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Release Candidate: Production Release with Multi-Tenant & Onboarding Enhancements

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ajaysi
2026-02-28 20:06:26 +05:30
parent 08a1f4a1d8
commit 4828274cbf
162 changed files with 19489 additions and 4300 deletions
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214
backend/api/podcast/handlers/analysis.py
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@@ -7,54 +7,238 @@ Analysis endpoint for podcast ideas.
from fastapi import APIRouter, Depends, HTTPException
from typing import Dict, Any
import json
import uuid
from sqlalchemy.orm import Session
from services.database import get_db
from middleware.auth_middleware import get_current_user
from api.story_writer.utils.auth import require_authenticated_user
from services.llm_providers.main_text_generation import llm_text_gen
from services.llm_providers.main_image_generation import generate_image
from services.podcast_bible_service import PodcastBibleService
from utils.asset_tracker import save_asset_to_library
from loguru import logger
from ..models import PodcastAnalyzeRequest, PodcastAnalyzeResponse
from ..constants import PODCAST_IMAGES_DIR
from ..models import (
PodcastAnalyzeRequest,
PodcastAnalyzeResponse,
PodcastEnhanceIdeaRequest,
PodcastEnhanceIdeaResponse
)
router = APIRouter()
@router.post("/idea/enhance", response_model=PodcastEnhanceIdeaResponse)
async def enhance_podcast_idea(
request: PodcastEnhanceIdeaRequest,
current_user: Dict[str, Any] = Depends(get_current_user),
):
"""
Take raw keywords/topic and use AI to craft a presentable, detailed podcast idea.
Uses the user's Podcast Bible for hyper-personalization if available.
"""
user_id = require_authenticated_user(current_user)
# Serialize Bible context if provided or generate from onboarding
bible_context = ""
try:
bible_service = PodcastBibleService()
if request.bible:
from models.podcast_bible_models import PodcastBible
bible_data = PodcastBible(**request.bible)
bible_context = bible_service.serialize_bible(bible_data)
else:
# Generate from onboarding data directly
bible_obj = bible_service.generate_bible(user_id, "temp_enhance")
bible_context = bible_service.serialize_bible(bible_obj)
except Exception as exc:
logger.warning(f"[Podcast Enhance] Failed to parse or generate bible context: {exc}")
prompt = f"""
You are a creative podcast producer. Your goal is to take a simple podcast idea or keywords
and transform it into a compelling, professional, and detailed episode concept.
{f"USER PERSONALIZATION CONTEXT (Podcast Bible):\n{bible_context}\n" if bible_context else ""}
RAW IDEA/KEYWORDS: "{request.idea}"
TASK:
1. Rewrite the idea into a professional, presentable 2-3 sentence episode pitch.
2. Focus on making it sound expert-led and audience-focused.
3. Ensure it aligns with the host's persona and target audience interests if context was provided.
4. Keep it concise but information-rich.
Return JSON with:
- enhanced_idea: the rewritten, professional episode pitch
- rationale: 1 sentence explaining why this version works better for the target audience
"""
try:
raw = llm_text_gen(prompt=prompt, user_id=user_id, json_struct=None)
# Normalize response
if isinstance(raw, str):
data = json.loads(raw)
else:
data = raw
return PodcastEnhanceIdeaResponse(
enhanced_idea=data.get("enhanced_idea", request.idea),
rationale=data.get("rationale", "Made it more professional and listener-focused.")
)
except Exception as exc:
logger.error(f"[Podcast Enhance] Failed for user {user_id}: {exc}")
return PodcastEnhanceIdeaResponse(
enhanced_idea=request.idea,
rationale="Failed to enhance idea with AI, using original."
)
@router.post("/analyze", response_model=PodcastAnalyzeResponse)
async def analyze_podcast_idea(
request: PodcastAnalyzeRequest,
current_user: Dict[str, Any] = Depends(get_current_user),
db: Session = Depends(get_db),
):
"""
Analyze a podcast idea and return podcast-oriented outlines, keywords, and titles.
This uses the shared LLM provider but with a podcast-specific prompt (not story format).
If no avatar_url is provided, it generates one automatically based on the host's look.
"""
user_id = require_authenticated_user(current_user)
# Serialize Bible context if provided or generate from onboarding
bible_context = ""
bible_obj = None
try:
bible_service = PodcastBibleService()
if request.bible:
from models.podcast_bible_models import PodcastBible
bible_data = PodcastBible(**request.bible)
bible_context = bible_service.serialize_bible(bible_data)
bible_obj = bible_data
else:
# Generate from onboarding data directly
bible_obj = bible_service.generate_bible(user_id, "temp_analyze")
bible_context = bible_service.serialize_bible(bible_obj)
bible_obj = bible_obj
except Exception as exc:
logger.warning(f"[Podcast Analyze] Failed to parse or generate bible context: {exc}")
# --- NEW: Generate Presenter Avatar if missing ---
final_avatar_url = request.avatar_url
final_avatar_prompt = None
if not final_avatar_url:
logger.info(f"[Podcast Analyze] No avatar_url provided, generating one for user {user_id}")
try:
# 1. PRE-FLIGHT VALIDATION: Check subscription limits for image generation
from services.subscription import PricingService
from services.subscription.preflight_validator import validate_image_generation_operations
pricing_service = PricingService(db)
validate_image_generation_operations(
pricing_service=pricing_service,
user_id=user_id,
num_images=1
)
# 2. Build avatar prompt from Bible host look or fallback
host_look = bible_obj.host.look if bible_obj and bible_obj.host.look else "A professional podcast host"
visual_style = bible_obj.visual_style.style_preset if bible_obj else "Realistic Photography"
final_avatar_prompt = f"Professional headshot of a podcast host, {host_look}, {visual_style} style, clean background, soft studio lighting, center-focused, high resolution, sharp focus, professional photography quality, 16:9 aspect ratio."
# 3. Generate the image
logger.info(f"[Podcast Analyze] Generating avatar with prompt: {final_avatar_prompt}")
image_result = generate_image(
prompt=final_avatar_prompt,
user_id=user_id,
width=1024,
height=1024
)
# 4. Save to disk and library
if image_result and image_result.image_bytes:
img_id = str(uuid.uuid4())[:8]
filename = f"presenter_podcast_{user_id}_{img_id}.png"
output_path = PODCAST_IMAGES_DIR / filename
PODCAST_IMAGES_DIR.mkdir(parents=True, exist_ok=True)
with open(output_path, "wb") as f:
f.write(image_result.image_bytes)
final_avatar_url = f"/api/podcast/images/avatars/{filename}"
# Save to asset library for reuse
save_asset_to_library(
db=db,
user_id=user_id,
asset_type="image",
file_url=final_avatar_url,
filename=filename,
title=f"Presenter Avatar - {request.idea[:40]}",
description=f"AI-generated podcast presenter for: {request.idea}",
provider=image_result.provider,
model=image_result.model,
cost=image_result.cost
)
logger.info(f"[Podcast Analyze] ✅ Generated and saved avatar to {final_avatar_url}")
except Exception as e:
logger.error(f"[Podcast Analyze] ❌ Failed to generate avatar: {e}")
# Non-fatal: continue analysis even if avatar generation fails
# --- END: Avatar Generation ---
# Incorporate user feedback if provided
feedback_context = ""
if request.feedback:
feedback_context = f"""
USER REGENERATION FEEDBACK:
The user was not satisfied with the previous analysis. They provided the following instructions for improvement:
"{request.feedback}"
Please prioritize this feedback and adjust the analysis accordingly.
"""
prompt = f"""
You are an expert podcast producer. Given a podcast idea, craft concise podcast-ready assets
You are an expert podcast producer and research strategist. Given a podcast idea, craft concise podcast-ready assets
that sound like episode plans (not fiction stories).
{f"USER PERSONALIZATION CONTEXT (Podcast Bible):\n{bible_context}\n" if bible_context else ""}
{feedback_context}
Podcast Idea: "{request.idea}"
Duration: ~{request.duration} minutes
Speakers: {request.speakers} (host + optional guest)
TASK:
1. Define the target audience and content type aligned with the Bible's "Audience DNA" and "Brand DNA".
2. Identify 5 high-impact keywords.
3. Propose 2 episode outlines with factual segments.
4. Suggest 3 titles.
5. IMPORTANT: Generate 4-6 specific research queries for Exa. These queries MUST be highly targeted to the episode's topic, the host's expertise level, and the audience's interests as defined in the Bible.
* Do NOT use generic queries like "latest trends in X".
* DO use queries that look for case studies, specific data points, expert opinions, or contrasting viewpoints that would make for a deep, insightful podcast conversation.
Return JSON with:
- audience: short target audience description
- content_type: podcast style/format
- top_keywords: 5 podcast-relevant keywords/phrases
- suggested_outlines: 2 items, each with title (<=60 chars) and 4-6 short segments (bullet-friendly, factual)
- title_suggestions: 3 concise episode titles (no cliffhanger storytelling)
- exa_suggested_config: suggested Exa search options to power research (keep conservative defaults to control cost), with:
- exa_search_type: "auto" | "neural" | "keyword" (prefer "auto" unless clearly news-heavy)
- title_suggestions: 3 concise episode titles
- research_queries: array of {{"query": "string", "rationale": "string"}}
- exa_suggested_config: suggested Exa search options with:
- exa_search_type: "auto" | "neural" | "keyword"
- exa_category: one of ["research paper","news","company","github","tweet","personal site","pdf","financial report","linkedin profile"]
- exa_include_domains: up to 3 reputable domains to prioritize (optional)
- exa_exclude_domains: up to 3 domains to avoid (optional)
- exa_include_domains: up to 3 reputable domains
- exa_exclude_domains: up to 3 domains
- max_sources: 6-10
- include_statistics: boolean (true if topic needs fresh stats)
- date_range: one of ["last_month","last_3_months","last_year","all_time"] (pick recent if time-sensitive)
- include_statistics: boolean
- date_range: one of ["last_month","last_3_months","last_year","all_time"]
Requirements:
- Keep language factual, actionable, and suited for spoken audio.
- Avoid narrative fiction tone; focus on insights, hooks, objections, and takeaways.
- Prefer 2024-2025 context when relevant.
- Avoid narrative fiction tone.
- Prefer 2024-2025 context.
"""
try:
@@ -82,7 +266,7 @@ Requirements:
top_keywords = data.get("top_keywords") or []
suggested_outlines = data.get("suggested_outlines") or []
title_suggestions = data.get("title_suggestions") or []
research_queries = data.get("research_queries") or []
exa_suggested_config = data.get("exa_suggested_config") or None
return PodcastAnalyzeResponse(
@@ -91,6 +275,10 @@ Requirements:
top_keywords=top_keywords,
suggested_outlines=suggested_outlines,
title_suggestions=title_suggestions,
research_queries=research_queries,
exa_suggested_config=exa_suggested_config,
bible=bible_obj.model_dump() if bible_obj else None,
avatar_url=final_avatar_url,
avatar_prompt=final_avatar_prompt,
)