#!/usr/bin/env python3

print("🚀 APP.PY STARTING - IMMEDIATE FEEDBACK", flush=True)

import sys
import os
import re
import time
from pathlib import Path

print("📦 BASIC IMPORTS COMPLETE", flush=True)

def generate_toc(markdown_content):
    """Generate a Table of Contents from markdown headers"""
    print("  📋 Generating Table of Contents...", flush=True)
    lines = markdown_content.split('\n')
    toc_lines = []
    toc_lines.append("## Table of Contents")
    toc_lines.append("")
    
    header_count = 0
    for line in lines:
        # Match headers (##, ###, etc.)
        header_match = re.match(r'^(#{2,})\s+(.+)$', line)
        if header_match:
            header_count += 1
            level = len(header_match.group(1)) - 2  # Convert ## to 0, ### to 1, etc.
            title = header_match.group(2)
            
            # Create anchor link
            anchor = re.sub(r'[^a-zA-Z0-9\s-]', '', title.lower())
            anchor = re.sub(r'\s+', '-', anchor.strip())
            
            # Add indentation based on header level
            indent = "  " * level
            toc_lines.append(f"{indent}- [{title}](#{anchor})")
    
    toc_lines.append("")
    toc_lines.append("---")
    toc_lines.append("")
    
    print(f"  ✅ Generated TOC with {header_count} headers", flush=True)
    return '\n'.join(toc_lines)

def main():
    """Simple pitch deck analyzer with comprehensive debugging"""
    print("🚀 PITCH DECK ANALYZER MAIN FUNCTION STARTING", flush=True)
    print("=" * 50, flush=True)
    
    if len(sys.argv) < 2:
        print("❌ Usage: python app.py <pdf_file>", flush=True)
        return
    
    pdf_path = sys.argv[1]
    if not os.path.exists(pdf_path):
        print(f"❌ Error: File '{pdf_path}' not found", flush=True)
        return
    
    print(f"📁 Processing file: {pdf_path}", flush=True)
    print(f"📁 File exists: {os.path.exists(pdf_path)}", flush=True)
    print(f"📁 File size: {os.path.getsize(pdf_path)} bytes", flush=True)
    
    # Import what we need directly (avoid __init__.py issues)
    print("\n📦 IMPORTING MODULES", flush=True)
    print("-" * 30, flush=True)
    
    sys.path.append('modules')
    
    print("  🔄 Importing client module...", flush=True)
    from client import get_openrouter_client
    print("  ✅ client module imported successfully", flush=True)
    
    print("  🔄 Importing pdf_processor module...", flush=True)
    from pdf_processor import extract_slides_from_pdf
    print("  ✅ pdf_processor module imported successfully", flush=True)
    
    print("  🔄 Importing analysis module...", flush=True)
    from analysis import analyze_slides_batch
    print("  ✅ analysis module imported successfully", flush=True)
    
    print("  🔄 Importing markdown_utils module...", flush=True)
    from markdown_utils import send_to_api_and_get_haste_link
    print("  ✅ markdown_utils module imported successfully", flush=True)
    
    print("✅ ALL MODULES IMPORTED SUCCESSFULLY", flush=True)
    
    # Extract slides
    print("\n📄 EXTRACTING SLIDES", flush=True)
    print("-" * 30, flush=True)
    print("  🔄 Calling extract_slides_from_pdf...", flush=True)
    start_time = time.time()
    
    slides = extract_slides_from_pdf(pdf_path, "processed", Path(pdf_path).stem)
    extraction_time = time.time() - start_time
    print(f"  ✅ extract_slides_from_pdf completed in {extraction_time:.2f}s", flush=True)
    print(f"  📊 Extracted {len(slides)} slides", flush=True)
    
    # LIMIT TO FIRST 3 SLIDES FOR TESTING
    print(f"  🔄 Limiting to first 3 slides for testing...", flush=True)
    slides = slides[:3]
    print(f"  📊 Processing {len(slides)} slides", flush=True)
    
    # Analyze slides
    print("\n🧠 ANALYZING SLIDES", flush=True)
    print("-" * 30, flush=True)
    print("  🔄 Initializing API client...", flush=True)
    
    client = get_openrouter_client()
    print("  ✅ API client initialized successfully", flush=True)
    
    print("  🔄 Calling analyze_slides_batch...", flush=True)
    analysis_start_time = time.time()
    
    analysis_results = analyze_slides_batch(client, slides)
    analysis_time = time.time() - analysis_start_time
    print(f"  ✅ analyze_slides_batch completed in {analysis_time:.2f}s", flush=True)
    print(f"  📊 Analysis results: {len(analysis_results)} slides analyzed", flush=True)
    
    # Create report
    print("\n📝 CREATING REPORT", flush=True)
    print("-" * 30, flush=True)
    print("  🔄 Building markdown content...", flush=True)
    
    markdown_content = f"# Pitch Deck Analysis: {Path(pdf_path).stem}\n\n"
    
    # Add analysis metadata
    markdown_content += "This analysis was generated using multiple AI agents, each specialized in different aspects of slide evaluation.\n\n"
    markdown_content += f"**Source File:** `{Path(pdf_path).name}` (PDF)\n"
    markdown_content += f"**Analysis Generated:** {len(slides)} slides processed (limited for testing)\n"
    markdown_content += "**Processing Method:** Individual processing with specialized AI agents\n"
    markdown_content += "**Text Extraction:** Docling-powered text transcription\n\n"
    
    # Add executive summary at the top (model-assisted with heuristic fallback)
    print("  🔄 Generating executive summary...", flush=True)
    def _build_heuristic_summary(analysis_results_local):
        categories = [
            ('problem_analyzer', 'Problem Analysis'),
            ('solution_evaluator', 'Solution Evaluation'),
            ('market_opportunity_assessor', 'Market Opportunity'),
            ('traction_evaluator', 'Traction'),
            ('funding_analyzer', 'Funding & Ask')
        ]
        lines = []
        lines.append("## Executive Summary\n")
        # Overall one-liner assembled from first sentences
        overall_bits = []
        for slide_num in sorted(analysis_results_local.keys()):
            slide_agents = analysis_results_local.get(slide_num, {})
            pa = slide_agents.get('problem_analyzer', {}).get('analysis', '')
            if pa:
                first_sentence = pa.split('. ')[0].strip()
                if first_sentence:
                    overall_bits.append(first_sentence)
            if len(overall_bits) >= 3:
                break
        if overall_bits:
            lines.append("" + " ".join(overall_bits) + "\n")
        # Coverage table-like bullets
        lines.append("### Coverage of Points of Interest\n")
        for key, title in categories:
            coverage_note = "Covered"
            for slide_agents in analysis_results_local.values():
                if key in slide_agents and slide_agents[key].get('analysis'):
                    coverage_note = "Covered"
                    break
            lines.append(f"- {title}: {coverage_note}")
        lines.append("\n### Slide Snapshots\n")
        for slide_num in sorted(analysis_results_local.keys()):
            slide_agents = analysis_results_local.get(slide_num, {})
            pa = slide_agents.get('problem_analyzer', {}).get('analysis', '')
            one_liner = (pa.split('\n')[0].split('. ')[0]).strip() if pa else "No clear problem statement identified."
            lines.append(f"- Slide {slide_num}: {one_liner}")
        lines.append("\n")
        return "\n".join(lines)
    def _build_model_summary(client_local, analysis_results_local):
        try:
            # Aggregate content for model
            blocks = []
            for slide_num in sorted(analysis_results_local.keys()):
                slide_agents = analysis_results_local[slide_num]
                parts = []
                for k, v in slide_agents.items():
                    agent_name = v.get('agent', k)
                    analysis_text = v.get('analysis', '')
                    parts.append(f"{agent_name}: {analysis_text}")
                blocks.append(f"Slide {slide_num}:\n" + "\n".join(parts))
            aggregate_text = "\n\n".join(blocks)
            messages = [
                {"role": "system", "content": "You are a senior pitch deck analyst. Create a concise executive summary."},
                {"role": "user", "content": [
                    {"type": "text", "text": "Summarize this deck. Provide: 1) 2-3 sentence overall summary of what the deck accomplishes; 2) A bullet list rating coverage of these points of interest: Problem, Solution, Market Opportunity, Traction, Funding & Ask (ratings: Strong/Covered/Weak/Not covered) with one short note each; 3) A one-line snapshot per slide. Return Markdown only."},
                    {"type": "text", "text": aggregate_text}
                ]}
            ]
            response = client_local.chat.completions.create(
                model="gpt-4o-mini",
                messages=messages,
                max_tokens=700
            )
            content = response.choices[0].message.content.strip()
            if content:
                return content + "\n\n"
        except Exception as _e:
            print(f"  ⚠️  Model summary generation failed: {_e}", flush=True)
        return None
    summary_md = _build_model_summary(client, analysis_results) or _build_heuristic_summary(analysis_results)
    markdown_content += summary_md

    print(f"  📊 Building markdown for {len(slides)} slides...", flush=True)
    
    for i, slide_data in enumerate(slides):
        slide_num = i + 1
        print(f"    🔄 Processing slide {slide_num}/{len(slides)}...", flush=True)
        
        analysis = analysis_results.get(slide_num, {})
        
        markdown_content += f"# Slide {slide_num}\n\n"
        markdown_content += f"![Slide {slide_num}](slides/{slide_data['filename']})\n\n"
        
        if analysis:
            markdown_content += "## Agentic Analysis\n\n"
            
            # Format each agent's analysis
            agent_count = 0
            for agent_key, agent_data in analysis.items():
                if isinstance(agent_data, dict) and 'agent' in agent_data and 'analysis' in agent_data:
                    agent_count += 1
                    agent_name = agent_data['agent']
                    agent_analysis = agent_data['analysis']
                    
                    markdown_content += f"### {agent_name}\n\n"
                    markdown_content += f"{agent_analysis}\n\n"
            
            print(f"    ✅ Added {agent_count} agent analyses for slide {slide_num}", flush=True)
        else:
            markdown_content += "## Agentic Analysis\n\n"
            markdown_content += "No analysis available\n\n"
            print(f"    ⚠️  No analysis available for slide {slide_num}", flush=True)
        
        markdown_content += "---\n\n"
    
    print("  ✅ Markdown content built successfully", flush=True)
    
    # Generate Table of Contents
    print("  🔄 Generating Table of Contents...", flush=True)
    toc = generate_toc(markdown_content)
    
    # Insert TOC after the main title
    print("  🔄 Inserting TOC into document...", flush=True)
    lines = markdown_content.split('\n')
    final_content = []
    final_content.append(lines[0])  # Main title
    final_content.append("")  # Empty line
    final_content.append(toc)  # TOC
    final_content.extend(lines[2:])  # Rest of content
    
    final_markdown = '\n'.join(final_content)
    print(f"  ✅ Final markdown created: {len(final_markdown)} characters", flush=True)
    
    # Save report
    print("\n💾 SAVING REPORT", flush=True)
    print("-" * 30, flush=True)
    output_file = f"processed/{Path(pdf_path).stem}_analysis.md"
    print(f"  🔄 Saving to: {output_file}", flush=True)
    
    os.makedirs("processed", exist_ok=True)
    with open(output_file, 'w', encoding='utf-8') as f:
        f.write(final_markdown)
    print(f"  ✅ Report saved successfully ({len(final_markdown)} characters)", flush=True)
    
    # Always upload the report
    print("\n🌐 UPLOADING REPORT", flush=True)
    print("-" * 30, flush=True)
    print("  🔄 Calling send_to_api_and_get_haste_link...", flush=True)
    
    upload_result = send_to_api_and_get_haste_link(final_markdown, Path(pdf_path).stem)
    if isinstance(upload_result, tuple) or isinstance(upload_result, list):
        raw_url, html_url = upload_result if len(upload_result) >= 2 else (upload_result[0], None)
        if raw_url:
            print(f"  ✅ Raw markdown URL: {raw_url}", flush=True)
        if html_url:
            print(f"  ✅ HTML URL: {html_url}", flush=True)
        if not raw_url and not html_url:
            print("  ❌ Upload failed - no URLs returned", flush=True)
    elif upload_result:
        print(f"  ✅ Report uploaded successfully: {upload_result}", flush=True)
    else:
        print("  ❌ Upload failed - no URL returned", flush=True)
    
    print("\n🎉 PROCESSING COMPLETE!", flush=True)
    print("=" * 50, flush=True)

if __name__ == "__main__":
    print("🎯 __main__ BLOCK ENTERED", flush=True)
    main()