228 lines
11 KiB
Python
228 lines
11 KiB
Python
import os
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import sys
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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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import config
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from . import utils
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# Suppress Vertex AI warnings
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warnings.filterwarnings("ignore", category=UserWarning, module="vertexai")
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try:
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import vertexai
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from vertexai.preview.vision_models import ImageGenerationModel as VertexImageModel
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HAS_VERTEX = True
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except ImportError:
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HAS_VERTEX = False
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try:
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from google.auth.transport.requests import Request
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from google.oauth2.credentials import Credentials
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from google_auth_oauthlib.flow import InstalledAppFlow
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HAS_OAUTH = True
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except ImportError:
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HAS_OAUTH = False
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model_logic = None
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model_writer = None
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model_artist = None
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model_image = None
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def get_optimal_model(base_type="pro"):
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try:
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models = [m for m in genai.list_models() if 'generateContent' in m.supported_generation_methods]
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candidates = [m.name for m in models 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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# Prioritize stable models (higher quotas) over experimental/beta ones
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if "exp" in n or "beta" in n: return 0
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if "latest" in n: return 50
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return 100
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return sorted(candidates, key=score, reverse=True)[0]
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except: 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-1.5-pro", "reason": "Fallback: Default Pro model selected."},
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"writer": {"model": "models/gemini-1.5-flash", "reason": "Fallback: Default Flash model selected."},
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"artist": {"model": "models/gemini-1.5-flash", "reason": "Fallback: Default Flash model selected."},
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"ranking": []
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}
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def select_best_models(force_refresh=False):
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"""
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Uses a safe bootstrapper model to analyze available models and pick the best ones.
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Caches the result for 24 hours.
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"""
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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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# 1. Check Cache
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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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# Check if within 24 hours (86400 seconds)
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if not force_refresh and time.time() - cached.get('timestamp', 0) < 86400:
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models = cached_models
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# Validate format (must be dicts with reasons, not just strings)
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if isinstance(models.get('logic'), dict) and 'reason' in models['logic']:
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utils.log("SYSTEM", "Using cached AI model selection (valid for 24h).")
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return models
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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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models = [m.name for m in genai.list_models() if 'generateContent' in m.supported_generation_methods]
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bootstrapper = "models/gemini-1.5-flash"
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if bootstrapper not in models:
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candidates = [m for m in models if 'flash' in m]
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bootstrapper = candidates[0] if candidates else "models/gemini-pro"
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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"Analyze this list of available Google Gemini models:\n{json.dumps(models)}\n\nSelect the best model for each of these three roles based on these criteria:\n- Most recent version with best features and ability.\n- Beta versions are okay, but avoid 'experimental' if a stable beta/prod version exists.\n- Consider quota efficiency (Flash is cheaper/faster, Pro is smarter).\n\nROLES:\n1. LOGIC: For complex reasoning, JSON structuring, and plot planning.\n2. WRITER: For creative fiction writing, prose generation, and speed.\n3. ARTIST: For generating visual art prompts and design instructions.\n\nAlso provide a 'ranking' list of ALL models analyzed, ordered from best/most useful to worst/least useful, with a short reason.\n\nReturn JSON: {{ 'logic': {{ 'model': 'model_name', 'reason': 'reasoning' }}, 'writer': {{ 'model': 'model_name', 'reason': 'reasoning' }}, 'artist': {{ 'model': 'model_name', 'reason': 'reasoning' }}, 'ranking': [ {{ 'model': 'model_name', 'reason': 'reasoning' }} ] }}"
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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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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({"timestamp": int(time.time()), "models": selection, "available_at_time": models}, 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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# 3. Fallback to Stale Cache if available (Better than heuristics)
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# Relaxed check: If we successfully loaded ANY JSON from the cache, use it.
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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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# Save fallback to cache if file doesn't exist OR if we couldn't load it (corrupt/None)
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# This ensures we have a valid file on disk for the web UI to read.
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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 model_logic, model_writer, model_artist, model_image
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if model_logic and not force: return
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genai.configure(api_key=config.API_KEY)
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# Check cache to skip frequent validation
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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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# Validate Gemini API Key
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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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# Check if we have a cache file we can rely on before exiting
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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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def get_model_name(role_data):
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if isinstance(role_data, dict): return role_data.get('model')
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return role_data
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logic_name = get_model_name(selected_models['logic']) if config.MODEL_LOGIC_HINT == "AUTO" else config.MODEL_LOGIC_HINT
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writer_name = get_model_name(selected_models['writer']) if config.MODEL_WRITER_HINT == "AUTO" else config.MODEL_WRITER_HINT
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artist_name = get_model_name(selected_models['artist']) if config.MODEL_ARTIST_HINT == "AUTO" else config.MODEL_ARTIST_HINT
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utils.log("SYSTEM", f"Models: Logic={logic_name} | Writer={writer_name} | Artist={artist_name}")
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model_logic = genai.GenerativeModel(logic_name, safety_settings=utils.SAFETY_SETTINGS)
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model_writer = genai.GenerativeModel(writer_name, safety_settings=utils.SAFETY_SETTINGS)
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model_artist = genai.GenerativeModel(artist_name, safety_settings=utils.SAFETY_SETTINGS)
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# Initialize Image Model (Default to None)
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model_image = None
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if hasattr(genai, 'ImageGenerationModel'):
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try: model_image = genai.ImageGenerationModel("imagen-3.0-generate-001")
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except: pass
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img_source = "Gemini API" if model_image else "None"
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# Auto-detect GCP Project from credentials if not set (Fix for Image Model)
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if 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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# Check common OAuth structures
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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 HAS_VERTEX and config.GCP_PROJECT:
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creds = None
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# Handle OAuth Client ID (credentials.json) if provided instead of Service Account
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if HAS_OAUTH:
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gac = config.GOOGLE_CREDS # Use persistent config, not volatile env var
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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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# It's an OAuth Client ID. Unset env var to avoid library crash.
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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 = 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(Request())
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except Exception:
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utils.log("SYSTEM", "Token refresh failed. Re-authenticating...")
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flow = 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 = 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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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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# Override with Vertex Image Model if available
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try:
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model_image = VertexImageModel.from_pretrained("imagen-3.0-generate-001")
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img_source = "Vertex AI"
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except: pass
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utils.log("SYSTEM", f"Image Generation Provider: {img_source}") |