file_processor.py

"""
File processor for attachment feature
Supports: txt, md, py, js, json, csv, pdf, docx, xlsx
"""

import os
from typing import Dict, Optional
import datetime


def process_file(file_path: str) -> Optional[Dict]:
    """
    Extract text content from uploaded file

    Args:
        file_path: Path to the uploaded file

    Returns:
        Dictionary with file metadata and content, or None if processing failed
    """
    try:
        filename = os.path.basename(file_path)
        file_size = os.path.getsize(file_path)
        file_ext = os.path.splitext(filename)[1].lower()

        # Process based on file type
        content = extract_content(file_path, file_ext)

        if content is None:
            return None

        # Truncate if too large (keep first 20,000 chars for context)
        original_length = len(content)
        max_chars = 20000

        if original_length > max_chars:
            content = content[:max_chars]
            content += f"\n\n[📏 Content truncated - original file was {original_length:,} characters, showing first {max_chars:,}]"

        return {
            "filename": filename,
            "content": content,
            "size_bytes": file_size,
            "file_type": file_ext[1:],  # Remove the dot
            "uploaded_at": datetime.datetime.now().isoformat(),
            "char_count": len(content)
        }

    except Exception as e:
        print(f"Error processing file {file_path}: {str(e)}")
        return None


def extract_content(file_path: str, file_ext: str) -> Optional[str]:
    """Extract text content based on file extension"""

    # Plain text files
    if file_ext in ['.txt', '.md', '.log', '.json', '.html', '.xml', '.css', '.sql']:
        return read_text_file(file_path)

    # Code files
    elif file_ext in ['.py', '.js', '.ts', '.jsx', '.tsx', '.java', '.cpp', '.c', '.h',
                      '.cs', '.php', '.rb', '.go', '.rs', '.swift', '.kt', '.sh', '.yml', '.yaml']:
        return read_text_file(file_path)

    # CSV files
    elif file_ext == '.csv':
        return read_csv_file(file_path)

    # PDF files
    elif file_ext == '.pdf':
        return read_pdf_file(file_path)

    # Word documents
    elif file_ext in ['.docx', '.doc']:
        return read_docx_file(file_path)

    # Excel files
    elif file_ext in ['.xlsx', '.xls']:
        return read_excel_file(file_path)

    else:
        return f"[❌ Unsupported file type: {file_ext}]"


def read_text_file(file_path: str) -> Optional[str]:
    """Read plain text file with multiple encoding attempts"""
    encodings = ['utf-8', 'utf-16', 'latin-1', 'cp1252']

    for encoding in encodings:
        try:
            with open(file_path, 'r', encoding=encoding) as f:
                return f.read()
        except UnicodeDecodeError:
            continue
        except Exception as e:
            print(f"Error reading text file with {encoding}: {e}")
            continue

    return "[❌ Could not decode text file - unsupported encoding]"


def read_csv_file(file_path: str) -> Optional[str]:
    """Read CSV file and convert to formatted text"""
    try:
        import csv

        with open(file_path, 'r', encoding='utf-8', newline='') as f:
            reader = csv.reader(f)
            rows = list(reader)

            if not rows:
                return "[Empty CSV file]"

            # Format as text with column alignment
            output = []
            output.append(f"CSV Data ({len(rows)} rows):\n")
            output.append("=" * 50)

            # Header
            if rows:
                output.append(" | ".join(rows[0]))
                output.append("-" * 50)

            # Data rows (limit to first 100 rows for context)
            for row in rows[1:101]:
                output.append(" | ".join(str(cell) for cell in row))

            if len(rows) > 101:
                output.append(f"\n[... {len(rows) - 101} more rows]")

            return "\n".join(output)

    except Exception as e:
        return f"[❌ Error reading CSV: {str(e)}]"


def read_pdf_file(file_path: str) -> Optional[str]:
    """Extract text from PDF file"""
    try:
        import pdfplumber

        with pdfplumber.open(file_path) as pdf:
            pages_text = []

            for i, page in enumerate(pdf.pages[:50]):  # Limit to first 50 pages
                text = page.extract_text()
                if text:
                    pages_text.append(f"--- Page {i + 1} ---\n{text}")

            if len(pdf.pages) > 50:
                pages_text.append(f"\n[... {len(pdf.pages) - 50} more pages not shown]")

            content = "\n\n".join(pages_text)

            if not content.strip():
                return "[❌ PDF appears to be empty or contains only images]"

            return content

    except ImportError:
        return "[❌ pdfplumber not installed - run: pip install pdfplumber]"
    except Exception as e:
        return f"[❌ Error reading PDF: {str(e)}]"


def read_docx_file(file_path: str) -> Optional[str]:
    """Extract text from Word document"""
    try:
        from docx import Document

        doc = Document(file_path)
        paragraphs = []

        for para in doc.paragraphs:
            if para.text.strip():
                paragraphs.append(para.text)

        # Also extract text from tables
        for table in doc.tables:
            for row in table.rows:
                row_text = " | ".join(cell.text.strip() for cell in row.cells)
                if row_text.strip():
                    paragraphs.append(row_text)

        content = "\n\n".join(paragraphs)

        if not content.strip():
            return "[❌ Word document appears to be empty]"

        return content

    except ImportError:
        return "[❌ python-docx not installed - run: pip install python-docx]"
    except Exception as e:
        return f"[❌ Error reading Word document: {str(e)}]"


def read_excel_file(file_path: str) -> Optional[str]:
    """Extract text from Excel file"""
    try:
        import pandas as pd

        # Read all sheets
        excel_file = pd.ExcelFile(file_path)
        output = []

        output.append(f"Excel File - {len(excel_file.sheet_names)} sheet(s)\n")
        output.append("=" * 50)

        for sheet_name in excel_file.sheet_names:
            df = pd.read_excel(file_path, sheet_name=sheet_name)

            output.append(f"\n📊 Sheet: {sheet_name}")
            output.append(f"Dimensions: {df.shape[0]} rows × {df.shape[1]} columns")
            output.append("-" * 50)

            # Convert to string representation (limit rows)
            if len(df) > 50:
                output.append(df.head(50).to_string(index=False))
                output.append(f"\n[... {len(df) - 50} more rows]")
            else:
                output.append(df.to_string(index=False))

            output.append("\n")

        return "\n".join(output)

    except ImportError:
        return "[❌ pandas/openpyxl not installed - run: pip install pandas openpyxl]"
    except Exception as e:
        return f"[❌ Error reading Excel file: {str(e)}]"


def get_file_icon(file_type: str) -> str:
    """Return emoji icon for file type"""
    icons = {
        'txt': '📄', 'md': '📝', 'pdf': '📕', 'doc': '📘', 'docx': '📘',
        'xls': '📊', 'xlsx': '📊', 'csv': '📊',
        'json': '📋', 'xml': '📋', 'html': '🌐',
        'py': '🐍', 'js': '📜', 'ts': '📜', 'java': '☕', 'cpp': '⚙️',
        'log': '📋', 'sql': '🗄️', 'yml': '⚙️', 'yaml': '⚙️'
    }
    return icons.get(file_type, '📎')


def format_file_size(size_bytes: int) -> str:
    """Format file size in human-readable format"""
    if size_bytes < 1024:
        return f"{size_bytes} B"
    elif size_bytes < 1024 * 1024:
        return f"{size_bytes / 1024:.1f} KB"
    else:
        return f"{size_bytes / (1024 * 1024):.1f} MB"