# [DEF:llm_core:Module]
import os
import logging
from typing import Type, TypeVar
from pydantic import BaseModel

# Pylance often fails to resolve dynamic exports in google-generativeai
import google.generativeai as genai # type: ignore

logger = logging.getLogger("LLM_Core")
T = TypeVar("T", bound=BaseModel)

class GeminiProcessor:
    def __init__(self, model_name: str = "gemini-1.5-flash"):
        api_key = os.getenv("GOOGLE_API_KEY")
        if not api_key:
            raise ValueError("[FATAL] GOOGLE_API_KEY not found.")
        
        # Explicit type ignore for Pylance strict mode
        genai.configure(api_key=api_key) # type: ignore
        self.model = genai.GenerativeModel(model_name) # type: ignore

    def generate_structured(self, prompt: str, content: str, schema: Type[T]) -> T:
        logger.info(f"[GeminiProcessor] Structured generation for {schema.__name__}")
        full_prompt = f"{prompt}\n\nINPUT TEXT:\n{content}"
        
        response = self.model.generate_content(
            full_prompt,
            generation_config=genai.GenerationConfig( # type: ignore
                response_mime_type="application/json",
                response_schema=schema
            )
        )
        return schema.model_validate_json(response.text)

    def generate_text(self, system_instruction: str, user_content: str) -> str:
        # Re-instantiate model with system instruction
        model_w_sys = genai.GenerativeModel( # type: ignore
            self.model.model_name,
            system_instruction=system_instruction
        )
        response = model_w_sys.generate_content(user_content)
        return response.text
# [/DEF:llm_core]