# storyverse_weaver/core/llm_services.py import os import google.generativeai as genai from huggingface_hub import InferenceClient # from dotenv import load_dotenv # load_dotenv() GOOGLE_API_KEY = os.getenv("STORYVERSE_GOOGLE_API_KEY") HF_TOKEN = os.getenv("STORYVERSE_HF_TOKEN") # For fallback GEMINI_TEXT_CONFIGURED = False HF_TEXT_CONFIGURED = False # For fallback text model hf_inference_text_client = None class LLMTextResponse: def __init__(self, text=None, error=None, success=True, model_id_used="unknown_text_llm"): self.text, self.error, self.success, self.model_id_used = text, error, success, model_id_used def __str__(self): return str(self.text) if self.success and self.text is not None else f"ERROR (Text Model: {self.model_id_used}): {self.error}" def initialize_text_llms(): global GOOGLE_API_KEY, HF_TOKEN, GEMINI_TEXT_CONFIGURED, HF_TEXT_CONFIGURED, hf_inference_text_client print("INFO: llm_services.py - Initializing Text LLM clients (Gemini primary)...") # Google Gemini (Primary) if GOOGLE_API_KEY and GOOGLE_API_KEY.strip(): print("INFO: llm_services.py - STORYVERSE_GOOGLE_API_KEY found in environment.") try: genai.configure(api_key=GOOGLE_API_KEY) # Simple test: list available models to confirm API key works and API is enabled models = [m for m in genai.list_models() if 'generateContent' in m.supported_generation_methods and "gemini" in m.name] if not models: raise Exception("No usable Gemini models found with this API key, or Generative Language API not fully enabled/propagated.") GEMINI_TEXT_CONFIGURED = True print(f"SUCCESS: llm_services.py - Google Gemini API (for text) configured. Found models like: {models[0].name}") except Exception as e: GEMINI_TEXT_CONFIGURED = False print(f"ERROR: llm_services.py - Failed to configure/validate Google Gemini API.") print(f" Gemini Init Error Details: {type(e).__name__}: {e}") else: GEMINI_TEXT_CONFIGURED = False print("WARNING: llm_services.py - STORYVERSE_GOOGLE_API_KEY not found or empty.") # Hugging Face (Fallback) if HF_TOKEN and HF_TOKEN.strip(): print("INFO: llm_services.py - STORYVERSE_HF_TOKEN found (for fallback text model).") try: hf_inference_text_client = InferenceClient(token=HF_TOKEN) HF_TEXT_CONFIGURED = True print("SUCCESS: llm_services.py - Hugging Face InferenceClient (for fallback text) initialized.") except Exception as e: HF_TEXT_CONFIGURED = False print(f"ERROR: llm_services.py - Failed to initialize HF InferenceClient for fallback text: {e}") hf_inference_text_client = None # Ensure client is None on failure else: HF_TEXT_CONFIGURED = False print("WARNING: llm_services.py - STORYVERSE_HF_TOKEN not found or empty (for fallback text model).") print(f"INFO: llm_services.py - Text LLM Init complete. Gemini Text Ready: {GEMINI_TEXT_CONFIGURED}, HF Text (Fallback) Ready: {HF_TEXT_CONFIGURED}") def is_gemini_text_ready(): return GEMINI_TEXT_CONFIGURED def is_hf_text_ready(): return HF_TEXT_CONFIGURED # Still useful to know if fallback is available def generate_text_gemini(prompt: str, model_id: str = "gemini-1.5-flash-latest", system_prompt: str = None, temperature: float = 0.7, max_tokens: int = 512) -> LLMTextResponse: if not is_gemini_text_ready(): return LLMTextResponse(error="Gemini text API not configured.", success=False, model_id_used=model_id) print(f"DEBUG: llm_services.py - Calling Gemini ({model_id}) for text. System prompt: {'Yes' if system_prompt else 'No'}") try: model = genai.GenerativeModel(model_name=model_id, system_instruction=system_prompt) config = genai.types.GenerationConfig(temperature=temperature, max_output_tokens=max_tokens) response = model.generate_content(prompt, generation_config=config) # Pass prompt directly if response.prompt_feedback and response.prompt_feedback.block_reason: return LLMTextResponse(error=f"Gemini: Prompt blocked ({response.prompt_feedback.block_reason_message or response.prompt_feedback.block_reason})", success=False, model_id_used=model_id) if not response.candidates or not response.candidates[0].content.parts: return LLMTextResponse(error=f"Gemini: No content generated (Finish reason: {response.candidates[0].finish_reason if response.candidates else 'Unknown'})", success=False, model_id_used=model_id) generated_text = response.text # Simpler access for Gemini print(f"DEBUG: llm_services.py - Gemini text generated successfully ({model_id}). Snippet: {generated_text[:50]}...") return LLMTextResponse(text=generated_text, model_id_used=model_id) except Exception as e: error_msg = f"Gemini API Error during text_generation ({model_id}): {type(e).__name__} - {str(e)}" # Add specific checks for Google API errors if "API key not valid" in str(e) or "PERMISSION_DENIED" in str(e): error_msg += " Check your GOOGLE_API_KEY and ensure Generative Language API is enabled in Google Cloud." print(f"ERROR: llm_services.py - {error_msg}") return LLMTextResponse(error=error_msg, success=False, model_id_used=model_id) def generate_text_hf(prompt: str, model_id: str = "mistralai/Mistral-7B-Instruct-v0.2", system_prompt: str = None, temperature: float = 0.7, max_tokens: int = 512) -> LLMTextResponse: # ... (This function remains the same as before, for fallback) if not is_hf_text_ready() or not hf_inference_text_client: return LLMTextResponse(error="HF text API not configured.", success=False, model_id_used=model_id) full_prompt = f"[INST] <>\n{system_prompt}\n<>\n\n{prompt} [/INST]" if system_prompt else prompt try: use_sample = temperature > 0.001 response_text = hf_inference_text_client.text_generation(full_prompt, model=model_id, max_new_tokens=max_tokens, temperature=temperature if use_sample else None, do_sample=use_sample) return LLMTextResponse(text=response_text, model_id_used=model_id) except Exception as e: return LLMTextResponse(error=f"HF API Error ({model_id}): {type(e).__name__} - {str(e)}", success=False, model_id_used=model_id) print("DEBUG: core.llm_services (Gemini Primary for StoryVerseWeaver) - Module defined.")