e59c77b221
Frontend: - Add progress modals with educational content for analysis and voice cloning - Improve tab navigation in AnalysisPanel (combine Titles, Hook, CTA into one tab) - Fix tab styling to make inactive tabs visible - Fix avatar 'Make Presentable' not updating preview (blob URL handling) - Improve mobile responsiveness for avatar tabs - Clean up verbose console logging (AnalysisPanel, demoMode, RobustCamera) - Add sequential progress messages instead of cycling Backend: - Fix 'Depends object has no attribute get' error in auth and image editing - Use get_session_for_user instead of get_db outside FastAPI DI context - Reduce WARNING logs to DEBUG in audio handler - Add proper emphasis boolean handling in script generation - Add missing fields to PodcastScene and PodcastSceneLine models - Fix voice cloning cost estimate display issue
279 lines
11 KiB
Python
279 lines
11 KiB
Python
"""
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Podcast Script Handlers
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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, 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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from api.story_writer.utils.auth import require_authenticated_user
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from services.llm_providers.main_text_generation import llm_text_gen
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from services.podcast_bible_service import PodcastBibleService
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from models.podcast_bible_models import PodcastBible
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from loguru import logger
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from ..models import (
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PodcastScriptRequest,
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PodcastScriptResponse,
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PodcastScene,
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PodcastSceneLine,
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)
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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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current_user: Dict[str, Any] = Depends(get_current_user),
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):
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"""
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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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if request.research:
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try:
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key_insights = request.research.get("keyword_analysis", {}).get("key_insights") or []
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fact_cards = request.research.get("factCards", []) or []
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mapped_angles = request.research.get("mappedAngles", []) or []
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sources = request.research.get("sources", []) or []
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top_facts = [f.get("quote", "") for f in fact_cards[:5] if f.get("quote")]
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angles_summary = [
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f"{a.get('title', '')}: {a.get('why', '')}" for a in mapped_angles[:3] if a.get("title") or a.get("why")
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]
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top_sources = [s.get("url") for s in sources[:3] if s.get("url")]
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research_parts = []
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if key_insights:
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research_parts.append(f"Key Insights: {', '.join(key_insights[:5])}")
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if top_facts:
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research_parts.append(f"Key Facts: {', '.join(top_facts)}")
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if angles_summary:
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research_parts.append(f"Research Angles: {' | '.join(angles_summary)}")
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if top_sources:
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research_parts.append(f"Top Sources: {', '.join(top_sources)}")
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research_context = "\n".join(research_parts)
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except Exception as exc:
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logger.warning(f"Failed to parse research context: {exc}")
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research_context = ""
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# Extract Podcast Bible context for hyper-personalization
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bible_context = ""
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if request.bible:
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try:
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bible_service = PodcastBibleService()
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bible_obj = PodcastBible(**request.bible)
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bible_context = bible_service.serialize_bible(bible_obj)
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except Exception as exc:
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logger.warning(f"Failed to serialize podcast bible: {exc}")
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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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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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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"""Create a podcast script with scenes and dialogue.
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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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Topic: "{request.idea}"
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Duration: {request.duration_minutes} min | Speakers: {request.speakers}
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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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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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json_struct=None,
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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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raise HTTPException(status_code=500, detail=f"Script generation failed: {exc}")
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if isinstance(raw, str):
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try:
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data = json.loads(raw)
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except json.JSONDecodeError:
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raise HTTPException(status_code=500, detail="LLM returned non-JSON output")
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elif isinstance(raw, dict):
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data = raw
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else:
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raise HTTPException(status_code=500, detail="Unexpected LLM response format")
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scenes_data = data.get("scenes") or []
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if not isinstance(scenes_data, list):
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raise HTTPException(status_code=500, detail="LLM response missing scenes array")
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if len(scenes_data) == 0:
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logger.warning("[ScriptGen] LLM returned empty scenes array")
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raise HTTPException(status_code=500, detail="LLM returned no scenes - please try again")
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logger.warning(f"[ScriptGen] Processing {len(scenes_data)} scenes from LLM response")
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valid_emotions = {"neutral", "happy", "excited", "serious", "curious", "confident"}
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# Normalize scenes
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scenes: list[PodcastScene] = []
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total_lines_input = 0
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total_lines_output = 0
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dropped_empty_lines = 0
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for idx, scene in enumerate(scenes_data):
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if not isinstance(scene, dict):
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logger.warning(f"[ScriptGen] Scene {idx} is not a dict, skipping")
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continue
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title = scene.get("title") or f"Scene {idx + 1}"
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duration = int(scene.get("duration") or max(30, (request.duration_minutes * 60) // max(1, len(scenes_data))))
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emotion = scene.get("emotion") or "neutral"
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if emotion not in valid_emotions:
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logger.warning(f"[ScriptGen] Invalid emotion '{emotion}' in scene {idx}, defaulting to 'neutral'")
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emotion = "neutral"
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lines_raw = scene.get("lines") or []
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total_lines_input += len(lines_raw)
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lines: list[PodcastSceneLine] = []
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for line_idx, line in enumerate(lines_raw):
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if not isinstance(line, dict):
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logger.warning(f"[ScriptGen] Line {line_idx} in scene {idx} is not a dict, skipping")
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continue
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speaker = line.get("speaker") or ("Host" if len(lines) % request.speakers == 0 else "Guest")
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text = line.get("text") or ""
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# Handle emphasis - convert various values to boolean
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emphasis_raw = line.get("emphasis", False)
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if isinstance(emphasis_raw, bool):
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emphasis = emphasis_raw
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elif isinstance(emphasis_raw, str):
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emphasis = emphasis_raw.lower() in ("true", "yes", "1")
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if emphasis_raw.lower() not in ("true", "false", "yes", "no", "1", "0"):
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logger.debug(f"[ScriptGen] Unusual emphasis value '{emphasis_raw}' converted to {emphasis}")
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else:
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emphasis = bool(emphasis_raw)
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# Generate line ID if not provided
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line_id = line.get("id") or f"line-{idx + 1}-{line_idx + 1}"
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# Get used fact IDs if provided
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used_fact_ids = line.get("usedFactIds") or line.get("used_fact_ids") or None
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if text:
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lines.append(PodcastSceneLine(
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speaker=speaker,
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text=text,
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emphasis=emphasis,
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id=line_id,
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usedFactIds=used_fact_ids
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))
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total_lines_output += 1
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else:
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dropped_empty_lines += 1
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logger.debug(f"[ScriptGen] Dropped empty line {line_idx} in scene {idx}")
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# Log scene status
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if scenes_data and isinstance(scene, dict):
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image_url_raw = scene.get("imageUrl") or scene.get("image_url")
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audio_url_raw = scene.get("audioUrl") or scene.get("audio_url")
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if image_url_raw:
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logger.warning(f"[ScriptGen] Scene {idx} has imageUrl - will be reset to None")
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if audio_url_raw:
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logger.warning(f"[ScriptGen] Scene {idx} has audioUrl - will be reset to None")
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scenes.append(
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PodcastScene(
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id=scene.get("id") or f"scene-{idx + 1}",
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title=title,
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duration=duration,
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lines=lines,
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approved=False,
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emotion=emotion,
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imageUrl=None, # Will be generated later
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audioUrl=None, # Will be generated later
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imagePrompt=None, # Will be generated during image generation
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)
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)
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# Summary logging
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logger.warning(f"[ScriptGen] Script generated: {len(scenes)} scenes, {total_lines_output}/{total_lines_input} lines")
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if dropped_empty_lines > 0:
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logger.warning(f"[ScriptGen] Dropped {dropped_empty_lines} empty lines")
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return PodcastScriptResponse(scenes=scenes)
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