feat: Implement mobile application and lead processing utilities.

scripts/analyze_leads_quality.py

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+
+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()