"""
Document Processor Service
Handles text extraction from various document types:
- PDF (text extraction + OCR fallback)
- DOCX (Word documents)
- Excel (XLS, XLSX)
- Images (via OCR)
- Plain text (TXT, MD)
"""

import os
import io
from pathlib import Path
from typing import Optional
import fitz  # PyMuPDF
from docx import Document
from pptx import Presentation
from pptx.util import Inches
import pandas as pd
from PIL import Image

from services.ocr_service import ocr_service
from config import Config


class DocumentProcessor:
    def __init__(self):
        self.supported_extensions = Config.ALLOWED_EXTENSIONS
    
    def get_file_type(self, filename: str) -> str:
        """Determine file type from extension"""
        ext = Path(filename).suffix.lower().lstrip('.')
        
        type_map = {
            'pdf': 'pdf',
            'doc': 'word',
            'docx': 'word',
            'ppt': 'powerpoint',
            'pptx': 'powerpoint',
            'xls': 'excel',
            'xlsx': 'excel',
            'txt': 'text',
            'md': 'text',
            'png': 'image',
            'jpg': 'image',
            'jpeg': 'image',
            'gif': 'image',
            'webp': 'image'
        }
        
        return type_map.get(ext, 'unknown')
    
    def is_supported(self, filename: str) -> bool:
        """Check if file type is supported"""
        ext = Path(filename).suffix.lower().lstrip('.')
        return ext in self.supported_extensions
    
    def process(self, file_path: str, filename: str) -> dict:
        """
        Process a document and extract text
        Returns: {"success": bool, "text": str, "method": str, "error": str}
        """
        file_type = self.get_file_type(filename)
        
        try:
            if file_type == 'pdf':
                return self._process_pdf(file_path)
            elif file_type == 'word':
                return self._process_word(file_path)
            elif file_type == 'powerpoint':
                return self._process_pptx(file_path)
            elif file_type == 'excel':
                return self._process_excel(file_path)
            elif file_type == 'image':
                return self._process_image(file_path)
            elif file_type == 'text':
                return self._process_text(file_path)
            else:
                return {
                    "success": False,
                    "error": f"Unsupported file type: {file_type}"
                }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    def _process_pdf(self, file_path: str) -> dict:
        """
        Process PDF - Always use complete OpenRouter vision OCR for best accuracy
        """
        try:
            doc = fitz.open(file_path)
            total_pages = len(doc)
            doc.close()
            
            print(f"Processing {total_pages} page PDF with OpenRouter vision OCR...")
            
            # Use OpenRouter vision models for OCR
            ocr_result = ocr_service.extract_text_from_pdf(file_path)
            
            if ocr_result['success']:
                print(f"PDF OCR successful")
                return {
                    "success": True,
                    "text": ocr_result['text'],
                    "method": ocr_result.get('model', 'OpenRouter Vision OCR'),
                    "page_count": total_pages
                }
            else:
                return {
                    "success": False,
                    "error": f"OCR failed: {ocr_result['error']}"
                }
            
        except Exception as e:
            return {"success": False, "error": f"PDF processing error: {str(e)}"}
    
    def _process_pdf_hybrid(self, file_path: str, text_pages: list, ocr_needed_pages: list) -> dict:
        """
        Hybrid PDF processing: combine text extraction with OCR for scanned pages only
        Used as fallback when full PDF OCR fails
        """
        try:
            doc = fitz.open(file_path)
            total_pages = len(doc)
            all_pages = {}
            
            # Add already extracted text pages
            for page_num, text in text_pages:
                all_pages[page_num] = f"--- Page {page_num + 1} ---\n{text}"
            
            # OCR the scanned pages in batches
            print(f"OCR processing {len(ocr_needed_pages)} scanned pages...")
            
            for i, page_num in enumerate(ocr_needed_pages):
                page = doc[page_num]
                
                # Render page to image
                mat = fitz.Matrix(2, 2)  # 2x zoom for better OCR
                pix = page.get_pixmap(matrix=mat)
                
                temp_path = f"{file_path}_page_{page_num}.png"
                pix.save(temp_path)
                
                ocr_result = ocr_service.extract_text(temp_path)
                
                # Clean up temp file
                if os.path.exists(temp_path):
                    os.remove(temp_path)
                
                if ocr_result['success']:
                    all_pages[page_num] = f"--- Page {page_num + 1} (OCR) ---\n{ocr_result['text']}"
                else:
                    all_pages[page_num] = f"--- Page {page_num + 1} ---\n[OCR failed: {ocr_result['error']}]"
                
                # Progress logging every 10 pages
                if (i + 1) % 10 == 0:
                    print(f"OCR progress: {i + 1}/{len(ocr_needed_pages)} pages")
            
            doc.close()
            
            # Combine all pages in order
            text_parts = [all_pages[i] for i in sorted(all_pages.keys())]
            
            return {
                "success": True,
                "text": "\n\n".join(text_parts),
                "method": "hybrid (text + OCR)",
                "page_count": total_pages
            }
            
        except Exception as e:
            return {"success": False, "error": f"Hybrid PDF processing error: {str(e)}"}
    
    def _process_word(self, file_path: str) -> dict:
        """Process Word documents (DOCX)"""
        try:
            doc = Document(file_path)
            
            text_parts = []
            
            # Extract paragraphs
            for para in doc.paragraphs:
                if para.text.strip():
                    text_parts.append(para.text)
            
            # Extract tables
            for table in doc.tables:
                table_text = []
                for row in table.rows:
                    row_text = [cell.text.strip() for cell in row.cells]
                    table_text.append(" | ".join(row_text))
                if table_text:
                    text_parts.append("\n[Table]\n" + "\n".join(table_text))
            
            return {
                "success": True,
                "text": "\n\n".join(text_parts),
                "method": "docx extraction"
            }
            
        except Exception as e:
            return {"success": False, "error": f"Word processing error: {str(e)}"}
    
    def _process_pptx(self, file_path: str) -> dict:
        """Process PowerPoint files (PPTX) - extracts all text from slides"""
        try:
            prs = Presentation(file_path)
            text_parts = []
            slide_count = 0
            
            for slide_num, slide in enumerate(prs.slides, 1):
                slide_count += 1
                slide_text_parts = []
                
                # Extract text from all shapes
                for shape in slide.shapes:
                    # Text frames (text boxes, titles, etc.)
                    if shape.has_text_frame:
                        for paragraph in shape.text_frame.paragraphs:
                            para_text = ""
                            for run in paragraph.runs:
                                para_text += run.text
                            if para_text.strip():
                                slide_text_parts.append(para_text.strip())
                    
                    # Tables in slides
                    if shape.has_table:
                        table = shape.table
                        table_rows = []
                        for row in table.rows:
                            row_cells = []
                            for cell in row.cells:
                                cell_text = ""
                                for paragraph in cell.text_frame.paragraphs:
                                    for run in paragraph.runs:
                                        cell_text += run.text
                                row_cells.append(cell_text.strip())
                            table_rows.append(" | ".join(row_cells))
                        if table_rows:
                            slide_text_parts.append("[Table]\n" + "\n".join(table_rows))
                
                # Speaker notes
                if slide.has_notes_slide:
                    notes_frame = slide.notes_slide.notes_text_frame
                    if notes_frame:
                        notes_text = ""
                        for paragraph in notes_frame.paragraphs:
                            for run in paragraph.runs:
                                notes_text += run.text
                        if notes_text.strip():
                            slide_text_parts.append(f"[Speaker Notes]\n{notes_text.strip()}")
                
                if slide_text_parts:
                    text_parts.append(f"--- Slide {slide_num} ---\n" + "\n".join(slide_text_parts))
            
            if not text_parts:
                return {
                    "success": False,
                    "error": "No text content found in PowerPoint file"
                }
            
            return {
                "success": True,
                "text": "\n\n".join(text_parts),
                "method": "pptx extraction",
                "slide_count": slide_count
            }
            
        except Exception as e:
            return {"success": False, "error": f"PowerPoint processing error: {str(e)}"}
    
    def _process_excel(self, file_path: str) -> dict:
        """Process Excel files"""
        try:
            # Read all sheets
            excel_file = pd.ExcelFile(file_path)
            text_parts = []
            
            for sheet_name in excel_file.sheet_names:
                df = pd.read_excel(excel_file, sheet_name=sheet_name)
                
                if not df.empty:
                    # Convert to string representation
                    sheet_text = f"=== Sheet: {sheet_name} ===\n"
                    sheet_text += df.to_string(index=False)
                    text_parts.append(sheet_text)
            
            return {
                "success": True,
                "text": "\n\n".join(text_parts),
                "method": "excel extraction",
                "sheet_count": len(excel_file.sheet_names)
            }
            
        except Exception as e:
            return {"success": False, "error": f"Excel processing error: {str(e)}"}
    
    def _process_image(self, file_path: str) -> dict:
        """Process images using OCR"""
        result = ocr_service.extract_text(file_path)
        
        if result['success']:
            return {
                "success": True,
                "text": result['text'],
                "method": f"OCR ({result.get('model', 'unknown')})"
            }
        else:
            return {"success": False, "error": result['error']}
    
    def _process_text(self, file_path: str) -> dict:
        """Process plain text files"""
        try:
            # Try different encodings
            encodings = ['utf-8', 'latin-1', 'cp1252']
            
            for encoding in encodings:
                try:
                    with open(file_path, 'r', encoding=encoding) as f:
                        text = f.read()
                    return {
                        "success": True,
                        "text": text,
                        "method": f"text read ({encoding})"
                    }
                except UnicodeDecodeError:
                    continue
            
            return {"success": False, "error": "Could not decode text file"}
            
        except Exception as e:
            return {"success": False, "error": f"Text processing error: {str(e)}"}


# Singleton instance
document_processor = DocumentProcessor()