import pandas as pd
import re

def analyze_leads():
    input_csv = 'leads/leads.csv'
    output_report = 'leads/analysis/red_flags.md'
    
    df = pd.read_csv(input_csv)
    
    # 1. Apply Specific Fixes
    # Dachdeckerinnung Unterfranken -> info@dachdecker-unterfranken.de
    mask = df['Firm/Innung'].str.contains('Dachdeckerinnung Unterfranken', case=False, na=False)
    if mask.any():
        print("Fixing Dachdeckerinnung Unterfranken email...")
        df.loc[mask, 'Email'] = 'info@dachdecker-unterfranken.de'
        
    # Save the fixes back to CSV
    df.to_csv(input_csv, index=False)
    
    # 2. Red Flag Analysis
    red_flags = []
    
    # Patterns
    freemail_domains = ['t-online.de', 'web.de', 'gmx.de', 'gmail.com', 'hotmail.com', 'yahoo.de', 'aol.com', 'freenet.de', 'arcor.de']
    kh_patterns = ['kh-', 'handwerk-', 'kreishandwerkerschaft', '-kh']
    
    # Check for Duplicates
    email_counts = df['Email'].value_counts()
    duplicate_emails = email_counts[email_counts > 1]
    
    if not duplicate_emails.empty:
        red_flags.append("## 🚩 Duplicate Emails (Potential Central Administration)")
        red_flags.append("| Email | Count | Innungen |")
        red_flags.append("|---|---|---|")
        for email, count in duplicate_emails.items():
            innungen = df[df['Email'] == email]['Firm/Innung'].tolist()
            innungen_str = "<br>".join(innungen[:3]) + ("..." if len(innungen) > 3 else "")
            red_flags.append(f"| `{email}` | {count} | {innungen_str} |")
        red_flags.append("")

    # Check for Freemail Addresses
    red_flags.append("## 🟡 Freemail Addresses (Check Professionality)")
    red_flags.append("| Innung | Contact | Email |")
    red_flags.append("|---|---|---|")
    
    found_freemail = False
    for idx, row in df.iterrows():
        email = str(row['Email']).lower()
        domain = email.split('@')[-1] if '@' in email else ''
        
        if domain in freemail_domains:
            red_flags.append(f"| {row['Firm/Innung']} | {row['Contact Person']} | `{email}` |")
            found_freemail = True
            
    if not found_freemail:
        red_flags.append("No freemail addresses found.")
    red_flags.append("")

    # Check for Generic KH Domains vs Specific Innung Name
    # Heuristic: If email has 'kh-' or 'handwerk' but Innung name is specific (like "Bäckerinnung")
    # This might indicate the email goes to the KH office, not the Obermeister directly.
    red_flags.append("## ℹ️ Kreishandwerkerschaft (KH) Generic Contacts")
    red_flags.append("These emails likely reach the administrative office, not necessarily the specific trade representative directly.")
    red_flags.append("| Innung | Email | Note |")
    red_flags.append("|---|---|---|")
    
    for idx, row in df.iterrows():
        email = str(row['Email']).lower()
        innung = str(row['Firm/Innung'])
        
        is_kh_email = any(p in email for p in kh_patterns)
        
        # If it's a specific guild but uses a generic KH email
        if is_kh_email:
             red_flags.append(f"| {innung} | `{email}` | Generic KH Domain |")

    # Domain Mismatch (Simple)
    # Check if the domain is totally unrelated to the Innung name
    # Difficult to do reliably without extensive lists, but we can look for "shop", "portal", etc.
    
    # Save Report
    import os
    os.makedirs(os.path.dirname(output_report), exist_ok=True)
    
    with open(output_report, 'w', encoding='utf-8') as f:
        f.write("# Lead Quality Audit & Red Flags\n\n")
        f.write("\n".join(red_flags))
        
    print(f"Report generated at {output_report}")

if __name__ == "__main__":
    analyze_leads()