134 lines
5.1 KiB
Python
134 lines
5.1 KiB
Python
"""
|
|
Podcast cost estimation helpers.
|
|
|
|
Builds user-facing podcast estimates from the subscription pricing catalog
|
|
instead of hard-coded frontend heuristics.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Any, Dict, Optional
|
|
from sqlalchemy.orm import Session
|
|
|
|
from models.subscription_models import APIProvider
|
|
from services.subscription.pricing_service import PricingService
|
|
|
|
|
|
def _round_money(value: float) -> float:
|
|
return round(float(value), 4)
|
|
|
|
|
|
def _load_pricing(
|
|
pricing_service: PricingService,
|
|
provider: APIProvider,
|
|
preferred_model: str,
|
|
) -> Optional[Dict[str, Any]]:
|
|
pricing = pricing_service.get_pricing_for_provider_model(provider, preferred_model)
|
|
if pricing:
|
|
return pricing
|
|
# Fallback to provider default model row (if configured).
|
|
return pricing_service.get_pricing_for_provider_model(provider, "default")
|
|
|
|
|
|
def estimate_podcast_cost(
|
|
*,
|
|
db: Session,
|
|
duration_minutes: int,
|
|
speakers: int,
|
|
query_count: int,
|
|
include_avatar_phase: bool = True,
|
|
) -> Optional[Dict[str, Any]]:
|
|
"""
|
|
Compute a backend estimate for podcast creation.
|
|
|
|
Returns None when pricing rows are unavailable so UI can display "Unavailable".
|
|
"""
|
|
pricing_service = PricingService(db)
|
|
|
|
gemini_pricing = _load_pricing(pricing_service, APIProvider.GEMINI, "gemini-2.5-flash")
|
|
exa_pricing = _load_pricing(pricing_service, APIProvider.EXA, "exa-search")
|
|
audio_pricing = _load_pricing(pricing_service, APIProvider.AUDIO, "minimax/speech-02-hd")
|
|
video_pricing = _load_pricing(pricing_service, APIProvider.VIDEO, "default")
|
|
image_pricing = _load_pricing(pricing_service, APIProvider.STABILITY, "qwen-image")
|
|
|
|
if not gemini_pricing:
|
|
return None
|
|
|
|
minutes = max(1, int(duration_minutes or 1))
|
|
speaker_count = max(1, int(speakers or 1))
|
|
research_queries = max(1, int(query_count or 1))
|
|
|
|
# Phase-level usage assumptions (token/request proxies for pre-creation estimate).
|
|
analysis_input_tokens = 1800
|
|
analysis_output_tokens = 1000
|
|
research_synthesis_input_tokens = 2200
|
|
research_synthesis_output_tokens = 900
|
|
script_input_tokens = max(1800, minutes * 300)
|
|
script_output_tokens = max(2200, minutes * 700)
|
|
|
|
# TTS token proxy: ~900 chars per minute per speaker.
|
|
estimated_tts_tokens = max(900, minutes * 900 * speaker_count)
|
|
|
|
analysis_cost = (
|
|
analysis_input_tokens * float(gemini_pricing.get("cost_per_input_token") or 0.0)
|
|
+ analysis_output_tokens * float(gemini_pricing.get("cost_per_output_token") or 0.0)
|
|
+ float(gemini_pricing.get("cost_per_request") or 0.0)
|
|
)
|
|
research_llm_cost = (
|
|
research_synthesis_input_tokens * float(gemini_pricing.get("cost_per_input_token") or 0.0)
|
|
+ research_synthesis_output_tokens * float(gemini_pricing.get("cost_per_output_token") or 0.0)
|
|
+ float(gemini_pricing.get("cost_per_request") or 0.0)
|
|
)
|
|
script_cost = (
|
|
script_input_tokens * float(gemini_pricing.get("cost_per_input_token") or 0.0)
|
|
+ script_output_tokens * float(gemini_pricing.get("cost_per_output_token") or 0.0)
|
|
+ float(gemini_pricing.get("cost_per_request") or 0.0)
|
|
)
|
|
|
|
research_search_cost = 0.0
|
|
if exa_pricing:
|
|
research_search_cost = research_queries * float(exa_pricing.get("cost_per_request") or 0.0)
|
|
|
|
tts_cost = 0.0
|
|
if audio_pricing:
|
|
tts_cost = (
|
|
estimated_tts_tokens * float(audio_pricing.get("cost_per_input_token") or 0.0)
|
|
+ float(audio_pricing.get("cost_per_request") or 0.0)
|
|
)
|
|
|
|
# Assume one video render request per minute (upper-bound planning estimate).
|
|
video_cost = 0.0
|
|
if video_pricing:
|
|
video_cost = minutes * float(video_pricing.get("cost_per_request") or 0.0)
|
|
|
|
avatar_cost = 0.0
|
|
if include_avatar_phase and image_pricing:
|
|
image_unit = float(image_pricing.get("cost_per_image") or image_pricing.get("cost_per_request") or 0.0)
|
|
avatar_cost = speaker_count * image_unit
|
|
|
|
research_cost = research_search_cost + research_llm_cost
|
|
total = analysis_cost + research_cost + script_cost + tts_cost + video_cost + avatar_cost
|
|
|
|
return {
|
|
"ttsCost": _round_money(tts_cost),
|
|
"avatarCost": _round_money(avatar_cost),
|
|
"videoCost": _round_money(video_cost),
|
|
"researchCost": _round_money(research_cost),
|
|
"analysisCost": _round_money(analysis_cost),
|
|
"scriptCost": _round_money(script_cost),
|
|
"total": _round_money(total),
|
|
"currency": "USD",
|
|
"source": "pricing_catalog",
|
|
"assumptions": {
|
|
"analysis_input_tokens": analysis_input_tokens,
|
|
"analysis_output_tokens": analysis_output_tokens,
|
|
"research_synthesis_input_tokens": research_synthesis_input_tokens,
|
|
"research_synthesis_output_tokens": research_synthesis_output_tokens,
|
|
"script_input_tokens": script_input_tokens,
|
|
"script_output_tokens": script_output_tokens,
|
|
"estimated_tts_tokens": estimated_tts_tokens,
|
|
"research_queries": research_queries,
|
|
"video_requests": minutes,
|
|
},
|
|
}
|