WIP: AI Podcast Maker and YouTube Creator Studio integration
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ajaysi
358
backend/services/youtube/planner.py
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358
backend/services/youtube/planner.py
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"""
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YouTube Video Planner Service
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Generates video plans, outlines, and insights using AI with persona integration.
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"""
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from typing import Dict, Any, Optional, List
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from loguru import logger
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from fastapi import HTTPException
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from services.llm_providers.main_text_generation import llm_text_gen
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from utils.logger_utils import get_service_logger
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logger = get_service_logger("youtube.planner")
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class YouTubePlannerService:
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"""Service for planning YouTube videos with AI assistance."""
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def __init__(self):
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"""Initialize the planner service."""
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logger.info("[YouTubePlanner] Service initialized")
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def generate_video_plan(
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self,
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user_idea: str,
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duration_type: str, # "shorts", "medium", "long"
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persona_data: Optional[Dict[str, Any]] = None,
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reference_image_description: Optional[str] = None,
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source_content_id: Optional[str] = None, # For blog/story conversion
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source_content_type: Optional[str] = None, # "blog", "story"
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user_id: str = None,
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include_scenes: bool = False, # For shorts: combine plan + scenes in one call
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) -> Dict[str, Any]:
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"""
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Generate a comprehensive video plan from user input.
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Args:
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user_idea: User's video idea or topic
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duration_type: "shorts" (≤60s), "medium" (1-4min), "long" (4-10min)
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persona_data: Optional persona data for tone/style
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reference_image_description: Optional description of reference image
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source_content_id: Optional ID of source content (blog/story)
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source_content_type: Type of source content
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user_id: Clerk user ID for subscription checking
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Returns:
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Dictionary with video plan, outline, insights, and metadata
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"""
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try:
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logger.info(
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f"[YouTubePlanner] Generating plan: idea={user_idea[:50]}..., "
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f"duration={duration_type}, user={user_id}"
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)
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# Build persona context
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persona_context = self._build_persona_context(persona_data)
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# Build duration context
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duration_context = self._get_duration_context(duration_type)
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# Build source content context if provided
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source_context = ""
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if source_content_id and source_content_type:
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source_context = f"""
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**Source Content:**
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- Type: {source_content_type}
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- ID: {source_content_id}
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- Note: This video should be based on the existing {source_content_type} content.
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"""
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# Build reference image context
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image_context = ""
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if reference_image_description:
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image_context = f"""
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**Reference Image:**
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{reference_image_description}
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- Use this as visual inspiration for the video
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"""
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# Generate comprehensive video plan
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planning_prompt = f"""You are an expert YouTube content strategist. Create a comprehensive video plan based on the user's idea.
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**User's Video Idea:**
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{user_idea}
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**Video Duration Type:**
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{duration_type} ({duration_context['description']})
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**Duration Guidelines:**
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- Target length: {duration_context['target_seconds']} seconds
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- Hook duration: {duration_context['hook_seconds']} seconds
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- Main content: {duration_context['main_seconds']} seconds
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- CTA duration: {duration_context['cta_seconds']} seconds
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- Maximum scenes: {duration_context['max_scenes']} (for shorts, keep 2-4 scenes total)
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{persona_context}
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{source_context}
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{image_context}
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**Your Task:**
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Create a detailed video plan that includes:
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1. **Video Summary**: A 2-3 sentence overview of what the video will cover
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2. **Target Audience**: Who this video is for
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3. **Video Goal**: Primary objective (educate, entertain, sell, inspire, etc.)
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4. **Key Message**: The main takeaway viewers should remember
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5. **Hook Strategy**: Attention-grabbing opening (first {duration_context['hook_seconds']} seconds)
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6. **Content Outline**: High-level structure with 3-5 main sections
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7. **Call-to-Action**: Clear CTA that fits the video goal
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8. **Visual Style**: Recommended visual approach (cinematic, tutorial, vlog, etc.)
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9. **Tone**: Recommended tone (professional, casual, energetic, etc.)
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10. **SEO Keywords**: 5-7 relevant keywords for YouTube SEO
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**Format your response as JSON:**
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{{
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"video_summary": "...",
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"target_audience": "...",
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"video_goal": "...",
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"key_message": "...",
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"hook_strategy": "...",
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"content_outline": [
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{{"section": "Section 1", "description": "...", "duration_estimate": 30}},
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{{"section": "Section 2", "description": "...", "duration_estimate": 45}}
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],
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"call_to_action": "...",
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"visual_style": "...",
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"tone": "...",
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"seo_keywords": ["keyword1", "keyword2", ...]
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}}
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Make sure the content outline fits within the {duration_type} duration constraints.
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"""
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system_prompt = (
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"You are an expert YouTube content strategist specializing in creating "
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"engaging, well-structured video plans. Your plans are data-driven, "
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"audience-focused, and optimized for YouTube's algorithm."
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)
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# For shorts, combine plan + scenes in one call to save API calls
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if include_scenes and duration_type == "shorts":
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planning_prompt += f"""
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**IMPORTANT: Since this is a SHORTS video, also generate the complete scene breakdown in the same response.**
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**Additional Task - Generate Detailed Scenes:**
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Create detailed scenes (up to {duration_context['max_scenes']} scenes) that include:
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1. Scene number and title
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2. Narration text (what will be spoken) - keep it concise for shorts
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3. Visual description (what viewers will see)
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4. Duration estimate (2-8 seconds each)
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5. Emphasis tags (hook, main_content, transition, cta)
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**Scene Format:**
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Each scene should be detailed enough for video generation. Total duration must fit within {duration_context['target_seconds']} seconds.
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**Update JSON structure to include "scenes" array:**
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Add a "scenes" field with the complete scene breakdown.
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"""
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json_struct = {
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"type": "object",
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"properties": {
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"video_summary": {"type": "string"},
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"target_audience": {"type": "string"},
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"video_goal": {"type": "string"},
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"key_message": {"type": "string"},
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"hook_strategy": {"type": "string"},
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"content_outline": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"section": {"type": "string"},
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"description": {"type": "string"},
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"duration_estimate": {"type": "number"}
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}
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}
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},
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"call_to_action": {"type": "string"},
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"visual_style": {"type": "string"},
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"tone": {"type": "string"},
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"seo_keywords": {
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"type": "array",
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"items": {"type": "string"}
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},
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"scenes": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"scene_number": {"type": "number"},
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"title": {"type": "string"},
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"narration": {"type": "string"},
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"visual_description": {"type": "string"},
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"duration_estimate": {"type": "number"},
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"emphasis": {"type": "string"},
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"visual_cues": {
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"type": "array",
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"items": {"type": "string"}
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}
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},
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"required": [
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"scene_number", "title", "narration", "visual_description",
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"duration_estimate", "emphasis"
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]
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}
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}
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},
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"required": [
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"video_summary", "target_audience", "video_goal", "key_message",
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"hook_strategy", "content_outline", "call_to_action",
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"visual_style", "tone", "seo_keywords", "scenes"
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]
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}
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else:
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json_struct = {
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"type": "object",
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"properties": {
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"video_summary": {"type": "string"},
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"target_audience": {"type": "string"},
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"video_goal": {"type": "string"},
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"key_message": {"type": "string"},
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"hook_strategy": {"type": "string"},
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"content_outline": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"section": {"type": "string"},
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"description": {"type": "string"},
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"duration_estimate": {"type": "number"}
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}
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}
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},
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"call_to_action": {"type": "string"},
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"visual_style": {"type": "string"},
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"tone": {"type": "string"},
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"seo_keywords": {
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"type": "array",
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"items": {"type": "string"}
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}
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},
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"required": [
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"video_summary", "target_audience", "video_goal", "key_message",
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"hook_strategy", "content_outline", "call_to_action",
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"visual_style", "tone", "seo_keywords"
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]
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}
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# Generate plan using LLM
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response = llm_text_gen(
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prompt=planning_prompt,
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system_prompt=system_prompt,
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user_id=user_id,
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json_struct=json_struct
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)
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# Parse response (handle both dict and JSON string)
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if isinstance(response, dict):
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plan_data = response
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else:
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import json
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plan_data = json.loads(response)
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# Add metadata
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plan_data["duration_type"] = duration_type
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plan_data["duration_metadata"] = duration_context
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plan_data["user_idea"] = user_idea
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# If scenes were included, mark them for scene builder
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if include_scenes and duration_type == "shorts" and "scenes" in plan_data:
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plan_data["_scenes_included"] = True
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logger.info(
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f"[YouTubePlanner] ✅ Plan + {len(plan_data.get('scenes', []))} scenes "
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f"generated in 1 AI call (optimized for shorts)"
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)
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else:
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if include_scenes and duration_type == "shorts":
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# LLM did not return scenes; downstream will regenerate
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plan_data["_scenes_included"] = False
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logger.warning(
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"[YouTubePlanner] Shorts optimization requested but no scenes returned; "
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"scene builder will generate scenes separately."
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)
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logger.info(f"[YouTubePlanner] ✅ Plan generated successfully")
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return plan_data
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"[YouTubePlanner] Error generating plan: {e}", exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=f"Failed to generate video plan: {str(e)}"
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)
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def _build_persona_context(self, persona_data: Optional[Dict[str, Any]]) -> str:
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"""Build persona context string for prompts."""
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if not persona_data:
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return """
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**Persona Context:**
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- Using default professional tone
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- No specific persona constraints
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"""
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core_persona = persona_data.get("core_persona", {})
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tone = core_persona.get("tone", "professional")
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voice = core_persona.get("voice_characteristics", {})
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return f"""
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**Persona Context:**
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- Tone: {tone}
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- Voice Style: {voice.get('style', 'professional')}
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- Communication Style: {voice.get('communication_style', 'clear and direct')}
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- Brand Values: {core_persona.get('core_belief', 'value-driven content')}
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- Use this persona to guide the video's tone, style, and messaging approach.
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"""
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def _get_duration_context(self, duration_type: str) -> Dict[str, Any]:
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"""Get duration-specific context and constraints."""
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contexts = {
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"shorts": {
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"description": "YouTube Shorts (15-60 seconds)",
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"target_seconds": 30,
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"hook_seconds": 3,
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"main_seconds": 24,
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"cta_seconds": 3,
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# Keep scenes tight for shorts to control cost and pacing
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"max_scenes": 4,
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"scene_duration_range": (2, 8)
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},
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"medium": {
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"description": "Medium-length video (1-4 minutes)",
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"target_seconds": 150, # 2.5 minutes
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"hook_seconds": 10,
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"main_seconds": 130,
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"cta_seconds": 10,
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"max_scenes": 12,
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"scene_duration_range": (5, 15)
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},
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"long": {
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"description": "Long-form video (4-10 minutes)",
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"target_seconds": 420, # 7 minutes
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"hook_seconds": 15,
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"main_seconds": 380,
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"cta_seconds": 25,
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"max_scenes": 20,
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"scene_duration_range": (10, 30)
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}
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}
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return contexts.get(duration_type, contexts["medium"])
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