3f984e8d0c
- Add POST /podcast/pre-estimate endpoint for cost estimation before analysis - Enhance cost_estimator.py with multi-model support (gemini, audio, voice clone, image, video) - Add detailed cost breakdown (llm, audio, media costs + per-phase breakdown) - Remove redundant pricing seeding from init_alpha_subscription_tiers.py - Add SSOT pricing via PricingService.initialize_default_pricing() - Update TopicUrlInput tooltip to show estimate details - Add debug logging for pricing seeding and pre-estimate - Clean up verbose podcast mode debug logs in app.py
216 lines
7.9 KiB
Python
216 lines
7.9 KiB
Python
"""
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Podcast cost estimation helpers.
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Builds user-facing podcast estimates from the subscription pricing catalog
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instead of hard-coded frontend heuristics.
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Supports multiple models for each component:
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- Audio TTS: minimax/speech-02-hd (default), qwen3-tts, cosyvoice-tts
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- Voice Clone: qwen3, cosyvoice, minimax
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- Image: qwen-image (default), ideogram-v3-turbo
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- Video: wan-2.5 (default), kling-v2.5, infinitetalk
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- LLM: gemini-2.5-flash (default)
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"""
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from __future__ import annotations
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from typing import Any, Dict, Optional
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from sqlalchemy.orm import Session
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from models.subscription_models import APIProvider
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from services.subscription.pricing_service import PricingService
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def _round_money(value: float) -> float:
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return round(float(value), 4)
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def _load_pricing(
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pricing_service: PricingService,
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provider: APIProvider,
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preferred_model: str,
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) -> Optional[Dict[str, Any]]:
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"""Load pricing for a provider and model, with fallback to default."""
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pricing = pricing_service.get_pricing_for_provider_model(provider, preferred_model)
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if pricing:
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return pricing
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# Fallback to provider default model row (if configured).
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return pricing_service.get_pricing_for_provider_model(provider, "default")
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# Default models used in podcast generation
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DEFAULT_MODELS = {
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"gemini": "gemini-2.5-flash",
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"exa": "exa-search",
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"audio_tts": "minimax/speech-02-hd",
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"voice_clone": "wavespeed-ai/qwen3-tts/voice-clone",
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"image": "qwen-image",
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"video": "wan-2.5",
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}
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def estimate_podcast_cost(
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*,
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db: Session,
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duration_minutes: int,
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speakers: int,
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query_count: int,
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include_avatar_phase: bool = True,
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# Optional model overrides
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gemini_model: str = "gemini-2.5-flash",
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audio_tts_model: str = "minimax/speech-02-hd",
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voice_clone_engine: str = "qwen3",
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image_model: str = "qwen-image",
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video_model: str = "wan-2.5",
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) -> Optional[Dict[str, Any]]:
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"""
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Compute a backend estimate for podcast creation.
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Supports customizable models for each component.
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Uses pricing_catalog for accurate cost calculation.
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"""
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pricing_service = PricingService(db)
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# Load pricing for each component and model
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gemini_pricing = _load_pricing(pricing_service, APIProvider.GEMINI, gemini_model)
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exa_pricing = _load_pricing(pricing_service, APIProvider.EXA, "exa-search")
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# Audio TTS pricing (minimax/speech-02-hd)
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audio_pricing = _load_pricing(pricing_service, APIProvider.AUDIO, audio_tts_model)
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# Voice clone pricing (different engines)
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voice_clone_model = f"wavespeed-ai/{voice_clone_engine}-tts/voice-clone"
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voice_clone_pricing = _load_pricing(pricing_service, APIProvider.AUDIO, voice_clone_model)
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if not voice_clone_pricing:
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# Try alternate model names
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voice_clone_pricing = _load_pricing(pricing_service, APIProvider.AUDIO, f"{voice_clone_engine}/voice-clone")
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# Image pricing (qwen-image or ideogram)
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image_pricing = _load_pricing(pricing_service, APIProvider.STABILITY, image_model)
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# Video pricing (wan-2.5, kling, or infinitetalk)
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video_pricing = _load_pricing(pricing_service, APIProvider.VIDEO, video_model)
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# Return None if critical pricing unavailable (fail fast)
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if not gemini_pricing:
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return None
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# Configuration
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minutes = max(1, int(duration_minutes or 1))
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speaker_count = max(1, int(speakers or 1))
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research_queries = max(1, int(query_count or 1))
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# Token usage assumptions per phase
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analysis_input_tokens = 1800
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analysis_output_tokens = 1000
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research_synthesis_input_tokens = 2200
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research_synthesis_output_tokens = 900
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script_input_tokens = max(1800, minutes * 300)
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script_output_tokens = max(2200, minutes * 700)
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# TTS: ~900 chars per minute per speaker
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estimated_tts_tokens = max(900, minutes * 900 * speaker_count)
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# Voice clone: 1 clone operation per speaker
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voice_clone_count = speaker_count
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# ===== COST CALCULATIONS =====
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# 1. Analysis phase (LLM)
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analysis_cost = (
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analysis_input_tokens * float(gemini_pricing.get("cost_per_input_token") or 0.0)
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+ analysis_output_tokens * float(gemini_pricing.get("cost_per_output_token") or 0.0)
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)
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# 2. Research phase
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# 2a. LLM for research synthesis
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research_llm_cost = (
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research_synthesis_input_tokens * float(gemini_pricing.get("cost_per_input_token") or 0.0)
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+ research_synthesis_output_tokens * float(gemini_pricing.get("cost_per_output_token") or 0.0)
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)
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# 2b. Search API (Exa)
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research_search_cost = 0.0
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if exa_pricing:
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research_search_cost = research_queries * float(exa_pricing.get("cost_per_request") or 0.0)
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research_cost = research_search_cost + research_llm_cost
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# 3. Script generation (LLM)
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script_cost = (
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script_input_tokens * float(gemini_pricing.get("cost_per_input_token") or 0.0)
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+ script_output_tokens * float(gemini_pricing.get("cost_per_output_token") or 0.0)
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)
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# 4. Audio TTS
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tts_cost = 0.0
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if audio_pricing:
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tts_cost = estimated_tts_tokens * float(audio_pricing.get("cost_per_input_token") or 0.0)
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# 5. Voice cloning (if needed)
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voice_clone_cost = 0.0
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if voice_clone_pricing:
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voice_clone_cost = voice_clone_count * (
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float(voice_clone_pricing.get("cost_per_request") or 0.0)
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+ estimated_tts_tokens * float(voice_clone_pricing.get("cost_per_input_token") or 0.0)
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)
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# 6. Avatar image generation
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avatar_cost = 0.0
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if include_avatar_phase and image_pricing:
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image_unit = float(image_pricing.get("cost_per_image") or image_pricing.get("cost_per_request") or 0.0)
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avatar_cost = speaker_count * image_unit
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# 7. Video rendering
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video_cost = 0.0
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if video_pricing:
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# Assume 1 video render per minute (upper bound)
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video_cost = minutes * float(video_pricing.get("cost_per_request") or 0.0)
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# ===== TOTALS =====
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llm_total = analysis_cost + research_llm_cost + script_cost
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audio_total = tts_cost + voice_clone_cost
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media_total = avatar_cost + video_cost
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total = llm_total + research_search_cost + audio_total + media_total
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return {
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# Cost breakdown
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"analysisCost": _round_money(analysis_cost),
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"researchCost": _round_money(research_cost),
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"researchSearchCost": _round_money(research_search_cost),
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"researchLlmCost": _round_money(research_llm_cost),
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"scriptCost": _round_money(script_cost),
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"ttsCost": _round_money(tts_cost),
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"voiceCloneCost": _round_money(voice_clone_cost),
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"avatarCost": _round_money(avatar_cost),
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"videoCost": _round_money(video_cost),
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"total": _round_money(total),
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# Totals by category
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"llmCost": _round_money(llm_total),
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"audioCost": _round_money(audio_total),
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"mediaCost": _round_money(media_total),
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# Currency
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"currency": "USD",
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"source": "pricing_catalog",
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# Models used for this estimate
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"models": {
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"llm": gemini_model,
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"research": "exa-search",
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"audio_tts": audio_tts_model,
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"voice_clone": voice_clone_model,
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"image": image_model,
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"video": video_model,
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},
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# Assumptions used
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"assumptions": {
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"analysis_input_tokens": analysis_input_tokens,
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"analysis_output_tokens": analysis_output_tokens,
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"research_synthesis_input_tokens": research_synthesis_input_tokens,
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"research_synthesis_output_tokens": research_synthesis_output_tokens,
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"script_input_tokens": script_input_tokens,
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"script_output_tokens": script_output_tokens,
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"estimated_tts_tokens": estimated_tts_tokens,
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"research_queries": research_queries,
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"voice_clone_count": voice_clone_count,
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"video_requests": minutes,
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"avatar_requests": speaker_count if include_avatar_phase else 0,
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},
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} |