97 lines
3.8 KiB
Python
97 lines
3.8 KiB
Python
"""
|
|
Podcast Analysis Handlers
|
|
|
|
Analysis endpoint for podcast ideas.
|
|
"""
|
|
|
|
from fastapi import APIRouter, Depends, HTTPException
|
|
from typing import Dict, Any
|
|
import json
|
|
|
|
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 loguru import logger
|
|
from ..models import PodcastAnalyzeRequest, PodcastAnalyzeResponse
|
|
|
|
router = APIRouter()
|
|
|
|
|
|
@router.post("/analyze", response_model=PodcastAnalyzeResponse)
|
|
async def analyze_podcast_idea(
|
|
request: PodcastAnalyzeRequest,
|
|
current_user: Dict[str, Any] = Depends(get_current_user),
|
|
):
|
|
"""
|
|
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).
|
|
"""
|
|
user_id = require_authenticated_user(current_user)
|
|
|
|
prompt = f"""
|
|
You are an expert podcast producer. Given a podcast idea, craft concise podcast-ready assets
|
|
that sound like episode plans (not fiction stories).
|
|
|
|
Podcast Idea: "{request.idea}"
|
|
Duration: ~{request.duration} minutes
|
|
Speakers: {request.speakers} (host + optional guest)
|
|
|
|
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)
|
|
- 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)
|
|
- 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)
|
|
|
|
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.
|
|
"""
|
|
|
|
try:
|
|
raw = llm_text_gen(prompt=prompt, user_id=user_id, json_struct=None)
|
|
except HTTPException:
|
|
# Re-raise HTTPExceptions (e.g., 429 subscription limit) - preserve error details
|
|
raise
|
|
except Exception as exc:
|
|
logger.error(f"[Podcast Analyze] Analysis failed for user {user_id}: {exc}")
|
|
raise HTTPException(status_code=500, detail=f"Analysis failed: {exc}")
|
|
|
|
# Normalize response (accept dict or JSON string)
|
|
if isinstance(raw, str):
|
|
try:
|
|
data = json.loads(raw)
|
|
except json.JSONDecodeError:
|
|
raise HTTPException(status_code=500, detail="LLM returned non-JSON output")
|
|
elif isinstance(raw, dict):
|
|
data = raw
|
|
else:
|
|
raise HTTPException(status_code=500, detail="Unexpected LLM response format")
|
|
|
|
audience = data.get("audience") or "Growth-focused professionals"
|
|
content_type = data.get("content_type") or "Interview + insights"
|
|
top_keywords = data.get("top_keywords") or []
|
|
suggested_outlines = data.get("suggested_outlines") or []
|
|
title_suggestions = data.get("title_suggestions") or []
|
|
|
|
exa_suggested_config = data.get("exa_suggested_config") or None
|
|
|
|
return PodcastAnalyzeResponse(
|
|
audience=audience,
|
|
content_type=content_type,
|
|
top_keywords=top_keywords,
|
|
suggested_outlines=suggested_outlines,
|
|
title_suggestions=title_suggestions,
|
|
exa_suggested_config=exa_suggested_config,
|
|
)
|
|
|