Mike Wichers
f7099cc3e4
Replaced monolithic modules/ package with a clean architecture:
- core/ config.py, utils.py
- ai/ models.py (ResilientModel), setup.py (init_models)
- story/ planner.py, writer.py, editor.py, style_persona.py, bible_tracker.py
- marketing/ cover.py, blurb.py, fonts.py, assets.py
- export/ exporter.py
- web/ app.py (Flask factory), db.py, helpers.py, tasks.py, routes/{auth,project,run,persona,admin}.py
- cli/ engine.py (run_generation), wizard.py (BookWizard)
Flask routes split into 5 Blueprints; all templates updated with blueprint-
prefixed url_for() calls. Dockerfile and docker-compose updated to use
web.app entry point and new package paths.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
290 lines
13 KiB
Python
290 lines
13 KiB
Python
import os
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import json
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import time
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import warnings
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import google.generativeai as genai
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from core import config, utils
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from ai import models
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def get_optimal_model(base_type="pro"):
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try:
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available = [m for m in genai.list_models() if 'generateContent' in m.supported_generation_methods]
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candidates = [m.name for m in available if base_type in m.name]
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if not candidates: return f"models/gemini-1.5-{base_type}"
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def score(n):
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gen_bonus = 0
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if "2.5" in n: gen_bonus = 300
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elif "2.0" in n: gen_bonus = 200
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elif "2." in n: gen_bonus = 150
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if "exp" in n or "beta" in n or "preview" in n: return gen_bonus + 0
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if "latest" in n: return gen_bonus + 50
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return gen_bonus + 100
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return sorted(candidates, key=score, reverse=True)[0]
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except Exception as e:
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utils.log("SYSTEM", f"⚠️ Error finding optimal model: {e}")
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return f"models/gemini-1.5-{base_type}"
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def get_default_models():
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return {
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"logic": {"model": "models/gemini-2.0-pro-exp", "reason": "Fallback: Gemini 2.0 Pro for complex reasoning and JSON adherence.", "estimated_cost": "$0.00/1M (Experimental)"},
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"writer": {"model": "models/gemini-2.0-flash", "reason": "Fallback: Gemini 2.0 Flash for fast, high-quality creative writing.", "estimated_cost": "$0.10/1M"},
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"artist": {"model": "models/gemini-2.0-flash", "reason": "Fallback: Gemini 2.0 Flash for visual prompt design.", "estimated_cost": "$0.10/1M"},
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"ranking": []
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}
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def select_best_models(force_refresh=False):
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cache_path = os.path.join(config.DATA_DIR, "model_cache.json")
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cached_models = None
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if os.path.exists(cache_path):
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try:
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with open(cache_path, 'r') as f:
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cached = json.load(f)
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cached_models = cached.get('models', {})
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if not force_refresh and time.time() - cached.get('timestamp', 0) < 86400:
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m = cached_models
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if isinstance(m.get('logic'), dict) and 'reason' in m['logic']:
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utils.log("SYSTEM", "Using cached AI model selection (valid for 24h).")
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return m
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except Exception as e:
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utils.log("SYSTEM", f"Cache read failed: {e}. Refreshing models.")
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try:
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utils.log("SYSTEM", "Refreshing AI model list from API...")
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all_models = list(genai.list_models())
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raw_model_names = [m.name for m in all_models]
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utils.log("SYSTEM", f"Found {len(all_models)} raw models from Google API.")
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compatible = [m.name for m in all_models if 'generateContent' in m.supported_generation_methods and 'gemini' in m.name.lower()]
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utils.log("SYSTEM", f"Identified {len(compatible)} compatible Gemini models: {compatible}")
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bootstrapper = get_optimal_model("flash")
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utils.log("SYSTEM", f"Bootstrapping model selection with: {bootstrapper}")
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model = genai.GenerativeModel(bootstrapper)
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prompt = f"""
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ROLE: AI Model Architect
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TASK: Select the optimal Gemini models for a book-writing application. Prefer newer Gemini 2.x models when available.
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AVAILABLE_MODELS:
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{json.dumps(compatible)}
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PRICING_CONTEXT (USD per 1M tokens, approximate):
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- Gemini 2.5 Pro/Flash: Best quality/speed; check current pricing.
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- Gemini 2.0 Flash: ~$0.10 Input / $0.40 Output. (Fast, cost-effective, excellent quality).
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- Gemini 2.0 Pro Exp: Free experimental tier with strong reasoning.
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- Gemini 1.5 Flash: ~$0.075 Input / $0.30 Output. (Legacy, still reliable).
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- Gemini 1.5 Pro: ~$1.25 Input / $5.00 Output. (Legacy, expensive).
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CRITERIA:
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- LOGIC: Needs complex reasoning, strict JSON adherence, plot consistency, and instruction following.
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-> Prefer: Gemini 2.5 Pro > 2.0 Pro > 2.0 Flash > 1.5 Pro
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- WRITER: Needs creativity, prose quality, long-form text generation, and speed.
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-> Prefer: Gemini 2.5 Flash/Pro > 2.0 Flash > 1.5 Flash (balance quality/cost)
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- ARTIST: Needs rich visual description, prompt understanding for cover art design.
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-> Prefer: Gemini 2.0 Flash > 1.5 Flash (speed and visual understanding)
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CONSTRAINTS:
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- Strongly prefer Gemini 2.x over 1.5 where available.
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- Avoid 'experimental' or 'preview' only if a stable 2.x version exists; otherwise experimental 2.x is fine.
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- 'thinking' models are too slow/expensive for Writer/Artist roles.
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- Provide a ranking of ALL available models from best to worst overall.
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OUTPUT_FORMAT (JSON only, no markdown):
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{{
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"logic": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX/1M" }},
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"writer": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX/1M" }},
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"artist": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX/1M" }},
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"ranking": [ {{ "model": "string", "reason": "string", "estimated_cost": "string" }} ]
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}}
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"""
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try:
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response = model.generate_content(prompt)
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selection = json.loads(utils.clean_json(response.text))
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except Exception as e:
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utils.log("SYSTEM", f"Model selection generation failed (Safety/Format): {e}")
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raise e
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if not os.path.exists(config.DATA_DIR): os.makedirs(config.DATA_DIR)
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with open(cache_path, 'w') as f:
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json.dump({
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"timestamp": int(time.time()),
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"models": selection,
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"available_at_time": compatible,
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"raw_models": raw_model_names
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}, f, indent=2)
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return selection
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except Exception as e:
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utils.log("SYSTEM", f"AI Model Selection failed: {e}.")
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if cached_models:
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utils.log("SYSTEM", "⚠️ Using stale cached models due to API failure.")
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return cached_models
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utils.log("SYSTEM", "Falling back to heuristics.")
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fallback = get_default_models()
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try:
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with open(cache_path, 'w') as f:
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json.dump({"timestamp": int(time.time()), "models": fallback, "error": str(e)}, f, indent=2)
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except: pass
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return fallback
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def init_models(force=False):
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global_vars = models.__dict__
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if global_vars.get('model_logic') and not force: return
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genai.configure(api_key=config.API_KEY)
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cache_path = os.path.join(config.DATA_DIR, "model_cache.json")
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skip_validation = False
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if not force and os.path.exists(cache_path):
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try:
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with open(cache_path, 'r') as f: cached = json.load(f)
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if time.time() - cached.get('timestamp', 0) < 86400: skip_validation = True
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except: pass
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if not skip_validation:
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utils.log("SYSTEM", "Validating credentials...")
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try:
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list(genai.list_models(page_size=1))
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utils.log("SYSTEM", "✅ Gemini API Key is valid.")
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except Exception as e:
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if os.path.exists(cache_path):
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utils.log("SYSTEM", f"⚠️ API check failed ({e}), but cache exists. Attempting to use cached models.")
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else:
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utils.log("SYSTEM", f"⚠️ API check failed ({e}). No cache found. Attempting to initialize with defaults.")
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utils.log("SYSTEM", "Selecting optimal models via AI...")
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selected_models = select_best_models(force_refresh=force)
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if not force:
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missing_costs = False
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for role in ['logic', 'writer', 'artist']:
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if 'estimated_cost' not in selected_models.get(role, {}) or selected_models[role].get('estimated_cost') == 'N/A':
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missing_costs = True
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if missing_costs:
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utils.log("SYSTEM", "⚠️ Missing cost info in cached models. Forcing refresh.")
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return init_models(force=True)
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def get_model_details(role_data):
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if isinstance(role_data, dict): return role_data.get('model'), role_data.get('estimated_cost', 'N/A')
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return role_data, 'N/A'
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logic_name, logic_cost = get_model_details(selected_models['logic'])
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writer_name, writer_cost = get_model_details(selected_models['writer'])
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artist_name, artist_cost = get_model_details(selected_models['artist'])
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logic_name = logic_name if config.MODEL_LOGIC_HINT == "AUTO" else config.MODEL_LOGIC_HINT
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writer_name = writer_name if config.MODEL_WRITER_HINT == "AUTO" else config.MODEL_WRITER_HINT
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artist_name = artist_name if config.MODEL_ARTIST_HINT == "AUTO" else config.MODEL_ARTIST_HINT
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models.logic_model_name = logic_name
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models.writer_model_name = writer_name
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models.artist_model_name = artist_name
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utils.log("SYSTEM", f"Models: Logic={logic_name} ({logic_cost}) | Writer={writer_name} ({writer_cost}) | Artist={artist_name}")
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utils.update_pricing(logic_name, logic_cost)
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utils.update_pricing(writer_name, writer_cost)
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utils.update_pricing(artist_name, artist_cost)
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if models.model_logic is None:
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models.model_logic = models.ResilientModel(logic_name, utils.SAFETY_SETTINGS, "Logic")
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models.model_writer = models.ResilientModel(writer_name, utils.SAFETY_SETTINGS, "Writer")
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models.model_artist = models.ResilientModel(artist_name, utils.SAFETY_SETTINGS, "Artist")
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else:
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models.model_logic.update(logic_name)
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models.model_writer.update(writer_name)
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models.model_artist.update(artist_name)
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models.model_image = None
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models.image_model_name = None
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models.image_model_source = "None"
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hint = config.MODEL_IMAGE_HINT if hasattr(config, 'MODEL_IMAGE_HINT') else "AUTO"
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if hasattr(genai, 'ImageGenerationModel'):
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candidates = [hint] if hint and hint != "AUTO" else ["imagen-3.0-generate-001", "imagen-3.0-fast-generate-001"]
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for candidate in candidates:
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try:
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models.model_image = genai.ImageGenerationModel(candidate)
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models.image_model_name = candidate
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models.image_model_source = "Gemini API"
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utils.log("SYSTEM", f"✅ Image model: {candidate} (Gemini API)")
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break
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except Exception:
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continue
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# Auto-detect GCP Project
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if models.HAS_VERTEX and not config.GCP_PROJECT and config.GOOGLE_CREDS and os.path.exists(config.GOOGLE_CREDS):
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try:
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with open(config.GOOGLE_CREDS, 'r') as f:
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cdata = json.load(f)
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for k in ['installed', 'web']:
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if k in cdata and 'project_id' in cdata[k]:
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config.GCP_PROJECT = cdata[k]['project_id']
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utils.log("SYSTEM", f"Auto-detected GCP Project ID: {config.GCP_PROJECT}")
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break
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except: pass
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if models.HAS_VERTEX and config.GCP_PROJECT:
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creds = None
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if models.HAS_OAUTH:
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gac = config.GOOGLE_CREDS
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if gac and os.path.exists(gac):
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try:
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with open(gac, 'r') as f: data = json.load(f)
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if 'installed' in data or 'web' in data:
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if "GOOGLE_APPLICATION_CREDENTIALS" in os.environ:
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del os.environ["GOOGLE_APPLICATION_CREDENTIALS"]
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token_path = os.path.join(os.path.dirname(os.path.abspath(gac)), 'token.json')
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SCOPES = ['https://www.googleapis.com/auth/cloud-platform']
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if os.path.exists(token_path):
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creds = models.Credentials.from_authorized_user_file(token_path, SCOPES)
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if not creds or not creds.valid:
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if creds and creds.expired and creds.refresh_token:
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try:
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creds.refresh(models.Request())
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except Exception:
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utils.log("SYSTEM", "Token refresh failed. Re-authenticating...")
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flow = models.InstalledAppFlow.from_client_secrets_file(gac, SCOPES)
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creds = flow.run_local_server(port=0)
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else:
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utils.log("SYSTEM", "OAuth Client ID detected. Launching browser to authenticate...")
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flow = models.InstalledAppFlow.from_client_secrets_file(gac, SCOPES)
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creds = flow.run_local_server(port=0)
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with open(token_path, 'w') as token: token.write(creds.to_json())
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utils.log("SYSTEM", "✅ Authenticated via OAuth Client ID.")
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except Exception as e:
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utils.log("SYSTEM", f"⚠️ OAuth check failed: {e}")
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import vertexai as _vertexai
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_vertexai.init(project=config.GCP_PROJECT, location=config.GCP_LOCATION, credentials=creds)
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utils.log("SYSTEM", f"✅ Vertex AI initialized (Project: {config.GCP_PROJECT})")
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vertex_candidates = [hint] if hint and hint != "AUTO" else ["imagen-3.0-generate-001", "imagen-3.0-fast-generate-001"]
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for candidate in vertex_candidates:
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try:
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models.model_image = models.VertexImageModel.from_pretrained(candidate)
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models.image_model_name = candidate
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models.image_model_source = "Vertex AI"
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utils.log("SYSTEM", f"✅ Image model: {candidate} (Vertex AI)")
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break
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except Exception:
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continue
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utils.log("SYSTEM", f"Image Generation Provider: {models.image_model_source} ({models.image_model_name or 'unavailable'})")
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