from typing import Dict, List, Any, Optional
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

class CSVQueryEngine:
    """Query engine for CSV data with multi-file support."""
    
    def __init__(self, index_manager, llm):
        """Initialize with index manager and language model."""
        self.index_manager = index_manager
        self.llm = llm
    
    def query(self, query_text: str) -> Dict[str, Any]:
        """Process a natural language query across CSV files."""
        # Find relevant CSV files
        relevant_csvs = self.index_manager.find_relevant_csvs(query_text)
        
        if not relevant_csvs:
            return {
                "answer": "No relevant CSV files found for your query.",
                "sources": []
            }
        
        # Prepare context from relevant CSVs
        context = self._prepare_context(query_text, relevant_csvs)
        
        # Generate prompt
        prompt = self._generate_prompt(query_text, context)
        
        # Get response from LLM
        response = self.llm.complete(prompt)
        
        # Return formatted response
        return {
            "answer": response.text,
            "sources": self._get_sources(relevant_csvs)
        }
    
    def _prepare_context(self, query: str, csv_ids: List[str]) -> str:
        """Prepare context from relevant CSV files."""
        context_parts = []
        
        for csv_id in csv_ids:
            # Get metadata
            if csv_id not in self.index_manager.indexes:
                continue
                
            metadata = self.index_manager.indexes[csv_id]["metadata"]
            file_path = self.index_manager.indexes[csv_id]["path"]
            
            # Add CSV metadata
            context_parts.append(f"CSV File: {metadata['filename']}")
            context_parts.append(f"Columns: {', '.join(metadata['columns'])}")
            context_parts.append(f"Row Count: {metadata['row_count']}")
            
            # Add sample data
            try:
                df = pd.read_csv(file_path)
                context_parts.append("\nSample Data:")
                context_parts.append(df.head(5).to_string())
                
                # Add some basic statistics that might be relevant
                context_parts.append("\nNumeric Column Statistics:")
                numeric_cols = df.select_dtypes(include=['number']).columns
                for col in numeric_cols:
                    stats = df[col].describe()
                    context_parts.append(f"{col} - mean: {stats['mean']:.2f}, min: {stats['min']:.2f}, max: {stats['max']:.2f}")
            except Exception as e:
                context_parts.append(f"Error reading CSV: {str(e)}")
        
        return "\n\n".join(context_parts)
    
    def _generate_prompt(self, query: str, context: str) -> str:
        """Generate a prompt for the LLM."""
        return f"""You are an AI assistant specialized in analyzing CSV data.
Your goal is to help users understand their data and extract insights.

Below is information about CSV files that might help answer the query:

{context}

User Query: {query}

Please provide a comprehensive and accurate answer based on the data.
If calculations are needed, explain your process.
If the data doesn't contain information to answer the query, say so clearly.

Answer:"""
    
    def _get_sources(self, csv_ids: List[str]) -> List[Dict[str, str]]:
        """Get source information for the response."""
        sources = []
        
        for csv_id in csv_ids:
            if csv_id not in self.index_manager.indexes:
                continue
                
            metadata = self.index_manager.indexes[csv_id]["metadata"]
            sources.append({
                "csv": metadata["filename"],
                "columns": ", ".join(metadata["columns"][:5]) + ("..." if len(metadata["columns"]) > 5 else "")
            })
        
        return sources