
||||
"480p": (854, 480),
|
||||
"720p": (1280, 720),
|
||||
}
|
||||
width, height = resolution_dims.get(resolution, (854, 480))
|
||||
result["width"] = width
|
||||
result["height"] = height
|
||||
|
||||
logger.info(
|
||||
f"[Hunyuan Avatar Adapter] ✅ Generated talking avatar: "
|
||||
f"resolution={resolution}, duration={result.get('duration', 5.0)}s, cost=${actual_cost:.2f}"
|
||||
)
|
||||
|
||||
return result
|
||||
156
backend/services/video_studio/platform_specs.py
Normal file
156
backend/services/video_studio/platform_specs.py
Normal file
@@ -0,0 +1,156 @@
|
||||
"""
|
||||
Platform specifications for Social Optimizer.
|
||||
|
||||
Defines aspect ratios, duration limits, file size limits, and other requirements
|
||||
for each social media platform.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import List, Optional
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class Platform(Enum):
|
||||
"""Social media platforms."""
|
||||
INSTAGRAM = "instagram"
|
||||
TIKTOK = "tiktok"
|
||||
YOUTUBE = "youtube"
|
||||
LINKEDIN = "linkedin"
|
||||
FACEBOOK = "facebook"
|
||||
TWITTER = "twitter"
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlatformSpec:
|
||||
"""Platform specification for video optimization."""
|
||||
platform: Platform
|
||||
name: str
|
||||
aspect_ratio: str # e.g., "9:16", "16:9", "1:1"
|
||||
width: int
|
||||
height: int
|
||||
max_duration: float # seconds
|
||||
max_file_size_mb: float # MB
|
||||
formats: List[str] # e.g., ["mp4", "mov"]
|
||||
description: str
|
||||
|
||||
|
||||
# Platform specifications
|
||||
PLATFORM_SPECS: List[PlatformSpec] = [
|
||||
PlatformSpec(
|
||||
platform=Platform.INSTAGRAM,
|
||||
name="Instagram Reels",
|
||||
aspect_ratio="9:16",
|
||||
width=1080,
|
||||
height=1920,
|
||||
max_duration=90.0, # 90 seconds
|
||||
max_file_size_mb=4000.0, # 4GB
|
||||
formats=["mp4"],
|
||||
description="Vertical video format for Instagram Reels",
|
||||
),
|
||||
PlatformSpec(
|
||||
platform=Platform.TIKTOK,
|
||||
name="TikTok",
|
||||
aspect_ratio="9:16",
|
||||
width=1080,
|
||||
height=1920,
|
||||
max_duration=60.0, # 60 seconds
|
||||
max_file_size_mb=287.0, # 287MB
|
||||
formats=["mp4", "mov"],
|
||||
description="Vertical video format for TikTok",
|
||||
),
|
||||
PlatformSpec(
|
||||
platform=Platform.YOUTUBE,
|
||||
name="YouTube Shorts",
|
||||
aspect_ratio="9:16",
|
||||
width=1080,
|
||||
height=1920,
|
||||
max_duration=60.0, # 60 seconds
|
||||
max_file_size_mb=256000.0, # 256GB (very high limit)
|
||||
formats=["mp4", "mov", "webm"],
|
||||
description="Vertical video format for YouTube Shorts",
|
||||
),
|
||||
PlatformSpec(
|
||||
platform=Platform.LINKEDIN,
|
||||
name="LinkedIn Video",
|
||||
aspect_ratio="16:9",
|
||||
width=1920,
|
||||
height=1080,
|
||||
max_duration=600.0, # 10 minutes
|
||||
max_file_size_mb=5000.0, # 5GB
|
||||
formats=["mp4"],
|
||||
description="Horizontal video format for LinkedIn",
|
||||
),
|
||||
PlatformSpec(
|
||||
platform=Platform.LINKEDIN,
|
||||
name="LinkedIn Video (Square)",
|
||||
aspect_ratio="1:1",
|
||||
width=1080,
|
||||
height=1080,
|
||||
max_duration=600.0, # 10 minutes
|
||||
max_file_size_mb=5000.0, # 5GB
|
||||
formats=["mp4"],
|
||||
description="Square video format for LinkedIn",
|
||||
),
|
||||
PlatformSpec(
|
||||
platform=Platform.FACEBOOK,
|
||||
name="Facebook Video",
|
||||
aspect_ratio="16:9",
|
||||
width=1920,
|
||||
height=1080,
|
||||
max_duration=240.0, # 240 seconds (4 minutes)
|
||||
max_file_size_mb=4000.0, # 4GB
|
||||
formats=["mp4", "mov"],
|
||||
description="Horizontal video format for Facebook",
|
||||
),
|
||||
PlatformSpec(
|
||||
platform=Platform.FACEBOOK,
|
||||
name="Facebook Video (Square)",
|
||||
aspect_ratio="1:1",
|
||||
width=1080,
|
||||
height=1080,
|
||||
max_duration=240.0, # 240 seconds
|
||||
max_file_size_mb=4000.0, # 4GB
|
||||
formats=["mp4", "mov"],
|
||||
description="Square video format for Facebook",
|
||||
),
|
||||
PlatformSpec(
|
||||
platform=Platform.TWITTER,
|
||||
name="Twitter/X Video",
|
||||
aspect_ratio="16:9",
|
||||
width=1920,
|
||||
height=1080,
|
||||
max_duration=140.0, # 140 seconds (2:20)
|
||||
max_file_size_mb=512.0, # 512MB
|
||||
formats=["mp4"],
|
||||
description="Horizontal video format for Twitter/X",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
def get_platform_specs(platform: Platform) -> List[PlatformSpec]:
|
||||
"""Get all specifications for a platform."""
|
||||
return [spec for spec in PLATFORM_SPECS if spec.platform == platform]
|
||||
|
||||
|
||||
def get_platform_spec(platform: Platform, aspect_ratio: Optional[str] = None) -> Optional[PlatformSpec]:
|
||||
"""Get a specific platform specification."""
|
||||
specs = get_platform_specs(platform)
|
||||
if aspect_ratio:
|
||||
for spec in specs:
|
||||
if spec.aspect_ratio == aspect_ratio:
|
||||
return spec
|
||||
return specs[0] if specs else None
|
||||
|
||||
|
||||
def get_all_platforms() -> List[Platform]:
|
||||
"""Get all available platforms."""
|
||||
return list(Platform)
|
||||
|
||||
|
||||
def get_platform_by_name(name: str) -> Optional[Platform]:
|
||||
"""Get platform enum by name."""
|
||||
name_lower = name.lower()
|
||||
for platform in Platform:
|
||||
if platform.value == name_lower:
|
||||
return platform
|
||||
return None
|
||||
269
backend/services/video_studio/social_optimizer_service.py
Normal file
269
backend/services/video_studio/social_optimizer_service.py
Normal file
@@ -0,0 +1,269 @@
|
||||
"""
|
||||
Social Optimizer service for platform-specific video optimization.
|
||||
|
||||
Creates optimized versions of videos for Instagram, TikTok, YouTube, LinkedIn, Facebook, and Twitter.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional
|
||||
from dataclasses import dataclass
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
from .platform_specs import Platform, PlatformSpec, get_platform_spec, get_platform_specs
|
||||
from .video_processors import (
|
||||
convert_aspect_ratio,
|
||||
trim_video,
|
||||
compress_video,
|
||||
extract_thumbnail,
|
||||
)
|
||||
|
||||
logger = get_service_logger("video_studio.social_optimizer")
|
||||
|
||||
|
||||
@dataclass
|
||||
class OptimizationOptions:
|
||||
"""Options for video optimization."""
|
||||
auto_crop: bool = True
|
||||
generate_thumbnails: bool = True
|
||||
compress: bool = True
|
||||
trim_mode: str = "beginning" # "beginning", "middle", "end"
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlatformResult:
|
||||
"""Result for a single platform optimization."""
|
||||
platform: str
|
||||
name: str
|
||||
aspect_ratio: str
|
||||
video_url: str
|
||||
thumbnail_url: Optional[str] = None
|
||||
duration: float = 0.0
|
||||
file_size: int = 0
|
||||
width: int = 0
|
||||
height: int = 0
|
||||
|
||||
|
||||
class SocialOptimizerService:
|
||||
"""Service for optimizing videos for social media platforms."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize Social Optimizer service."""
|
||||
logger.info("[SocialOptimizer] Service initialized")
|
||||
|
||||
async def optimize_for_platforms(
|
||||
self,
|
||||
video_bytes: bytes,
|
||||
platforms: List[str],
|
||||
options: OptimizationOptions,
|
||||
user_id: str,
|
||||
video_studio_service: Any, # VideoStudioService
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Optimize video for multiple platforms.
|
||||
|
||||
Args:
|
||||
video_bytes: Source video as bytes
|
||||
platforms: List of platform names (e.g., ["instagram", "tiktok"])
|
||||
options: Optimization options
|
||||
user_id: User ID for file storage
|
||||
video_studio_service: VideoStudioService instance for saving files
|
||||
|
||||
Returns:
|
||||
Dict with results for each platform
|
||||
"""
|
||||
logger.info(
|
||||
f"[SocialOptimizer] Optimizing video for platforms: {platforms}, "
|
||||
f"user={user_id}"
|
||||
)
|
||||
|
||||
results: List[PlatformResult] = []
|
||||
errors: List[Dict[str, str]] = []
|
||||
|
||||
# Process each platform
|
||||
for platform_name in platforms:
|
||||
try:
|
||||
platform_enum = Platform(platform_name.lower())
|
||||
platform_specs = get_platform_specs(platform_enum)
|
||||
|
||||
# Process each format variant for the platform
|
||||
for spec in platform_specs:
|
||||
try:
|
||||
result = await self._optimize_for_spec(
|
||||
video_bytes=video_bytes,
|
||||
spec=spec,
|
||||
options=options,
|
||||
user_id=user_id,
|
||||
video_studio_service=video_studio_service,
|
||||
)
|
||||
results.append(result)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"[SocialOptimizer] Failed to optimize for {spec.name}: {e}",
|
||||
exc_info=True
|
||||
)
|
||||
errors.append({
|
||||
"platform": platform_name,
|
||||
"format": spec.name,
|
||||
"error": str(e),
|
||||
})
|
||||
except ValueError:
|
||||
logger.warning(f"[SocialOptimizer] Unknown platform: {platform_name}")
|
||||
errors.append({
|
||||
"platform": platform_name,
|
||||
"error": f"Unknown platform: {platform_name}",
|
||||
})
|
||||
|
||||
# Calculate total cost (free - FFmpeg processing)
|
||||
total_cost = 0.0
|
||||
|
||||
logger.info(
|
||||
f"[SocialOptimizer] Optimization complete: "
|
||||
f"{len(results)} successful, {len(errors)} errors"
|
||||
)
|
||||
|
||||
return {
|
||||
"success": len(results) > 0,
|
||||
"results": [
|
||||
{
|
||||
"platform": r.platform,
|
||||
"name": r.name,
|
||||
"aspect_ratio": r.aspect_ratio,
|
||||
"video_url": r.video_url,
|
||||
"thumbnail_url": r.thumbnail_url,
|
||||
"duration": r.duration,
|
||||
"file_size": r.file_size,
|
||||
"width": r.width,
|
||||
"height": r.height,
|
||||
}
|
||||
for r in results
|
||||
],
|
||||
"errors": errors,
|
||||
"cost": total_cost,
|
||||
}
|
||||
|
||||
async def _optimize_for_spec(
|
||||
self,
|
||||
video_bytes: bytes,
|
||||
spec: PlatformSpec,
|
||||
options: OptimizationOptions,
|
||||
user_id: str,
|
||||
video_studio_service: Any,
|
||||
) -> PlatformResult:
|
||||
"""
|
||||
Optimize video for a specific platform specification.
|
||||
|
||||
Args:
|
||||
video_bytes: Source video as bytes
|
||||
spec: Platform specification
|
||||
options: Optimization options
|
||||
user_id: User ID for file storage
|
||||
video_studio_service: VideoStudioService instance
|
||||
|
||||
Returns:
|
||||
PlatformResult with optimized video URL and metadata
|
||||
"""
|
||||
logger.info(
|
||||
f"[SocialOptimizer] Optimizing for {spec.name} "
|
||||
f"({spec.aspect_ratio}, max {spec.max_duration}s)"
|
||||
)
|
||||
|
||||
processed_video = video_bytes
|
||||
original_size_mb = len(video_bytes) / (1024 * 1024)
|
||||
|
||||
# Step 1: Convert aspect ratio if needed
|
||||
if options.auto_crop:
|
||||
processed_video = await asyncio.to_thread(
|
||||
convert_aspect_ratio,
|
||||
processed_video,
|
||||
spec.aspect_ratio,
|
||||
"center", # Use center crop for social media
|
||||
)
|
||||
logger.debug(f"[SocialOptimizer] Aspect ratio converted to {spec.aspect_ratio}")
|
||||
|
||||
# Step 2: Trim if video exceeds max duration
|
||||
if spec.max_duration > 0:
|
||||
# Get video duration (we'll need to check this)
|
||||
# For now, we'll trim if the video is likely too long
|
||||
# In a real implementation, we'd use MoviePy to get duration first
|
||||
processed_video = await asyncio.to_thread(
|
||||
trim_video,
|
||||
processed_video,
|
||||
start_time=0.0,
|
||||
end_time=None,
|
||||
max_duration=spec.max_duration,
|
||||
trim_mode=options.trim_mode,
|
||||
)
|
||||
logger.debug(f"[SocialOptimizer] Video trimmed to max {spec.max_duration}s")
|
||||
|
||||
# Step 3: Compress if needed and file size exceeds limit
|
||||
if options.compress:
|
||||
current_size_mb = len(processed_video) / (1024 * 1024)
|
||||
if current_size_mb > spec.max_file_size_mb:
|
||||
# Calculate target size (90% of max to be safe)
|
||||
target_size_mb = spec.max_file_size_mb * 0.9
|
||||
processed_video = await asyncio.to_thread(
|
||||
compress_video,
|
||||
processed_video,
|
||||
target_size_mb=target_size_mb,
|
||||
quality="medium",
|
||||
)
|
||||
logger.debug(
|
||||
f"[SocialOptimizer] Video compressed: "
|
||||
f"{current_size_mb:.2f}MB -> {len(processed_video) / (1024 * 1024):.2f}MB"
|
||||
)
|
||||
|
||||
# Step 4: Save optimized video
|
||||
save_result = video_studio_service._save_video_file(
|
||||
video_bytes=processed_video,
|
||||
operation_type=f"social_optimizer_{spec.platform.value}",
|
||||
user_id=user_id,
|
||||
)
|
||||
video_url = save_result["file_url"]
|
||||
|
||||
# Step 5: Generate thumbnail if requested
|
||||
thumbnail_url = None
|
||||
if options.generate_thumbnails:
|
||||
try:
|
||||
thumbnail_bytes = await asyncio.to_thread(
|
||||
extract_thumbnail,
|
||||
processed_video,
|
||||
time_position=None, # Middle of video
|
||||
width=spec.width,
|
||||
height=spec.height,
|
||||
)
|
||||
|
||||
# Save thumbnail
|
||||
thumbnail_save_result = video_studio_service._save_video_file(
|
||||
video_bytes=thumbnail_bytes,
|
||||
operation_type=f"social_optimizer_thumbnail_{spec.platform.value}",
|
||||
user_id=user_id,
|
||||
)
|
||||
thumbnail_url = thumbnail_save_result["file_url"]
|
||||
logger.debug(f"[SocialOptimizer] Thumbnail generated: {thumbnail_url}")
|
||||
except Exception as e:
|
||||
logger.warning(f"[SocialOptimizer] Failed to generate thumbnail: {e}")
|
||||
|
||||
# Get video metadata (duration, file size)
|
||||
# For now, we'll estimate based on file size
|
||||
# In a real implementation, we'd use MoviePy to get actual duration
|
||||
file_size = len(processed_video)
|
||||
estimated_duration = spec.max_duration if spec.max_duration > 0 else 10.0
|
||||
|
||||
logger.info(
|
||||
f"[SocialOptimizer] Optimization complete for {spec.name}: "
|
||||
f"video_url={video_url}, size={file_size} bytes"
|
||||
)
|
||||
|
||||
return PlatformResult(
|
||||
platform=spec.platform.value,
|
||||
name=spec.name,
|
||||
aspect_ratio=spec.aspect_ratio,
|
||||
video_url=video_url,
|
||||
thumbnail_url=thumbnail_url,
|
||||
duration=estimated_duration,
|
||||
file_size=file_size,
|
||||
width=spec.width,
|
||||
height=spec.height,
|
||||
)
|
||||
@@ -0,0 +1,129 @@
|
||||
"""
|
||||
Video Background Remover service for Video Studio.
|
||||
|
||||
Removes or replaces video backgrounds using WaveSpeed Video Background Remover.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
from typing import Dict, Any, Optional, Callable
|
||||
from fastapi import HTTPException
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
from ..wavespeed.client import WaveSpeedClient
|
||||
|
||||
logger = get_service_logger("video_studio.video_background_remover")
|
||||
|
||||
|
||||
class VideoBackgroundRemoverService:
|
||||
"""Service for video background removal/replacement operations."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize Video Background Remover service."""
|
||||
self.wavespeed_client = WaveSpeedClient()
|
||||
logger.info("[VideoBackgroundRemover] Service initialized")
|
||||
|
||||
def calculate_cost(self, duration: float = 10.0) -> float:
|
||||
"""
|
||||
Calculate cost for video background removal operation.
|
||||
|
||||
Pricing from WaveSpeed documentation:
|
||||
- Rate: $0.01 per second
|
||||
- Minimum: $0.05 for ≤5 seconds
|
||||
- Maximum: $6.00 for 600 seconds (10 minutes)
|
||||
|
||||
Args:
|
||||
duration: Video duration in seconds
|
||||
|
||||
Returns:
|
||||
Cost in USD
|
||||
"""
|
||||
# Pricing: $0.01 per second
|
||||
# Minimum charge: $0.05 for ≤5 seconds
|
||||
# Maximum: $6.00 for 600 seconds (10 minutes)
|
||||
cost_per_second = 0.01
|
||||
if duration <= 5.0:
|
||||
return 0.05 # Minimum charge
|
||||
elif duration >= 600.0:
|
||||
return 6.00 # Maximum charge
|
||||
else:
|
||||
return duration * cost_per_second
|
||||
|
||||
async def remove_background(
|
||||
self,
|
||||
video_data: bytes,
|
||||
background_image_data: Optional[bytes] = None,
|
||||
user_id: str = None,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Remove or replace video background.
|
||||
|
||||
Args:
|
||||
video_data: Source video as bytes
|
||||
background_image_data: Optional replacement background image as bytes
|
||||
user_id: User ID for tracking
|
||||
progress_callback: Optional callback for progress updates
|
||||
|
||||
Returns:
|
||||
Dict with processed video_url, cost, and metadata
|
||||
"""
|
||||
try:
|
||||
logger.info(f"[VideoBackgroundRemover] Background removal request: user={user_id}, has_background={background_image_data is not None}")
|
||||
|
||||
# Convert video to base64 data URI
|
||||
video_b64 = base64.b64encode(video_data).decode('utf-8')
|
||||
video_uri = f"data:video/mp4;base64,{video_b64}"
|
||||
|
||||
# Convert background image to base64 if provided
|
||||
background_image_uri = None
|
||||
if background_image_data:
|
||||
image_b64 = base64.b64encode(background_image_data).decode('utf-8')
|
||||
background_image_uri = f"data:image/jpeg;base64,{image_b64}"
|
||||
|
||||
# Call WaveSpeed API
|
||||
processed_video_bytes = await asyncio.to_thread(
|
||||
self.wavespeed_client.remove_background,
|
||||
video=video_uri,
|
||||
background_image=background_image_uri,
|
||||
enable_sync_mode=False, # Always use async with polling
|
||||
timeout=600, # 10 minutes max for long videos
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
|
||||
# Estimate video duration (rough estimate: 1MB ≈ 1 second at 1080p)
|
||||
estimated_duration = max(5, len(video_data) / (1024 * 1024)) # Minimum 5 seconds
|
||||
cost = self.calculate_cost(estimated_duration)
|
||||
|
||||
# Save processed video
|
||||
from .video_studio_service import VideoStudioService
|
||||
video_service = VideoStudioService()
|
||||
save_result = video_service._save_video_file(
|
||||
video_bytes=processed_video_bytes,
|
||||
operation_type="background_removal",
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
logger.info(f"[VideoBackgroundRemover] Background removal successful: user={user_id}, cost=${cost:.4f}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": save_result["file_url"],
|
||||
"video_bytes": processed_video_bytes,
|
||||
"cost": cost,
|
||||
"has_background_replacement": background_image_data is not None,
|
||||
"metadata": {
|
||||
"original_size": len(video_data),
|
||||
"processed_size": len(processed_video_bytes),
|
||||
"estimated_duration": estimated_duration,
|
||||
},
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoBackgroundRemover] Background removal failed: {e}", exc_info=True)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Video background removal failed: {str(e)}"
|
||||
)
|
||||
647
backend/services/video_studio/video_processors.py
Normal file
647
backend/services/video_studio/video_processors.py
Normal file
@@ -0,0 +1,647 @@
|
||||
"""
|
||||
Video processing utilities for Transform Studio.
|
||||
|
||||
Handles format conversion, aspect ratio conversion, speed adjustment,
|
||||
resolution scaling, and compression using MoviePy/FFmpeg.
|
||||
"""
|
||||
|
||||
import io
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
from typing import Optional, Tuple, Dict, Any
|
||||
from fastapi import HTTPException
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("video_studio.video_processors")
|
||||
|
||||
try:
|
||||
from moviepy import VideoFileClip
|
||||
MOVIEPY_AVAILABLE = True
|
||||
except ImportError:
|
||||
MOVIEPY_AVAILABLE = False
|
||||
logger.warning("[VideoProcessors] MoviePy not available. Video processing will not work.")
|
||||
|
||||
|
||||
def _check_moviepy():
|
||||
"""Check if MoviePy is available."""
|
||||
if not MOVIEPY_AVAILABLE:
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail="MoviePy is not installed. Please install it: pip install moviepy imageio imageio-ffmpeg"
|
||||
)
|
||||
|
||||
|
||||
def _get_resolution_dimensions(resolution: str) -> Tuple[int, int]:
|
||||
"""Get width and height for a resolution string."""
|
||||
resolution_map = {
|
||||
"480p": (854, 480),
|
||||
"720p": (1280, 720),
|
||||
"1080p": (1920, 1080),
|
||||
"1440p": (2560, 1440),
|
||||
"4k": (3840, 2160),
|
||||
}
|
||||
return resolution_map.get(resolution.lower(), (1280, 720))
|
||||
|
||||
|
||||
def _get_aspect_ratio_dimensions(aspect_ratio: str, target_height: int = 720) -> Tuple[int, int]:
|
||||
"""Get width and height for an aspect ratio."""
|
||||
aspect_map = {
|
||||
"16:9": (16, 9),
|
||||
"9:16": (9, 16),
|
||||
"1:1": (1, 1),
|
||||
"4:5": (4, 5),
|
||||
"21:9": (21, 9),
|
||||
}
|
||||
|
||||
if aspect_ratio not in aspect_map:
|
||||
return (1280, 720) # Default to 16:9
|
||||
|
||||
width_ratio, height_ratio = aspect_map[aspect_ratio]
|
||||
width = int((width_ratio / height_ratio) * target_height)
|
||||
return (width, target_height)
|
||||
|
||||
|
||||
def convert_format(
|
||||
video_bytes: bytes,
|
||||
output_format: str = "mp4",
|
||||
codec: str = "libx264",
|
||||
quality: str = "medium",
|
||||
audio_codec: str = "aac",
|
||||
) -> bytes:
|
||||
"""
|
||||
Convert video to a different format.
|
||||
|
||||
Args:
|
||||
video_bytes: Input video as bytes
|
||||
output_format: Output format (mp4, mov, webm, gif)
|
||||
codec: Video codec (libx264, libvpx-vp9, etc.)
|
||||
quality: Quality preset (high, medium, low)
|
||||
audio_codec: Audio codec (aac, mp3, opus, etc.)
|
||||
|
||||
Returns:
|
||||
Converted video as bytes
|
||||
"""
|
||||
_check_moviepy()
|
||||
|
||||
quality_presets = {
|
||||
"high": {"bitrate": "5000k", "preset": "slow"},
|
||||
"medium": {"bitrate": "2500k", "preset": "medium"},
|
||||
"low": {"bitrate": "1000k", "preset": "fast"},
|
||||
}
|
||||
preset = quality_presets.get(quality, quality_presets["medium"])
|
||||
|
||||
# Save input to temp file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as input_file:
|
||||
input_file.write(video_bytes)
|
||||
input_path = input_file.name
|
||||
|
||||
try:
|
||||
# Load video
|
||||
clip = VideoFileClip(input_path)
|
||||
|
||||
# Format-specific codec selection
|
||||
if output_format == "webm":
|
||||
codec = "libvpx-vp9"
|
||||
audio_codec = "libopus"
|
||||
elif output_format == "gif":
|
||||
# For GIF, we need to handle differently
|
||||
codec = None
|
||||
audio_codec = None
|
||||
elif output_format == "mov":
|
||||
codec = "libx264"
|
||||
audio_codec = "aac"
|
||||
else: # mp4
|
||||
codec = codec or "libx264"
|
||||
audio_codec = audio_codec or "aac"
|
||||
|
||||
# Write to temp output file
|
||||
output_suffix = f".{output_format}" if output_format != "gif" else ".gif"
|
||||
with tempfile.NamedTemporaryFile(suffix=output_suffix, delete=False) as output_file:
|
||||
output_path = output_file.name
|
||||
|
||||
if output_format == "gif":
|
||||
# For GIF, use write_gif
|
||||
clip.write_gif(output_path, fps=15, logger=None)
|
||||
else:
|
||||
# For video formats
|
||||
clip.write_videofile(
|
||||
output_path,
|
||||
codec=codec,
|
||||
audio_codec=audio_codec,
|
||||
bitrate=preset["bitrate"],
|
||||
preset=preset["preset"],
|
||||
threads=4,
|
||||
logger=None,
|
||||
)
|
||||
|
||||
# Read output file
|
||||
with open(output_path, "rb") as f:
|
||||
output_bytes = f.read()
|
||||
|
||||
# Cleanup
|
||||
clip.close()
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True)
|
||||
|
||||
logger.info(f"[VideoProcessors] Format conversion successful: {output_format}, size={len(output_bytes)} bytes")
|
||||
return output_bytes
|
||||
|
||||
except Exception as e:
|
||||
# Cleanup on error
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True) if 'output_path' in locals() else None
|
||||
logger.error(f"[VideoProcessors] Format conversion failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Format conversion failed: {str(e)}")
|
||||
|
||||
|
||||
def convert_aspect_ratio(
|
||||
video_bytes: bytes,
|
||||
target_aspect: str,
|
||||
crop_mode: str = "center",
|
||||
) -> bytes:
|
||||
"""
|
||||
Convert video to a different aspect ratio.
|
||||
|
||||
Args:
|
||||
video_bytes: Input video as bytes
|
||||
target_aspect: Target aspect ratio (16:9, 9:16, 1:1, 4:5, 21:9)
|
||||
crop_mode: Crop mode (center, smart, letterbox)
|
||||
|
||||
Returns:
|
||||
Converted video as bytes
|
||||
"""
|
||||
_check_moviepy()
|
||||
|
||||
# Save input to temp file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as input_file:
|
||||
input_file.write(video_bytes)
|
||||
input_path = input_file.name
|
||||
|
||||
try:
|
||||
# Load video
|
||||
clip = VideoFileClip(input_path)
|
||||
original_width, original_height = clip.size
|
||||
|
||||
# Calculate target dimensions
|
||||
target_width, target_height = _get_aspect_ratio_dimensions(target_aspect, original_height)
|
||||
target_aspect_ratio = target_width / target_height
|
||||
original_aspect_ratio = original_width / original_height
|
||||
|
||||
# Determine crop dimensions
|
||||
if crop_mode == "letterbox":
|
||||
# Letterboxing: add black bars
|
||||
if target_aspect_ratio > original_aspect_ratio:
|
||||
# Target is wider, add horizontal bars
|
||||
new_height = int(original_width / target_aspect_ratio)
|
||||
y_offset = (original_height - new_height) // 2
|
||||
clip = clip.crop(y1=y_offset, y2=y_offset + new_height)
|
||||
else:
|
||||
# Target is taller, add vertical bars
|
||||
new_width = int(original_height * target_aspect_ratio)
|
||||
x_offset = (original_width - new_width) // 2
|
||||
clip = clip.crop(x1=x_offset, x2=x_offset + new_width)
|
||||
else:
|
||||
# Center crop (default)
|
||||
if target_aspect_ratio > original_aspect_ratio:
|
||||
# Need to crop height
|
||||
new_height = int(original_width / target_aspect_ratio)
|
||||
y_offset = (original_height - new_height) // 2
|
||||
clip = clip.crop(y1=y_offset, y2=y_offset + new_height)
|
||||
else:
|
||||
# Need to crop width
|
||||
new_width = int(original_height * target_aspect_ratio)
|
||||
x_offset = (original_width - new_width) // 2
|
||||
clip = clip.crop(x1=x_offset, x2=x_offset + new_width)
|
||||
|
||||
# Resize to target dimensions (maintain quality)
|
||||
clip = clip.resize((target_width, target_height))
|
||||
|
||||
# Write to temp output file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as output_file:
|
||||
output_path = output_file.name
|
||||
|
||||
clip.write_videofile(
|
||||
output_path,
|
||||
codec="libx264",
|
||||
audio_codec="aac",
|
||||
preset="medium",
|
||||
threads=4,
|
||||
logger=None,
|
||||
)
|
||||
|
||||
# Read output file
|
||||
with open(output_path, "rb") as f:
|
||||
output_bytes = f.read()
|
||||
|
||||
# Cleanup
|
||||
clip.close()
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True)
|
||||
|
||||
logger.info(f"[VideoProcessors] Aspect ratio conversion successful: {target_aspect}, size={len(output_bytes)} bytes")
|
||||
return output_bytes
|
||||
|
||||
except Exception as e:
|
||||
# Cleanup on error
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True) if 'output_path' in locals() else None
|
||||
logger.error(f"[VideoProcessors] Aspect ratio conversion failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Aspect ratio conversion failed: {str(e)}")
|
||||
|
||||
|
||||
def adjust_speed(
|
||||
video_bytes: bytes,
|
||||
speed_factor: float,
|
||||
) -> bytes:
|
||||
"""
|
||||
Adjust video playback speed.
|
||||
|
||||
Args:
|
||||
video_bytes: Input video as bytes
|
||||
speed_factor: Speed multiplier (0.25, 0.5, 1.0, 1.5, 2.0, 4.0)
|
||||
|
||||
Returns:
|
||||
Speed-adjusted video as bytes
|
||||
"""
|
||||
_check_moviepy()
|
||||
|
||||
if speed_factor <= 0:
|
||||
raise HTTPException(status_code=400, detail="Speed factor must be greater than 0")
|
||||
|
||||
# Save input to temp file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as input_file:
|
||||
input_file.write(video_bytes)
|
||||
input_path = input_file.name
|
||||
|
||||
try:
|
||||
# Load video
|
||||
clip = VideoFileClip(input_path)
|
||||
|
||||
# Adjust speed using MoviePy's speedx effect
|
||||
try:
|
||||
# Try MoviePy v2 API first
|
||||
from moviepy.video.fx.speedx import speedx
|
||||
clip = clip.fx(speedx, speed_factor)
|
||||
except (ImportError, AttributeError):
|
||||
try:
|
||||
# Fallback: try direct import
|
||||
from moviepy.video.fx import speedx
|
||||
clip = clip.fx(speedx, speed_factor)
|
||||
except (ImportError, AttributeError):
|
||||
# Fallback: Manual speed adjustment (less accurate but works)
|
||||
# This maintains audio sync by adjusting fps and duration
|
||||
original_fps = clip.fps
|
||||
new_fps = original_fps * speed_factor
|
||||
original_duration = clip.duration
|
||||
new_duration = original_duration / speed_factor
|
||||
clip = clip.with_fps(new_fps).with_duration(new_duration)
|
||||
|
||||
# Write to temp output file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as output_file:
|
||||
output_path = output_file.name
|
||||
|
||||
clip.write_videofile(
|
||||
output_path,
|
||||
codec="libx264",
|
||||
audio_codec="aac",
|
||||
preset="medium",
|
||||
threads=4,
|
||||
logger=None,
|
||||
)
|
||||
|
||||
# Read output file
|
||||
with open(output_path, "rb") as f:
|
||||
output_bytes = f.read()
|
||||
|
||||
# Cleanup
|
||||
clip.close()
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True)
|
||||
|
||||
logger.info(f"[VideoProcessors] Speed adjustment successful: {speed_factor}x, size={len(output_bytes)} bytes")
|
||||
return output_bytes
|
||||
|
||||
except Exception as e:
|
||||
# Cleanup on error
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True) if 'output_path' in locals() else None
|
||||
logger.error(f"[VideoProcessors] Speed adjustment failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Speed adjustment failed: {str(e)}")
|
||||
|
||||
|
||||
def scale_resolution(
|
||||
video_bytes: bytes,
|
||||
target_resolution: str,
|
||||
maintain_aspect: bool = True,
|
||||
) -> bytes:
|
||||
"""
|
||||
Scale video to target resolution.
|
||||
|
||||
Args:
|
||||
video_bytes: Input video as bytes
|
||||
target_resolution: Target resolution (480p, 720p, 1080p, 1440p, 4k)
|
||||
maintain_aspect: Whether to maintain aspect ratio
|
||||
|
||||
Returns:
|
||||
Scaled video as bytes
|
||||
"""
|
||||
_check_moviepy()
|
||||
|
||||
# Save input to temp file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as input_file:
|
||||
input_file.write(video_bytes)
|
||||
input_path = input_file.name
|
||||
|
||||
try:
|
||||
# Load video
|
||||
clip = VideoFileClip(input_path)
|
||||
target_width, target_height = _get_resolution_dimensions(target_resolution)
|
||||
|
||||
# Resize
|
||||
if maintain_aspect:
|
||||
clip = clip.resize(height=target_height) # Maintain aspect ratio
|
||||
else:
|
||||
clip = clip.resize((target_width, target_height))
|
||||
|
||||
# Write to temp output file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as output_file:
|
||||
output_path = output_file.name
|
||||
|
||||
clip.write_videofile(
|
||||
output_path,
|
||||
codec="libx264",
|
||||
audio_codec="aac",
|
||||
preset="medium",
|
||||
threads=4,
|
||||
logger=None,
|
||||
)
|
||||
|
||||
# Read output file
|
||||
with open(output_path, "rb") as f:
|
||||
output_bytes = f.read()
|
||||
|
||||
# Cleanup
|
||||
clip.close()
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True)
|
||||
|
||||
logger.info(f"[VideoProcessors] Resolution scaling successful: {target_resolution}, size={len(output_bytes)} bytes")
|
||||
return output_bytes
|
||||
|
||||
except Exception as e:
|
||||
# Cleanup on error
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True) if 'output_path' in locals() else None
|
||||
logger.error(f"[VideoProcessors] Resolution scaling failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Resolution scaling failed: {str(e)}")
|
||||
|
||||
|
||||
def compress_video(
|
||||
video_bytes: bytes,
|
||||
target_size_mb: Optional[float] = None,
|
||||
quality: str = "medium",
|
||||
) -> bytes:
|
||||
"""
|
||||
Compress video to reduce file size.
|
||||
|
||||
Args:
|
||||
video_bytes: Input video as bytes
|
||||
target_size_mb: Target file size in MB (optional)
|
||||
quality: Quality preset (high, medium, low)
|
||||
|
||||
Returns:
|
||||
Compressed video as bytes
|
||||
"""
|
||||
_check_moviepy()
|
||||
|
||||
quality_presets = {
|
||||
"high": {"bitrate": "5000k", "preset": "slow"},
|
||||
"medium": {"bitrate": "2500k", "preset": "medium"},
|
||||
"low": {"bitrate": "1000k", "preset": "fast"},
|
||||
}
|
||||
preset = quality_presets.get(quality, quality_presets["medium"])
|
||||
|
||||
# Save input to temp file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as input_file:
|
||||
input_file.write(video_bytes)
|
||||
input_path = input_file.name
|
||||
|
||||
try:
|
||||
# Load video
|
||||
clip = VideoFileClip(input_path)
|
||||
|
||||
# Calculate bitrate if target size is specified
|
||||
if target_size_mb:
|
||||
duration = clip.duration
|
||||
target_size_bits = target_size_mb * 8 * 1024 * 1024 # Convert MB to bits
|
||||
calculated_bitrate = int(target_size_bits / duration)
|
||||
# Ensure reasonable bitrate (min 500k, max 10000k)
|
||||
bitrate = f"{max(500, min(10000, calculated_bitrate // 1000))}k"
|
||||
else:
|
||||
bitrate = preset["bitrate"]
|
||||
|
||||
# Write to temp output file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as output_file:
|
||||
output_path = output_file.name
|
||||
|
||||
clip.write_videofile(
|
||||
output_path,
|
||||
codec="libx264",
|
||||
audio_codec="aac",
|
||||
bitrate=bitrate,
|
||||
preset=preset["preset"],
|
||||
threads=4,
|
||||
logger=None,
|
||||
)
|
||||
|
||||
# Read output file
|
||||
with open(output_path, "rb") as f:
|
||||
output_bytes = f.read()
|
||||
|
||||
# Cleanup
|
||||
clip.close()
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True)
|
||||
|
||||
original_size_mb = len(video_bytes) / (1024 * 1024)
|
||||
compressed_size_mb = len(output_bytes) / (1024 * 1024)
|
||||
compression_ratio = (1 - compressed_size_mb / original_size_mb) * 100
|
||||
|
||||
logger.info(
|
||||
f"[VideoProcessors] Compression successful: "
|
||||
f"{original_size_mb:.2f}MB -> {compressed_size_mb:.2f}MB ({compression_ratio:.1f}% reduction)"
|
||||
)
|
||||
return output_bytes
|
||||
|
||||
except Exception as e:
|
||||
# Cleanup on error
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True) if 'output_path' in locals() else None
|
||||
logger.error(f"[VideoProcessors] Compression failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Compression failed: {str(e)}")
|
||||
|
||||
|
||||
def trim_video(
|
||||
video_bytes: bytes,
|
||||
start_time: float = 0.0,
|
||||
end_time: Optional[float] = None,
|
||||
max_duration: Optional[float] = None,
|
||||
trim_mode: str = "beginning",
|
||||
) -> bytes:
|
||||
"""
|
||||
Trim video to specified duration or time range.
|
||||
|
||||
Args:
|
||||
video_bytes: Input video as bytes
|
||||
start_time: Start time in seconds (default: 0.0)
|
||||
end_time: End time in seconds (optional, uses video duration if not provided)
|
||||
max_duration: Maximum duration in seconds (trims if video is longer)
|
||||
trim_mode: How to trim if max_duration is set ("beginning", "middle", "end")
|
||||
|
||||
Returns:
|
||||
Trimmed video as bytes
|
||||
"""
|
||||
_check_moviepy()
|
||||
|
||||
# Save input to temp file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as input_file:
|
||||
input_file.write(video_bytes)
|
||||
input_path = input_file.name
|
||||
|
||||
try:
|
||||
# Load video
|
||||
clip = VideoFileClip(input_path)
|
||||
original_duration = clip.duration
|
||||
|
||||
# Determine trim range
|
||||
if max_duration and original_duration > max_duration:
|
||||
# Need to trim to max_duration
|
||||
if trim_mode == "beginning":
|
||||
# Keep the beginning
|
||||
start_time = 0.0
|
||||
end_time = max_duration
|
||||
elif trim_mode == "end":
|
||||
# Keep the end
|
||||
start_time = original_duration - max_duration
|
||||
end_time = original_duration
|
||||
else: # middle
|
||||
# Keep the middle
|
||||
start_time = (original_duration - max_duration) / 2
|
||||
end_time = start_time + max_duration
|
||||
else:
|
||||
# Use provided times or full video
|
||||
if end_time is None:
|
||||
end_time = original_duration
|
||||
|
||||
# Ensure valid range
|
||||
start_time = max(0.0, min(start_time, original_duration))
|
||||
end_time = max(start_time, min(end_time, original_duration))
|
||||
|
||||
# Trim video
|
||||
trimmed_clip = clip.subclip(start_time, end_time)
|
||||
|
||||
# Write to temp output file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as output_file:
|
||||
output_path = output_file.name
|
||||
|
||||
trimmed_clip.write_videofile(
|
||||
output_path,
|
||||
codec="libx264",
|
||||
audio_codec="aac",
|
||||
preset="medium",
|
||||
threads=4,
|
||||
logger=None,
|
||||
)
|
||||
|
||||
# Read output file
|
||||
with open(output_path, "rb") as f:
|
||||
output_bytes = f.read()
|
||||
|
||||
# Cleanup
|
||||
trimmed_clip.close()
|
||||
clip.close()
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True)
|
||||
|
||||
logger.info(
|
||||
f"[VideoProcessors] Video trimmed: {start_time:.2f}s-{end_time:.2f}s, "
|
||||
f"duration={end_time - start_time:.2f}s, size={len(output_bytes)} bytes"
|
||||
)
|
||||
return output_bytes
|
||||
|
||||
except Exception as e:
|
||||
# Cleanup on error
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
Path(output_path).unlink(missing_ok=True) if 'output_path' in locals() else None
|
||||
logger.error(f"[VideoProcessors] Video trimming failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Video trimming failed: {str(e)}")
|
||||
|
||||
|
||||
def extract_thumbnail(
|
||||
video_bytes: bytes,
|
||||
time_position: Optional[float] = None,
|
||||
width: int = 1280,
|
||||
height: int = 720,
|
||||
) -> bytes:
|
||||
"""
|
||||
Extract a thumbnail frame from video.
|
||||
|
||||
Args:
|
||||
video_bytes: Input video as bytes
|
||||
time_position: Time position in seconds (default: middle of video)
|
||||
width: Thumbnail width (default: 1280)
|
||||
height: Thumbnail height (default: 720)
|
||||
|
||||
Returns:
|
||||
Thumbnail image as bytes (JPEG format)
|
||||
"""
|
||||
_check_moviepy()
|
||||
|
||||
# Save input to temp file
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as input_file:
|
||||
input_file.write(video_bytes)
|
||||
input_path = input_file.name
|
||||
|
||||
try:
|
||||
# Load video
|
||||
clip = VideoFileClip(input_path)
|
||||
|
||||
# Determine time position
|
||||
if time_position is None:
|
||||
time_position = clip.duration / 2 # Middle of video
|
||||
|
||||
# Ensure valid time position
|
||||
time_position = max(0.0, min(time_position, clip.duration))
|
||||
|
||||
# Get frame at specified time
|
||||
frame = clip.get_frame(time_position)
|
||||
|
||||
# Convert numpy array to PIL Image
|
||||
from PIL import Image
|
||||
img = Image.fromarray(frame)
|
||||
|
||||
# Resize if needed
|
||||
if img.size != (width, height):
|
||||
img = img.resize((width, height), Image.Resampling.LANCZOS)
|
||||
|
||||
# Convert to bytes (JPEG)
|
||||
output_buffer = io.BytesIO()
|
||||
img.save(output_buffer, format="JPEG", quality=90)
|
||||
output_bytes = output_buffer.getvalue()
|
||||
|
||||
# Cleanup
|
||||
clip.close()
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
|
||||
logger.info(
|
||||
f"[VideoProcessors] Thumbnail extracted: time={time_position:.2f}s, "
|
||||
f"size={width}x{height}, image_size={len(output_bytes)} bytes"
|
||||
)
|
||||
return output_bytes
|
||||
|
||||
except Exception as e:
|
||||
# Cleanup on error
|
||||
Path(input_path).unlink(missing_ok=True)
|
||||
logger.error(f"[VideoProcessors] Thumbnail extraction failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Thumbnail extraction failed: {str(e)}")
|
||||
1063
backend/services/video_studio/video_studio_service.py
Normal file
1063
backend/services/video_studio/video_studio_service.py
Normal file
File diff suppressed because it is too large
Load Diff
135
backend/services/video_studio/video_translate_service.py
Normal file
135
backend/services/video_studio/video_translate_service.py
Normal file
@@ -0,0 +1,135 @@
|
||||
"""
|
||||
Video Translate service for Video Studio.
|
||||
|
||||
Uses HeyGen Video Translate (heygen/video-translate) for video translation.
|
||||
"""
|
||||
|
||||
import base64
|
||||
from typing import Dict, Any, Optional, Callable
|
||||
from fastapi import HTTPException
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
from ..wavespeed.client import WaveSpeedClient
|
||||
|
||||
logger = get_service_logger("video_studio.video_translate")
|
||||
|
||||
|
||||
class VideoTranslateService:
|
||||
"""Service for video translation operations."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize Video Translate service."""
|
||||
self.wavespeed_client = WaveSpeedClient()
|
||||
logger.info("[VideoTranslate] Service initialized")
|
||||
|
||||
def calculate_cost(self, duration: float = 10.0) -> float:
|
||||
"""
|
||||
Calculate cost for video translation operation.
|
||||
|
||||
Args:
|
||||
duration: Video duration in seconds
|
||||
|
||||
Returns:
|
||||
Cost in USD
|
||||
"""
|
||||
# HeyGen Video Translate pricing: $0.0375/s
|
||||
# No minimum charge mentioned in docs, but we'll use 1 second minimum
|
||||
cost_per_second = 0.0375
|
||||
billed_duration = max(1.0, duration)
|
||||
return cost_per_second * billed_duration
|
||||
|
||||
async def translate_video(
|
||||
self,
|
||||
video_data: bytes,
|
||||
output_language: str = "English",
|
||||
user_id: str = None,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Translate video to target language using HeyGen Video Translate.
|
||||
|
||||
Args:
|
||||
video_data: Source video as bytes
|
||||
output_language: Target language for translation
|
||||
user_id: User ID for tracking
|
||||
progress_callback: Optional callback for progress updates
|
||||
|
||||
Returns:
|
||||
Dict with translated video_url, cost, and metadata
|
||||
"""
|
||||
try:
|
||||
logger.info(
|
||||
f"[VideoTranslate] Video translate request: user={user_id}, "
|
||||
f"output_language={output_language}"
|
||||
)
|
||||
|
||||
if not user_id:
|
||||
raise ValueError("user_id is required for video translation")
|
||||
|
||||
# Convert video to base64 data URI
|
||||
video_b64 = base64.b64encode(video_data).decode('utf-8')
|
||||
video_uri = f"data:video/mp4;base64,{video_b64}"
|
||||
|
||||
# Estimate duration (we'll use a default, actual duration would come from video metadata)
|
||||
estimated_duration = 10.0 # Default estimate, should be improved with actual video duration
|
||||
|
||||
# Calculate cost estimate
|
||||
cost = self.calculate_cost(estimated_duration)
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(10.0, f"Submitting video translation request to HeyGen ({output_language})...")
|
||||
|
||||
# Perform video translation
|
||||
# video_translate is synchronous (uses sync_mode internally)
|
||||
translated_video_bytes = self.wavespeed_client.video_translate(
|
||||
video=video_uri,
|
||||
output_language=output_language,
|
||||
enable_sync_mode=True,
|
||||
timeout=600, # 10 minutes timeout
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(90.0, "Video translation complete, saving video...")
|
||||
|
||||
# Save translated video
|
||||
from . import VideoStudioService
|
||||
video_service = VideoStudioService()
|
||||
save_result = video_service._save_video_file(
|
||||
video_bytes=translated_video_bytes,
|
||||
operation_type="video_translate",
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
# Recalculate cost with actual duration if available
|
||||
# For now, use estimated cost
|
||||
actual_cost = cost
|
||||
|
||||
logger.info(
|
||||
f"[VideoTranslate] Video translate successful: user={user_id}, "
|
||||
f"output_language={output_language}, cost=${actual_cost:.4f}"
|
||||
)
|
||||
|
||||
metadata = {
|
||||
"original_video_size": len(video_data),
|
||||
"translated_video_size": len(translated_video_bytes),
|
||||
"output_language": output_language,
|
||||
}
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"video_url": save_result["file_url"],
|
||||
"video_bytes": translated_video_bytes,
|
||||
"cost": actual_cost,
|
||||
"output_language": output_language,
|
||||
"metadata": metadata,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoTranslate] Video translate error: {e}", exc_info=True)
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
1
backend/services/wavespeed/generators/__init__.py
Normal file
1
backend/services/wavespeed/generators/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
"""WaveSpeed API generators for different content types."""
|
||||
374
backend/services/wavespeed/generators/image.py
Normal file
374
backend/services/wavespeed/generators/image.py
Normal file
@@ -0,0 +1,374 @@
|
||||
"""
|
||||
Image generation generator for WaveSpeed API.
|
||||
"""
|
||||
|
||||
import time
|
||||
import requests
|
||||
from typing import Optional
|
||||
from requests import exceptions as requests_exceptions
|
||||
from fastapi import HTTPException
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("wavespeed.generators.image")
|
||||
|
||||
|
||||
class ImageGenerator:
|
||||
"""Image generation generator."""
|
||||
|
||||
def __init__(self, api_key: str, base_url: str, polling):
|
||||
"""Initialize image generator.
|
||||
|
||||
Args:
|
||||
api_key: WaveSpeed API key
|
||||
base_url: WaveSpeed API base URL
|
||||
polling: WaveSpeedPolling instance for async operations
|
||||
"""
|
||||
self.api_key = api_key
|
||||
self.base_url = base_url
|
||||
self.polling = polling
|
||||
|
||||
def _get_headers(self) -> dict:
|
||||
"""Get HTTP headers for API requests."""
|
||||
return {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
}
|
||||
|
||||
def generate_image(
|
||||
self,
|
||||
model: str,
|
||||
prompt: str,
|
||||
width: int = 1024,
|
||||
height: int = 1024,
|
||||
num_inference_steps: Optional[int] = None,
|
||||
guidance_scale: Optional[float] = None,
|
||||
negative_prompt: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
enable_sync_mode: bool = True,
|
||||
timeout: int = 120,
|
||||
**kwargs
|
||||
) -> bytes:
|
||||
"""
|
||||
Generate image using WaveSpeed AI models (Ideogram V3 or Qwen Image).
|
||||
|
||||
Args:
|
||||
model: Model to use ("ideogram-v3-turbo" or "qwen-image")
|
||||
prompt: Text prompt for image generation
|
||||
width: Image width (default: 1024)
|
||||
height: Image height (default: 1024)
|
||||
num_inference_steps: Number of inference steps
|
||||
guidance_scale: Guidance scale for generation
|
||||
negative_prompt: Negative prompt (what to avoid)
|
||||
seed: Random seed for reproducibility
|
||||
enable_sync_mode: If True, wait for result and return it directly (default: True)
|
||||
timeout: Request timeout in seconds (default: 120)
|
||||
**kwargs: Additional parameters
|
||||
|
||||
Returns:
|
||||
bytes: Generated image bytes
|
||||
"""
|
||||
# Map model names to WaveSpeed API paths
|
||||
model_paths = {
|
||||
"ideogram-v3-turbo": "ideogram-ai/ideogram-v3-turbo",
|
||||
"qwen-image": "wavespeed-ai/qwen-image/text-to-image",
|
||||
}
|
||||
|
||||
model_path = model_paths.get(model)
|
||||
if not model_path:
|
||||
raise ValueError(f"Unsupported image model: {model}. Supported: {list(model_paths.keys())}")
|
||||
|
||||
url = f"{self.base_url}/{model_path}"
|
||||
|
||||
payload = {
|
||||
"prompt": prompt,
|
||||
"width": width,
|
||||
"height": height,
|
||||
"enable_sync_mode": enable_sync_mode,
|
||||
}
|
||||
|
||||
# Add optional parameters
|
||||
if num_inference_steps is not None:
|
||||
payload["num_inference_steps"] = num_inference_steps
|
||||
if guidance_scale is not None:
|
||||
payload["guidance_scale"] = guidance_scale
|
||||
if negative_prompt:
|
||||
payload["negative_prompt"] = negative_prompt
|
||||
if seed is not None:
|
||||
payload["seed"] = seed
|
||||
|
||||
# Add any extra parameters
|
||||
for key, value in kwargs.items():
|
||||
if key not in payload:
|
||||
payload[key] = value
|
||||
|
||||
logger.info(f"[WaveSpeed] Generating image via {url} (model={model}, prompt_length={len(prompt)})")
|
||||
response = requests.post(url, headers=self._get_headers(), json=payload, timeout=timeout)
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.error(f"[WaveSpeed] Image generation failed: {response.status_code} {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "WaveSpeed image generation failed",
|
||||
"status_code": response.status_code,
|
||||
"response": response.text,
|
||||
},
|
||||
)
|
||||
|
||||
response_json = response.json()
|
||||
data = response_json.get("data") or response_json
|
||||
|
||||
# Check status - if "created" or "processing", we need to poll even in sync mode
|
||||
status = data.get("status", "").lower()
|
||||
outputs = data.get("outputs") or []
|
||||
prediction_id = data.get("id")
|
||||
|
||||
# Handle sync mode - result should be directly in outputs
|
||||
if enable_sync_mode:
|
||||
# If we have outputs and status is "completed", use them directly
|
||||
if outputs and status == "completed":
|
||||
logger.info(f"[WaveSpeed] Got immediate results from sync mode (status: {status})")
|
||||
image_url = self._extract_image_url(outputs)
|
||||
return self._download_image(image_url, timeout)
|
||||
|
||||
# Sync mode returned "created" or "processing" status - need to poll
|
||||
if not prediction_id:
|
||||
logger.error(f"[WaveSpeed] Sync mode returned status '{status}' but no prediction ID: {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed sync mode returned async response without prediction ID",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"[WaveSpeed] Sync mode returned status '{status}' with no outputs. "
|
||||
f"Falling back to polling (prediction_id: {prediction_id})"
|
||||
)
|
||||
|
||||
# Async mode OR sync mode that returned "created"/"processing" - poll for result
|
||||
if not prediction_id:
|
||||
logger.error(f"[WaveSpeed] No prediction ID in response: {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed response missing prediction id",
|
||||
)
|
||||
|
||||
# Poll for result (use longer timeout for image generation)
|
||||
logger.info(f"[WaveSpeed] Polling for image generation result (prediction_id: {prediction_id}, status: {status})")
|
||||
result = self.polling.poll_until_complete(prediction_id, timeout_seconds=240, interval_seconds=1.0)
|
||||
outputs = result.get("outputs") or []
|
||||
|
||||
if not outputs:
|
||||
raise HTTPException(status_code=502, detail="WaveSpeed image generator returned no outputs")
|
||||
|
||||
image_url = self._extract_image_url(outputs)
|
||||
return self._download_image(image_url, timeout=60)
|
||||
|
||||
def generate_character_image(
|
||||
self,
|
||||
prompt: str,
|
||||
reference_image_bytes: bytes,
|
||||
style: str = "Auto",
|
||||
aspect_ratio: str = "16:9",
|
||||
rendering_speed: str = "Default",
|
||||
timeout: Optional[int] = None,
|
||||
) -> bytes:
|
||||
"""
|
||||
Generate image using Ideogram Character API to maintain character consistency.
|
||||
Creates variations of a reference character image while respecting the base appearance.
|
||||
|
||||
Note: This API is always async and requires polling for results.
|
||||
|
||||
Args:
|
||||
prompt: Text prompt describing the scene/context for the character
|
||||
reference_image_bytes: Reference image bytes (base avatar)
|
||||
style: Character style type ("Auto", "Fiction", or "Realistic")
|
||||
aspect_ratio: Aspect ratio ("1:1", "16:9", "9:16", "4:3", "3:4")
|
||||
rendering_speed: Rendering speed ("Default", "Turbo", "Quality")
|
||||
timeout: Total timeout in seconds for submission + polling (default: 180)
|
||||
|
||||
Returns:
|
||||
bytes: Generated image bytes with consistent character
|
||||
"""
|
||||
import base64
|
||||
|
||||
# Encode reference image to base64
|
||||
image_base64 = base64.b64encode(reference_image_bytes).decode('utf-8')
|
||||
# Add data URI prefix
|
||||
image_data_uri = f"data:image/png;base64,{image_base64}"
|
||||
|
||||
url = f"{self.base_url}/ideogram-ai/ideogram-character"
|
||||
|
||||
payload = {
|
||||
"prompt": prompt,
|
||||
"image": image_data_uri,
|
||||
"style": style,
|
||||
"aspect_ratio": aspect_ratio,
|
||||
"rendering_speed": rendering_speed,
|
||||
}
|
||||
|
||||
logger.info(f"[WaveSpeed] Generating character image via Ideogram Character (prompt_length={len(prompt)})")
|
||||
|
||||
# Retry on transient connection failures
|
||||
max_retries = 2
|
||||
retry_delay = 2.0
|
||||
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
response = requests.post(
|
||||
url,
|
||||
headers=self._get_headers(),
|
||||
json=payload,
|
||||
timeout=(30, 30)
|
||||
)
|
||||
break
|
||||
except (requests_exceptions.ConnectTimeout, requests_exceptions.ConnectionError) as e:
|
||||
if attempt < max_retries:
|
||||
logger.warning(f"[WaveSpeed] Connection attempt {attempt + 1}/{max_retries + 1} failed, retrying in {retry_delay}s: {e}")
|
||||
time.sleep(retry_delay)
|
||||
retry_delay *= 2
|
||||
continue
|
||||
else:
|
||||
error_type = "Connection timeout" if isinstance(e, requests_exceptions.ConnectTimeout) else "Connection error"
|
||||
logger.error(f"[WaveSpeed] {error_type} to Ideogram Character API after {max_retries + 1} attempts: {e}")
|
||||
raise HTTPException(
|
||||
status_code=504 if isinstance(e, requests_exceptions.ConnectTimeout) else 502,
|
||||
detail={
|
||||
"error": f"{error_type} to WaveSpeed Ideogram Character API",
|
||||
"message": "Unable to establish connection to the image generation service after multiple attempts. Please check your network connection and try again.",
|
||||
"exception": str(e),
|
||||
"retry_recommended": True,
|
||||
},
|
||||
)
|
||||
except requests_exceptions.Timeout as e:
|
||||
logger.error(f"[WaveSpeed] Request timeout to Ideogram Character API: {e}")
|
||||
raise HTTPException(
|
||||
status_code=504,
|
||||
detail={
|
||||
"error": "Request timeout to WaveSpeed Ideogram Character API",
|
||||
"message": "The image generation request took too long. Please try again.",
|
||||
"exception": str(e),
|
||||
},
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.error(f"[WaveSpeed] Character image generation failed: {response.status_code} {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "WaveSpeed Ideogram Character generation failed",
|
||||
"status_code": response.status_code,
|
||||
"response": response.text,
|
||||
},
|
||||
)
|
||||
|
||||
response_json = response.json()
|
||||
data = response_json.get("data") or response_json
|
||||
|
||||
# Extract prediction ID
|
||||
prediction_id = data.get("id")
|
||||
if not prediction_id:
|
||||
logger.error(f"[WaveSpeed] No prediction ID in response: {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed Ideogram Character response missing prediction id",
|
||||
)
|
||||
|
||||
# Ideogram Character API is always async - check status and poll if needed
|
||||
outputs = data.get("outputs") or []
|
||||
status = data.get("status", "unknown")
|
||||
|
||||
logger.info(f"[WaveSpeed] Ideogram Character task created: prediction_id={prediction_id}, status={status}")
|
||||
|
||||
# If status is already completed, use outputs directly (unlikely but possible)
|
||||
if outputs and status == "completed":
|
||||
logger.info(f"[WaveSpeed] Got immediate results from Ideogram Character")
|
||||
else:
|
||||
# Always need to poll for results (API is async)
|
||||
logger.info(f"[WaveSpeed] Polling for Ideogram Character result (status: {status}, prediction_id: {prediction_id})")
|
||||
polling_timeout = timeout if timeout else None
|
||||
result = self.polling.poll_until_complete(
|
||||
prediction_id,
|
||||
timeout_seconds=polling_timeout,
|
||||
interval_seconds=0.5,
|
||||
)
|
||||
|
||||
if not isinstance(result, dict):
|
||||
logger.error(f"[WaveSpeed] Unexpected result type: {type(result)}, value: {result}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed Ideogram Character returned unexpected response format",
|
||||
)
|
||||
|
||||
outputs = result.get("outputs") or []
|
||||
status = result.get("status", "unknown")
|
||||
|
||||
if status != "completed":
|
||||
error_msg = "Unknown error"
|
||||
if isinstance(result, dict):
|
||||
error_msg = result.get("error") or result.get("message") or str(result.get("details", "Unknown error"))
|
||||
else:
|
||||
error_msg = str(result)
|
||||
|
||||
logger.error(f"[WaveSpeed] Ideogram Character task did not complete: status={status}, error={error_msg}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "WaveSpeed Ideogram Character task failed",
|
||||
"status": status,
|
||||
"message": error_msg,
|
||||
}
|
||||
)
|
||||
|
||||
# Extract image URL from outputs
|
||||
if not outputs:
|
||||
logger.error(f"[WaveSpeed] No outputs after polling: status={status}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed Ideogram Character returned no outputs",
|
||||
)
|
||||
|
||||
image_url = self._extract_image_url(outputs)
|
||||
return self._download_image(image_url, timeout=60)
|
||||
|
||||
def _extract_image_url(self, outputs: list) -> str:
|
||||
"""Extract image URL from outputs."""
|
||||
if not isinstance(outputs, list) or len(outputs) == 0:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed image generator output format not recognized",
|
||||
)
|
||||
|
||||
first_output = outputs[0]
|
||||
if isinstance(first_output, str):
|
||||
image_url = first_output
|
||||
elif isinstance(first_output, dict):
|
||||
image_url = first_output.get("url") or first_output.get("image_url") or first_output.get("output")
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed image generator output format not recognized",
|
||||
)
|
||||
|
||||
if not image_url or not (image_url.startswith("http://") or image_url.startswith("https://")):
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed image generator output format not recognized",
|
||||
)
|
||||
|
||||
return image_url
|
||||
|
||||
def _download_image(self, image_url: str, timeout: int = 60) -> bytes:
|
||||
"""Download image from URL."""
|
||||
logger.info(f"[WaveSpeed] Fetching image from URL: {image_url}")
|
||||
image_response = requests.get(image_url, timeout=timeout)
|
||||
if image_response.status_code == 200:
|
||||
image_bytes = image_response.content
|
||||
logger.info(f"[WaveSpeed] Image generated successfully (size: {len(image_bytes)} bytes)")
|
||||
return image_bytes
|
||||
else:
|
||||
logger.error(f"[WaveSpeed] Failed to fetch image from URL: {image_response.status_code}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="Failed to fetch generated image from WaveSpeed URL",
|
||||
)
|
||||
164
backend/services/wavespeed/generators/prompt.py
Normal file
164
backend/services/wavespeed/generators/prompt.py
Normal file
@@ -0,0 +1,164 @@
|
||||
"""
|
||||
Prompt optimization generator for WaveSpeed API.
|
||||
"""
|
||||
|
||||
import requests
|
||||
from typing import Optional
|
||||
from fastapi import HTTPException
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("wavespeed.generators.prompt")
|
||||
|
||||
|
||||
class PromptGenerator:
|
||||
"""Prompt optimization generator."""
|
||||
|
||||
def __init__(self, api_key: str, base_url: str, polling):
|
||||
"""Initialize prompt generator.
|
||||
|
||||
Args:
|
||||
api_key: WaveSpeed API key
|
||||
base_url: WaveSpeed API base URL
|
||||
polling: WaveSpeedPolling instance for async operations
|
||||
"""
|
||||
self.api_key = api_key
|
||||
self.base_url = base_url
|
||||
self.polling = polling
|
||||
|
||||
def _get_headers(self) -> dict:
|
||||
"""Get HTTP headers for API requests."""
|
||||
return {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
}
|
||||
|
||||
def optimize_prompt(
|
||||
self,
|
||||
text: str,
|
||||
mode: str = "image",
|
||||
style: str = "default",
|
||||
image: Optional[str] = None,
|
||||
enable_sync_mode: bool = True,
|
||||
timeout: int = 30,
|
||||
) -> str:
|
||||
"""
|
||||
Optimize a prompt using WaveSpeed prompt optimizer.
|
||||
|
||||
Args:
|
||||
text: The prompt text to optimize
|
||||
mode: "image" or "video" (default: "image")
|
||||
style: "default", "artistic", "photographic", "technical", "anime", "realistic" (default: "default")
|
||||
image: Base64-encoded image for context (optional)
|
||||
enable_sync_mode: If True, wait for result and return it directly (default: True)
|
||||
timeout: Request timeout in seconds (default: 30)
|
||||
|
||||
Returns:
|
||||
Optimized prompt text
|
||||
"""
|
||||
model_path = "wavespeed-ai/prompt-optimizer"
|
||||
url = f"{self.base_url}/{model_path}"
|
||||
|
||||
payload = {
|
||||
"text": text,
|
||||
"mode": mode,
|
||||
"style": style,
|
||||
"enable_sync_mode": enable_sync_mode,
|
||||
}
|
||||
|
||||
if image:
|
||||
payload["image"] = image
|
||||
|
||||
logger.info(f"[WaveSpeed] Optimizing prompt via {url} (mode={mode}, style={style})")
|
||||
response = requests.post(url, headers=self._get_headers(), json=payload, timeout=timeout)
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.error(f"[WaveSpeed] Prompt optimization failed: {response.status_code} {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "WaveSpeed prompt optimization failed",
|
||||
"status_code": response.status_code,
|
||||
"response": response.text,
|
||||
},
|
||||
)
|
||||
|
||||
response_json = response.json()
|
||||
data = response_json.get("data") or response_json
|
||||
|
||||
# Handle sync mode - result should be directly in outputs
|
||||
if enable_sync_mode:
|
||||
outputs = data.get("outputs") or []
|
||||
if not outputs:
|
||||
logger.error(f"[WaveSpeed] No outputs in sync mode response: {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed prompt optimizer returned no outputs",
|
||||
)
|
||||
|
||||
# Extract optimized prompt from outputs
|
||||
optimized_prompt = self._extract_prompt_from_outputs(outputs, timeout)
|
||||
if not optimized_prompt:
|
||||
logger.error(f"[WaveSpeed] Could not extract optimized prompt from outputs: {outputs}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed prompt optimizer output format not recognized",
|
||||
)
|
||||
|
||||
logger.info(f"[WaveSpeed] Prompt optimized successfully (length: {len(optimized_prompt)} chars)")
|
||||
return optimized_prompt
|
||||
|
||||
# Async mode - return prediction ID for polling
|
||||
prediction_id = data.get("id")
|
||||
if not prediction_id:
|
||||
logger.error(f"[WaveSpeed] No prediction ID in async response: {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed response missing prediction id for async mode",
|
||||
)
|
||||
|
||||
# Poll for result
|
||||
result = self.polling.poll_until_complete(prediction_id, timeout_seconds=60, interval_seconds=0.5)
|
||||
outputs = result.get("outputs") or []
|
||||
|
||||
if not outputs:
|
||||
raise HTTPException(status_code=502, detail="WaveSpeed prompt optimizer returned no outputs")
|
||||
|
||||
# Extract optimized prompt from outputs
|
||||
optimized_prompt = self._extract_prompt_from_outputs(outputs, timeout)
|
||||
if not optimized_prompt:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed prompt optimizer output format not recognized",
|
||||
)
|
||||
|
||||
logger.info(f"[WaveSpeed] Prompt optimized successfully (length: {len(optimized_prompt)} chars)")
|
||||
return optimized_prompt
|
||||
|
||||
def _extract_prompt_from_outputs(self, outputs: list, timeout: int) -> Optional[str]:
|
||||
"""Extract optimized prompt from outputs, handling URLs and direct text."""
|
||||
if not isinstance(outputs, list) or len(outputs) == 0:
|
||||
return None
|
||||
|
||||
first_output = outputs[0]
|
||||
|
||||
# If it's a string that looks like a URL, fetch it
|
||||
if isinstance(first_output, str):
|
||||
if first_output.startswith("http://") or first_output.startswith("https://"):
|
||||
logger.info(f"[WaveSpeed] Fetching optimized prompt from URL: {first_output}")
|
||||
url_response = requests.get(first_output, timeout=timeout)
|
||||
if url_response.status_code == 200:
|
||||
return url_response.text.strip()
|
||||
else:
|
||||
logger.error(f"[WaveSpeed] Failed to fetch prompt from URL: {url_response.status_code}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="Failed to fetch optimized prompt from WaveSpeed URL",
|
||||
)
|
||||
else:
|
||||
# It's already the text
|
||||
return first_output
|
||||
elif isinstance(first_output, dict):
|
||||
return first_output.get("text") or first_output.get("prompt") or first_output.get("output")
|
||||
|
||||
return None
|
||||
223
backend/services/wavespeed/generators/speech.py
Normal file
223
backend/services/wavespeed/generators/speech.py
Normal file
@@ -0,0 +1,223 @@
|
||||
"""
|
||||
Speech generation generator for WaveSpeed API.
|
||||
"""
|
||||
|
||||
import time
|
||||
import requests
|
||||
from typing import Optional
|
||||
from requests import exceptions as requests_exceptions
|
||||
from fastapi import HTTPException
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("wavespeed.generators.speech")
|
||||
|
||||
|
||||
class SpeechGenerator:
|
||||
"""Speech generation generator."""
|
||||
|
||||
def __init__(self, api_key: str, base_url: str, polling):
|
||||
"""Initialize speech generator.
|
||||
|
||||
Args:
|
||||
api_key: WaveSpeed API key
|
||||
base_url: WaveSpeed API base URL
|
||||
polling: WaveSpeedPolling instance for async operations
|
||||
"""
|
||||
self.api_key = api_key
|
||||
self.base_url = base_url
|
||||
self.polling = polling
|
||||
|
||||
def _get_headers(self) -> dict:
|
||||
"""Get HTTP headers for API requests."""
|
||||
return {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
}
|
||||
|
||||
def generate_speech(
|
||||
self,
|
||||
text: str,
|
||||
voice_id: str,
|
||||
speed: float = 1.0,
|
||||
volume: float = 1.0,
|
||||
pitch: float = 0.0,
|
||||
emotion: str = "happy",
|
||||
enable_sync_mode: bool = True,
|
||||
timeout: int = 120,
|
||||
**kwargs
|
||||
) -> bytes:
|
||||
"""
|
||||
Generate speech audio using Minimax Speech 02 HD via WaveSpeed.
|
||||
|
||||
Args:
|
||||
text: Text to convert to speech (max 10000 characters)
|
||||
voice_id: Voice ID (e.g., "Wise_Woman", "Friendly_Person", etc.)
|
||||
speed: Speech speed (0.5-2.0, default: 1.0)
|
||||
volume: Speech volume (0.1-10.0, default: 1.0)
|
||||
pitch: Speech pitch (-12 to 12, default: 0.0)
|
||||
emotion: Emotion ("happy", "sad", "angry", etc., default: "happy")
|
||||
enable_sync_mode: If True, wait for result and return it directly (default: True)
|
||||
timeout: Request timeout in seconds (default: 60)
|
||||
**kwargs: Additional parameters (sample_rate, bitrate, format, etc.)
|
||||
|
||||
Returns:
|
||||
bytes: Generated audio bytes
|
||||
"""
|
||||
model_path = "minimax/speech-02-hd"
|
||||
url = f"{self.base_url}/{model_path}"
|
||||
|
||||
payload = {
|
||||
"text": text,
|
||||
"voice_id": voice_id,
|
||||
"speed": speed,
|
||||
"volume": volume,
|
||||
"pitch": pitch,
|
||||
"emotion": emotion,
|
||||
"enable_sync_mode": enable_sync_mode,
|
||||
}
|
||||
|
||||
# Add optional parameters
|
||||
optional_params = [
|
||||
"english_normalization",
|
||||
"sample_rate",
|
||||
"bitrate",
|
||||
"channel",
|
||||
"format",
|
||||
"language_boost",
|
||||
]
|
||||
for param in optional_params:
|
||||
if param in kwargs:
|
||||
payload[param] = kwargs[param]
|
||||
|
||||
logger.info(f"[WaveSpeed] Generating speech via {url} (voice={voice_id}, text_length={len(text)})")
|
||||
|
||||
# Retry on transient connection issues
|
||||
max_retries = 2
|
||||
retry_delay = 2.0
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
response = requests.post(
|
||||
url,
|
||||
headers=self._get_headers(),
|
||||
json=payload,
|
||||
timeout=(30, 60), # connect, read
|
||||
)
|
||||
break
|
||||
except (requests_exceptions.ConnectTimeout, requests_exceptions.ConnectionError) as e:
|
||||
if attempt < max_retries:
|
||||
logger.warning(
|
||||
f"[WaveSpeed] Speech connection attempt {attempt + 1}/{max_retries + 1} failed, "
|
||||
f"retrying in {retry_delay}s: {e}"
|
||||
)
|
||||
time.sleep(retry_delay)
|
||||
retry_delay *= 2
|
||||
continue
|
||||
logger.error(f"[WaveSpeed] Speech connection failed after {max_retries + 1} attempts: {e}")
|
||||
raise HTTPException(
|
||||
status_code=504,
|
||||
detail={
|
||||
"error": "Connection to WaveSpeed speech API timed out",
|
||||
"message": "Unable to reach the speech service. Please try again.",
|
||||
"exception": str(e),
|
||||
"retry_recommended": True,
|
||||
},
|
||||
)
|
||||
except requests_exceptions.Timeout as e:
|
||||
logger.error(f"[WaveSpeed] Speech request timeout: {e}")
|
||||
raise HTTPException(
|
||||
status_code=504,
|
||||
detail={
|
||||
"error": "WaveSpeed speech request timed out",
|
||||
"message": "The speech generation request took too long. Please try again.",
|
||||
"exception": str(e),
|
||||
},
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.error(f"[WaveSpeed] Speech generation failed: {response.status_code} {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "WaveSpeed speech generation failed",
|
||||
"status_code": response.status_code,
|
||||
"response": response.text,
|
||||
},
|
||||
)
|
||||
|
||||
response_json = response.json()
|
||||
data = response_json.get("data") or response_json
|
||||
|
||||
# Handle sync mode - result should be directly in outputs
|
||||
if enable_sync_mode:
|
||||
outputs = data.get("outputs") or []
|
||||
if not outputs:
|
||||
logger.error(f"[WaveSpeed] No outputs in sync mode response: {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed speech generator returned no outputs",
|
||||
)
|
||||
|
||||
audio_url = self._extract_audio_url(outputs)
|
||||
return self._download_audio(audio_url, timeout)
|
||||
|
||||
# Async mode - return prediction ID for polling
|
||||
prediction_id = data.get("id")
|
||||
if not prediction_id:
|
||||
logger.error(f"[WaveSpeed] No prediction ID in async response: {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed response missing prediction id for async mode",
|
||||
)
|
||||
|
||||
# Poll for result
|
||||
result = self.polling.poll_until_complete(prediction_id, timeout_seconds=120, interval_seconds=0.5)
|
||||
outputs = result.get("outputs") or []
|
||||
|
||||
if not outputs:
|
||||
raise HTTPException(status_code=502, detail="WaveSpeed speech generator returned no outputs")
|
||||
|
||||
audio_url = self._extract_audio_url(outputs)
|
||||
return self._download_audio(audio_url, timeout)
|
||||
|
||||
def _extract_audio_url(self, outputs: list) -> str:
|
||||
"""Extract audio URL from outputs."""
|
||||
if not isinstance(outputs, list) or len(outputs) == 0:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed speech generator output format not recognized",
|
||||
)
|
||||
|
||||
first_output = outputs[0]
|
||||
if isinstance(first_output, str):
|
||||
audio_url = first_output
|
||||
elif isinstance(first_output, dict):
|
||||
audio_url = first_output.get("url") or first_output.get("output")
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed speech generator output format not recognized",
|
||||
)
|
||||
|
||||
if not audio_url or not (audio_url.startswith("http://") or audio_url.startswith("https://")):
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="WaveSpeed speech generator output format not recognized",
|
||||
)
|
||||
|
||||
return audio_url
|
||||
|
||||
def _download_audio(self, audio_url: str, timeout: int) -> bytes:
|
||||
"""Download audio from URL."""
|
||||
logger.info(f"[WaveSpeed] Fetching audio from URL: {audio_url}")
|
||||
audio_response = requests.get(audio_url, timeout=timeout)
|
||||
if audio_response.status_code == 200:
|
||||
audio_bytes = audio_response.content
|
||||
logger.info(f"[WaveSpeed] Speech generated successfully (size: {len(audio_bytes)} bytes)")
|
||||
return audio_bytes
|
||||
else:
|
||||
logger.error(f"[WaveSpeed] Failed to fetch audio from URL: {audio_response.status_code}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail="Failed to fetch generated audio from WaveSpeed URL",
|
||||
)
|
||||
1330
backend/services/wavespeed/generators/video.py
Normal file
1330
backend/services/wavespeed/generators/video.py
Normal file
File diff suppressed because it is too large
Load Diff
253
backend/services/wavespeed/hunyuan_avatar.py
Normal file
253
backend/services/wavespeed/hunyuan_avatar.py
Normal file
@@ -0,0 +1,253 @@
|
||||
"""
|
||||
Hunyuan Avatar Service
|
||||
|
||||
Service for creating talking avatars using Hunyuan Avatar model.
|
||||
Reference: https://wavespeed.ai/models/wavespeed-ai/hunyuan-avatar
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import base64
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import requests
|
||||
from fastapi import HTTPException
|
||||
from loguru import logger
|
||||
|
||||
from .client import WaveSpeedClient
|
||||
|
||||
HUNYUAN_AVATAR_MODEL_PATH = "wavespeed-ai/hunyuan-avatar"
|
||||
HUNYUAN_AVATAR_MODEL_NAME = "wavespeed-ai/hunyuan-avatar"
|
||||
MAX_IMAGE_BYTES = 10 * 1024 * 1024 # 10MB
|
||||
MAX_AUDIO_BYTES = 50 * 1024 * 1024 # 50MB safety cap
|
||||
MAX_DURATION_SECONDS = 120 # 2 minutes maximum
|
||||
MIN_DURATION_SECONDS = 5 # Minimum billable duration
|
||||
|
||||
|
||||
def _as_data_uri(content_bytes: bytes, mime_type: str) -> str:
|
||||
"""Convert bytes to data URI."""
|
||||
encoded = base64.b64encode(content_bytes).decode("utf-8")
|
||||
return f"data:{mime_type};base64,{encoded}"
|
||||
|
||||
|
||||
def calculate_hunyuan_avatar_cost(resolution: str, duration: float) -> float:
|
||||
"""
|
||||
Calculate cost for Hunyuan Avatar video.
|
||||
|
||||
Pricing:
|
||||
- 480p: $0.15 per 5 seconds
|
||||
- 720p: $0.30 per 5 seconds
|
||||
- Minimum charge: 5 seconds
|
||||
- Maximum billable: 120 seconds
|
||||
|
||||
Args:
|
||||
resolution: Output resolution (480p or 720p)
|
||||
duration: Video duration in seconds
|
||||
|
||||
Returns:
|
||||
Cost in USD
|
||||
"""
|
||||
# Clamp duration to valid range
|
||||
actual_duration = max(MIN_DURATION_SECONDS, min(duration, MAX_DURATION_SECONDS))
|
||||
|
||||
# Calculate cost per 5 seconds
|
||||
cost_per_5_seconds = 0.15 if resolution == "480p" else 0.30
|
||||
|
||||
# Round up to nearest 5 seconds
|
||||
billable_5_second_blocks = (actual_duration + 4) // 5 # Ceiling division
|
||||
|
||||
return cost_per_5_seconds * billable_5_second_blocks
|
||||
|
||||
|
||||
def create_hunyuan_avatar(
|
||||
*,
|
||||
image_bytes: bytes,
|
||||
audio_bytes: bytes,
|
||||
resolution: str = "480p",
|
||||
prompt: Optional[str] = None,
|
||||
seed: Optional[int] = None,
|
||||
user_id: str = "video_studio",
|
||||
image_mime: str = "image/png",
|
||||
audio_mime: str = "audio/mpeg",
|
||||
client: Optional[WaveSpeedClient] = None,
|
||||
progress_callback: Optional[callable] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Create talking avatar video using Hunyuan Avatar.
|
||||
|
||||
Reference: https://wavespeed.ai/docs/docs-api/wavespeed-ai/hunyuan-avatar
|
||||
|
||||
Args:
|
||||
image_bytes: Portrait image as bytes
|
||||
audio_bytes: Audio file as bytes
|
||||
resolution: Output resolution (480p or 720p, default: 480p)
|
||||
prompt: Optional text to guide expression or style
|
||||
seed: Optional random seed (-1 for random)
|
||||
user_id: User ID for tracking
|
||||
image_mime: MIME type of image
|
||||
audio_mime: MIME type of audio
|
||||
client: Optional WaveSpeedClient instance
|
||||
progress_callback: Optional progress callback function
|
||||
|
||||
Returns:
|
||||
Dictionary with video_bytes, prompt, duration, model_name, cost, etc.
|
||||
"""
|
||||
if not image_bytes:
|
||||
raise HTTPException(status_code=400, detail="Image bytes are required for Hunyuan Avatar.")
|
||||
if not audio_bytes:
|
||||
raise HTTPException(status_code=400, detail="Audio bytes are required for Hunyuan Avatar.")
|
||||
|
||||
if len(image_bytes) > MAX_IMAGE_BYTES:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Image exceeds {MAX_IMAGE_BYTES / (1024 * 1024):.0f}MB limit required by Hunyuan Avatar.",
|
||||
)
|
||||
if len(audio_bytes) > MAX_AUDIO_BYTES:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Audio exceeds {MAX_AUDIO_BYTES / (1024 * 1024):.0f}MB limit allowed for Hunyuan Avatar requests.",
|
||||
)
|
||||
|
||||
if resolution not in {"480p", "720p"}:
|
||||
raise HTTPException(status_code=400, detail="Resolution must be '480p' or '720p'.")
|
||||
|
||||
# Build payload
|
||||
payload: Dict[str, Any] = {
|
||||
"image": _as_data_uri(image_bytes, image_mime),
|
||||
"audio": _as_data_uri(audio_bytes, audio_mime),
|
||||
"resolution": resolution,
|
||||
}
|
||||
|
||||
if prompt:
|
||||
payload["prompt"] = prompt.strip()
|
||||
if seed is not None:
|
||||
payload["seed"] = seed
|
||||
|
||||
client = client or WaveSpeedClient()
|
||||
|
||||
# Progress callback: submission
|
||||
if progress_callback:
|
||||
progress_callback(10.0, "Submitting Hunyuan Avatar request to WaveSpeed...")
|
||||
|
||||
prediction_id = client.submit_image_to_video(HUNYUAN_AVATAR_MODEL_PATH, payload, timeout=60)
|
||||
|
||||
try:
|
||||
# Poll for completion
|
||||
if progress_callback:
|
||||
progress_callback(20.0, f"Polling for completion (prediction_id: {prediction_id})...")
|
||||
|
||||
result = client.poll_until_complete(
|
||||
prediction_id,
|
||||
timeout_seconds=600, # 10 minutes max
|
||||
interval_seconds=0.5, # Poll every 0.5 seconds
|
||||
progress_callback=progress_callback,
|
||||
)
|
||||
except HTTPException as exc:
|
||||
detail = exc.detail or {}
|
||||
if isinstance(detail, dict):
|
||||
detail.setdefault("prediction_id", prediction_id)
|
||||
detail.setdefault("resume_available", True)
|
||||
raise
|
||||
|
||||
outputs = result.get("outputs") or []
|
||||
if not outputs:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "Hunyuan Avatar completed but returned no outputs",
|
||||
"prediction_id": prediction_id,
|
||||
}
|
||||
)
|
||||
|
||||
video_url = outputs[0]
|
||||
if not isinstance(video_url, str) or not video_url.startswith("http"):
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": f"Invalid video URL format: {video_url}",
|
||||
"prediction_id": prediction_id,
|
||||
}
|
||||
)
|
||||
|
||||
# Progress callback: downloading video
|
||||
if progress_callback:
|
||||
progress_callback(90.0, "Downloading generated video...")
|
||||
|
||||
# Download video
|
||||
try:
|
||||
video_response = requests.get(video_url, timeout=180)
|
||||
if video_response.status_code != 200:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "Failed to download Hunyuan Avatar video",
|
||||
"status_code": video_response.status_code,
|
||||
"response": video_response.text[:200],
|
||||
"prediction_id": prediction_id,
|
||||
}
|
||||
)
|
||||
except requests.exceptions.RequestException as e:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": f"Failed to download video: {str(e)}",
|
||||
"prediction_id": prediction_id,
|
||||
}
|
||||
)
|
||||
|
||||
video_bytes = video_response.content
|
||||
if len(video_bytes) == 0:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "Downloaded video is empty",
|
||||
"prediction_id": prediction_id,
|
||||
}
|
||||
)
|
||||
|
||||
# Estimate duration (we don't get exact duration from API, so estimate from audio or use default)
|
||||
# For now, we'll use a default estimate - in production, you might want to analyze the audio file
|
||||
estimated_duration = 10.0 # Default estimate
|
||||
|
||||
# Calculate cost
|
||||
cost = calculate_hunyuan_avatar_cost(resolution, estimated_duration)
|
||||
|
||||
# Get video dimensions from resolution
|
||||
resolution_dims = {
|
||||
"480p": (854, 480),
|
||||
"720p": (1280, 720),
|
||||
}
|
||||
width, height = resolution_dims.get(resolution, (854, 480))
|
||||
|
||||
# Extract metadata
|
||||
metadata = result.get("metadata", {})
|
||||
metadata.update({
|
||||
"has_nsfw_contents": result.get("has_nsfw_contents", []),
|
||||
"created_at": result.get("created_at"),
|
||||
"resolution": resolution,
|
||||
"max_duration": MAX_DURATION_SECONDS,
|
||||
})
|
||||
|
||||
logger.info(
|
||||
f"[Hunyuan Avatar] ✅ Generated video: {len(video_bytes)} bytes, "
|
||||
f"resolution={resolution}, cost=${cost:.2f}"
|
||||
)
|
||||
|
||||
# Progress callback: completed
|
||||
if progress_callback:
|
||||
progress_callback(100.0, "Avatar generation completed!")
|
||||
|
||||
return {
|
||||
"video_bytes": video_bytes,
|
||||
"prompt": prompt or "",
|
||||
"duration": estimated_duration,
|
||||
"model_name": HUNYUAN_AVATAR_MODEL_NAME,
|
||||
"cost": cost,
|
||||
"provider": "wavespeed",
|
||||
"resolution": resolution,
|
||||
"width": width,
|
||||
"height": height,
|
||||
"metadata": metadata,
|
||||
"source_video_url": video_url,
|
||||
"prediction_id": prediction_id,
|
||||
}
|
||||
203
backend/services/wavespeed/polling.py
Normal file
203
backend/services/wavespeed/polling.py
Normal file
@@ -0,0 +1,203 @@
|
||||
"""
|
||||
Polling utilities for WaveSpeed API.
|
||||
"""
|
||||
|
||||
import time
|
||||
from typing import Any, Dict, Optional, Callable
|
||||
|
||||
import requests
|
||||
from fastapi import HTTPException
|
||||
from requests import exceptions as requests_exceptions
|
||||
|
||||
from utils.logger_utils import get_service_logger
|
||||
|
||||
logger = get_service_logger("wavespeed.polling")
|
||||
|
||||
|
||||
class WaveSpeedPolling:
|
||||
"""Polling utilities for WaveSpeed API predictions."""
|
||||
|
||||
def __init__(self, api_key: str, base_url: str):
|
||||
"""Initialize polling utilities.
|
||||
|
||||
Args:
|
||||
api_key: WaveSpeed API key
|
||||
base_url: WaveSpeed API base URL
|
||||
"""
|
||||
self.api_key = api_key
|
||||
self.base_url = base_url
|
||||
|
||||
def _get_headers(self) -> Dict[str, str]:
|
||||
"""Get HTTP headers for API requests."""
|
||||
return {"Authorization": f"Bearer {self.api_key}"}
|
||||
|
||||
def get_prediction_result(self, prediction_id: str, timeout: int = 30) -> Dict[str, Any]:
|
||||
"""
|
||||
Fetch the current status/result for a prediction.
|
||||
Matches the example pattern: simple GET request, check status_code == 200, return data.
|
||||
"""
|
||||
url = f"{self.base_url}/predictions/{prediction_id}/result"
|
||||
headers = self._get_headers()
|
||||
|
||||
try:
|
||||
response = requests.get(url, headers=headers, timeout=timeout)
|
||||
except requests_exceptions.Timeout as exc:
|
||||
raise HTTPException(
|
||||
status_code=504,
|
||||
detail={
|
||||
"error": "WaveSpeed polling request timed out",
|
||||
"prediction_id": prediction_id,
|
||||
"resume_available": True,
|
||||
"exception": str(exc),
|
||||
},
|
||||
) from exc
|
||||
except requests_exceptions.RequestException as exc:
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "WaveSpeed polling request failed",
|
||||
"prediction_id": prediction_id,
|
||||
"resume_available": True,
|
||||
"exception": str(exc),
|
||||
},
|
||||
) from exc
|
||||
|
||||
# Match example pattern: check status_code == 200, then get data
|
||||
if response.status_code == 200:
|
||||
result = response.json().get("data")
|
||||
if not result:
|
||||
raise HTTPException(status_code=502, detail={"error": "WaveSpeed polling response missing data"})
|
||||
return result
|
||||
else:
|
||||
# Non-200 status - log and raise error (matching example's break behavior)
|
||||
logger.error(f"[WaveSpeed] Polling failed: {response.status_code} {response.text}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "WaveSpeed prediction polling failed",
|
||||
"status_code": response.status_code,
|
||||
"response": response.text,
|
||||
},
|
||||
)
|
||||
|
||||
def poll_until_complete(
|
||||
self,
|
||||
prediction_id: str,
|
||||
timeout_seconds: Optional[int] = None,
|
||||
interval_seconds: float = 1.0,
|
||||
progress_callback: Optional[Callable[[float, str], None]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Poll WaveSpeed until the job completes or fails.
|
||||
Matches the example pattern: simple polling loop until status is "completed" or "failed".
|
||||
|
||||
Args:
|
||||
prediction_id: The prediction ID to poll for
|
||||
timeout_seconds: Optional timeout in seconds. If None, polls indefinitely until completion/failure.
|
||||
interval_seconds: Seconds to wait between polling attempts (default: 1.0, faster than 2.0)
|
||||
progress_callback: Optional callback function(progress: float, message: str) for progress updates
|
||||
|
||||
Returns:
|
||||
Dict containing the completed result
|
||||
|
||||
Raises:
|
||||
HTTPException: If the task fails, polling fails, or times out (if timeout_seconds is set)
|
||||
"""
|
||||
start_time = time.time()
|
||||
consecutive_errors = 0
|
||||
max_consecutive_errors = 6 # safety guard for non-transient errors
|
||||
|
||||
while True:
|
||||
try:
|
||||
result = self.get_prediction_result(prediction_id)
|
||||
consecutive_errors = 0 # Reset error counter on success
|
||||
except HTTPException as exc:
|
||||
detail = exc.detail or {}
|
||||
if isinstance(detail, dict):
|
||||
detail.setdefault("prediction_id", prediction_id)
|
||||
detail.setdefault("resume_available", True)
|
||||
detail.setdefault("error", detail.get("error", "WaveSpeed polling failed"))
|
||||
|
||||
# Determine underlying status code (WaveSpeed vs proxy)
|
||||
status_code = detail.get("status_code", exc.status_code)
|
||||
|
||||
# Treat 5xx as transient: keep polling indefinitely with backoff
|
||||
if 500 <= int(status_code) < 600:
|
||||
consecutive_errors += 1
|
||||
backoff = min(30.0, interval_seconds * (2 ** (consecutive_errors - 1)))
|
||||
logger.warning(
|
||||
f"[WaveSpeed] Transient polling error {consecutive_errors} for {prediction_id}: "
|
||||
f"{status_code}. Backing off {backoff:.1f}s"
|
||||
)
|
||||
time.sleep(backoff)
|
||||
continue
|
||||
|
||||
# For non-transient (typically 4xx) errors, apply safety cap
|
||||
consecutive_errors += 1
|
||||
if consecutive_errors >= max_consecutive_errors:
|
||||
logger.error(
|
||||
f"[WaveSpeed] Too many polling errors ({consecutive_errors}) for {prediction_id}, "
|
||||
f"status_code={status_code}. Giving up."
|
||||
)
|
||||
raise HTTPException(status_code=exc.status_code, detail=detail) from exc
|
||||
|
||||
backoff = min(30.0, interval_seconds * (2 ** (consecutive_errors - 1)))
|
||||
logger.warning(
|
||||
f"[WaveSpeed] Polling error {consecutive_errors}/{max_consecutive_errors} for {prediction_id}: "
|
||||
f"{status_code}. Backing off {backoff:.1f}s"
|
||||
)
|
||||
time.sleep(backoff)
|
||||
continue
|
||||
|
||||
# Extract status from result (matching example pattern)
|
||||
status = result.get("status")
|
||||
|
||||
if status == "completed":
|
||||
elapsed = time.time() - start_time
|
||||
logger.info(f"[WaveSpeed] Prediction {prediction_id} completed in {elapsed:.1f}s")
|
||||
return result
|
||||
|
||||
if status == "failed":
|
||||
error_msg = result.get("error", "Unknown error")
|
||||
logger.error(f"[WaveSpeed] Prediction {prediction_id} failed: {error_msg}")
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
"error": "WaveSpeed task failed",
|
||||
"prediction_id": prediction_id,
|
||||
"message": error_msg,
|
||||
"details": result,
|
||||
},
|
||||
)
|
||||
|
||||
# Check timeout only if specified
|
||||
if timeout_seconds is not None:
|
||||
elapsed = time.time() - start_time
|
||||
if elapsed > timeout_seconds:
|
||||
logger.error(f"[WaveSpeed] Prediction {prediction_id} timed out after {timeout_seconds}s")
|
||||
raise HTTPException(
|
||||
status_code=504,
|
||||
detail={
|
||||
"error": "WaveSpeed task timed out",
|
||||
"prediction_id": prediction_id,
|
||||
"timeout_seconds": timeout_seconds,
|
||||
"current_status": status,
|
||||
"message": f"Task did not complete within {timeout_seconds} seconds. Status: {status}",
|
||||
},
|
||||
)
|
||||
|
||||
# Log progress periodically (every 30 seconds)
|
||||
elapsed = time.time() - start_time
|
||||
if int(elapsed) % 30 == 0 and elapsed > 0:
|
||||
logger.info(f"[WaveSpeed] Polling {prediction_id}: status={status}, elapsed={elapsed:.0f}s")
|
||||
|
||||
# Call progress callback if provided
|
||||
if progress_callback:
|
||||
# Map elapsed time to progress (20-80% range during polling)
|
||||
# Assume typical completion time is timeout_seconds or 120s default
|
||||
estimated_total = timeout_seconds or 120
|
||||
progress = min(80.0, 20.0 + (elapsed / estimated_total) * 60.0)
|
||||
progress_callback(progress, f"Video generation in progress... ({elapsed:.0f}s)")
|
||||
|
||||
# Poll faster (1.0s instead of 2.0s) to match example's responsiveness
|
||||
time.sleep(interval_seconds)
|
||||
@@ -107,26 +107,136 @@ class YouTubeVideoRendererService:
|
||||
try:
|
||||
from pathlib import Path
|
||||
from urllib.parse import urlparse
|
||||
import requests
|
||||
|
||||
logger.info(f"[YouTubeRenderer] Attempting to load existing audio for scene {scene_number} from URL: {scene_audio_url}")
|
||||
|
||||
# Extract filename from URL (e.g., /api/youtube/audio/filename.mp3)
|
||||
parsed_url = urlparse(scene_audio_url)
|
||||
audio_filename = Path(parsed_url.path).name
|
||||
|
||||
# Load audio file
|
||||
# Try to load from local file system first
|
||||
base_dir = Path(__file__).parent.parent.parent.parent
|
||||
youtube_audio_dir = base_dir / "youtube_audio"
|
||||
audio_path = youtube_audio_dir / audio_filename
|
||||
|
||||
if audio_path.exists():
|
||||
# Debug: If file not found, try to find it with flexible matching
|
||||
if not audio_path.exists():
|
||||
logger.debug(f"[YouTubeRenderer] Audio file not found at {audio_path}. Searching for alternative matches...")
|
||||
if youtube_audio_dir.exists():
|
||||
all_files = list(youtube_audio_dir.glob("*.mp3"))
|
||||
logger.debug(f"[YouTubeRenderer] Found {len(all_files)} MP3 files in directory")
|
||||
|
||||
# Try to find a file that matches the scene (by scene number or title pattern)
|
||||
# The filename format is: scene_{scene_number}_{clean_title}_{unique_id}.mp3
|
||||
# Extract components from expected filename
|
||||
expected_parts = audio_filename.replace('.mp3', '').split('_')
|
||||
if len(expected_parts) >= 3:
|
||||
scene_num_str = expected_parts[1] if expected_parts[0] == 'scene' else None
|
||||
title_part = expected_parts[2] if len(expected_parts) > 2 else None
|
||||
|
||||
# Try to find files matching scene number or title
|
||||
matching_files = []
|
||||
for f in all_files:
|
||||
file_parts = f.stem.split('_')
|
||||
if len(file_parts) >= 3 and file_parts[0] == 'scene':
|
||||
file_scene_num = file_parts[1]
|
||||
file_title = file_parts[2] if len(file_parts) > 2 else ''
|
||||
|
||||
# Match by scene number (try both 0-indexed and 1-indexed)
|
||||
if scene_num_str:
|
||||
scene_num_int = int(scene_num_str)
|
||||
file_scene_int = int(file_scene_num) if file_scene_num.isdigit() else None
|
||||
if file_scene_int == scene_num_int or file_scene_int == scene_num_int - 1 or file_scene_int == scene_num_int + 1:
|
||||
matching_files.append(f.name)
|
||||
# Or match by title
|
||||
elif title_part and title_part.lower() in file_title.lower():
|
||||
matching_files.append(f.name)
|
||||
|
||||
if matching_files:
|
||||
logger.info(
|
||||
f"[YouTubeRenderer] Found potential audio file matches for scene {scene_number}: {matching_files[:3]}. "
|
||||
f"Expected: {audio_filename}"
|
||||
)
|
||||
# Try using the first match
|
||||
alternative_path = youtube_audio_dir / matching_files[0]
|
||||
if alternative_path.exists() and alternative_path.is_file():
|
||||
logger.info(f"[YouTubeRenderer] Using alternative audio file: {matching_files[0]}")
|
||||
audio_path = alternative_path
|
||||
audio_filename = matching_files[0]
|
||||
else:
|
||||
logger.warning(f"[YouTubeRenderer] Alternative match found but file doesn't exist: {alternative_path}")
|
||||
else:
|
||||
# Show sample files for debugging
|
||||
sample_files = [f.name for f in all_files[:10] if f.name.startswith("scene_")]
|
||||
if sample_files:
|
||||
logger.debug(f"[YouTubeRenderer] Sample scene audio files in directory: {sample_files}")
|
||||
|
||||
if audio_path.exists() and audio_path.is_file():
|
||||
with open(audio_path, "rb") as f:
|
||||
audio_bytes = f.read()
|
||||
audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
|
||||
logger.info(f"[YouTubeRenderer] Using existing audio for scene {scene_number} from {audio_filename}")
|
||||
logger.info(f"[YouTubeRenderer] ✅ Using existing audio for scene {scene_number} from local file: {audio_filename} ({len(audio_bytes)} bytes)")
|
||||
else:
|
||||
logger.warning(f"[YouTubeRenderer] Audio file not found: {audio_path}, will generate new audio")
|
||||
raise FileNotFoundError(f"Audio file not found: {audio_path}")
|
||||
# File not found locally - try loading from asset library
|
||||
logger.warning(
|
||||
f"[YouTubeRenderer] Audio file not found locally at {audio_path}. "
|
||||
f"Attempting to load from asset library (filename: {audio_filename})"
|
||||
)
|
||||
|
||||
try:
|
||||
from services.content_asset_service import ContentAssetService
|
||||
from services.database import get_db
|
||||
from models.content_asset_models import AssetType, AssetSource
|
||||
|
||||
db = next(get_db())
|
||||
try:
|
||||
asset_service = ContentAssetService(db)
|
||||
# Try to find the asset by filename and source
|
||||
assets = asset_service.get_assets(
|
||||
user_id=user_id,
|
||||
asset_type=AssetType.AUDIO,
|
||||
source_module=AssetSource.YOUTUBE_CREATOR,
|
||||
limit=100,
|
||||
)
|
||||
|
||||
# Find matching asset by filename
|
||||
matching_asset = None
|
||||
for asset in assets:
|
||||
if asset.filename == audio_filename:
|
||||
matching_asset = asset
|
||||
break
|
||||
|
||||
if matching_asset and matching_asset.file_path:
|
||||
asset_path = Path(matching_asset.file_path)
|
||||
if asset_path.exists() and asset_path.is_file():
|
||||
with open(asset_path, "rb") as f:
|
||||
audio_bytes = f.read()
|
||||
audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
|
||||
logger.info(
|
||||
f"[YouTubeRenderer] ✅ Loaded audio for scene {scene_number} from asset library: "
|
||||
f"{audio_filename} ({len(audio_bytes)} bytes)"
|
||||
)
|
||||
else:
|
||||
raise FileNotFoundError(f"Asset library file path does not exist: {asset_path}")
|
||||
else:
|
||||
raise FileNotFoundError(f"Audio asset not found in library for filename: {audio_filename}")
|
||||
finally:
|
||||
db.close()
|
||||
except Exception as asset_error:
|
||||
logger.warning(
|
||||
f"[YouTubeRenderer] Failed to load audio from asset library: {asset_error}. "
|
||||
f"Original path attempted: {audio_path}"
|
||||
)
|
||||
raise FileNotFoundError(
|
||||
f"Audio file not found at {audio_path} and not found in asset library: {asset_error}"
|
||||
)
|
||||
|
||||
except FileNotFoundError as e:
|
||||
logger.warning(f"[YouTubeRenderer] ❌ Audio file not found: {e}. Will generate new audio if enabled.")
|
||||
scene_audio_url = None # Fall back to generation
|
||||
except Exception as e:
|
||||
logger.warning(f"[YouTubeRenderer] Failed to load existing audio: {e}, will generate new audio")
|
||||
logger.warning(f"[YouTubeRenderer] ❌ Failed to load existing audio: {e}. Will generate new audio if enabled.", exc_info=True)
|
||||
scene_audio_url = None # Fall back to generation
|
||||
|
||||
# Generate audio if not available and generation is enabled
|
||||
|
||||
Reference in New Issue
Block a user