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Merge remote-tracking branch 'origin/codex/task-title-3y5pbt'

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ajaysi
2026-04-20 08:47:19 +05:30
parent f748c081c2 e68c289901
commit 7f7279f903
7 changed files with 147 additions and 9 deletions
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141
backend/api/podcast/handlers/script.py
View File

@@ -8,6 +8,7 @@ from fastapi import APIRouter, Depends, HTTPException
from typing import Dict, Any, Optional
from pydantic import BaseModel, Field
import json
import re
from middleware.auth_middleware import get_current_user
from api.story_writer.utils.auth import require_authenticated_user
@@ -23,6 +24,8 @@ from ..models import (
)
router = APIRouter()
MAX_TTS_CHARS_PER_REQUEST = 10_000
TARGET_TTS_CHARS_PER_SCENE = 8_500
class SceneApprovalRequest(BaseModel):
@@ -60,28 +63,43 @@ async def generate_podcast_script(
logger.warning(f"[ScriptGen] ========== SCRIPT GENERATION START ==========")
logger.warning(f"[ScriptGen] Topic: {request.idea[:60]}...")
logger.warning(f"[ScriptGen] Duration: {request.duration_minutes} min, Speakers: {request.speakers}")
logger.warning(f"[ScriptGen] Has research: {bool(request.research)}, Has bible: {bool(request.bible)}, Has analysis: {bool(request.analysis)}")
podcast_mode = (request.podcast_mode or "video_only").strip().lower()
logger.warning(f"[ScriptGen] Has research: {bool(request.research)}, Has bible: {bool(request.bible)}, Has analysis: {bool(request.analysis)}, Mode: {podcast_mode}")
research_fact_cards = request.research.get("factCards", []) if request.research else []
# Build comprehensive research context for higher-quality scripts
research_context = ""
if request.research:
try:
key_insights = request.research.get("keyword_analysis", {}).get("key_insights") or []
fact_cards = request.research.get("factCards", []) or []
fact_cards = research_fact_cards or []
mapped_angles = request.research.get("mappedAngles", []) or []
sources = request.research.get("sources", []) or []
top_facts = [f.get("quote", "") for f in fact_cards[:5] if f.get("quote")]
top_facts = [
f"[{f.get('id') or f'fact_{idx + 1}'}] {f.get('quote', '')}"
for idx, f in enumerate(fact_cards[:10])
if f.get("quote")
]
angles_summary = [
f"{a.get('title', '')}: {a.get('why', '')}" for a in mapped_angles[:3] if a.get("title") or a.get("why")
]
top_sources = [s.get("url") for s in sources[:3] if s.get("url")]
numeric_signals = []
for f in fact_cards[:12]:
quote = (f.get("quote") or "").strip()
if any(ch.isdigit() for ch in quote):
numeric_signals.append(quote[:180])
if len(numeric_signals) >= 5:
break
research_parts = []
if key_insights:
research_parts.append(f"Key Insights: {', '.join(key_insights[:5])}")
if top_facts:
research_parts.append(f"Key Facts: {', '.join(top_facts)}")
if numeric_signals:
research_parts.append(f"Numeric Signals (prefer for chart scenes): {' | '.join(numeric_signals)}")
if angles_summary:
research_parts.append(f"Research Angles: {' | '.join(angles_summary)}")
if top_sources:
@@ -92,6 +110,51 @@ async def generate_podcast_script(
logger.warning(f"Failed to parse research context: {exc}")
research_context = ""
def _normalize_fact_ids(value: Any) -> Optional[list[str]]:
if not value:
return None
if isinstance(value, list):
cleaned = [str(v).strip() for v in value if str(v).strip()]
return cleaned or None
if isinstance(value, str) and value.strip():
return [value.strip()]
return None
def _default_chart_data(scene_title: str) -> Dict[str, Any]:
numeric_pairs: list[tuple[str, float]] = []
for fact in research_fact_cards[:12]:
quote = (fact.get("quote") or "").strip()
if not quote:
continue
nums = re.findall(r"\d+(?:\.\d+)?", quote.replace(",", ""))
if not nums:
continue
label = quote[:48] + ("…" if len(quote) > 48 else "")
try:
numeric_pairs.append((label, float(nums[0])))
except ValueError:
continue
if len(numeric_pairs) >= 5:
break
if numeric_pairs:
labels = [p[0] for p in numeric_pairs]
values = [p[1] for p in numeric_pairs]
return {
"type": "bar_comparison",
"title": scene_title,
"labels": labels,
"values": values,
"takeaway": "Data points sourced from research facts used in this scene.",
}
return {
"type": "bullet",
"title": scene_title,
"bullet_points": ["Key point 1", "Key point 2", "Key point 3"],
"takeaway": "Narration summary for this scene.",
}
# Extract Podcast Bible context for hyper-personalization
bible_context = ""
if request.bible:
@@ -122,25 +185,58 @@ async def generate_podcast_script(
except:
pass
mode_instructions = ""
if podcast_mode == "audio_only":
mode_instructions = f"""
AUDIO-ONLY MODE RULES (CRITICAL):
- This is an audio-only episode. Do NOT include avatar/image/camera instructions.
- Keep each scene's total dialogue under {TARGET_TTS_CHARS_PER_SCENE} chars to stay below TTS max request size ({MAX_TTS_CHARS_PER_REQUEST}).
- For every scene include chart_data so B-roll charts can be generated while narration plays.
- Build script STRICTLY from RESEARCH context and cite fact linkage via usedFactIds.
- If evidence is weak, say uncertainty explicitly rather than inventing facts.
- Add natural TTS pacing in dialogue with markers like [pause:300ms], [pause:700ms], [emote:curious], [emote:serious].
"""
elif podcast_mode == "audio_video":
mode_instructions = """
AUDIO+VIDEO MODE:
- Include rich narration that works for both listening and visual storytelling.
- Use a balanced pace suitable for TTS and scene visuals.
"""
else:
mode_instructions = """
VIDEO-ONLY MODE:
- Prioritize visual rhythm and concise narration per scene.
"""
prompt = f"""Create a podcast script with scenes and dialogue.
{f"BIBLE: {bible_context[:1500]}" if bible_context else ""}
{f"{analysis_context}" if analysis_context else ""}
{f"{outline_context}" if outline_context else ""}
{f"RESEARCH: {research_context[:1200]}" if research_context else ""}
{f"RESEARCH: {research_context[:2500]}" if research_context else ""}
{mode_instructions}
Topic: "{request.idea}"
Duration: {request.duration_minutes} min | Speakers: {request.speakers}
Podcast mode: {podcast_mode}
Return JSON with scenes array. Each scene:
- id: string
- title: short title (<=50 chars)
- duration: seconds (total/5)
- emotion: neutral|happy|excited|serious|curious|confident
- lines: array of {{speaker, text, emphasis}}
- lines: array of {{speaker, text, emphasis, usedFactIds, ttsHints}}
- Use 2-4 LINES PER SCENE (shorter script = lower TTS costs)
- Each line: 1-3 sentences, conversational
- usedFactIds: include related fact ids when research facts are available (example: ["fact_1", "fact_3"])
- ttsHints: optional list from [pause_300ms, pause_700ms, smile, serious_tone, emphasize_data]
- Plain text only, no markdown
- chart_data: object for B-roll mapping (required in audio_only)
- type: bar_comparison|line_trend|bullet_points
- title: short chart title
- labels: list
- values: list (same length as labels)
- takeaway: one sentence tying chart to narration
COST OPTIMIZATION:
- 5-6 scenes max for {request.duration_minutes} min episode
@@ -231,7 +327,8 @@ COST OPTIMIZATION:
line_id = line.get("id") or f"line-{idx + 1}-{line_idx + 1}"
# Get used fact IDs if provided
used_fact_ids = line.get("usedFactIds") or line.get("used_fact_ids") or None
used_fact_ids = _normalize_fact_ids(line.get("usedFactIds") or line.get("used_fact_ids"))
tts_hints = line.get("ttsHints") or line.get("tts_hints") or None
if text:
lines.append(PodcastSceneLine(
@@ -239,7 +336,8 @@ COST OPTIMIZATION:
text=text,
emphasis=emphasis,
id=line_id,
usedFactIds=used_fact_ids
usedFactIds=used_fact_ids,
ttsHints=tts_hints if isinstance(tts_hints, list) else None,
))
total_lines_output += 1
else:
@@ -255,6 +353,33 @@ COST OPTIMIZATION:
if audio_url_raw:
logger.warning(f"[ScriptGen] Scene {idx} has audioUrl - will be reset to None")
# Keep each scene under TTS request size to prevent failures
scene_char_count = sum(len((l.text or "").strip()) for l in lines)
if scene_char_count > TARGET_TTS_CHARS_PER_SCENE and lines:
logger.warning(
f"[ScriptGen] Scene {idx} text too long ({scene_char_count} chars). "
f"Trimming to {TARGET_TTS_CHARS_PER_SCENE} target."
)
trimmed_lines: list[PodcastSceneLine] = []
remaining = TARGET_TTS_CHARS_PER_SCENE
for l in lines:
if remaining <= 0:
break
line_text = (l.text or "").strip()
if len(line_text) <= remaining:
trimmed_lines.append(l)
remaining -= len(line_text)
continue
l.text = f"{line_text[:max(0, remaining - 1)].rstrip()}…"
trimmed_lines.append(l)
remaining = 0
lines = trimmed_lines
chart_data = scene.get("chart_data") or scene.get("chartData") or None
if podcast_mode == "audio_only" and not chart_data:
# Ensure audio-only always has a B-roll mapping fallback
chart_data = _default_chart_data(title)
scenes.append(
PodcastScene(
id=scene.get("id") or f"scene-{idx + 1}",
@@ -266,6 +391,7 @@ COST OPTIMIZATION:
imageUrl=None, # Will be generated later
audioUrl=None, # Will be generated later
imagePrompt=None, # Will be generated during image generation
chart_data=chart_data if isinstance(chart_data, dict) else None,
)
)
@@ -275,4 +401,3 @@ COST OPTIMIZATION:
logger.warning(f"[ScriptGen] Dropped {dropped_empty_lines} empty lines")
return PodcastScriptResponse(scenes=scenes)

3
backend/api/podcast/models.py
View File

@@ -97,6 +97,7 @@ class PodcastScriptRequest(BaseModel):
bible: Optional[Dict[str, Any]] = Field(None, description="Podcast Bible for hyper-personalization")
outline: Optional[Dict[str, Any]] = Field(None, description="The refined episode outline to follow")
analysis: Optional[Dict[str, Any]] = Field(None, description="The full analysis context (audience, keywords, etc.)")
podcast_mode: Optional[str] = Field(default="video_only", description="Podcast mode: audio_only, video_only, or audio_video")
class PodcastSceneLine(BaseModel):
@@ -105,6 +106,7 @@ class PodcastSceneLine(BaseModel):
emphasis: Optional[bool] = False
id: Optional[str] = None # Optional line ID for frontend tracking
usedFactIds: Optional[List[str]] = None # Facts referenced in this line
ttsHints: Optional[List[str]] = None # Optional TTS hints, e.g. pause_300ms, smile, emphasize_data
class PodcastScene(BaseModel):
@@ -117,6 +119,7 @@ class PodcastScene(BaseModel):
imageUrl: Optional[str] = None # Generated image URL for video generation
audioUrl: Optional[str] = None # Generated audio URL for this scene
imagePrompt: Optional[str] = None # Original image generation prompt for video context
chart_data: Optional[Dict[str, Any]] = None # Optional chart mapping for B-roll scenes
class PodcastExaConfig(BaseModel):