feat: podcast demo mode with ALWRITY_ENABLED_FEATURES support
- Add ALWRITY_ENABLED_FEATURES env var for feature gating - Podcast-only mode: skip LLM bootstrap, scheduler, persona services - Enhance video generation prompt with scene context, analysis, narration - Add voice cloning support via custom_voice_id in WaveSpeed - Add text-to-speech for research results (browser speechSynthesis) - Fix render queue to sync images from script phase - Add WaveSpeed LLM pricing (gpt-oss-120b) - Fix podcast bible generation error handling - Refactor RouterManager for feature-based router loading
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@@ -1,11 +1,12 @@
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"""
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Podcast Script Handlers
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Script generation endpoint.
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Script generation and approval endpoints.
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"""
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from fastapi import APIRouter, Depends, HTTPException
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from typing import Dict, Any
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from typing import Dict, Any, Optional
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from pydantic import BaseModel, Field
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import json
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from middleware.auth_middleware import get_current_user
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@@ -24,6 +25,29 @@ from ..models import (
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router = APIRouter()
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class SceneApprovalRequest(BaseModel):
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project_id: str = Field(..., min_length=1)
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scene_id: str = Field(..., min_length=1)
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approved: bool = True
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notes: Optional[str] = None
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@router.post("/script/approve")
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async def approve_podcast_scene(
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request: SceneApprovalRequest,
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current_user: Dict[str, Any] = Depends(get_current_user),
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) -> Dict[str, Any]:
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"""Persist scene approval metadata for auditing (podcast-specific)."""
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user_id = require_authenticated_user(current_user)
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logger.warning(f"[Podcast] Scene approval recorded user={user_id} project={request.project_id} scene={request.scene_id} approved={request.approved}")
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return {
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"success": True,
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"project_id": request.project_id,
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"scene_id": request.scene_id,
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"approved": request.approved,
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}
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@router.post("/script", response_model=PodcastScriptResponse)
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async def generate_podcast_script(
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request: PodcastScriptRequest,
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@@ -33,6 +57,10 @@ async def generate_podcast_script(
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Generate a podcast script outline (scenes + lines) using podcast-oriented prompting.
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"""
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user_id = require_authenticated_user(current_user)
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logger.warning(f"[ScriptGen] ========== SCRIPT GENERATION START ==========")
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logger.warning(f"[ScriptGen] Topic: {request.idea[:60]}...")
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logger.warning(f"[ScriptGen] Duration: {request.duration_minutes} min, Speakers: {request.speakers}")
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logger.warning(f"[ScriptGen] Has research: {bool(request.research)}, Has bible: {bool(request.bible)}, Has analysis: {bool(request.analysis)}")
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# Build comprehensive research context for higher-quality scripts
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research_context = ""
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@@ -77,55 +105,53 @@ async def generate_podcast_script(
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# Extract Analysis and Outline context for grounding
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analysis_context = ""
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if request.analysis:
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analysis_context = f"""
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TARGET AUDIENCE: {request.analysis.get('audience', 'General')}
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CONTENT TYPE: {request.analysis.get('contentType', 'Conversational')}
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TOP KEYWORDS: {', '.join(request.analysis.get('topKeywords', []))}
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"""
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try:
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audience = request.analysis.get('audience', '') or ''
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content_type = request.analysis.get('contentType', '') or ''
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keywords = request.analysis.get('topKeywords', []) or []
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analysis_context = f"ANALYSIS: Audience={audience} | Type={content_type} | Keywords={', '.join(keywords[:8])}"
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except:
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pass
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outline_context = ""
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if request.outline:
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outline_context = f"""
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REFINED EPISODE OUTLINE (Follow this structure closely):
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Title: {request.outline.get('title', 'N/A')}
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Segments: {' | '.join(request.outline.get('segments', []))}
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"""
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try:
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title = request.outline.get('title', '') or ''
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segments = request.outline.get('segments', []) or []
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outline_context = f"OUTLINE: {title} - {' | '.join(segments[:5])}"
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except:
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pass
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prompt = f"""You are an expert podcast script planner. Create natural, conversational podcast scenes.
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prompt = f"""Create a podcast script with scenes and dialogue.
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{f"PODCAST BIBLE (Hyper-Personalization Context):\n{bible_context}\n" if bible_context else ""}
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{f"ANALYSIS CONTEXT:\n{analysis_context}\n" if analysis_context else ""}
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{f"REFINED OUTLINE:\n{outline_context}\n" if outline_context else ""}
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{f"BIBLE: {bible_context[:1500]}" if bible_context else ""}
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{f"{analysis_context}" if analysis_context else ""}
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{f"{outline_context}" if outline_context else ""}
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{f"RESEARCH: {research_context[:1200]}" if research_context else ""}
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Podcast Idea: "{request.idea}"
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Duration: ~{request.duration_minutes} minutes
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Speakers: {request.speakers} (Host + optional Guest)
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Topic: "{request.idea}"
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Duration: {request.duration_minutes} min | Speakers: {request.speakers}
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{f"RESEARCH CONTEXT:\n{research_context}\n" if research_context else ""}
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Return JSON with scenes array. Each scene:
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- id: string
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- title: short title (<=50 chars)
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- duration: seconds (total/5)
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- emotion: neutral|happy|excited|serious|curious|confident
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- lines: array of {{speaker, text, emphasis}}
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- Use 2-4 LINES PER SCENE (shorter script = lower TTS costs)
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- Each line: 1-3 sentences, conversational
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- Plain text only, no markdown
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Return JSON with:
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- scenes: array of scenes. Each scene has:
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- id: string
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- title: short scene title (<= 60 chars)
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- duration: duration in seconds (evenly split across total duration)
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- emotion: string (one of: "neutral", "happy", "excited", "serious", "curious", "confident")
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- lines: array of {{"speaker": "...", "text": "...", "emphasis": boolean}}
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* Write natural, conversational dialogue
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* Each line can be a sentence or a few sentences that flow together
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* Use plain text only - no markdown formatting (no asterisks, underscores, etc.)
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* Mark "emphasis": true for key statistics or important points
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Guidelines:
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- Write for spoken delivery: conversational, natural, with contractions.
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- Follow the interaction tone specified in the Bible.
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- Ensure the Host persona matches the background and personality traits from the Bible.
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- Structure the intro and outro scenes according to the Bible's "Intro Format" and "Outro Format".
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- Adhere to any constraints mentioned in the Bible.
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- Use insights from the Research Context to ground the conversation in facts.
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- IMPORTANT: Follow the REFINED OUTLINE segments as the primary structure for the episode.
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COST OPTIMIZATION:
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- 5-6 scenes max for {request.duration_minutes} min episode
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- Concise, information-dense dialogue
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- Skip filler words and redundant phrases
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- Focus on unique insights from research
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- Make every line count toward value delivery
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"""
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try:
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logger.warning(f"[ScriptGen] Calling LLM to generate script (prompt length: {len(prompt)})...")
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raw = llm_text_gen(
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prompt=prompt,
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user_id=user_id,
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@@ -133,6 +159,7 @@ Guidelines:
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preferred_provider=None,
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flow_type="premium_tool",
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)
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logger.warning(f"[ScriptGen] LLM response received, length: {len(raw) if raw else 0}")
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except HTTPException:
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raise
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except Exception as exc:
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