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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ajaysi
@@ -104,6 +104,16 @@ async def generate_podcast_scene_image(
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# Otherwise, generate from scratch with podcast-optimized prompt
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image_prompt = "" # Initialize prompt variable
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# Emotion to lighting mapping for visual tone
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emotion_lighting = {
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"happy": "warm, bright lighting, cheerful atmosphere",
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"excited": "dynamic, energetic lighting with highlights",
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"serious": "professional, balanced lighting, authoritative feel",
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"curious": "soft, inviting lighting, thoughtful atmosphere",
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"confident": "strong, dramatic lighting, authoritative look",
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"neutral": "professional, balanced lighting"
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}
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if base_avatar_bytes:
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# Use Ideogram Character API for consistent character generation
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# Use custom prompt if provided, otherwise build scene-specific prompt
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@@ -127,6 +137,28 @@ async def generate_podcast_scene_image(
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if bible_obj.host.look:
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prompt_parts.append(f"Host Look: {bible_obj.host.look}")
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# Scene emotion for visual tone
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emotion_lighting = {
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"happy": "warm, bright lighting, cheerful atmosphere",
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"excited": "dynamic, energetic lighting with highlights",
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"serious": "professional, balanced lighting, authoritative feel",
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"curious": "soft, inviting lighting, thoughtful atmosphere",
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"confident": "strong, dramatic lighting, authoritative look",
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"neutral": "professional, balanced lighting"
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}
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scene_emotion = request.scene_emotion
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if scene_emotion and scene_emotion in emotion_lighting:
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prompt_parts.append(emotion_lighting[scene_emotion])
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# AI Analysis context for visual relevance
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if request.analysis:
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keywords = request.analysis.get("topKeywords", [])[:5]
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if keywords:
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prompt_parts.append(f"Keywords: {', '.join(keywords)}")
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audience = request.analysis.get("audience", "")
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if audience:
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prompt_parts.append(f"Target: {audience}")
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# Scene content insights for visual context
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if request.scene_content:
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content_preview = request.scene_content[:200].replace("\n", " ").strip()
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@@ -139,6 +171,12 @@ async def generate_podcast_scene_image(
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visual_keywords.append("modern tech studio setting")
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if any(word in content_lower for word in ["business", "growth", "strategy", "market"]):
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visual_keywords.append("professional business studio")
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if any(word in content_lower for word in ["nature", "outdoor", "environment", "green"]):
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visual_keywords.append("natural outdoor setting")
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if any(word in content_lower for word in ["medical", "health", "wellness"]):
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visual_keywords.append("clean medical studio")
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if any(word in content_lower for word in ["education", "learning", "students"]):
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visual_keywords.append("classroom or educational setting")
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if visual_keywords:
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prompt_parts.append(", ".join(visual_keywords))
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@@ -265,6 +303,19 @@ async def generate_podcast_scene_image(
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if request.scene_title:
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prompt_parts.append(f"Scene theme: {request.scene_title}")
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# Scene emotion for visual tone (no avatar branch)
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if request.scene_emotion and request.scene_emotion in emotion_lighting:
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prompt_parts.append(emotion_lighting[request.scene_emotion])
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# AI Analysis context (no avatar branch)
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if request.analysis:
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keywords = request.analysis.get("topKeywords", [])[:5]
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if keywords:
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prompt_parts.append(f"Keywords: {', '.join(keywords)}")
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audience = request.analysis.get("audience", "")
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if audience:
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prompt_parts.append(f"Target: {audience}")
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# Content context for visual relevance
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if request.scene_content:
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content_preview = request.scene_content[:150].replace("\n", " ").strip()
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@@ -276,6 +327,12 @@ async def generate_podcast_scene_image(
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visual_keywords.append("modern technology aesthetic")
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if any(word in content_lower for word in ["business", "growth", "strategy", "market"]):
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visual_keywords.append("professional business environment")
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if any(word in content_lower for word in ["nature", "outdoor", "environment"]):
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visual_keywords.append("natural outdoor setting")
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if any(word in content_lower for word in ["medical", "health", "wellness"]):
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visual_keywords.append("clean medical studio")
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if any(word in content_lower for word in ["education", "learning", "students"]):
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visual_keywords.append("classroom or educational setting")
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if visual_keywords:
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prompt_parts.append(", ".join(visual_keywords))
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@@ -379,6 +436,7 @@ async def generate_podcast_scene_image(
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provider=result.provider,
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model=result.model,
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cost=cost,
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image_prompt=image_prompt,
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)
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except HTTPException:
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