"""
Data extraction module using Google Gemini Pro and OpenAI Whisper

This module handles:
- PDF parsing (Life Plan reports in Japanese)
- Video transcription and timestamp extraction
- Structured data extraction using LLMs
"""

import os
from typing import Dict, Any, List, Optional
from pathlib import Path


class PDFExtractor:
    """Extract structured data from Life Plan PDF reports using Gemini Pro"""
    
    def __init__(self, api_key: Optional[str] = None):
        self.api_key = api_key or os.getenv("GEMINI_API_KEY")
        if not self.api_key:
            raise ValueError("GEMINI_API_KEY not found in environment")
    
    def extract(self, pdf_path: str) -> Dict[str, Any]:
        """
        Extract structured data from PDF
        
        Args:
            pdf_path: Path to the PDF file
            
        Returns:
            Dictionary containing:
            - client_info: Name, age, family structure
            - financial_data: Income, expenses, assets
            - life_events: Major events (retirement, education, housing)
            - insurance_data: Current policies and recommendations
        """
        # TODO: Implement Gemini Pro PDF parsing
        # 1. Upload PDF to Gemini API
        # 2. Use prompt engineering to extract structured JSON
        # 3. Validate against schema
        
        raise NotImplementedError("PDF extraction not yet implemented")


class VideoExtractor:
    """Extract transcription and timestamps from interview videos"""
    
    def __init__(self, api_key: Optional[str] = None):
        self.api_key = api_key or os.getenv("OPENAI_API_KEY")
        if not self.api_key:
            raise ValueError("OPENAI_API_KEY not found in environment")
    
    def extract(self, video_path: str) -> Dict[str, Any]:
        """
        Extract audio transcription and meaningful timestamps
        
        Args:
            video_path: Path to the video file
            
        Returns:
            Dictionary containing:
            - transcription: Full text transcription
            - timestamps: List of {time, text, keywords} objects
            - segments: Recommended video segments for B-Roll
        """
        # TODO: Implement Whisper transcription
        # 1. Extract audio from video using FFmpeg
        # 2. Run Whisper API
        # 3. Parse timestamps and keywords
        
        raise NotImplementedError("Video extraction not yet implemented")


def extract_pdf_data(pdf_path: str) -> Dict[str, Any]:
    """Convenience function for PDF extraction"""
    extractor = PDFExtractor()
    return extractor.extract(pdf_path)


def extract_video_data(video_path: str) -> Dict[str, Any]:
    """Convenience function for video extraction"""
    extractor = VideoExtractor()
    return extractor.extract(video_path)