"""LLM-powered research assistant"""

import logging
from typing import List, Dict, Any, Optional
from dataclasses import dataclass

logger = logging.getLogger(__name__)


@dataclass
class ResearchQuery:
    """Structure for research queries"""
    question: str
    context: Optional[str] = None
    domain: str = "neuroscience"
    max_results: int = 10


class ResearchAssistant:
    """AI-powered research assistant for neuroscience"""
    
    def __init__(
        self,
        model: str = "gpt-4",
        api_key: Optional[str] = None
    ):
        """
        Initialize research assistant
        
        Args:
            model: LLM model to use
            api_key: API key for model provider
        """
        self.model = model
        self.api_key = api_key
        self._setup_model()
    
    def _setup_model(self) -> None:
        """Set up LLM connection"""
        logger.info(f"Setting up {self.model} model")
        # TODO: Initialize actual LLM connection
    
    def analyze_findings(
        self,
        results: Dict[str, Any],
        analysis_type: str = "comprehensive"
    ) -> str:
        """
        Analyze research findings using LLM
        
        Args:
            results: Research results to analyze
            analysis_type: Type of analysis to perform
            
        Returns:
            Natural language analysis
        """
        logger.info(f"Performing {analysis_type} analysis")
        
        # Placeholder implementation
        return (
            "Based on the provided results, the analysis suggests "
            "a significant correlation between microbiome diversity "
            "and Alzheimer's disease progression. Further investigation "
            "is recommended to establish causal relationships."
        )
    
    def generate_hypothesis(
        self,
        background: str,
        data: Dict[str, Any]
    ) -> List[str]:
        """
        Generate research hypotheses based on data
        
        Args:
            background: Research background/context
            data: Available data
            
        Returns:
            List of generated hypotheses
        """
        logger.info("Generating research hypotheses")
        
        # Placeholder implementation
        hypotheses = [
            "Gut microbiome dysbiosis precedes cognitive decline in AD",
            "Specific microbial metabolites modulate neuroinflammation",
            "Microbiome-based interventions may slow AD progression"
        ]
        
        # TODO: Implement actual hypothesis generation
        
        return hypotheses
    
    def literature_search(
        self,
        query: ResearchQuery
    ) -> Dict[str, Any]:
        """
        Perform intelligent literature search
        
        Args:
            query: Research query
            
        Returns:
            Search results with summaries
        """
        logger.info(f"Searching literature for: {query.question}")
        
        # Placeholder implementation
        results = {
            "query": query.question,
            "n_results": 5,
            "papers": [
                {
                    "title": "Gut microbiome alterations in Alzheimer's disease",
                    "authors": ["Smith et al."],
                    "year": 2024,
                    "summary": "Placeholder summary"
                }
            ],
            "synthesis": "Recent studies show growing evidence..."
        }
        
        # TODO: Implement actual literature search
        
        return results
    
    def explain_results(
        self,
        results: Dict[str, Any],
        audience: str = "researcher"
    ) -> str:
        """
        Explain results for different audiences
        
        Args:
            results: Results to explain
            audience: Target audience (researcher/clinician/patient)
            
        Returns:
            Tailored explanation
        """
        explanations = {
            "researcher": "Technical analysis with methodological details...",
            "clinician": "Clinical implications and treatment considerations...",
            "patient": "Simple explanation focusing on practical outcomes..."
        }
        
        return explanations.get(audience, explanations["researcher"])
