import pandas as pd
from reportlab.lib.pagesizes import letter, A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, PageBreak
from reportlab.lib import colors
from reportlab.lib.enums import TA_CENTER, TA_LEFT
from datetime import datetime
from typing import List, Dict
import os

def generate_csv(data, path="report.csv"):
    """Legacy function - kept for backwards compatibility"""
    return generate_enhanced_csv(data, "Other", path)


def generate_enhanced_csv(
    data: List[Dict],
    interviewee_type: str,
    path: str = "report.csv"
) -> str:
    """
    Generate enhanced CSV with proper formatting and data validation
    """
    
    if not data:
        # Create empty CSV with headers
        df = pd.DataFrame(columns=["Transcript ID", "Status"])
        df.to_csv(path, index=False)
        return path
    
    # Create DataFrame
    df = pd.DataFrame(data)
    
    # Reorder columns for better readability
    priority_cols = ["Transcript ID", "File Name", "Quality Score", "Word Count"]
    other_cols = [col for col in df.columns if col not in priority_cols]
    ordered_cols = [col for col in priority_cols if col in df.columns] + other_cols
    
    df = df[ordered_cols]
    
    # Save with proper encoding
    df.to_csv(path, index=False, encoding='utf-8-sig')
    
    return path


def generate_pdf(summary, details, path="report.pdf"):
    """Legacy function - kept for backwards compatibility"""
    # Create minimal results structure
    results = [{
        "transcript_id": "Transcript 1",
        "file_name": "analysis.txt",
        "full_text": details,
        "quality_score": 0.8,
        "word_count": len(details.split())
    }]
    return generate_enhanced_pdf(summary, results, "Other", [], path)


def generate_enhanced_pdf(
    summary: str,
    results: List[Dict],
    interviewee_type: str,
    processing_errors: List[str],
    path: str = "report.pdf"
) -> str:
    """
    Generate professional PDF report with proper formatting
    """
    
    # Create document
    doc = SimpleDocTemplate(
        path,
        pagesize=letter,
        rightMargin=0.75*inch,
        leftMargin=0.75*inch,
        topMargin=0.75*inch,
        bottomMargin=0.75*inch
    )
    
    # Container for the 'Flowable' objects
    story = []
    
    # Define styles
    styles = getSampleStyleSheet()
    
    # Custom styles
    title_style = ParagraphStyle(
        'CustomTitle',
        parent=styles['Heading1'],
        fontSize=24,
        textColor=colors.HexColor('#1a1a1a'),
        spaceAfter=30,
        alignment=TA_CENTER,
        fontName='Helvetica-Bold'
    )
    
    heading_style = ParagraphStyle(
        'CustomHeading',
        parent=styles['Heading2'],
        fontSize=16,
        textColor=colors.HexColor('#2c3e50'),
        spaceAfter=12,
        spaceBefore=20,
        fontName='Helvetica-Bold'
    )
    
    subheading_style = ParagraphStyle(
        'CustomSubheading',
        parent=styles['Heading3'],
        fontSize=13,
        textColor=colors.HexColor('#34495e'),
        spaceAfter=8,
        spaceBefore=12,
        fontName='Helvetica-Bold'
    )
    
    body_style = ParagraphStyle(
        'CustomBody',
        parent=styles['BodyText'],
        fontSize=11,
        leading=14,
        textColor=colors.HexColor('#2c3e50'),
        alignment=TA_LEFT
    )
    
    # Title page
    story.append(Paragraph("Transcript Analysis Report", title_style))
    story.append(Spacer(1, 0.2*inch))
    
    # Metadata table
    metadata = [
        ["Report Generated:", datetime.now().strftime("%B %d, %Y at %I:%M %p")],
        ["Interviewee Type:", interviewee_type],
        ["Total Transcripts:", str(len(results))],
        ["Successfully Processed:", str(sum(1 for r in results if r.get("quality_score", 0) > 0))]
    ]
    
    metadata_table = Table(metadata, colWidths=[2*inch, 4*inch])
    metadata_table.setStyle(TableStyle([
        ('BACKGROUND', (0, 0), (0, -1), colors.HexColor('#ecf0f1')),
        ('TEXTCOLOR', (0, 0), (-1, -1), colors.HexColor('#2c3e50')),
        ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
        ('FONTNAME', (0, 0), (0, -1), 'Helvetica-Bold'),
        ('FONTSIZE', (0, 0), (-1, -1), 10),
        ('BOTTOMPADDING', (0, 0), (-1, -1), 8),
        ('TOPPADDING', (0, 0), (-1, -1), 8),
        ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#bdc3c7'))
    ]))
    
    story.append(metadata_table)
    story.append(Spacer(1, 0.3*inch))
    
    # Executive Summary
    story.append(Paragraph("Executive Summary", heading_style))
    story.append(Spacer(1, 0.1*inch))
    
    # Split summary into paragraphs
    summary_paragraphs = summary.split('\n\n')
    for para in summary_paragraphs:
        if para.strip():
            # Clean up text for PDF
            clean_para = para.strip().replace('&', '&amp;').replace('<', '&lt;').replace('>', '&gt;')
            story.append(Paragraph(clean_para, body_style))
            story.append(Spacer(1, 0.1*inch))
    
    # Processing errors section (if any)
    if processing_errors:
        story.append(PageBreak())
        story.append(Paragraph("Processing Issues", heading_style))
        story.append(Spacer(1, 0.1*inch))
        
        for error in processing_errors:
            clean_error = error.replace('&', '&amp;').replace('<', '&lt;').replace('>', '&gt;')
            story.append(Paragraph(f"• {clean_error}", body_style))
            story.append(Spacer(1, 0.05*inch))
    
    # Individual transcript details
    story.append(PageBreak())
    story.append(Paragraph("Detailed Transcript Analysis", heading_style))
    story.append(Spacer(1, 0.2*inch))
    
    for result in results:
        # Transcript header
        transcript_title = f"{result['transcript_id']} - {result['file_name']}"
        story.append(Paragraph(transcript_title, subheading_style))
        
        # Stats
        stats_data = [
            ["Quality Score:", f"{result['quality_score']:.2f}/1.00"],
            ["Word Count:", f"{result['word_count']:,}"]
        ]
        
        stats_table = Table(stats_data, colWidths=[1.5*inch, 2*inch])
        stats_table.setStyle(TableStyle([
            ('FONTSIZE', (0, 0), (-1, -1), 9),
            ('BOTTOMPADDING', (0, 0), (-1, -1), 4),
            ('TOPPADDING', (0, 0), (-1, -1), 4),
        ]))
        
        story.append(stats_table)
        story.append(Spacer(1, 0.1*inch))
        
        # Analysis text
        text = result['full_text']
        
        # Split into manageable chunks and clean
        chunks = text.split('\n\n')
        for chunk in chunks[:10]:  # Limit to prevent overly long PDFs
            if chunk.strip():
                clean_chunk = chunk.strip().replace('&', '&amp;').replace('<', '&lt;').replace('>', '&gt;')
                # Limit paragraph length
                if len(clean_chunk) > 1000:
                    clean_chunk = clean_chunk[:1000] + "..."
                story.append(Paragraph(clean_chunk, body_style))
                story.append(Spacer(1, 0.1*inch))
        
        story.append(Spacer(1, 0.2*inch))
        
        # Page break between transcripts (except last)
        if result != results[-1]:
            story.append(PageBreak())
    
    # Build PDF
    try:
        doc.build(story)
        return path
    except Exception as e:
        print(f"[PDF Error] Failed to generate PDF: {e}")
        # Create a minimal fallback PDF
        simple_doc = SimpleDocTemplate(path, pagesize=letter)
        simple_story = [
            Paragraph("Transcript Analysis Report", title_style),
            Paragraph(f"Error generating full report: {str(e)}", body_style),
            Paragraph(summary, body_style)
        ]
        simple_doc.build(simple_story)
        return path